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The growing importance of research infrastructures

March 9, 2023 | 7 min read

By Federica Rosetta

Research infrastructures are critical for science, the economy and researchers; two experts weigh in on why and how to meet the challenges

Research infrastructures — also known as core facilities or shared facilities — are critical for science, the economy and researchers themselves. They give researchers access to the high-quality resources and services they need to foster innovation and develop cutting-edge technologies to address global challenges and drive the green and digital transition. Until recently, however, many scientific disciplines and many parts of the world have underestimated the critical role such facilities can play. But that has been changing. As  Ondřej Hradil opens in new tab/window , Research Infrastructure Manager at  Masaryk University opens in new tab/window  in the Czech Republic, explained:

"Historically only certain fields of science, typically physics, have recognized the value of research infrastructures."

Ondřej Hradil

Research Infrastructure Manager at Masaryk University

Ondřej noted that in astrophysics or particle physics, one cannot imagine modern, cutting-edge research without the “mega-science projects/facilities” such as  CERN opens in new tab/window  in Geneva or big telescopes. Now, other disciplines are recognizing that it makes sense to organize the necessary technology and expertise into research infrastructures. The urgency is heightened as research organizations feel the pressure of increasing costs for cutting-edge equipment with higher resolution and innovative features that requires specific expertise to be managed.

The increasingly essential nature of research infrastructure has earned it a place at the top of the EU research agenda. The  European Council’s conclusions on research infrastructures opens in new tab/window , adopted in December 2022, address the need to broaden access to RIs and further advance the European research infrastructure ecosystem. Similarly, the  Declaration on fostering a Global Ecosystem of Research Infrastructures opens in new tab/window  — the so called Brno Declaration — represents a call for action to support the development of a thriving global Research Infrastructures ecosystem. That declaration was launched in October under the Czech Presidency of the EU Council and during the  International Conference on Research Infrastructures 2022 (ICRI) opens in new tab/window , which Ondřej helped organize.

Similarly the current Swedish EU Council Presidency has put RIs as a top priority in the Research & Innovation agenda and is planning a major  event in June in Lund opens in new tab/window  focusing on how RIs can provide “new opportunities and benefits for society.”

Alberto Zigoni opens in new tab/window , Product Director for Academic Information Systems at Elsevier, agreed with Ondřej on the importance of infrastructure as a critical input to the research system and a tool for fostering collaboration. He said the roundtable Elsevier hosted with Science|Business in June —  The investment challenge: How to assess the impact of research infrastructures opens in new tab/window  — confirmed  the importance of putting research infrastructure at the center of the research policy agenda:

"It ensures the continuation of current initiatives while planning for new ones that can foster future competitiveness. This is particularly true for Europe, which is lagging behind the United States and China on this front, so I’m pleased to see that research infrastructure is a priority area for the European Research Area Policy Agenda."

Alberto Zigoni

Portfolio Integration Director at Elsevier

Report: Attracting investment for research infrastructures

This report, by Elsevier and Science|Business, covers topics such as:

Pitching for investment

Assessing the impact of research infrastructure

Determining where to invest

Alberto emphasized the need for a structured, multi-stakeholder approach to impact assessment. That, he said, requires a multi-faceted definition of “impact” and the ability to create a compelling narrative that combines qualitative and quantitative elements and can be tailored to the specific audience, be it government, the private sector or local communities:

While some aspects of economic and societal impact — especially local ones such as new jobs created — are not necessarily directly related to scientific impact, other forms of economic and societal impact, with a broader reach and longer timeframes to develop, are related to scientific impact. For instance, scientific publications are routinely cited in patents to uphold their claims; likewise, many policy documents on major issues such as climate change and global health issues use scientific research to support political decisions. These are all pathways to impact that can be recognized by starting with an assessment of the scientific impact. The latter can be evaluated by tracking publications that report results of scientific research which involved research infrastructures.

The need for impact assessment is vital, Ondřej agreed, emphasizing that without it, funding would be hard to raise and sustain:

Research infrastructures require a substantial investment to establish and cover their operating costs. So, it does not come as a surprise that funders need to make some tough choices, and their expectation is that research infrastructures that are funded will deliver value and perform well, especially with regards to providing and the needed services required by the wide scientific community to advance science. Next to that, funders look also at wider impacts on society and the economy. Nowadays public budgets are under unprecedented pressure and must be carefully prioritized. The scientific, economic and societal impact of research infrastructures is more important than ever and needs to be proven. Depending on the country or funder, the relative importance of the impacts will differ.

Necessary as it may be, evaluating the impact of research infrastructure has its challenges. The typical metrics for measuring scientific impact are user publications, patents and other bibliometric indicators, which can usually be done with the help of database such as  Scopus . However, Ondřej pointed out that this does not offer the complete picture:

What is still missing are reliable data to link publications to individual facilities and research infrastructures. This is due to a lack of practice in the academic community around acknowledging facilities, which is not always at the desirable level among authors and facilities. Secondly, the acknowledgement of facilities is not yet widely supported by journals. At the end of the day, the scientific impact evaluation can only be performed manually using full-text search of articles — which is frustrating.

Alberto agreed, commenting:

The need for data exposes the main challenge, which is data collection, as indicated by Ondřej. If we limit the scope to the evaluation of scientific impact, data collection translates in the ability to collect scientific publications that describe research activities where the RI in scope was used. As simple and obvious as this sounds, it is extremely difficult to track those papers in an automated manner: with the exception of the very large RIs such as the Large Hadron Collider, traditional methods based on article metadata such as authors and their affiliations, or funding acknowledgments, don’t work.

There are two key reasons for that, he suggested:

Firstly, lab staff is usually not included in the authors list; likewise, facilities where the experiments took place may or may not be mentioned in the acknowledgements section of the article. Furthermore, if a funder wants to evaluate what instruments have been used, those mentions usually occur in the body of the article, in a section called ‘Materials and Methods’ or something similar. That makes it extremely difficult to collect those papers using databases such as Scopus or PubMed. The remaining option is to reach out to researchers and lab managers to try to collect publications, which leads in general to a significant underrepresentation of the impact a piece of infrastructure may have had.

How can we support impact assessment?

So what can be done to address these issues? Ondřej shared his thoughts:

I mentioned two issues. The culture of acknowledging facilities can be improved by ongoing communication with the users. This is the task of facilities and their staff to remind users to do this. As facilities contribute to the experiments, I do consider this an ethical obligation, too. In fact, getting access to facilities has its financial value, which is often overlooked, but shall be clearly stated in the publication in the same way as funding/grant acknowledgements. Facilities shall also establish policies when and how the facility shall be, given their contribution, acknowledged or the facility staff shall be among the co-authors of the paper. This prevents future conflicts. 

The second issue, Ondřej said, is closely linked to publishers and journals. He suggested that publishers include facility acknowledgements in their publication processes by integrating them into their submission checklists and relevant acknowledgement sections. “Many journals and publishers do not consider this to be important so far. There is much work ahead of us to change this,” he said. “At the end of the day, having proper acknowledgements in publications can also improve the research quality and reproducibility. Many facilities are actively engaged in helping users with their data — co-designing experiments, acquisition of raw data, data analysis and interpretation from initial treatment to the creation of figures, data archiving and sharing of raw and processed data. Giving a quality label of reliable data management by the facilities is important.”

Alberto, meanwhile, pointed to pilots that were already underway as a possible solution:

Over the past year, we have worked together with academic institutions on pilot projects where we are trying to answer the question: ‘What is the scientific impact of my institution’s research infrastructure?’ A good example is the  partnership with Dutch institutions opens in new tab/window , as part of a broader collaboration around open science. Our goal is to provide the quantitative evidence that research leaders at institutions can use to inform their qualitative assessment and provide data for evidence-based decision making. We are not developing a new assessment methodology: (for that) we rely on industry best practices and  Elsevier's Research Intelligence portfolio . We focus instead on automating  the task of collecting publications so they can be fed into our systems for analysis.

The team has developed a sophisticated Natural Language Processing algorithm that is trained to identify those mentions in the “materials and methods” or equivalent section in scientific literature and link them to a taxonomy of research infrastructure. By using this approach, the team been able to collect up to four times more links between equipment and publications compared to traditional methods.

Both Alberto and Ondřej predicted that interest in research infrastructure would continue to grow, and with it the need for measurement as a way of demonstrating impact. As Ondřej noted, it’s a cause the research community would do well to rally behind:

This is yet an unexplored topic of interest to a wide community of facilities, irrespective of their scientific domain or size. Not only to the big ones such as  ESFRI opens in new tab/window  projects but also to smaller and mid-sized facilities that are typically hosted by universities and research institutes. So I hope we will get together a critical mass to move this topic further, work together with journals and publishers and possibly establish well-accepted standards.

Contributor

Federica Rosetta

VP of Academic & Research Relations, EU

2022 RESEARCH

Infrastructure, september 13 – 16, 2022, boulder, colorado.

The National Science Foundation’s (NSF) Large Facilities Office (LFO) hosted the 2022 Research Infrastructure Workshop from September 13 to September 16, 2022, in Boulder, CO. The National Center for Atmospheric Research (NCAR), the Geodetic Facility for the Advancement of Geoscience (GAGE), the National Ecological Observatory Network (NEON), and the National Solar Observatory (NSO) were joint co-hosts for the event. 

Attendees had the option to attend the workshop in-person or virtually.

The Research Infrastructure Workshop is a collaborative forum for all the National Science Foundation’s Research Infrastructure Projects. We strive to support NSF’s mission and promote the scientific endeavor with the following desired outcomes: 

• Provide a forum to collect and share best practices and lessons learned 
• Discuss new initiatives and collect community input 
• Demonstrate project management, operations, and business-related tools and techniques 
• Expand our community of practice by connecting colleagues across disciplines and organizations to promote collaboration between facilities 

The workshop featured four tracks: Award Management and Guidance; Program and Project Management; Facilities and Operations Management; and Education and Public Outreach. 

In 2022, workshop participants had the option to register for four specialty workshops on the last day of the event: a Cyberinfrastructure Workshop led by CI Compass ; a Facility Safety Officer Workshop led by NSF’s Large Facilities Office; a Cyber Security Workshop led by Trusted CI ; and a Science Communication Workshop led by NSF’s Division of Astronomical Sciences.

For additional information regarding program content, visit the agenda page or contact us at: rioutreach@nsf.gov . To sign up for notifications about the 2023 Research Infrastructure Workshop, sign up to Get Notified .

Research Infrastructure Workshops - Who Should Attend?

Join us for collaboration, knowledge sharing, and discussion!

Professional development units.

Speaking Opportunities

If you are interested in speaking, have examples to share, questions to ask, or have other related topic suggestions, please contact us at rioutreach@nsf.gov .

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Article Contents

1 introduction, 2 conceptual background, 5 discussion, 6 conclusion, supplementary data.

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Making a Research Infrastructure: Conditions and Strategies to Transform a Service into an Infrastructure

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Benedikt Fecher, Rebecca Kahn, Nataliia Sokolovska, Teresa Völker, Philip Nebe, Making a Research Infrastructure: Conditions and Strategies to Transform a Service into an Infrastructure, Science and Public Policy , Volume 48, Issue 4, August 2021, Pages 499–507, https://doi.org/10.1093/scipol/scab026

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In this article, we examine the making of research infrastructures for digital research. In line with many scholars in this field, we understand research infrastructures as deeply relational and adaptive systems that are embedded in research practice. Our aim was to identify the relevant context factors, actor constellations, organizational settings, and strategies which contribute to the evolution of a basic service into an actual infrastructure. To this end, we conducted thirty-three case studies of non-commercial and commercial research services along the research life cycle. By examining how these services emerge, we hope to gain a better understanding of the conditions and strategies to transform a service into an infrastructure. We are able to identify competitive disadvantages for publicly financed infrastructure projects with regard to the mode of implementation and the resources invested in development and marketing. We suggest that the results of this study are of practical relevance, especially for individuals, communities, and organizations wanting to create research infrastructures, as well as for funders and policy makers wanting to support innovative and sustainable infrastructures.

Digital communication technologies have proved instrumental in changing practices across all sectors of society, including academia. The hope of many researchers and science policy makers alike is that the Internet will help foster scientific progress and ultimately to make science more open, that is more inclusive, accessible, and transparent (cf. Fecher and Friesike 2014 ; Heck 2021 ). However, realizing efforts such as this require concrete policy initiatives behind them, if they are to endure and become part of everyday research practice. To date, many policies tend to focus on getting the technical aspect of research infrastructures off the ground, such as the development of major scientific equipment, sets of archival or scientific data, or communication and computing networks ( European Commission, 2016 ). As a result, we have seen a plethora of services emerge in recent years, which stand as a testament to the firm belief in scientific progress due to technology. While these are a valuable step in trying to meet new user and stakeholder needs and thereby integrate into the research life cycle (and, in some cases, attempt to reconfigure it), we argue in this article that there is more to research infrastructures than technical black boxes.

Infrastructure studies offer a fruitful perspective from which to study how technical innovations might generate effects which loop back upon the social organization of science. Scholars in this field largely agree that only when a technical service is embedded in practice, when it becomes ‘invisible’ ( Star and Ruhleder 1996 ; Bowker and Star 1999 ), can it be considered part of an infrastructure. In this understanding, infrastructures are much more than the technical assemblage of things; only when these are part of practice, can they be considered part of the infrastructure. Bowker and Star (1999) refer to the depths of interdependence between the technical networks and the real work of knowledge production as ‘infrastructural inversion’ and suggest that infrastructures become examinable, when they break down. In this light, the transformative potential of the Internet on scholarly practice can be seen as an ongoing irritation for routinized academic work, which offers us an opportunity to study changes in scholarly practice through the infrastructural lense ( Kaltenbrunner 2015 ).

In this article, we present the results of an empirical study on the emergence of research infrastructures for digital science that we conducted as part of a research project funded by the German Federal Ministry of Education and Research (BMBF). In particular, we are interested in the relevant environmental (i.e. legal, political, and social) factors for research services (RQ1), the strategies services apply to engage users and stakeholders (RQ2), and the typical organizational characteristics (i.e. team constellation, workflows, and financing) that services feature (RQ3). To approach these questions, we conducted thirty-three case studies of emerging services along the research life cycle between March and December 2018. We used desk research and semi-structured interviews with representatives of these services (mostly founders, CEOs, and project leads). Our results shed light on the motivations and logics behind infrastructure development and the interdependencies between new technical services and academic knowledge production. We are able to identify competitive disadvantages for publicly financed infrastructure projects with regard to the modes of implementation and the resources invested in development and marketing. The results of this study are of practical relevance, especially for persons and organizations which want to create and sustain research infrastructures and for funders and policy makers who aim to create the conditions for research in the twenty-first century.

2.1 Defining research infrastructures

For the purposes of this article, it is necessary to review the scholarly discourse on infrastructures and to derive a robust definition for an empirical investigation. To this extent, we conducted an extensive literature review drawing from infrastructure and information studies (see Online Appendix Table 1 ).

We find that there is a consensus in the scholarly discourse that infrastructures go beyond the pure material framework and also take into account social and environmental factors. Bowker and Star (1999) understand an infrastructure as a practical match among routines of work practice, technology, and wider-scale organizational resources. In their understanding, infrastructures are sunk into other structures of social arrangements and technologies and support communities of practice (cf. Bowker and Star 1998 ). In that line, Wouters (2014) defines infrastructures as a routinized and relational set of human interactions that are multilayered and cannot be constructed top-down. This echoes the work of Pollock and Williams (2010) who argue that infrastructures should be viewed iteratively over time, as entities with their own biographies and which only exist in social contexts. The bottom-up nature of infrastructures is further explored by Blanke and Hedges (2013) who argue that such an understanding is essential if an infrastructure is to adequately meet the needs of its users. Edwards (2013) describes infrastructures as ecologies or complex adaptive systems that incorporate technological standards, social practices, and norms. Similarly, Hanseth et al. (1996) propose that infrastructures rely on a degree of standardization and compatibility if they are to function effectively (see also Larkin 2013 ). Drawing on Strauss (1985 , 1988 ), Kaltenbrunner (2015) describes infrastructures as a result of articulation work, that is the activity of meshing distributed elements of labor in cooperative settings. He differentiates the production task (e.g. a research report) from the articulation work (i.e. everything that is necessary to write the report). These settings, as previously described by Schmidt and Bannon (1992) , are increasingly distributed, thus requiring the kinds of cooperative, digitized support infrastructures that form the basis of this study.

We suggest that these general conceptions of infrastructures can be transferred to research infrastructures. Drawing from this, we proceed from an understanding of research infrastructures as deeply relational and adaptive systems where the material and social aspects are in permanent interplay. They are embedded in the social practice of research and influenced by environmental factors. This allows us to consider the examined services as infrastructures in the making, that is they are not (yet) part of research practice but try to become part of it, and informs our central research interest: by examining how these services emerge, we hope to gain a better understanding of the conditions and strategies to transform a service into an infrastructure.

2.2 Conceptual framework

Three conceptual dimensions appear particularly relevant in the context of this study and for answering our three research questions:

Environmental perspective, that is the ecology in which services operate.

This conceptual dimension relates to the first research question and thus which and to what extent environmental factors play a role in the development of an infrastructures for digital science. As adaptive systems, it can be assumed that research infrastructures do not emerge without context and are indeed influenced by environmental factors. Here, we distinguish between legal norms (e.g. with regard to data protection) as well as societal and political discourses (e.g. science policy developments) with regard to the influence of digitalization on science.

Social perspective, that is the practice that services try to penetrate.

This conceptual dimension relates to the second research question, that is the strategies services apply to engage users and stakeholders. Services must be embedded into the social practice of research in order to be part of the research infrastructure. In this context, two large (and occasionally overlapping) groups of social actors appear crucial to us. These are the actual users (i.e. people who use a service) and relevant stakeholders (i.e. people who do not use a service but are directly relevant to its provision). For example, repositories are used by researchers (i.e. they are the users), but they are funded by research funders and hosted by libraries (i.e. they are stakeholders). We assume that both groups are relevant for a service to become part of practice. Empirically, we are interested in what practical problems a service wants to solve (i.e. motivation), which users and stakeholders they address and what strategies they employ to engage them, i.e. to become part of the practice.

Organizational perspective, that is the resources that services have to adapt.

This conceptual dimension relates to the third research question, that is the organizational characteristics that services feature. Taking the perspective of technical services, we are interested in the organizational capacities that a service has with regard to the team constellation, modes of implementation of changes, as well as the financial resources. Thereby, we assume that the interplay between the material and the social does not only relate to the relationship between the service and its (external) users and stakeholders but also to the internal, social, and material, capacities.

This study is part of the BMBF-funded research project DREAM (Digital Research Mining), which deals with infrastructures for digital science (i.e. scholarly practices that rely on digital resources). 1 The aim of this study was to better understand the conditions and strategies to transform a service into an infrastructure. We assume that the transformative potential of the Internet makes it possible to study infrastructures for scholarly practice insofar as new services challenge existing infrastructures and seek to become part of the infrastructure themselves.

To this end, we conducted thirty-three case studies of non-commercial and commercial research services along the research life cycle between March and December 2018. We used a purposeful, theoretical sampling, guided by three criteria: size, source of funding, and functionality. Regarding functionality, we chose cases that can be assigned to different phases of the well-established research cycle (cf. Wilkinson 2000 ; Humphrey 2006 ). This is to ensure that sufficient cases are included in our analysis for all practices and phases in a typical research project. Accordingly, we differentiated five broad phases (think and plan; discover; gather and analyze; write and publish; share and impact). Many services in our sample cover more than one phase. For instance, the service Knowledge Unlatched offers features for discovering and publishing. We approximated the size of a service by the numbers of employees indicated in the interviews and other available information such as profit and number of users. It was important to include both large and small services in order to better assess the impact of organizational resources on infrastructure development. Similarly, it was important to include both commercial and publicly financed services, as the two are subject to fundamentally different operational conditions (e.g. accountability to a research funder versus accountability to shareholders). It has to be said that many services have mixed business models. For instance, it is quite typical that services that receive public funds also receive individual payments by customers. A table of the cases in our sample can be found in the Online Appendix Table 2 .

We conducted semi-structured interviews with representatives of the services (mostly CEOs, founders, or project managers). For the instrument, we converted the aforementioned conceptual categories into questions. This resulted in three topics:

Environment (i.e. relevant political and societal discourses, and legal frameworks),

Social practice (i.e. motivations, user, and stakeholder strategies), and

Organization (i.e. team constellation, business model, and technical implementation).

The personal interviews have resulted in rich, textual data for the comparative analysis. We used a word-exact transcription of the interviews for our qualitative content analysis (cf. Mayring 2004 ). To this extent, we proceeded from a rough, deductive framework informed by the aforementioned categories and research interests and refined the category system through multiple rounds of thematic coding and coder discussions. In order to establish inter-coder reliability, all interviews were analyzed by two coders, using MAXQDA. Not all interviewees agreed to allow us to use their institutions’ names or to publish the full transcripts. In these cases, we speak generally of ‘service + number’ and avoid identifiers in quotations. In general, the results will not refer to the interviewed persons by name, but to the services they represent.

Here, we present the main findings of our research, relating to (1) environment (i.e. relevant political and societal discourses, legal frameworks), (2) social practice (i.e. motivations, user, and stakeholder strategies), and (3) organization (i.e. team constellation, modes of implementation, and business model).

4.1 Environment

We defined the external context in which the services operate as their environment, which consists of the legal frameworks within which it may operate, as well as relevant political and societal discourses. How the service anticipates these influences its ability to become embedded in research practice.

4.1.1 Legal framework

When asked about which legal provisions are of relevance for running their service, the respondents largely referred to copyright, privacy, and standard licenses. The majority of codes refer to privacy regulations (forty codes), followed by copyright compliance (twenty-three codes), and references to standard licenses (seven codes). The core operational challenge here is presented by different national legal regimes, to which the services—most of which operate internationally—must respond. In addition, when it comes to copyright, services aim to keep the threshold for sharing material low and often try to avoid individual licensing solutions by using standard licenses (e.g. Creative Commons). In order to comply with this set of legal obligations, research services need to invest in monitoring, compliance, and implementation work, as the interview with Service 6, a service that offers a unique identifier for individual researchers, demonstrates:

We do a huge amount of work around privacy. Privacy regulations in every country are different. […] We’ve gone through an external privacy audit since 2013 to ensure that we’re meeting international standards. […] We are fully compliant with GDPR, we also have to look outside of Europe, what are the other privacy regulations that we need to comply with.

It is noteworthy that the three legal categories identified are central legal concerns for any web-based service (also in non-academic contexts). This reveals the digitally enabled nature of the observed services. As with other web services, a key challenge is anticipating different legal regimes.

Open science is the dominant theme that the respondents refer to when asked about the relevance of political developments to their services. At the time of the interviews, this largely referred to policies that advocate for open access and open data. Multiple respondents, for example, refer to transformative open access agreements (e.g. the German DEAL negotiations between major scientific publishers and consortia of scientific institutions) and data policies (e.g. FAIR). When it comes to the geographic scope, respondents refer mostly to national policies passed by governmental institutions or national funders (twelve codes), supra-national policies, such as those passed by the European Union (ten codes) and institutional mandates at the level of the library, university, or company (three codes). Many of the respondents state that they are monitoring policy developments closely, as these affect their business models. Here for example, a representative from Altmetric, a service that provides attention metrics for scholarly outputs, refers to developments in the realm of research evaluation.

We pay attention in the UK and Australia and Hong Kong, the Research Excellence Framework type of thing. So in Australia it is ERA, in the UK it is REF. So the guidelines on how to assess research. Obviously, we want to be the people you go to as a research admin at the university, to get the evidence to write this case and so you can get the money you deserve.

Most services align themselves to open science and the aforementioned dimensions (i.e. transparency, accessibility, and inclusivity). Some of the respondents even lobby for open science, which can be seen as creating favorable environmental conditions for the service and are thereby beneficial for becoming an infrastructure. This becomes obvious in the interview with the Directory for Open Access Journals (DOAJ), an online directory that indexes and provides access to open access, peer-reviewed journals:

We have been very much involved in pushing for open access policies, open access mandates in the European Union, for instance. At a national level we have been active behind the scenes lobbying for open access policies. We, together with many other organizations, have been quite successful in the last decade to motivate decision makers to go in the direction of open access and open science.

Interestingly, different understandings of open science stand out, especially when it comes to commercialization. Commercial services describe open science (implicitly and explicitly) as a business opportunity, whereas some non-commercial services articulate reservations about the commercialization of open science and even try to counter it strategically. This becomes obvious in the following quote from a representative of Dryad, a non-commercial repository for research data:

Universities and university libraries are concerned about commercial publishers and commercial entities sort of taking over the research infrastructure space. That’s part of what we are trying to combat with this new partnership with [name of a non-profit service] is how do we make nonprofit infrastructure that is more aligned with values of academia?

On the one hand, the results show how closely digital science is associated with Open Science by the interviewees. On the other hand, the results show a divergence in what is perceived as open science. In particular, non-commercial services are dedicated to the early activist understanding of open science as articulated in the Berlin Declaration in 2003 2 or the Budapest open access Initiative in 2002. 3 They often see open science as liberation from commercial interests. Commercial services, on the other hand, relate to open science as a practice (e.g. sharing data, making articles openly accessible) and not necessarily to the underlying ideologies.

4.2 Social practice

For services to become infrastructures, they must be embedded within the social practice of research. Accordingly, our aim here was to identify how exactly services intend to become part of research infrastructure, that is which motivations they have and what strategies they employ in order to engage users and stakeholders.

4.2.1 Motivations

We found that interviewees referred to eight different types of motivations. It is noteworthy that many of the motivations relate to the aforementioned open science dimensions, that is accessibility (e.g. access), inclusivity (e.g. dissemination and collaboration), and transparency (e.g. transparency). Beyond that, the motivations mirror efficiency (e.g. orientation) and research governance considerations (e.g. compliance, recognition, and efficiency). These motivations are further delineated in Table 1 .

Subcodes for ‘Motivation’.

You hand over the finished articles to publishers, including all rights. The publisher prints and distributes, so the rights are gone. The state basically paid twice, for paying the people who do the editing and for the libraries that buy the articles back. On the Internet, researchers have the opportunity to do this themselves. Service 1.

The motivations are of importance here because they show where the services see problems in current practice and thus how they justify their raison d’être. In many cases, services position themselves against other, already established services and in some cases even articulate a need to replace them.

4.2.2 Users and stakeholder strategies

Discovering how these motivations are translated into a strategy required identifying users and stakeholders and the activities designed to engage with them and meet their needs. It is important to distinguish between users and stakeholders when analyzing strategies, because user strategies tend to refer to technical adaptation needs (i.e. making a service useful), whereas stakeholder strategies tend to refer to outreach activities and customer relations (i.e. making a service accepted). Based on the responses, we identified eight user and six stakeholder groups (see Fig. 1 ). It became clear that researchers are by far the most important user group, bearing in mind that there are potential overlaps between the researchers and authors categories. The most important stakeholder groups are customers and data providers. The latter has potential overlaps with the other service category and shows how important other technical services and their APIs are for a service (e.g. Altmetric uses the Facebook and Twitter APIs to build an impact metric).

Users and stakeholders.

To a certain extent, the illustration of users and stakeholders provides a map of the relevant actors for digital research infrastructures. It shows that, in addition to the actors already expected, the platform and cloud services play a significant role in the making of research infrastructures and that services relate to other services outside of the academic sphere.

We identified eight strategies implemented by the services to adapt to user needs. We differentiated these between pull (i.e. when a service reaches out to users or monitors their behavior), push (i.e. when users reach out to the service), and dialog strategies (i.e. when user and service engage in a dialog)—see Table 2 .

Strategies to anticipate user needs.

If the customers are still interested, there will be another very intensive discussion, in which we really discuss all features and go into the contractual details, so that everything is really transparent and clear. The customers can then do a training session. We currently offer a basic training course, which ideally takes place before commissioning. As soon as the installation has gone online, after a while we offer intensive training in which individual questions can be answered. Service 3.

We find the stakeholder strategies particularly intriguing because they demonstrate what a service is doing in order to become interwoven with the research environment. We identified four different strategies to engage stakeholders (see Table 3 ).

Strategies to anticipate stakeholder needs.

Non-commercial services articulate problems in engaging stakeholders due to a lack of resources. Both for-profit and non-commercial services attempt to influence discourses in their favor (i.e. awareness work). The largest category, mediation work , shows that services go to great lengths in order to connect and translate between different stakeholder groups which are considered relevant to the service. These are generally users and customers (e.g. researchers and librarians at an institution), between a service and other services (e.g. to be technically connectable), and finally between the programmers and users (e.g. in order to match technical possibilities with user requirements). The latter illustrates the negotiation of the technically possible with the socially desired as indicated in the working definition for infrastructure. This becomes obvious in an excerpt from an interview with a representative from Knowledge Unlatched, a platform that supports open access to books:

I was with a team of very young developers, they all knew about the latest technologies and of course, they wanted to use these technologies, because that is most interesting for them […]. That was a challenge, because these designers and front-end developers; they all wanted to have some fancy moving buttons. When we asked librarians to login and to use it, they were like, what is this? They have no idea, give me an Excel sheet, and I’ll do it. Knowledge unlatched.

It becomes apparent that, in addition to the research communities as the biggest user group, other actors are of great relevance for the services—for example, because they guarantee the technical operation (e.g. data providers) or grant favorable institutional conditions (e.g. research institutions and research libraries). Furthermore, remarkable differences between commercial and non-commercial services can be seen, in that non-commercial or publicly funded services in particular articulate a lack of resources for outreach and implementation.

4.3 Organization

Here, we focus on the internal aspects of research infrastructures, in particular the roles that organizational design, team background, financing models, and technical adaptation play for the emergence of an infrastructure.

4.3.1 Team constellation

One of the problems we have had is that it is always hard to have sufficient developers. People have a lot of demands on a service naturally. They start using it, they like things, they have ideas for how they would like to innovate and it is hard to always have sufficient developers and to be able to offer people everything they would like. DCC.

In contrast to non-commercial services, for-profit entities described sales teams as an important part of their staff. These teams help the service to adapt by ensuring they are able to fulfill user and customer needs, thereby deepening their ability to embed themselves into the research practice. There are also indications that non-commercial services struggle to recruit staff who have technical expertise. This may be due to the fact that the salaries in non-commercial services (which are mostly based within scientific institutions) are typically lower than those in the private sector and that there are limited reputative gains for infrastructure work in academia.

4.3.2 Business models

Regarding the business models, we broadly distinguish between rather non-profit and profit-oriented services. Among non-profit services (sixty-six codes), we differentiated between those who received institutional funding (eighteen codes), public funding (seventeen codes), charged fees (five codes), accepted donations (eight codes), and services that were exclusively financed by the founder/s (four codes). Profit-oriented services (forty-nine codes) included subscription models and licensing (twenty-six codes), individual payments (five codes), and private investments (thirteen codes) as their funding sources. Most services have mixed funding models, or at least emphasized the intention to seek other/additional sources of funding.

[…] currently there’s just sort of the grant model, temporary funding that is designed to do some special project and then it ends and you’re left with no means for continuing the work DRYAD.

Access to initial seed funding was common to both types of entities, but while non-profits often received initial funding from public funders, profit-oriented services often relied on investments from external companies in their startup stages. Several services started with seed investment (e.g. Tetrascience) and angel investment or were part of a startup incubator. The issue of sustainability for services that receive public funding is notable. There appears to be a need for follow-up funding that has not been satisfactorily addressed by research funders. Strategic partnerships are another feature of the organizational design. In some cases, strategic partnerships led to services becoming merged (e.g. Sharelatex, Dryad, and the Dash platform) or were partly acquired by a larger service (e.g. figshare by Digital Science).

4.3.3 Technical implementation

We are able to differentiate two modes of technical implementation: phased and iterative implementation. Phased implementation (six codes) describes an approach that begins with the users, that is screening their needs and then building the service accordingly. Iterative implementation (fifteen codes) is a process whereby user needs are constantly screened and adaptations are continuously made. Generally, we observe that it was mainly non-commercial services which used the phased implementation approach, whereas for-profit services exclusively referred to iterative implementation. Below, in Table 4 , there are two example quotes, the first referring to iterative implementation, and the second to phased implementation:

Iterative implementation versus phased implementation.

We consider this to be an important result, since it seems to reflect the funding logic of many non-commercial services, who typically expect implementation in consecutive work packages, whereas for-profit services appear to have to search for exposure earlier and permanently. This, we suggest, may further limit the adaptability and thereby competitiveness of non-commercial services.

In our observations, it became clear that open science is the dominant discourse to which new online services for research refer. They use open science as an umbrella term to describe possible solutions to what they perceive as the shortcomings of the established system and infrastructures of the scholarly research life cycle, such as a lack of access to articles and the lack of recognition for alternative scholarly outputs (cf. Fecher and Frieske, 2014 ). What differs, however, are the services’ responses to this discourse: although open science was initiated as a movement against the commercialization of research, it has been anticipated as a business model by many of the commercial services we observed. Meanwhile, non-profit services see open science as a set of principles, which framed an activist approach to research support. This finding echoes critical voices that have pointed to the appropriation of open science by commercial players (cf. Mirowski 2011 ).

The differences between commercial services and non-profit services permeated almost every aspect of their responses to their environment’ (e.g. which public debates they participate in), how they engage with users and stakeholders, and how they implement changes. For instance, it is noteworthy that commercial services devote more resources to marketing and sales. Non-commercial services, on the other hand, articulate a lack of resources for marketing and sales. The distinctions between commercial and non-commercial services were also clear in the observations related to organization: Both types of services followed a fairly straightforward version of a decentralized digital service and both place similar importance on the need to hire staff with strong technical backgrounds. However, non-commercial services report that they do not have the resources to hire highly qualified programmers on a long-term basis. Further, non-commercial services often adopt phased implementation, possibly due to the funding logic of many public research funders. Commercial services generally adopt an agile implementation logic, possibly to be responsive to changing market needs.

Herein, we see a severe competitive disadvantage for non-commercial services. We suggest that there are three reasons for this: the first might have something to do with the phased implementation logic of public research funders, which restricts the capacity of a service to adapt to user needs. The second is a general lack of resources for hiring highly skilled staff, which puts non-commercial services at a disadvantage in a competitive market, and the third is a short-funding runway, which makes it difficult for non-commercial services to plan for future continuation. The implications of these three factors might be that in a competitive landscape, it is the commercial services, and their market-driven approach to open science, who have a better chance of embedding themselves in the research life cycle, and thereby co-shaping the scientific practices of the future.

In this research paper, we examined the making of research infrastructures for digital science, that is the relevant environmental factors, the strategies deployed to penetrate practice, and the organizational conditions necessary for a service to become part of a research infrastructure. We defined infrastructures as deeply relational and adaptive systems where the material and social aspects are in permanent interplay and which are influenced by environmental factors. The ways in which the services respond to these environmental factors and anticipate user and stakeholder needs create effects that might loop back into the overall social organization of science. It can be seen in our study that the services position themselves against shortcomings of the established infrastructures with regard to the access and transparency of research or the dissemination and curation of results online. In this regard, the study of emerging infrastructures might provide us with a glimpse into the future of an increasingly ‘open’ academic value creation.

At the same time, however, many services hold ties to established infrastructures, including mergers and acquisitions by the established publishers. In addition, the non-agile funding logic of public infrastructures and the limited financial possibilities of public institutions for highly trained staff could mean competitive disadvantages for publicly funded services. It therefore remains to be assumed that although the range of available services will change, the dominant players for research infrastructures may remain unchanged with digitization. This might explain why some scholars see open science as a neoliberal project in which market logics define the shape of research and non-lucrative services (e.g. for niche communities) are neglected ( Mirowski 2018 ). In this respect, the dependence on commercial research infrastructures seems to be reproduced for digital science. If there is an interplay between research policy developments and research infrastructures, and if public funding for infrastructure works do not take community needs sufficiently into account, then certain communities who coalesce around non-commercial services risk being left out of research policy debates. The risk of funding logics contradicting infrastructure logics, especially for digital services, increases as the relative dominance of commercial services grows (cf. Morris and Rip 2006 ; Fry et al. 2009 ; Lilja 2020 ). Although our study is limited in terms of the cases studied and the depth of survey, it gives reason to critically reflect on public research infrastructure investments, for instance by revising funding policies and increasing incentives for highly skilled non-research staff. It appears sensible to us to revive infrastructure research as a meta-scientific field of research especially now, in a time of transition to an increasingly digital ecosystem for scholarly work. This could help to ensure that public funds are used sustainably and moreover help to understand how possible futures of academic work might look like. Future, and in our eyes highly relevant, research questions could, for instance, concern the increasing interconnectedness and dependence on platforms, the long-term success of public infrastructure funding, and new governance models for critical infrastructures.

Supplementary data are available at Science and Public Policy Journal online.

Conflict of interest statement . None declared.

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Research Infrastructures

Policy, strategy, how to apply and work programmes.

Policy and strategy

Research infrastructures are facilities that provide resources and services for the research communities to conduct research and foster innovation in their fields.

These include

• major equipment or sets of instruments
• knowledge-related facilities such as collections,
• archives or scientific data infrastructures
• computing systems
• communication networks

Horizon Europe will endow Europe with world-class sustainable research infrastructures which are open and accessible to the best researchers from Europe and beyond.

It will also encourage the use of existing research infrastructures, including those financed from funds under the EU's Cohesion Policy .

In so doing, enhancing the potential of the research infrastructures to support scientific advance and innovation, and to enable open and excellent science in accordance with the FAIR principles, alongside activities related to EU policies and international cooperation.

Research Infrastructures will also contribute to achieving the 4 key strategic orientations of the Horizon Europe strategic plan .

Areas of intervention

• consolidating and developing the landscape of European research infrastructures
• opening, integrating and interconnecting research infrastructures
• reinforcing European research infrastructure policy and international cooperation
• consolidating and developing the innovation potential of European research infrastructures and activities for innovation and training

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Research Infrastructures policy  

Access to European Research Infrastructures

Apply for funding

All funding information and details on how to apply are on the Funding and Tenders portal .

Work programme

Funding opportunities under Horizon Europe are set out in multiannual work programmes, which cover the large majority of support available.

Research Infrastructures work programme

The first pillar of Horizon Europe supports frontier research and breakthrough scientific ideas, teams up the best researchers from Europe and beyond and equips them with skills and word-class research infrastructures.

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Research infrastructure

Research Infrastructures (RIs) are increasingly diverse, play a key role in enabling and developing research in all scientific domains and represent a large share of research investment. They also play an important role in supporting research to addressing complex socio-technical challenges. Optimising their organisation, sustainability and impact has become of prime importance for research funders and decision-makers.

• Very large research infrastructures: policy issues and options
• Mobilising and coordinating diverse research infrastructures during the COVID-19 crisis

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Key messages, research infrastructures are long-term endeavours that require sustainable public investment.

Research infrastructures are often operational for several decades. They represent long term strategic investments which are indispensable for enabling and developing research in all scientific domains and often have broader socio-economic impacts. They therefore require careful planning and continuous and stable support, which is not limited to financial considerations. Robust business models, adequate construction and operation management procedures, open data management policies and a favourable research policy environment are needed for the proper development and sustainability of research infrastructures.

The impact of research infrastructures is often underestimated

Although research infrastructures are designed to support research needs, their impact goes beyond the production of scientific results and knowledge. Their conception, construction and operation can involve and require unique technological developments, data management systems and highly skilled staff. RIs offer opportunities for innovation and market development, can attract investments and contribute broadly to socio-economic development. In some cases, they can constitute a focal point for the development of an innovation ecosystem.

Recent crises, such as the COVID-19 pandemic, have shown that RIs can contribute to solving complex challenges and provide critical support to decision-making. Robust and comprehensive impact assessment methodologies are essential to capture the breadth of RIs’ potential impact and support RI managers in optimising the value of their facilities.

Synergistic collaboration and partnerships can empower RIs to better address complex scientific and societal challenges

The COVID-19 crisis demonstrated the capacity of RIs to work together in a complementary way to address complex challenges beyond their traditional scientific domain and to open up to new categories of users who require data and observations for complex interdisciplinary questions. New collaborative agreements between RIs of various types can support excellent science and inform decision-makers addressing grand societal challenges. 

However, while the development of integrated RI ecosystems holds promising potential, there are a number of practical challenges that have yet to be fully overcome. Most of these relate to the different models and funding contexts in which RIs operate. It is important to optimise frameworks for the development of these ecosystems so that their full added value can be realised.

The value of research infrastructures can be enhanced through the optimisation of management strategies, user-base policies and strategic portfolio management

In a context of limited research budgets, governments and funding agencies are confronted with the challenge of supporting increasingly large and complex RI portfolios. Potential users of RIs are also increasingly diverse and numerous, particularly as the data produced by RIs becomes progressively more complex and varied. The operation and use of RIs therefore requires careful balancing and optimisation: very large and international RIs, national RIs and smaller core institutional facilities each need specific strategies adapted to their characteristics.

Objectives of national policies for research and technical infrastructures

Research infrastructures represent considerable investments for governments, which expect in return a number of benefits. The analysis of the objective of national policies directed towards the support of RIs, using the STIP Compass database, matches the results of several surveys undertaken within OECD Global Science Forum activities on RIs. Besides the production of scientific knowledge in accordance with national priorities, RIs are expected to foster innovation at local and national level, as well as to facilitate international collaboration and, increasingly, address complex societal challenges.

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• Global Science Forum The Global Science Forum (GSF) supports countries to improve their science policies and share in the benefits of international collaboration. Learn more

Related policy issues

• Citizen science and engagement There is growing interest in citizen science and engagement to improve research efficiency and relevance, as well as to enhance trust in science. Learn more
• International collaboration in science Science is inherently international. Researchers and research institutions co-operate with each other across national borders and a high proportion of scientific publications include co-authors from different countries. Maintaining a well-functioning international research ecosystem is an important focus for science policy. Learn more
• Research careers and mobility There is fierce international competition for scientific talent, and building and maintaining a productive and inclusive research workforce is a major challenge for science policymakers. Learn more
• Research workforce diversity The research workforce is not representative of the diversity of societies as a whole. Whilst the pattern differs across disciplines, countries and employers, the overall picture is that women tend to be under-represented and are confronted with a glass-ceiling that blocks them from acceding to higher grade positions. The situation is even worse for a number of other groups. Factors such as socio-economic and ethnic backgrounds have a major influence on decisions to enter and persevere in research. Learn more

• Research Infrastructures

Research Infrastructures (RIs) are facilities, resources, and services that are used by the research communities to conduct research. The majority of funding for RI construction and operation is granted at national level, hence a co-ordinated development of future policies and funding schemes is crucial to improve efficiency.

Why do Research Infrastructures matter?

Research Infrastructures (RIs) are of fundamental strategic importance in the context of the European Research Area (ERA). Excellence in research requires top infrastructures for data collection, management, processing, analysing, and archiving.

What are the current priorities?

Science Europe aims to facilitate meaningful dialogue between RI funders, operators, and the scientific communities; provide guidance and promote mutual learning by sharing best-practices; ensure relevant consolidated contributions to ERA initiatives related to RIs; and liaise with the relevant stakeholders at European and global levels.

What is Science Europe doing to achieve these aims?

In November 2018, Science Europe, together with the Organisation for Economic Co-operation and Development (OECD) Global Science Forum (GSF), launched a joint activity on ‘ Optimising the Use and Operation of National Research Infrastructures ’ and produced guiding Models for optimising the operation and use of research infrastructures at national level. 

This activity builds on Science Europe’s previous exploratory work on the notion of portfolio of RIs conducted in 2016-2017 by a dedicated Working Group on RIs.

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Does large-scale research infrastructure affect regional knowledge innovation, and how? A case study of the National Supercomputing Center in China

• Haodong Yang 1 ,
• Li Liu 2 &
• Gaofeng Wang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  338 ( 2024 ) Cite this article

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• Science, technology and society

Large-scale research infrastructures (LSRIs) are widely acknowledged as a crucial instrument for venturing into the uncharted territories of science and technology, as well as contributing to the well-being of society. However, only a limited number of literature have scrutinized the impact of LSRIs, founded upon a causal inference framework. Moreover, the function of LSRIs in the advancement of innovation at the regional level remains inadequately identified. Drawing on the resource-based view, this study develops a conceptual framework that links the scientific effect of LSRIs to innovation resources in order to assess their impact on knowledge innovation (KI). Taking China’s National Supercomputing Center (NSC) as a case, three major mechanism hypotheses are proposed for the impact of NSC on KI, including basic effect, network effect, and technology effect. Using panel data from 283 cities in China from 2000 to 2020, we employ a spatial difference-in-differences estimation model to examine the impact of NSC on KI. The research finds that: (1) The construction of NSC stimulates KI in local and surrounding areas. (2) The main mechanisms by which NSC promotes KI include the increase in fiscal investment and talents in science, the improvement of digital infrastructure, as well as the enhancement of urban network centrality and innovation efficiency. (3) Geographical proximity, cooperation proximity, and digitization proximity constitute the main channels of policy spillover. (4) NSC has not shown significant promotion of regional innovation convergence, and its radiation influence needs further improvement. (5) The knowledge innovation effects of NSCs manifest heterogeneity based on the distinct knowledge orientation and innovation environment, with this impact being notably pronounced in application innovation-oriented cities such as Shenzhen. The results of this study reveal the positive yet limited impact of NSC on KI and provide a reference for other economies in the areas of LSRIs, digital infrastructure, and the formulation of place-based innovation policy.

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Introduction.

In the era of the knowledge economy, the value of scientific knowledge far surpasses any previous era. As the environment, health, energy, and population issues become increasingly complex, the information entropy of research objects is showing exponential growth. Against this backdrop, Large-scale research infrastructures (LSRIs) serve as important instruments for exploring the forefront of science and technology (S&T) and providing social public value. Their role in social and economic development is becoming more prominent (Michalowski, 2014 ; Beck and Charitos, 2021 ). LSRIs are regarded as large scientific platforms or systems consisting of clusters of large scientific instruments, facilities, and equipment (Michalowski, 2014 ; Qiao et al., 2016 ; D’ippolito and Rüling, 2019 ). The construction and operation level of LSRIs represents the strength of a country or region’s core original innovation ability (Marcelli, 2014 ). Therefore, LSRIs are particularly important for emerging countries that hope to catch up with developed countries in the field of S&T. Despite their significant demonstration and radiation effects, LSRIs have long been a topic of controversy due to their high technological complexity, long development cycles, and huge investment (Jiang et al., 2018 ; D’ippolito and Rüling, 2019 ). During the construction and operation stages, LSRIs usually face new and variable challenges involving multiple disciplines. The high complexity and uncertainty make failure easy and cause huge economic losses (Beck and Charitos, 2021 ). Therefore, it is particularly important to scientifically evaluate the knowledge effect produced by LSRIs. Existing research has explored the definition (Michalowski, 2014 ; Qiao et al., 2016 ; D’ippolito and Rüling, 2019 ), type (Qiao et al., 2016 ), and distribution (Marcelli, 2014 ) of LSRIs, analyzed the scientific effect that LSRIs possess in theory (Michalowski, 2014 ; Qiao et al., 2016 ) and investigated specific infrastructure using scientometrics or case study methods (Lozano et al., 2014 ; Carrazza et al., 2016 ; Caliari et al., 2020 ). However, as some studies have pointed out, there are few systematic evaluating the effect of LSRIs based on a causal inference framework (Bollen et al., 2011 ), and further efforts are needed to identify the role of LSRIs in innovation growth at the regional level (Caliari et al., 2020 ; Beck and Charitos, 2021 ).

In modern scientific research and technology engineering, complex mathematical calculations beyond human cognitive abilities are frequently encountered and must be solved using computers (Bollen et al., 2011 ; LeDuc et al., 2014 ). High-performance computing applications have integrated modeling, algorithms, software development, and computational simulation, serving as a necessary link for the application of high-performance computers in cutting-edge basic scientific research and becoming a third scientific method apart from theoretical research and scientific experiments. This study focuses on the impact and mechanisms of China’s National Supercomputing Center (NSC) on knowledge innovation (KI). The reasons for choosing NSC in China as the research object are: on the one hand, China has attached great importance to large-scale scientific facilities and their associated research in recent years, particularly in the field of supercomputing. The national and local governments have cumulatively invested billions of dollars and constructed more than ten national-level supercomputing centers. Among them, the Tianhe-1, Tianhe-2, and Sunway TaihuLight supercomputers are representative examples that have consistently ranked among the top ten on the TOP500 list, making China one of the few developing countries to achieve this level in large-scale scientific infrastructure investment. Investigating whether LSRIs investment enhances computational capabilities and contributes to scientific productivity or is merely an ambitious “image project” (public image campaign) is of great significance. On the other hand, since 2009, China has established NSCs in some cities (not traditional first-tier cities such as Beijing and Shanghai), which can be viewed as an external shock for local development. This provides a prerequisite for evaluating the scientific effect of NSCs under a causal inference framework, especially given that the goals and application fields of NSCs are rooted in local innovation endowment and industrial foundation (as a place-based policy). Taking Tianjin (one of the four municipalities directly under the central government of China) as an example, the construction of NSC has promoted the establishment of Tianhe S&T Park and the Industrial Big Data Application Innovation Center, aiming to build an industrial innovation system that integrates industry, academia, and research, promoting local talent cultivation and international cooperation.

Considering the unique attributes of NSC, this study’s research topic and objectives not only aim to address the ongoing debate regarding the role of LSRIs but also encompass the following two aspects:

As an extension of digital infrastructure.

Influenced by Schumpeterian innovation theory, introducing new technology and utilizing the power of “creative destruction” to enhance production levels are regarded as crucial factors for regional economic growth (Cardona et al., 2013 ; Batabyal and Nijkamp, 2016 ). The emergence and widespread use of information and communication technology (ICT) have fostered the digital economy. With the development of 5G communication, big data, and artificial intelligence, digital technology is increasingly viewed as a radical new technology. Existing literature confirms the broader effects of digital infrastructure construction on economic growth, urban innovation, corporate transformation, and social development (Cardona et al., 2013 ; Balcerzak and Bernard, 2017 ; Zhou et al., 2021 ; Zhang et al., 2022 ; Tang and Zhao, 2023 ). The majority of this literature are focused on scrutinizing the effects of network infrastructure, while relatively less attention has been given to the role of computing infrastructure, specifically its impact on promoting scientific knowledge production. This study seeks to provide evidence of the NSC’s influence on regional knowledge innovation as computing infrastructure.

As a practice of place-based innovation policy.

The scale effect of agglomeration leads to an increase in research and development (R&D) factor demand and releases the self-reinforcing characteristics of innovation, which may hinder the catching up of backward areas with advanced areas and have a negative impact on overall regional competitiveness and inclusiveness. Place-based innovation policy plays a crucial role in promoting coordinated regional innovation with the aim of achieving innovation convergence (Barca et al., 2012 ; Liu and Li, 2021 ). Although this policy model follows the principle of differentiation, some scholars are cautious about intervention, believing that government intervention may distort resource allocation, resulting in a loss of innovation efficiency, or point out that the impact of local policies is limited (Neumark and Simpson, 2015 ; Lu et al., 2022 ). As Marcelli ( 2014 ) mentioned in his study, scientific infrastructure is often situated in specific geographic locations, as evidenced by the establishment of multiple NSCs in different cities. Therefore, this study can also be seen as an evaluation of place-based innovation policy, involving identifying how supercomputing centers contribute to the development of local and regional knowledge innovation.

Compared with existing literature, this study aims to make several theoretical contributions:

First, drawing on the resource-based view, we categorize urban innovation resources into tangible resources such as human, financial, and physical capital, and intangible resources including social capital and resource utilization efficiency. This process not only shifts the focus from enterprise strategic resources to regional innovation resources but also integrates the resource-based view with social network theory and innovation efficiency research. Second, we establish a link between the scientific effect of LSRIs and the resource-based view, mapping the four scientific effect dimensions of S&T advancement effect, capability cultivation effect, networking effect, and clustering effect to innovation resources (both tangible and intangible). By extending the evaluation of LSRIs to the regional level, we provide empirical evidence for the causal relationship between LSRI and their innovation performance. Third, we classify the mechanism of NSC’s impact on regional knowledge innovation into three representative effects: the basic effect represented by R&D expenditure, S&T human resources, and digital infrastructure; the network effect represented by urban innovation network centrality; and the technological effect represented by innovation efficiency. By utilizing the convergence model, we verify the policy spillover of LSRIs and elucidate the role of computing infrastructure construction as a place-based innovation policy in regional innovation.

This article first discusses the definition and scientific effect of LSRIs, and based on the resource-based view, constructs a conceptual framework of LSRIs’ impact on knowledge innovation through mapping the scientific effect to different innovation resources. Next, we propose three mechanisms of NSC that affect KI (basic effect, network effect, and technology effect) and briefly review the development history of NSC in China. Then, the data, methods, and estimation results are presented. Finally, we discuss, and summarize the research results, and suggest policy implications.

Theoretical basis and evaluation framework

Scientific effect of large research infrastructures.

LSRIs, which are scientific research facilities built to meet the needs of modern “big science” research, aim to expand human cognitive abilities, discover new laws, and incubate new technologies. Some studies have divided the roles of LSRIs into categories including, but not limited to, scientific, technological, economic, educational, and other social aspects (Marcelli, 2014 ; Michalowski, 2014 ; Qiao et al., 2016 ; Carrazza et al., 2016 ; Caliari et al., 2020 ; Beck and Charitos, 2021 ). The OECD report in 2014 partitioned the impacts of LSRIs into scientific achievements, impacts of construction and operation, personnel training, scientific cooperation, technological innovation, and education (Michalowski, 2014 ). Qiao et al. ( 2016 ) established an analytical framework to evaluate the implementation effects of LSRIs, deconstructing the scientific effect of LSRIs from the perspectives of the S&T advancement effect, capability cultivation effect, networking effect, and clustering effect. Caliari et al. ( 2020 ) considered that LSRIs can make significant contributions to the economic growth of developing countries through technology and innovation, with specific roles involving scientific output and technological progress, supporting the development of industrial, health, and agricultural sectors. some scholars have explored the specific impacts of LSRIs in a targeted manner, such as D’ippolito and Rüling ( 2019 ) who discussed the types and formation of cooperation and their impact under the background of LSRIs sharing. Scarrà and Piccaluga ( 2022 ), aiming to understand how big science affects innovation through transfer mechanism and spillover effect, reviewed the relevant research directions through literature surveys, covering six major themes including technology transfer methods and mechanisms, cooperation with the public sector, and spillover effects of LSRIs, etc.

This article aims to examine the impact of NSC, a type of LSRI, on regional knowledge innovation and its mechanism. Establishing a framework is a prerequisite for conducting the evaluation. Given that the area of the research sample is China, in order to better fit the institutional and developmental background, we build the framework based on Michalowski ( 2014 ) and Qiao et al. ( 2016 ) and integrate the resource-based view to construct the path of NSC’s impact on knowledge innovation.

Resource-based view, social network theory, and innovation efficiency

The resource-based view emphasizes that an organization’s success is rooted in its specific resources, which constitute the logical starting point for strategic decision-making. The impact of resources on an organization’s competitive advantage applies not only to the enterprise level but also to the competitiveness of regions and countries, which depend on their resource endowments (Porter, 1990 ; Fatima et al., 2022 ; Ge and Liu, 2022 ). The study of the firm has identified specific forms of resources, with Grant ( 1991 ) proposing six major resources, including financial resources, physical resources, human resources, technological resources, reputation resources, and organizational resources. Das and Teng ( 1998 ) divided resources into financial, managerial, material, and technological categories. These heterogeneous resources can be classified into different groups based on different criteria, such as tangibility or whether they are protected by property law. In the context of innovation, Del Canto and Gonzalez ( 1999 ) categorized R&D resources into three types: financial, physical (capital intensity), and human resources. Auranen and Nieminen ( 2010 ) argued that organizations ensure the continuous development of R&D activities by acquiring and possessing equipment, funding, and personnel. Of course, the development of urban knowledge innovation not only depends on the direct input of local R&D resources but also the interaction with other regions. Social network theory asserts that the manner in which events unfold is contingent upon the context in which they take place. From the perspective of social capital, networks have significant value in transmitting resources (Beck and Charitos, 2021 ; Wei et al., 2022 ). Thus, relationships established through interaction and the networks formed through accumulation become important channels for information and knowledge diffusion. Similar views also appear in studies of the knowledge-based view (KBV), with the practicality of knowledge determining the need for interaction with external groups, and regions can effectively supplement their local knowledge resources through external knowledge spillover channels (Das and Teng, 1998 ; Ge and Liu, 2022 ). Overall, the RBV regards the creation and maintenance of networks as a mechanism for acquiring scarce resources, and the degree of embedding of regions in knowledge innovation networks reflects their implicit social capital resources.

However, in many instances, an organization’s success is not determined by its possession of superior resources, but rather by its ability to effectively utilize them. Simply possessing specific resources does not guarantee an organization’s competitive advantage, thus rendering resource utilization a critical issue in resource-based theory research (Majumdar, 1998 ; Arbelo et al., 2021 ). The high uncertainty of R&D activities and the limited quantity of R&D resources make it insufficient to merely explore resource input. This issue is relevant to the use of resources at the micro level, like in businesses, universities, and laboratories, as well as at the macro level, including in cities, regions, and even countries. It involves how to optimize resource input efficiency, such as using fewer resources to support the same level of business (output) or using existing resources to support more business (output). Relevant literature in the field of innovation suggests that knowledge production efficiency or innovation efficiency can be understood as the level of innovation potential formed by different R&D resources, i.e., the degree to which innovation input is converted into actual innovation output (Bai et al., 2020 ). The efficiency level is often related to the institutional background, organizational model, and internal structure of the research subject (Li, 2009 ). For a city, the innovation efficiency of a region is influenced by the internal innovation organization and element structure, as well as the innovation environment.

Based on the above discussion, we consider that the RBV provides a conceptual framework for examining the impact of NSC on knowledge innovation. This view is aligned with numerous dimensions of scientific effect in LSRIs. At the regional level, innovation factors, including R&D funding, and human and material capital, are regarded as the basic components of inter-regional innovation capacity, or as the core inputs for knowledge production. In this study, we take these factors as innovation resources unique to the local area and possessing tangible characteristics. This can be mapped to the capability cultivation effect (talent cultivation) and clustering effect (innovation agglomeration) in scientific effect. Furthermore, considering that the cross-regional networks formed by the interaction of cities with other regions constitute one of the main channels for information exchange and knowledge spillover, we view the embedding of cities in the innovation network as one of the main ways to obtain external resources (networking effect in LSRIs scientific effect), or as understanding the social capital resources of cities possessing intangible characteristics. Finally, given that the utilization of resources (affected by technological progress, institutional factors, and agglomeration, mapping to the capability cultivation effect and clustering effect) in increasing regional competitive advantages is as important as resource acquisition, we consider it as another intangible resource besides social capital at the regional level. In this way, we have achieved an integration of the resource-based view, social network theory, and innovation efficiency, forming a conceptual framework for NSC to influence knowledge innovation by changing regional innovation resources, which is linked to existing dimensions of scientific effect (Fig. 1 ).

The mapping of resource possession and utilization, along with the various dimensions of scientific effects within LSRIs, has achieved the integration of the resource-based view, social network theory, and innovation efficiency. The conceptual framework, wherein NSC influences KI, is thereby constructed.

Research hypothesis

Basic effect.

Similar to other infrastructures, the construction of NSC also has a knowledge spillover effect (across technological fields). NSC not only provides high-performance computing services but also has a complete application software environment. With accumulated research achievements and industry big data, the center can achieve the integration of supercomputing, big data, and artificial intelligence through R&D, construct a supercomputing application network, provide resources and platforms for digital services, and foster emerging industries in the supercomputing field. The NSC in Tianjin includes the supercomputing center, cloud computing center, e-government center, big data, and artificial intelligence R&D environment, aiming to promote the rapid development of the digital industry in the local and surrounding areas. It should be emphasized that the digital infrastructure requires financial support from the government, which often provides funding for R&D activities conducted by both public and private institutions (Gao and Yuan, 2020 ). The social benefits of R&D activities cannot be fully internalized by market mechanisms, making the government’s fiscal intervention somewhat reasonable. The construction of LSRIs represented by NSC requires a large investment and involves high risks, and there is a lack of sufficient motivation for private capital involvement. Given that the establishment of NSC relies heavily on fiscal investment as a key driver, the increase in government fiscal expenditures is needed to provide support for R&D and operation. Furthermore, the government may raise its S&T expenditures by increasing the number of project applications, which could provide financial support for universities, research institutions, and enterprises to purchase computing power, thereby facilitating efficient scientific research. Finally, talent is the key to influencing a city’s innovation and learning abilities. On the one hand, the construction and operation of the NSC require professionals in high-performance computing, computer networks, parallel software, and distributed systems, who should possess relevant industry experience and professional knowledge. On the other hand, investment in the new digital technologies and knowledge spillover from the NSC will drive the development of digital and other emerging industries. These high value-added and knowledge-intensive industries will in turn attract more S&T talents and enhance the regional innovation competitiveness. Based on the above, this study hypothesizes:

H1. NSC can promote the development of knowledge innovation by influencing regional financial resources (fiscal S&T expenditure), human resources (S&T talents), and material resources (digital infrastructure). We also define this as the basic effect of NSC on KI.

Network effect

Digital infrastructure characterized by informatization and networking can overcome the barriers of temporal and spatial distance in scientific research activities. It not only connects innovative fields that were previously isolated, promoting knowledge convergence and recombination, but also facilitates long-distance knowledge dissemination that would otherwise be constrained by geographic limitations (Qiao et al., 2016 ; da Silva Neto and Chiarini, 2023 ). To acquire high-end digital technology services, other cities are often more willing to establish cooperative relationships with NSC cities. Such cooperation can not only enhance the strength of existing collaborative interactions but also potentially form new cooperative relationships. The embedding of the innovation network structure can increase the centrality of the city in the network, which contributes to the attainment of more information and resource benefits (Han et al., 2021 ; Wen et al., 2021 ). Cross-regional cooperation connects innovation organizations with heterogeneous knowledge, which can alleviate the innovation reduction caused by homogeneous knowledge at the local level (Hazır et al., 2018 ). Existing research has revealed the negative impacts of excessive centrality. The establishment and maintenance of social relationships incur a certain cost, while excessive embedding of network structures may lead to increased maintenance costs, potentially crowding out innovation resources and generating diseconomies of scale (Wang et al., 2014 ). Complex connections imply exposure to more information, which poses challenges to information screening, and integration, and even leads to information overload. NSC relies on the supercomputer consisting of thousands of processors and extends the development of new digital technologies such as big data, artificial intelligence, and cloud computing, enabling the storage and recognition of massive amounts of information and knowledge. This reduces the cost of network maintenance, and cities can improve efficiency in the processes of capturing external information, absorbing knowledge, and maintaining external relationships, thereby enhancing the positive impact of urban research network embedding and encouraging cities to be involved in scientific research cooperation networks actively. Empirical evidence shows that the NSC in Chengdu has offered computing services to over 760 users across 35 cities, including major metropolitan areas such as Beijing, Shanghai, Guangzhou, and Chongqing. The NSC in Tianjin provides computing services to over 30 provinces, municipalities, and autonomous regions across the country, with more than ten partner institutions, including universities like Peking University, Dalian University of Technology, Jilin University, and Harbin Engineering University, as well as local government such as Linyi City. Additionally, it has established joint laboratories with 17 institutions, to support basic research and technological innovation. Based on the above analysis, it is reasonable to propose the hypothesis:

H2. The construction and operation of NSC can promote the development of knowledge innovation by affecting the embedding of the region in the national scientific research network (i.e., social capital resources), which is also defined in this study as the network effect of NSC’s impact on KI.

Technology effect

The impact of NSC on the utilization capacity of regional S&T resources is mainly reflected in two aspects: (1) computing efficiency. The emergence of new data features has brought about governance challenges such as how to handle, store, transmit, and analyze data, while also driving a paradigm shift in scientific research. In fields such as drug testing, genomics research, climate simulation, energy exploration, molecular modeling, and astrophysics, high-dimensional and massive data impose higher demands on computing power and memory. The main features of supercomputers include two aspects: fast data processing speed and large data storage capacity. For the former, the computing speed of supercomputers can currently reach more than hundreds of billions of times per second in China, which is millions of times faster than ordinary computers. The peak computing speed of the “Tianhe-2” system is 100.7 PFlops, and the sustained computing speed is 61.4 PFlops; for the Sunway TaihuLight supercomputer, these two data are 125.4 Pflops and 93.1 Pflops, respectively. As for the latter, the total storage capacity has also reached dozens of PetaBytes. The supercomputing center is equipped with various peripheral devices and high-functional software systems, which will greatly shorten the cycle of innovation, and reduce the cost and uncertainty of innovation. (2) Allocation efficiency. The limited ability of the public sector to acquire information and knowledge, along with the potential for policy lag, may result in interventions that fail to produce the expected effects. In the context of NSC construction, supporting the entire process of data collection, sharing, computation, analysis, and application with computing power can significantly improve the level of intelligence, precision, and scientific decision-making in social governance. Governments can use digital technology tools to better plan and formulate S&T policies. Through multi-disciplinary and cross-departmental information sharing, supercomputing centers can leverage new technologies such as cloud computing, big data, and the Internet of Things to optimize the integration and allocation of scientific and technological resources and achieve scientific decision-making for S&T expenditures. By helping to build digital platform for urban S&T resources, the correlation mapping of different data such as projects, expenditures, and results can be achieved, providing support for project progress, result evaluation, and further funding decisions. NSC and its supporting digital infrastructure construction can significantly improve the efficiency of data collection, processing, transmission, storage, and other aspects on both the supply and demand sides. The cloud platform is configured with multiple databases such as scientific research results and talents, achieving “dual-linkage” between scientific research results and innovation needs. This model mitigates the temporal and spatial limitations of the interaction between supply and demand for R&D elements and reduces the cost of search, effectively improving the city’s ability to integrate S&T resources. Based on the above analysis, hypotheses are put forward:

H3. The construction and operation of NSC can promote the development of knowledge innovation through the improvement of computing and allocation efficiency. We also define this as the technology effect of NSC affecting KI.

Innovation convergence and knowledge diffusion

The importance of knowledge in modern economic development is increasing, and regional innovation is becoming increasingly reliant on close spatial associations. In other words, the growth of innovation in a region depends not only on the local accumulation of knowledge but also on the diffusion of knowledge from neighboring areas, which is one of the potential opportunities for promoting regional innovation development (Tang and Cui, 2023 ). Existing literature indicate that the construction of digital infrastructure can help spread and diffuse knowledge (Batabyal and Nijkamp, 2016 ; Balcerzak and Bernard, 2017 ), while S&T centers can promote regional collaborative development through knowledge spillovers generated by innovation clusters (Gao and Yuan, 2020 ). Therefore, the construction of NSC, which combines digital infrastructure and scientific infrastructure, may also have an impact on the knowledge innovation development of neighboring areas. Firstly, this type of proximity relationship can be general geographic adjacency, as the flow of R&D factors and the effects of knowledge learning (especially tacit knowledge) are still influenced by geographical distance. The establishment of NSC in Kunshan aims to undertake advanced computing and scientific big data processing business in the Yangtze River Delta region, and engage in strategic cooperation with Suzhou Deep-time Digital Earth Research Center, Shanghai Neuroscience Research Center, and other institutions, to carry out applied computing research and services in scientific fields including artificial intelligence and biomedicine. Secondly, proximity can also refer to the economic distance, which in the context of this study can be understood as differences in the level of urban digitization. Research at the regional level applying the theory of innovation absorption indicates that the region requires relevant prior knowledge and a compatible cognitive structure to accurately identify, integrate, and effectively absorb valuable knowledge (Ge and Liu, 2022 ). If there is too large of a gap in digitalization levels between regions, latecomer regions lacking sufficient digital technology and a complete information communication environment are unlikely to benefit from the computational power services provided by NSC. Finally, the cross-regional cooperative network formed by interaction among knowledge production organizations is regarded as another channel for knowledge diffusion. Advanced regions can obtain exogenous knowledge that is different from local knowledge through cooperation. If knowledge, experience, and resources from advanced regions can diffuse to less advanced regions, innovators in the latter can use the absorbed knowledge to create new scientific outputs, bridging the knowledge gap with the scientific forefront (De Noni et al., 2018 ; Erdil et al., 2022 ). The establishment of NSC could create policy spillover through the provision of computational power services to previously closely connected partners, thereby promoting local knowledge innovation and development. Based on the above analysis, we propose the following hypotheses:

H4a. The establishment of the NSC may facilitate policy spillover through proximity relations.

H4b. Building on existing policy spillover, the NSC fosters regional knowledge innovation convergence.

Development process and institutional background of NSCs

Main supercomputers in china.

(1) The Tianhe series: In 1978, the Chinese government put forward the aim of creating a supercomputer and assigned the National University of Defense Technology with the task. The first computer in China that performs calculations at a speed of more than 100 million times per second, named “Yinhe-I”, was successfully appraised in Changsha in 1983, and subsequent models were released in succession. With the accumulation of previous experience and technology, the “Tianhe-1” and “Tianhe-2” were developed by the university and achieved top ranking in the TOP500 list in 2010, 2013, and 2015 respectively (please refer to Table 1 for details, including serial number 1, 3, and 4). (2) Dawning series: The “Dawning” series of supercomputers was developed by the Institute of Computer Science at the Chinese Academy of Sciences. In 1993, the “Dawning-I” was successfully developed, making a breakthrough for China in the field of Symmetric Multi-Processing. In 2010, the “Dawning Nebula” supercomputer achieved second place on the world’s supercomputer list (TOP500), representing the most significant accomplishment of the “Dawning” series (refer to number 2 in Table 1 for details). (3) Sunway series. In 1996, the National Research Center for Parallel Computer Engineering and Technology was established, signaling the beginning of the development of the “Sunway” supercomputers. In 2010, the “Sunway Blue Light” was created, and subsequently situated at the NSC in Jinan. In 2016, the “Sunway TaihuLight” supercomputer achieved first place on the TOP500 list (refer to number 6 in Table 1 for details). For a comprehensive exposition on the intricate trajectory of high-performance computing development in China, kindly refer to the work by Wang ( 2023 ).

Supercomputing center in China

The NSCs were established by the Ministry of Science and Technology with the aim of providing high-performance computational resources for scientific research in China. Since 2009, NSCs have been approved and constructed in different cities, including Tianjin, Shenzhen, Changsha, Guangzhou, Jinan, Wuxi, and Zhengzhou. The central government cooperates with local governments to jointly fund the construction of NSC. During the operation phase, some operating expenses are subsidized by local government finances. At the same time, central and local governments open topic applications to some researchers, who use part of the research funds to purchase computing resources. Currently, the NSCs, along with their supporting data storage and backup centers, have been applied in various fields such as biomedicine, genetics, aerospace, climate, marine science, artificial intelligence, new materials, new energy, neuroscience, and smart cities. Taking the NSC in Tianjin as an example, the “Tianhe-1” supercomputer has supported more than 2000 national science and technology major projects and national key R&D programs during its operation, with total funding exceeding 2 billion yuan. It has been recognized with both national and provincial-level awards, contributing to thousands of published academic achievements. (refer to https://www.nscc-tj.cn/index ).

Data and methodology

Data and variables, dependent variables.

This paper aims to explore the impact of NSC on regional knowledge innovation. Broadly speaking, KI involves multiple aspects including knowledge acquisition, creation, and transformation, among which knowledge creation is the core. Innovation organizations such as universities and research Institutes engage in basic and applied research to pursue new scientific discoveries and generate new scientific knowledge. Scientific publications serve as the primary carrier of scientific knowledge, reflecting the latest advances in scientific research, and also an important channel for knowledge diffusion across different research fields and geographic locations (Qiao et al., 2016 ). The output of scientific papers in a city to some extent reflects the level of knowledge innovation in the region (Li, 2009 ; Yang et al., 2022a ). Meanwhile, the publications database provides public access to the city information to which the researchers belong. Therefore, this study characterizes the level of knowledge innovation ( KI ) by the per capita number of scientific publications in a city and specifically measures it by the number of papers included in the Science Citation Index (SCI). In the robustness test, the criterion for highly cited SCI papers is defined as follows: sorting the number of citations of SCI papers in the same discipline and the same year, the papers ranked in the top 1% are considered as highly cited papers.

Independent variable

The variable NSC is a dummy variable that takes a value of 1 if the city approves the construction of an NSC in the current year or any year thereafter, and 0 otherwise.

Mechanism variables

(1)The basic effect variables. Firstly, considering the significant role of financial investment in the construction and operation stages of LSRIs, government financial support for S&T ( R&D_exp, billion yuan ) is selected as the proxy variable for R&D expenditure (Li, 2009 ; Liu and Li, 2021 ; Ge and Liu, 2022 ). Secondly, the scientific and technological talent (Li, 2009 ; Gao and Yuan, 2020 ) (human resources) is represented by the number of employees who conduct scientific research and technology services in the city ( R&D_talent , 10,000 persons ), which is essential for the construction, operation, and radiation effects of the NSC, requiring a sufficient number of knowledge-based personnel, particularly in STEM fields. Thirdly, digital infrastructure construction (physical resources) covers areas such as 5G, artificial intelligence, and industrial internet, reflecting the development of technological and material resources in the context of NSC (Zhang et al., 2022 ; Tang and Zhao, 2023 ). The construction of digital infrastructure is indirectly characterized by the city’s digitalization index, constructed using the entropy method based on secondary indicators including the number of mobile phone users and internet users, revenue of postal and telecommunications industry, number of relevant employees (in information transmission, computer services, and software industry). (2) The network effect variables. The network effect variable is related to the centrality and structural hole of actors in the network. Generally, an actor’s position in a network is considered more important if they have higher centrality and more structural holes. This study focuses on whether the construction of NSC can affect the direct connection between NSC cities and other regions, as well as its embedding in the network structure, rather than focusing on the control of knowledge flow between nodes. Therefore, we select indicators that reflect urban centrality, including degree centrality and closeness centrality (Wang et al., 2014 ; Han et al., 2021 ). (3) The technology effect variable. To measure the technology effect of NSC in terms of innovation efficiency ( Innova_effi ), we employ the stochastic frontier analysis (SFA) method, which is rooted in economic theory and allows for a more rigorous measurement of innovation efficiency (Li, 2009 ).

Control variables

(1) Economic development (Liu and Li, 2021 ; Lu et al., 2022 ; Gao and Yuan, 2020 ). Characterized by per capita GDP ( Econ, 10,000 yuan ). (2) Industrial structure (Tang and Cui, 2023 ; Lu et al., 2022 ): Measured by the share of the secondary industry in the GDP ( Industry_sec , % ). (3) Comprehensive growth rate ( n  +  g  +  δ ): Modeled according to the research of Yang (2021), and calculated as the sum of natural growth rate, technological progress rate, and capital depreciation rate, assuming that the sum of technological progress rate and capital depreciation rate equals 5%. (4) Financial sector development: Gauged by the aggregate amount of loans and deposits held by financial institutions ( Finan , 10,000 yuan). (5) Human capital (Tang and Cui, 2023 ; Liu and Li, 2021 ; Gao and Yuan, 2020 ): Proxied by the number of university students per 10,000 individuals ( H_cap ). (6) Traffic and Openness (Yang et al., 2021 ; Gao and Yuan, 2020 ): Indicated by the total volume of passenger transport via road, water, and air transport for openness and commuting ( Trans , 10,000 people).

Data sources and processing

The research sample in this study consists of panel data for 283 Chinese cities from 2000 to 2020. The number of papers indexed by the Science Citation Index (SCI) for each city is obtained from the Web of Science (WoS) database ( https://www.webofscience.com ). The centrality measure is based on constructing a city-level scientific research network matrix. To construct the matrix, information on scientific research collaborations among cities in China is obtained through Python. If authors from different cities appear in the same paper, it is considered as a collaboration between the cities, and the centrality of cities is then calculated using Ucinet software. The control and mechanism variables, including government financial support for S&T, the number of employees in scientific research and technology services, the number of mobile phone users and internet users, revenue of postal and telecommunications industry, number of relevant employees (in information transmission, computer services, and software industry), are obtained from the “China Urban Statistical Yearbook”.

Missing values are handled by the interpolation method or replaced with the mean of the city. As for the measurement of Innova_effi , we use government financial support for S&T, and the number of employees in scientific research and technology services as input indicators for financial and human resources. The output indicator is represented by knowledge innovation. Two models, the Cobb-Douglas production function model and the stochastic frontier analysis with translog function, are respectively used for calculation, and the generalized likelihood ratio test is used for verification. The results (LR chi2 = 77.76, P = 0.000) indicate that the stochastic frontier analysis with translog function is more suitable for calculating innovation efficiency. Descriptive statistics of the variables are presented in Table 2 , and natural logarithms are taken for some variables (including Econ, Finan, H_cap , Trans ) with values affected by price factors. As per Table 2 , it is discernible that the standard deviations of a series of variables, including KI, are notably higher than the means. This indicates significant data dispersion, unveiling substantial inter-city disparities, a fact also elucidated by the distinctions between the maximum and minimum values. To scrutinize the influence of skewed data on the estimation results, in the section “Robustness test”, we conduct a robustness examination by substituting models. Additionally, recognizing the disparities between the treatment and control groups (descriptive statistics of the groups are retained), we acknowledge potential disruptions from bidirectional causality and sample self-selection biases in the baseline regression results. To address this issue, the study employs methods including IV, 2SLS, PSM-DID, and placebo tests.

Methodology

Since 2009, the Ministry of Science and Technology has successively approved the establishment of NSCs in several cities. We take this as a quasi-natural experiment and regard cities with supercomputing centers as the treatment group and other cities as the control group to examine the effect of NSC on regional knowledge innovation. Due to differences in the construction time of supercomputing centers, the study first constructs a time-varying difference-in-differences (DID) model:

In the above equation, i and t represent specific cities and specific years, respectively. KI it is the knowledge innovation level of city i in t years. \({Z}_{{it}}^{{\prime} }\) represents other control variables that may impact the level of urban knowledge innovation. v i and μ t represent individual-fixed effects that do not vary over time and time-fixed effects that do not vary across individuals, respectively. Theoretical analysis indicates that the establishment of NSCs not only could promote local knowledge innovation but may also affect neighboring areas. Thus, this study uses the spatial DID method to relax the assumption that individuals are independent in classical DID. The most common spatial econometric models are the Spatial Lag Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). Based on the spatial autocorrelation of the dependent variable (significant Moran’s I at 1% level for each year, as shown in Table 3 ), this study follows the selection criteria proposed by Elhorst ( 2014 ) and conducts LM and Wald tests on the sample. Moreover, The Hausman and LR tests are also been conducted to assess the appropriateness of using a two-way-fixed effects model. Ultimately, the benchmark regression model adopts the SDM, as presented below:

In formula (2), ρ represents the spatial autoregressive coefficient, β 2 and β 3 indicate the impact of NSC construction and control variables in spatially related areas on knowledge innovation in the focal area, respectively. W is the spatial weight matrix. The inverse distance matrix between cities is mainly used as the spatial weight matrix in this study. The matrix is calculated by using the longitude and latitude data of each city (obtained from Baidu Map API) to calculate the spherical distance between two cities.

To investigate the mechanism through which NSC affects knowledge innovation, this study employs Alesina and Zhuravskaya’s ( 2011 ) mechanism test method. Utilizing a linear model, we established the impact of NSC on mechanism variables. Subsequently, it conducts a comparative analysis of the estimated coefficients of NSC in equations that control for mechanism variables and those that do not, aiming to validate the existence of such mechanisms (Gao and Yuan, 2020 ; Zhang and Wang, 2022 ; Chen et al. 2023a ).

Assuming that the coefficient of NSC in Eq. ( 2 ) is significant, the mechanism variable is used as the dependent variable, and the treatment variable ( NSC ) is used as the independent variable for regression analysis. The specific formula is as follows:

If the coefficient of the NSC in Eq. ( 3 ) is significant, then NSC and each mechanism variable will be included in the regression model with knowledge innovation as the dependent variable, as shown in Eq. ( 4 ). If the estimated coefficient of NSC decreases or is not significant, it means that the construction of NSC can affect the development of urban knowledge innovation through the mediating variable path.

In addition to studying the diffusion of knowledge across geographic proximity, this study constructs two types of adjacency matrices: one based on digitization distance (constructed from the reciprocal of the absolute difference of each city’s digital infrastructure index) and the other based on collaborative frequency (constructed from the number of collaborations between each city and other cities). Furthermore, following the method of Sala-i-Martin ( 1996 ), this paper examines the impact of NSC on knowledge innovation convergence. A detailed description of the process is provided in the section “Methodology”. Based on the conceptual framework, mechanism analysis, and research design presented earlier, the final research framework of this our study is illustrated in Fig. 2 below:

NSC influences KI through basic effect, technology effect, and network effect, with the potential to shape regional knowledge innovation convergence through various proximities.

Benchmarking

Table 4 reports the estimated results of NSC’s impact on knowledge innovation under the geographic proximity matrix. Columns (1) and (2) present the OLS regression results controlling for time and city-fixed effects. It can be observed that the estimated coefficient of the treatment effect variable NSC is significantly positive at the 5% level or higher (10.191/8.958), regardless of whether control variables are included. Columns (3) and (4) show the results of the spatial econometric model, with both the LM test and Wald test statistics being significant at the 1% level, ensuring the validity of the SDM used. Specifically, the estimated coefficients of NSC are significantly positive at the 1% level (10.184/8.934), suggesting that the establishment of NSC promotes local knowledge innovation. The estimated coefficients of the interaction term NSC×w are also significantly positive at the 1% level (47.319/37.966), indicating that NSC construction has a positive impact on knowledge innovation in surrounding areas. This can be attributed to the continuous improvement of transportation infrastructure and digital networks (Yang et al. 2021 ; Zhang et al. 2022 ), as well as the regional development strategy represented by urban agglomerations (Tang and Cui, 2023 ). The estimated results in Table 4 provide empirical evidence about the impact of the supercomputing center on regional scientific knowledge production and suggest that neglecting policy spillover effects would underestimate the influence of NSC on urban knowledge innovation, which is unfavorable for policy evaluation.

Mechanism test

Test of basic effect.

To explore the mechanisms through which NSC affects knowledge innovation, we conduct empirical tests from three levels: basics effect, network effect, and technology effect, based on the theoretical analysis in the section “Research hypothesis”.

Table 4 focuses on the basic effect of NSC, and column (4) in Table 4 indicates that NSC construction significantly improves urban knowledge innovation performance. The second-stage regression results, shown in columns (5), (7), and (9) of Table 5 , indicate that the policy treatment effects are all significantly positive at the 1% level (6.216/3.228/0.057). This result suggests that NSC construction not only promotes regional S&T investment and an increase in R&D personnel but also helps improve digital infrastructure. Furthermore, similar to the knowledge innovation performance, the construction of NSC can also have a positive effect on the financial, human, and material resources of innovation in geographically adjacent regions. The phenomenon can be interpreted through existing literature. For instance, the Chinese government’s integration of S&T investment targets in the evaluation criteria for local officials has stimulated innovation competition, compelling neighboring city governments to augment their S&T investment (Liu et al., 2020 ; Gao and Yuan, 2020 ). Alternatively, the regional integration development strategies have minimized inter-regional transit time, facilitating the flow of R&D elements (Tang and Cui, 2023 ; Yang et al., 2021 ). The increase in S&T investment, as well as the aggregation of talent, will also propel industrial structure upgrading (Gao and Yuan, 2020 ), expedite the construction of digital infrastructure, and enable urban digital transformation.

Finally, columns (6), (8), and (10) in Table 5 show the third-step regression results, in which the S&T investment, R&D personnel, digital infrastructure, and treatment effect variables are all simultaneously included in the regression equation. The estimated coefficients of the mechanism variables are all significantly positive at the 1% level (1.023/1.369/90.641), and the promotion effects of NSC on knowledge innovation are still significant, but the absolute values of the coefficients have decreased. Theoretically, the innovation effects of investments in S&T, R&D personnel, and digital infrastructure have substantial empirical support. Firstly, concerning the impact of investments in S&T on innovation, a substantial body of research has demonstrated the stimulating impact of government subsidies on the R&D activities of enterprises. Studies have also focused on the innovation effects of public sector S&T investments, encompassing different dimensions like research institutes and cities, supporting that fiscal investment in S&T has led to an increase in both the quantity of scientific publications and patents (Link and Scott, 2021 ; Chen et al., 2023b ). Secondly, since the proposition of endogenous growth theory, the accumulation of human capital has been considered the fountainhead of economic growth, significantly determining a nation’s innovative capacity. Liu and White ( 1997 ) have emphasized that innovation is driven by both absorptive capacity and new knowledge sources, with R&D personnel serving as a crucial manifestation of the former (Liu and White, 1997 ). Studies by Suseno et al. ( 2020 ), Lao et al. ( 2021 ), and Wen et al. ( 2023 ) have elucidated the innovation effects of high-level human capital from different perspectives. Thirdly, the innovation effects of digital (information) infrastructure are primarily realized through two mechanisms (Liu and Li, 2021 ; Zhang et al., 2022 ; Guo and Zhong, 2022 ; Ma and Lin, 2023 ; Tang and Zhao, 2023 ): (1) by reducing information asymmetry; (2) by breaking through administrative boundaries and geographical distances, facilitating information exchange and knowledge spillover among innovative entities.

Given the aforementioned statistical results and theoretical foundation, we are justified in deducing that NSC can promote the development of knowledge innovation through the impact on regional financial resources, human resources, and material resources, and the basic effect in hypothesis 1 has been tested.

Test of network effect and technology effect

Following the same methodology as the basic effect test, Table 6 reports the regression results of network effect and technology effect, using column (4) in Table 3 as the benchmark test (first step).

On the one hand, taking Centrality_Degree and Centrality_Closeness as dependent variables, it can be seen from columns (11) and (13) in Table 6 that the regression coefficients of NSC are both significantly positive at the 1% level (9.069/8.680), indicating that the construction of NSC has improved the centrality of city in the regional research cooperation network, that is, promoting the embedding of the city in the network structure. By relying on the supercomputer comprising thousands of processors and extending the development of new digital technologies such as big data, artificial intelligence, and cloud computing, NSC construction has not only expanded the city’s computing power services but also enhanced the region’s information and knowledge processing capabilities. Other cities are also more willing to establish cooperative relationships with NSC cities, thus promoting the embedding of the regional innovation network. Columns (12) and (14) in Table 6 report the regression results for the third step of the network effect, where Centrality_Degree , Centrality_Closeness , and NSC are simultaneously included in the regression equation. The estimated coefficients of the mechanism variables are both significantly positive at the 1% level (0.736/0.528). The promotion effect of NSC on knowledge innovation remains significant, and the estimated coefficients decrease from 8.934 to 2.226 and 4.329. In accordance with the social network theory, disparities in the positioning of individuals within a network can significantly influence the quantity and quality of information and resources they acquire, which leads to variations in innovative performance. Existing literature, based on diverse samples, has unveiled the augmented innovative performance associated with higher centrality in networks (Han et al., 2021 ; Wang et al., 2019 ). When entities occupy more central positions, they can engage in multidimensional technical collaborations and knowledge exchanges with various members. This enhances their capacity to absorb, transform, and reconfigure knowledge.

This result confirms that the construction of NSC can enhance the level of knowledge innovation by promoting the city’s embedding in the scientific cooperation network, thereby verifying hypothesis 2.

On the other hand, column (15) in Table 6 presents the second step estimation result of the technology effect mechanism. It shows that the estimated coefficient of NSC is significantly positive at the 1% level (0.091). After including Innova_effi and NSC in the regression equation, the estimated coefficient of the mechanism variable remains significantly positive at the 1% level (55.269). The absolute value of NSC ’s estimated coefficient decreases from 8.934 to 3.884 while still being significant. Indeed, the significance of knowledge production efficiency in innovation is primarily manifested through two dimensions. Firstly, R&D activities entail considerable risks and uncertainties, and high-efficiency aids in mitigating the costs associated with the knowledge innovation process. Secondly, there is the constraint of limited R&D resources. High efficiency implies a more effective utilization of funds and human resources, enabling the realization of a greater quantity and higher quality of innovative outcomes with the same inputs. Existing studies not only reveal the positive impact of technological efficiency enhancement on innovation performance but also underscore the crucial roles played by management efficiency and resource allocation efficiency in the innovation process (Bughin and Jacques, 1994 ; Hu and Chen, 2016 ; Yang et al., 2022b ).

The theoretical analysis and statistical results above indicate that NSC promotes the development of knowledge innovation by improving regional innovation efficiency, which confirms the technology effect (hypothesis 3). By enhancing the city’s computing power and allocation efficiency, NSC not only shortens the cycle of knowledge innovation and reduces its costs, but also optimizes the allocation of S&T resources, achieving scientific decision-making for urban innovation development.

Further analysis

Policy spillover.

Geographical proximity is not the only pathway that affects the diffusion of knowledge. With the rapid development of digital technology and the increasing improvement of infrastructure, the cross-regional flow of R&D elements makes the spatial connection of different cities closer. This study further examines the policy spillover of NSC from two aspects: cooperation proximity and digitization proximity. To address the problem of unclear coefficient economic implications, LeSage and Pace ( 2009 ) proposed the use of the partial derivative matrix method to divide the impact of the independent variable on the dependent variable into direct effect and indirect effect. In this study, the impact of the local NSC on knowledge innovation is considered a direct effect, while the impact of other regional NSCs on local knowledge innovation is regarded as an indirect effect.

The estimated results are shown in Table 7 , revealing that regardless of whether the geographical proximity matrix, cooperation proximity matrix, or digitization matrix is utilized, the estimated coefficients of NSC in both direct and indirect effects are significantly positive, confirming the existence of policy spillovers of NSC under different matrices (H4a is verified). This indicates that, in addition to radiating to geographically adjacent areas, NSC provides computing services to closely connected partners, thereby promoting local knowledge innovation development. Furthermore, the policy spillover effect of the supercomputing center is more effective when cities have comparable levels of digitization. The above results verify the policy spillover at the cooperative dimension, indirectly indicating that if the digital technology level of the city is limited, local knowledge innovation development is difficult to benefit from the NSC construction in the advanced areas. Considering the size and significance of the indirect effect coefficient, it can be seen that under the geographic and digitization matrices, the indirect effect of NSC is stronger, reflecting that both geographic distance and economic (digitization) distance are still the primary factors influencing policy spillover.

Given the existence of policy spillover in NSC, we construct β-convergence model based on the way of Sala-i-Martin ( 1996 ), examining the impact of NSC on regional knowledge innovation convergence. As depicted in Eq. ( 5 ) below, where \(L.{\mathrm{ln}{KI}}_{{it}}\) represents the lagged term of knowledge innovation, and \(D.{\mathrm{ln}{KI}}_{{it}}\) is the first-order differencing term for it. Our focal point lies in the alteration of \({\beta }_{0}\) before and after the inclusion of NSC . Should it be statistically significant (less than zero), notably increased in absolute value, it would signify that the establishment of NSC contributes to fostering inter-regional convergence in knowledge innovation.

The regression results are depicted in Table 8 . Whether using spatial econometric models or ordinary least squares, the coefficient of the lagged term L.lnY for knowledge innovation is significantly negative at the 1% level, implying that, after taking into account factors such as per capita GDP, comprehensive growth rate, and industrial structure, the latecomer regions have a higher knowledge growth rate than the knowledge-intensive regions. Columns (24) and (26) present the regression results after incorporating NSC , where the coefficient of L.lnY remains directionally and significantly unchanged, with only a slight increase in absolute value from 0.632 and 0.630 to 0.634 and 0.652. This indicates that although NSC can achieve policy spillover through geographical proximity, cooperation proximity, and digitization proximity, the impact is not sufficient to drive regional knowledge innovation convergence. Hypothesis 4b is not significantly supported by the results.

Heterogeneity analysis

The antecedent findings corroborate the knowledge innovation effects of NSC and unveil its primary mechanisms. Nevertheless, this impact may vary due to differences in urban knowledge orientation and scientific environments. This paper examines this heterogeneity in three distinct ways.

In comparison to research in aerospace, meteorology, engineering simulation, and other fields, basic scientific research may be less affected by NSC, despite collaborative research in areas like new energy, new materials, particle-liquid simulation, and condensed matter physics within Chinese NSCs. In erecting a single NSC in China, the government typically invests tens of millions of dollars at least, aspiring that NSC advancements will tackle tangible societal challenges and propel economic innovation. Yet, per information gleaned from prominent Chinese supercomputer portals and media coverage, the utilization of supercomputing in the realm of Mathematics seems relatively rare. Thus, based on “Research Area” information from the WoS database, scientific publications affiliated with each city under “Mathematics” are obtained. Firstly, cities are then classified into high-percentage groups (City_B) and low-percentage groups (City_NB) based on the proportion of publications (see “Critical value” in Table 9 , where 0.03 signifies that if the city’s mathematics publications exceed 0.03 in proportion, it is categorized as “City_B” with a value of 0). Despite Bdiff command tests indicating no significant differences in NSC coefficients between the two groups, the absolute value of the NSC coefficient in the “City_NB” group is slightly higher than that in the “City_B” group.

Secondly, this paper constructs the interaction term ( NSC×Basic ) to examine the moderating effect of urban knowledge orientation on the impact of NSC, as shown in columns (31) and (32) in Table 9 . The estimated coefficients of the interaction term ( NSC×Basic ) are significantly negative at the 1% level (−2.956/−2.573), indicating that the knowledge innovation effects of NSC tend to be lower in cities with a high proportion of “Mathematics” publications.

Thirdly, heterogeneity tests conducted through grouping or moderating effects cannot be precise for each individual and often pale in comparison when dealing with fewer groups. This paper further employs the synthetic difference in differences proposed by Arkhangelsky et al. ( 2021 ) to estimate the individual treatment effects (ITE) of cities. The estimation results in Table 10 provide the average treatment effects, T -values, and 95% confidence intervals for each city in the treatment group. It is noteworthy that only Shenzhen and Guangzhou exhibit significant treatment effects (8.938/3.685), and the Basic values in these two major cities are lower than the mean of the treatment group. Among them, Shenzhen stands out as a typical application innovation-oriented city (while also facing criticism for lacking a layout in basic research).

Robustness test

Parallel trend test.

The selection of an NSC site requires consideration of both the economic foundation of the city itself and its radiating influence in the region. Typically, the chosen city already possesses a relatively advanced knowledge base. To ensure the SDID model satisfies the “parallel trend” assumption prior to shock, we further examine the trend changes in both NSC and non-NSC cities. The equation is set as follows:

The study focuses on the coefficient β 1 of the interaction term between the time dummy variable and the NSC city dummy variable (if the city has NSC, the value is 1; otherwise, it is 0), as shown in Fig. 3 . The observation of the treatment effect can be divided into two stages. The first stage is before 2009, where it can be observed that the estimated coefficients of the interaction term are not significant, indicating no statistically significant differences in knowledge innovation changes between the treatment and control groups before policy implementation. The second stage is from 2009 to 2020, during which the policy treatment effect began to emerge in the second year of NSC construction and has been increasing year by year. The model satisfies the pre-assumption of “parallel trends,” while also presenting the dynamic changes of the treatment effect.

Following the construction of NSC, the estimated coefficients gradually become significant, and the effect of policy begin to manifest. This also indicates that the DID model satisfies the assumption of pre-parallel trends.

Endogenous processing

Due to the potential inclination of NSC construction sites toward cities with superior digital infrastructure, these urban centers often exhibit a heightened level of knowledge production. To mitigate the bias stemming from sample selection, reverse causality, and omitted variables, we try to address this issue under both OLS and spatial econometric models:

On the one hand, regarding the NSC as an endogenous variable, this study selects the per capita-fixed telephone ownership in 1984 ( FT ) (Li and Wang, 2022 ), relief degree of land surface ( Rdls ) (Zhang et al., 2022 ), and the frequency of digital economic policy terms ( FDEPT ) (Jin et al., 2022 ; Tao and Ding, 2022 ) as instrumental variables. These are chosen as instruments based on their correlation with the endogenous variable and independence from the error term. The historical level of information infrastructure influences the subsequent development of digital technology in the region; computing efficiency depends on data transfer speed (connectivity), which is influenced by the Rdls of the city (the cost and difficulty of constructing digital infrastructure); whether a region is selected as an NSC construction city is also influenced by the degree of emphasis on digitization in public sector policies. In terms of exogeneity, historical variable represented by FT and the geographical variable represented by Rdls have exclusive characteristics. Given that FT and Rdls are both cross-sectional data, this study adopts the approach outlined by Nunn and Qian ( 2014 ). We multiply the previous year’s nationwide total of internet and mobile phone users by FT , while Rdls is multiplied by the time trend terms.

The validity tests for the instrument variable selection are presented in Panel A of Table 11 , where the Kleibergen-Paap rk LM statistics are significant at the 1% level, F -values are all greater than 10, both Cragg-Donald Wald and Kleibergen-Paap rk Wald statistics exceed the critical values of the Stock-Yogo weak ID test (10% maximal IV size). This suggests that the three types of instrumental variables do not suffer from “under-identification” and “weak instrument” problems. Columns (33), (34), and (35) show the regression results for the first stage, indicating that the estimated coefficients of FT and PWF are significantly positive at the 1% level (0.249/5.016). It implies that the historical level of information infrastructure and the policy attention of the public sector to digitization indeed have a positive impact on whether a city is selected as an NSC. In contrast, the estimated coefficient for Rdls is significantly negative at the 1% level (−0.015), reflecting that higher Rdls do hinder a city from being selected as an NSC city. The results of the second-stage regression are shown in columns (36), (37), and (38), with NSC estimated coefficients all being significantly positive at the 1% level (69.125/37.768/20.310).

On the other hand, we try to address the endogeneity issue in the spatial econometric model in three different ways. (1) Dynamic SDM. Compared to static models, the dynamic SDM is advantageous in its more comprehensive consideration of time factors. This study sequentially includes the time-lagged dependent variable (dlag_1), the space-time-lagged dependent variable (dlag_2), and both of them (dlag_3) as explanatory variables in the regression model. The results shown in columns (39), (40), and (41) of Table 12 indicate that the estimated coefficients of the NSC are significantly positive at the 1% level (98.422/8.886/99.982). (2) Generalized spatial two-stage least squares method. Following the approach of Wang et al. ( 2022 ) we select the independent variable and its spatial lag term as instrumental variables. The regression results are shown in column (42) of Table 9 . Whether using first-order (1st order), second-order (2nd order), or third-order (3rd order) lagged independent variables (present only the results for the 1st order), the estimated coefficients of the NSC are also significantly positive at the 1% level. (3) Incorporating instrumental variables like FT , Rdls , and FDEPT into the G2SLS model, the results of the subsequent regression demonstrate that the NSC estimated coefficients still remain significantly positive at the 1% level (68.396/31.614/23.229).

The above outcomes indicate that potential endogeneity concerns do not significantly affect the validity of the baseline results.

Placebo and PSM-SDID

Building upon parallel trend tests, this study employs counterfactual analysis to further perform placebo analysis. By changing the construction time of the NSC and investigating the treatment effect determines whether the improvement in urban knowledge innovation is caused by the NSC. If the coefficient is significant, it suggests that the improvement of urban knowledge innovation level may not be caused by NSC, and the conclusion is not robust. Referring to Gao and Yuan’s research ( 2020 ), we only retain the samples from the period between 2000 and 2008, estimating them again by respectively moving the policy time forward one period (2008), two periods (2007), and three periods (2006). The results are shown in columns (46), (47), and (48) of Table 13 , with the NSC being insignificant, indirectly proving that the improvement in knowledge innovation level is attributed to the NSC. Moreover, this study employs the PSM-SDID method to conduct robustness checks on the original model, to overcome potential endogeneity issues caused by selection bias, and to enhance the accuracy of causal identification results. Using the year-by-year method to perform kernel matching, Econ , Industry_sec , n  +  g  +  δ , and the proportion of fiscal S&T expenditure to GDP are selected as a covariate. The standardized bias of each covariate after matching is less than 20 percent. Considering the requirements of spatial econometric models for balanced panel data, the samples with missing data in the year are removed, and ultimately, 714 samples are retained. As shown in columns (49), (50), and (51) of Table 13 , whether the spatial econometric model is adopted or not, the estimation results are consistent with the benchmark regression results, indicating the robustness of the positive impact NSC has on urban knowledge innovation obtained in the previous analysis.

Replacing the estimation method, variable, and sample

(1) Change the estimation method. Given that some cities have a value of zero for knowledge innovation, which accounts for a certain proportion of observations, the dependent variable being clustered on the left side of the value range may lead to biased estimation. Therefore, Tobit and negative binomial models are used to re-estimate the results. As shown in columns (52) and (53) of Table 14 , the estimated coefficients of NSC are significantly positive at least at the 1% level (8.958/0.236), indicating that the benchmark test results are not significantly affected by the structural characteristics of data. (2) Replace the dependent variable. Firstly, the knowledge innovation variable in our study is constructed by taking the ratio of urban S&T publications to the number of permanent urban residents. We replace permanent residents with urban employees to construct a new knowledge innovation variable and conduct another estimation. The estimated coefficient of the NSC is also significantly positive at the 1% level (25.857). Secondly, by using the number of highly cited papers in urban as the dependent variable, the estimated coefficient of NSC is significantly positive at the 1% level (0.491). The treatment effect of the policy remains robust, and both the quantity and quality of knowledge innovation are measured, achieving cross-validation. (3) Change sample. Considering that the small number of treatment groups in the sample may cause bias to the estimation, this paper deals with it by changing the sample in two ways: on the one hand, the number of samples (control group) is deleted. The study only retains 35 large and medium-sized cities in China and deletes samples of other cities for re-estimation. On the other hand, change the sample dimension. We raise the dimension to inter-provincial (31 provinces, municipalities, and autonomous regions), and Tianjin, Guangdong, Shandong, Jiangsu, Hunan, and Henan are respectively used as treatment groups (the data on the publication of S&T in the provincial area comes from the “China Science and Technology Statistical Yearbook”). Table 14 shows the SDID regression results (columns (56) and (57)), and the estimation coefficients of NSC are all significantly positive (3.020 /3.338), indicating that the previous research results are very robust.

Conclusion and policy implications

Discussion and conclusion.

As an important component of the national innovation system, LSRIs possess the capability to explore the unknown world, discover natural laws, and achieve S&T outputs. Existing research has revealed the impact of LSRIs on socio-economic development (especially in S&T innovation) from different perspectives (Marcelli, 2014 ; Michalowski, 2014 ; Qiao et al., 2016 ; Beck and Charitos, 2021 ), and theoretically explored the various dimensions of LSRIs’ scientific effect (Michalowski 2014 ; Qiao et al., 2016 ). However, there are two primary challenges in evaluating the impacts of construction: firstly, insufficient examination of the link between LSRIs and regional knowledge production; secondly, limited testing conducted within a causal inference framework. This study examines the impact of LSRIs on regional knowledge innovation with the backdrop of the Chinese NSC. The research results reveal the positive significance of this effect to a certain extent, directly confirming the scientific effect or S&T advancement effect of LSRIs mentioned in existing literature (Michalowski 2014 ; Qiao et al., 2016 ). Through mechanism testing, the identification of network effect (Lozano et al., 2014 ; Qiao et al., 2016 ; D’ippolito and Rüling, 2019 ; Beck and Charitos, 2021 ), capability cultivation (Michalowski 2014 ; Qiao et al., 2016 ), and clustering effect (Qiao et al., 2016 ; Beck and Charitos, 2021 ) is indirectly achieved. While increasing regional scientific financial, human, and material resources, LSRIs also contribute to the embedding of cities in regional innovation networks and the efficiency of utilizing innovation resources. Qiao et al. ( 2016 ) considered that the network effect is an important mechanism for LSRIs to interact with science stakeholders and strengthen scientific cooperation. Based on the co-publication of scientific publications, this study extends such network effects to the scientific cooperation connections established between cities. Other cities are more willing to establish cooperative relationships with NSC cities because they can benefit from computing power services. The improvement of data processing capabilities will also alleviate information overload problems and stimulate NSC cities to actively integrate into the innovation network. Some studies have mentioned the function of LSRIs in technology promotion and knowledge diffusion (Beck and Charitos, 2021 ; Scarrà and Piccaluga, 2022 ). Based on the spatial econometric model, our research results reveal the spillover effect of LSRI implementation, and this diffusion mechanism exists on multiple levels, including geographical proximity, cooperation proximity, and digitization proximity.

As a new productivity in the digital economy era, computing power plays an important role in promoting S&T progress, industry digital transformation, and economic and social development. NSC has the dual attributes of LSRI and digital infrastructure. Therefore, the research findings of this study also complement the literature regarding how digital infrastructure impacts the growth of innovation. Previous research has examined the impact of digital infrastructure on productivity and innovation from different dimensions including region and enterprise (Cardona et al., 2013 ; Balcerzak and Bernard, 2017 ; Zhou et al., 2021 ; Zhang et al., 2022 ; Tang and Zhao, 2023 ). However, the definition of digital infrastructure is mainly focused on network and communication infrastructure, lacking involvement in computing power. This paper provides direct evidence of how computing infrastructure impacts regional knowledge. NSC supplies high-performance computing services for scientific research, improving research and development efficiency, and shortening the output cycle of scientific research results (Marcelli, 2014 ). Moreover, it drives the development of new digital technologies represented by 5G, big data, cloud computing, and artificial intelligence, promoting the development of regional knowledge innovation. In addition, NSC is a national-level computing power hub established by the government based on urban innovation ecosystems in specific geographic locations, undertakes multiple missions of promoting local digital innovation, and accelerating knowledge spillover. Therefore, NSC can also be regarded as a place-based innovation policy. The research findings of this study reveal the significant impact of the intervention on local knowledge production. However, the driving effect of local knowledge growth on the convergence of regional innovation is limited, which is different from the evaluation results of other place-based innovation policies like “National Innovative City” and “urban cluster” (Tang and Cui, 2023 ; Gao and Yuan, 2020 ). The main reasons could be that the number of cities with NSC is still limited, and the construction of provincial and even more microscopic-level supercomputing centers has not been considered, which may lead to an underestimation of the radiation effect from the center. It is noteworthy that, akin to certain assessments of policy or digital innovation effects (Zhou et al., 2021 ; Liu and Li, 2021 ; Zhang and Wang, 2022 ; Tang and Zhao, 2023 ), our study encapsulates the inter-regional heterogeneity of NSC knowledge innovation effects. Diverging from existing research that relies on economic or geographical heterogeneity analysis (Yang et al., 2021 ; Gao and Yuan, 2020 ; Chen et al., 2023b ), our findings further unveil potential disparities in NSC innovation effects due to differences in urban scientific knowledge development emphasis.

In summary, the findings of this study can be distilled into the following key points:

NSC construction promotes local and surrounding area knowledge innovation.

The main mechanisms by which NSC promotes regional knowledge innovation include the increase in fiscal investment and talents in S&T (basic effect), the improvement of digital infrastructure (basic effect), as well as the enhancement of urban network centrality(network effect), and innovation efficiency(technology effect).

Geographical proximity, cooperation proximity, and digitization proximity constitute the main channels of policy spillover.

NSC has not shown a significant promoting effect on regional innovation convergence, and the radiation influence needs to be further improved.

Knowledge innovation effects of NSCs vary based on differences in urban knowledge orientation and scientific environments, with the treatment effects being notably pronounced in application innovation-oriented cities, exemplified by Shenzhen.

Policy implications

Firstly, considering the facilitating role of NSC in scientific knowledge production, it is necessary to enhance the supporting effect of LSRIs in scientific basic research and technological application research. While improving R&D efficiency, releasing the attraction of the large-scale scientific projects to innovative factors, and increasing the investment in S&T and the number of R&D personnel, improving urban digital infrastructure, and promoting the deep embedding of cities in scientific research collaboration networks.

Secondly, The study emphasizes the need to strengthen the policy spillover effect through various channels. This can be achieved by developing a city cluster strategy that coordinates the collaborative network of computing power within and around urban areas such as the Beijing-Tianjin-Hebei, Yangtze River Delta, and Greater Bay Area regions. For cities that have not yet established NSC, efforts should be made to optimize regional digital infrastructure and actively integrate into inter-regional cooperation networks, in order to create a favorable environment and basic conditions for cross-regional computing power scheduling, as well as to expand knowledge spillover in digitization and cooperation proximity.

Thirdly, given the weak promotion of NSC on regional knowledge innovation convergence, in the future, to strengthen the role of the national computing power hub as a connector and coordinator in the overall layout of the national computing power network, the computing infrastructure layout should be systematically optimized, with a focus on guiding the reasonable hierarchy for the layout of general data centers, supercomputing centers and intelligent computing centers. In this process, addressing the “computing power island” problem and expanding the influence of the center is an urgent issue that requires providing inter-city computing power collaboration and on-demand scheduling solutions.

Fourthly, the findings of this study reveal that the implementation of a place-based innovation policy, through the strategic establishment of NSCs in different regions, can effectively facilitate the growth of local knowledge creation. However, in order to achieve inter-regional convergence of knowledge, a concerted effort to refine inter-regional coordination mechanisms needs to be undertaken, simultaneously with the expansion of the centers. The limited yet positive contributions of NSCs also furnish valuable insights for other countries in the construction of LSRIs, digital infrastructure development, and implementation of place-based innovation policies. Particularly, in the selection of NSC locations, there should be consideration for regional disciplinary emphasis and the innovation environment, coupled with increased support for fundamental research.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Fund of China (Grant No. 71810107004).

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Haodong Yang & Gaofeng Wang

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Yang, H., Liu, L. & Wang, G. Does large-scale research infrastructure affect regional knowledge innovation, and how? A case study of the National Supercomputing Center in China. Humanit Soc Sci Commun 11 , 338 (2024). https://doi.org/10.1057/s41599-024-02850-8

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European Research Infrastructures in the International Landscape

What is a Research Infrastructure?

The term “Research Infrastructure” is not uniformly and globally defined. As an example, during the COOPEUS FP7 project (and associated US NSF COOPEUS project), a significant amount of time was spent mapping the US environmental research infrastructures. However, communication in the US side was hampered by the determining what to include in the analysis: Different funding agencies in the US had very different characterizations on the term infrastructure. For this reason RISCAPE defines the Research Infrastructure based on the European viewpoint, and selecting the international RIs from this perspective to maintain some degree of comparability.

The Directorate-General for Research European Commission defines to the Research Infrastructures as [1]

“research infrastructure” means facilities, resources and related services that are used by the scientific community to conduct top-level research in their respective fields and covers major scientific equipment or sets of instruments; knowledge-based resources such as collections, archives or structures for scientific information; enabling Information and Communications Technology-based infrastructures such as Grid, computing, software and communication, or any other entity of a unique nature essential to achieve excellence in research. Such infrastructures may be “single-sited” or “distributed” (an organised network of resources),

which is also corresponds to earlier FP7 definition of an RI.

However, for the purpose of RISCAPE, the following draft requirements are derived from the above definitions:

• To be a Research Infrastructure for RISCAPE purposes, the international RI must provide facilities, resources or related services to researchers also from outside of the Research Infrastructure institution itself . From the perspective of ESFRI RIs, this seems nonsensical: ESFRI RIs are typically meant to act as providers of services to researchers, not to act as researching institutions. However, in many non-European RI organizations, the infrastructure-like part is embedded into other research institutions and to maintain some degree of comparability with the European RIs, the need of general service provision is needed (even with requirements of registration, user fees, etc.).
• The purpose of the RI services must be to conduct or facilitate research . This requirement is needed to maintain the focus on research-orientation;
• The international RI must also have some degree of longevity . This requirement does not come from directly from the above definition, but is a major part of the whole European ESFRI process, and is one of the underlying implicit expectations for an RI. This requirement is also important for the usability of the RISCAPE report: short term projects with no long-term longevity or sustainability plans, would only have a of very brief use period in such a report and are therefore excluded.

These draft requirements will be further defined by the RISCAPE Stakeholder Panel and a level of freedom in choosing the international targets is retained in the disciplinary Work Packages (WPs 3-10).

[1] European Commission: Legal framework for a European Research Infrastructure Consortium – ERIC Practical Guidelines, DOI: 10.2777/79873

Scope definition (work in progress)

The scope defines which Targets (international RIs) will be included in the Landscape analysis. It provides the boundary conditions to be used our analysis. The idea is that all organisations which meet the scope criteria are included for analysis and the ones outside of it are excluded .

Aims of the project

Aims for the landscape analysis define why this analysis is done and how it is supposed to be used. Determining these aims together with identified Stakeholder groups is crucial to define the next steps of the analysis. However, we should start from the point of view of the RISCAPE project objectives:

The objective of the RISCAPE is to provide systematic, focused, high quality, comprehensive, consistent and peer-reviewed international landscape analysis report on the position and complementarities of the major European research infrastructures in the international research infrastructure landscape.

This is also reflected by the call requirements to supplement the ESFRI landscape process, i.e. our aims are to identify RIs internationally which are at a similar level of development to the RIs in ESFRI, or at least ones which bring us closer to understanding complementarities (even in some cases different size or importance) globally even if there are some deviations from the ESFRI definition. Such deviations are to be expected since Europe is very advanced in terms of RI provisions having the most extensive cross-national arrangements of RIs anywhere in the world.

Scope aspects

Some of the key aspects relevant to research infrastructure scope are

Existence of international research infrastructures which have complementarities with existing ESFRI RIs (and Key International Initiatives which are of a size and scope that would make them eligible for the ESFRI roadmap eg CERN). This is quite difficult to determine in some cases, but crucial to the later RISCAPE analysis. The aim is to find a definition to determine when an infrastructure can be considered to exist from an RISCAPE perspective. Very strict criteria could be applied, e.g. limiting this to RIs that have legal personality however this will not even capture some very important European research infrastructures, and will not necessarily reflect how RIs are organised globally. Similarly choosing a very loose definition (e.g. defining an RI as any group with a common name and a webpage or a social media hashtag) is not constructive either. Existence criteria could be demonstrated via many ways (depending on the selected minimum level for analysis), for example

• Based on a national law;
• Existence of legal organisation structure (i.e. being a legal person) or a confirmed part of a legal organisation structure (e.g. a separate division in a research institution for a specific purpose);
• Binding contract or a set of Memorandum of understandings between legal persons (research institutions, etc.);
• Support from national or regional power structures, e.g. part of the national/regional infrastructure roadmap, being a strategic priority of a regional government, etc.
• Documentation of existence, founding documents, rules, etc.
• (for looser networks) webpage, activity reports, publications, internal standards documents, best practices documents
• Outputs – evidence of facilities which can be accessed or the availability of outputs (eg data)

Temporal persistence must also be defined. In many cases, the landscape analysis is meant to be used for some period of time, so including upcoming activities needs to be considered. Similarly, a major existing RI could be fit into the scope of the analysis, but if it is set to be shut down soon, analysing the activities might not be fruitful (although a replacement could be, or the gap left by missing this target could also be relevant). Sustainability criteria can also be defined to limit the scope of the analysis for initiatives which have at least the intent and a plan for some scale of longevity. Temporal criteria could be demonstrated via

• Existing funding sources / service commitments, their time span
• Sustainability model, user fees, etc.
• Estimated operation time
• Scheduled shutdown time
• (for looser networks) activity plans, participation history, individual commitments
• (indirect) large scale investments (sign of long term commitment)

Relationship with the user communities can also be an important requirement for the scope setting. As the landscape analysis can require some sort of service portfolio meant to be usable with a worldwide scientific audience outside of the target organisation, this scope definition can be very important. The relationship to the user communities can be demonstrated by e.g.

• User manuals
• Web page information on the access (see below) methods
• Freely available documentation of the facilities and capabilities
• External advertisment (inc. social media)
• Stakeholder involvement (eg user groups, advisory boards)

Product scope is to make sure that the products (data, access, methods, etc.) are suitable for the landscape analysis planned. This could indicate that the products of the RI are connected to the scientific research, and that this is not an organisation with only non-scientific products. Demonstration by means of e.g.

• Product catalog
• Dissemination material
• User community products (e.g. publications)

Geographical (or location) relevance is cases crucial for the selection of the infrastructures in somc cases. Due to scientific, political or other reasons, the analysis can concentrate on specific areas, or disregard some areas. This limitation can be more political tham actually geographical, in which case the “location” might indicate ownership of e.g. a moving observatory or a web-based tool, regardless of its actual location. A regional (in the global sense) approach can be used to monitor the intensity of levels of engagement with RIs in different regions. This is distinct to ‘coverage’ and may be used to identify ‘hub’ locations.  Jurisdiction may be relevant for collaborative actions. Demonstration via e.g.

• Infrastructure documentation or mission statements
• International agreements (e.g. “covering observations in regions x and y”)

Access and openness can be also considered as a key element in terms of  fostering international collaboration consisting in being able to: i. attract the best researchers coming from over the world; ii. design of a new generation of interconnected infrastructures; ensure global interoperability.

• Data policies
• Access policies
• Access to international users (can be a scope criteria)

Impact / Significance requirement This is a catch-all requirement which is meant to be more determining the overall role of the infrastructure in the field. The main idea of this requirement is to make sure that the usage aspects and the position of the RI is considered in the process to include them in the analysis. However, to determining these aspects is much more difficult. Some possible ways to get documentation on these could be

• No of users
• Position in the field, Scientific significance (very hard to determine, perhaps using peer comments from European ESFRIs)
• Significance to the societal challenges
• Socio/economic impact
• Scientific and societal impact

What is a research Infrastructure? Commonalities and differences across science and policy, from mega-science to shared facilities

Since the beginning of the 21st century, the concept of Research Infrastructure (RI) has gained popularity in the research policy literature and in the political discussion about research funding. The emergence of the concept has been promoted by the European Union (EU), while most developed countries have integrated RIs and their funding within their set of research policy instruments. Despite its prominence, the notion of RI remains relatively ambiguous and subject to debate. Frequently, definitions are of operational nature and are driven by the interests of actors to receive funding for their own infrastructure. In our work, we investigate the different meanings of the concept of ‘research infrastructure’ in the scholarly literature and in the political process at the EU and national levels. First, we propose to challenge the presumption that the concept is purely procedural and look into meanings attributed by science and policy actors and what they have in common. Second, we explore differences in meanings across disciplinary communities, and between science and policy. Third, we identify some ideas that seem to constitute the core of the notion of research infrastructure that is widely shared by different scientific communities and by policy, such as the idea of open access, the one of sharing tools for research and the existence of a community of users. Our work will contribute to national and international debates on RIs, by inquiring into commonalities between their different conceptions across disciplines and sectors.

What is a research Infrastructure?

Commonalities and differences across science and policy, from mega-science to shared facilities.

Benedetto Lepori * and Marco Cavallaro **

* [email protected]

https://orcid.org/0000-0002-4178-4687

Institute for Communication and Public Policy, Faculty of Communication, Culture and Society Department, Università della Svizzera italiana, Switzerland

** [email protected]

https://orcid.org/0000-0003-4810-5525

1. Introduction

Since the beginning of the 21st century, the concept of Research Infrastructure (RI) has gained popularity in the research policy literature (Hallonsten and Cramer 2020), and in the political discussion about research funding (Franssen 2020). The emergence of the concept has been promoted by the European Union (EU) through the establishment of the European Strategic Forum for Research Infrastructures (ESFRI), while most developed countries have integrated RIs and their funding within their set of research policy instruments (Bolliger and Hallonsten 2020).

Despite its prominence, the notion of RI remains relatively ambiguous and subject to debate. Frequently, definitions are of operational nature and are driven by the interests of actors to receive funding for their own infrastructure. Some scholars even argue that the RI concept represents a purely political construct to fund initiatives that have little in common besides being awarded the RI label (Hallonsten 2020).

In our work, we investigate the different meanings of the concept of ‘research infrastructure’ in the scholarly literature and in the political process at the EU and national levels. First, we propose to challenge the presumption that the concept is purely procedural and look into meanings attributed by science and policy actors and what they have in common. Second, we explore differences in meanings across disciplinary communities, and between science and policy. Third, we identify some ideas that seem to constitute the core of the notion of research infrastructure that is widely shared by different scientific communities and by policy, such as the idea of open access, the one of sharing tools for research and the existence of a community of users.

While struggles over resources are an unavoidable and even necessary dimension of research funding policies, the lack of clarity about the concept and its different extensions makes an informed discussion and prioritization more difficult and might affect the legitimacy and accountability of funding decisions. Our work will contribute to national and international debates on RIs, by inquiring into commonalities between their different conceptions across disciplines and sectors.

2. Literature review

There is a small number of works dealing specifically with the origin of the concept of RIs, showing how it was historically linked to the concepts of “big science” or “mega-science” ( Cramer & Hallonsten 2020 ), where RIs were mostly considered to be large and single-sited facilities such as particle accelerators and telescopes, and to the emergence of European research policy and its goal of coordinating national initiatives ( Ulnicane 2020 ).

This literature emphasizes the political and processual nature of the RI definition. As put forward by Hallonsten ( 2020 ), the criteria proposed in the ESFRI definition of RIs as ‘facilities, resources or services of a unique nature that have been identified by European research communities to conduct top-level activities in all fields ( ESFRI Forum 2018 ) are not easily applicable to the European RI landscape. No RI included in the ESFRI roadmap is unique worldwide, and most of them have ‘competitors’ even at the national level, and the criterion of top-level activities hardly applies to most of them. In Hallonsten’s perspective, the core of the definition is in fact the identification of a process through which RIs are, first, identified by research communities and, second, prioritized in a political process involving countries, which are in principle willing to commit resources to their establishment and maintenance. The RI concept as used in the political process of roadmapping represents therefore a way of labelling and prioritizing some initiatives to channel them European and national funding. Accordingly, the (political) RI definition is closely associated with the establishment of roadmaps and related funding instruments ( Bolliger and Hallonsten 2020 ).

According to the literature, the labelling of RIs is, therefore, by and large, a tool for the governance and funding of research by public authorities ( Franssen 2020 ). Its emergence can be seen as a response by public authorities to two emerging issues. On the one hand, structural changes in public research funding implied a reduction of baseline funding to universities and public research organizations, through which many RIs were funded in the past ( Lepori, Jongbloed and Hicks 2023 ). This generated the need for a specific funding channel for RIs beyond the few very large infrastructures, which always required specific arrangements because of their size. On the other hand, current research policy increasingly emphasizes the need for coordination, achieving a critical mass and avoiding duplications ( Elzinga 2012 ); accordingly, it has become less acceptable to finance, in parallel, similar initiatives in a decentralized way. As shown by the case of digital humanities in the Netherlands, funding for RIs can be used as an incentive for scholarly communities, particularly in traditionally fragmented fields such as social sciences and humanities, to develop stronger forms of cooperation ( Franssen 2020 ). Conversely, success in putting RIs on national and European roadmaps largely depends on the ability of the related communities to organize their activities jointly.

The heterogeneity of RIs has led to efforts to develop typologies or classifications of RIs to create some order and to identify common patterns. A typology in terms of the RIs’ functions has been proposed by Hallonsten (2020 ). He distinguished between systems to perform measurements ( instruments ), facilities to observe the real world ( observatories ), collections of data to be used in research ( repositories ) and, finally, support that allows research at remote sites, such as aircraft ( vessels ).

Applying these classifications to the 60 RIs included in the ESFRI roadmaps, he was able to identify some patterns. Expectedly, the single-sited RIs are mostly instruments and observatories and concentrated in sciences (astronomy, physics, material sciences, engineering). On the contrary, the multiple-site category is very heterogeneous in terms of functions and organization; this applies particularly to the multiple-sited and multiple-purpose RIs, where it is hardly possible to find any commonalities – some of them being simple collections of national facilities. In Hallonsten’s view, this shows how flexible the RI concept is, but also questions whether overstretching it to this extent makes the concept useless. From a slightly different perspective, this analysis suggests that the concept of RIs might still be rather clear when dealing with specific and localized infrastructures, where concentrating facilities in one place allows the construction of more powerful telescopes, accelerators or test facilities; as soon as the single-sited constraint is lifted, it becomes more and more difficult to distinguish in practice between RIs and networks of laboratories or researchers sharing some facilities.

Clearly, the previous analysis shows that the mega-science concept does not any more adequately describe RIs. Has the concept been so overstretched to become virtually meaningless or can we identify emerging constitutive dimensions of a new concept?

In our work, we propose to delve further into the meaning of the RI concept as understood by scientific and policy communities and how it is associated with other patterns in science policy. Thereby, we aim to identify commonalities that could contribute to a core definition of RIs.

3. Methodology

To this aim, we used a mixed-method cross-sectional research design, using both quantitative and qualitative approaches (Schoonenboom & Johnson, 2017).

Firstly, to trace the use of the RI concept throughout the years and across scientific disciplines, we conducted a search query in November 2022 on the Scopus database ( www.scopus.com ) for the publications including the sentence ‘research infrastructure’ in the title or the keywords. To identify specific sets of meanings associated with RIs, we analyzed the co-occurrence of words used in the title and abstract of the publications and defined clusters through the association of neighboring words. The analysis was performed using the VoS viewer software (Van Eck and Waltman 2010).

Secondly, to analyze patterns in the use of the RI concept in policymaking, we considered a body of policy documents from ESFRI, the InRoad project ( https://www.inroad.eu/ ) - which notably collected a set of national definitions of RIs - supranational entities (EU, OECD) and national RI roadmaps in Europe. Based on these documents, we compare definitions across countries and supranational entities, and conduct a linguistic analysis, by looking into the prototypicality of words used in the definitions of RIs. For the latter, we will use the ProtAnt software which can be used to analyse neighboring words to a core concept and identify its meaning (Anthony & Baker 2015). For this analysis, European countries were selected based on the size of their research ecosystem and on available information.

4. Preliminary results

4.1. ri definition across scientific disciplines.

Our search query retrieved 1,578 documents, most of them being journal papers (655) or conference papers (650). As shown in Figure 1, the term was very rarely used before 2005, while the number of documents exceeded 150 in 2020 (data for the years 2021 and 2022 are still incomplete). The introduction of the term in European research policy, with the foundation of ESFRI in 2002, therefore pre-dates its scholarly usage, which started with Papon’s paper on European research cooperation (Papon 2004). Most disciplinary papers deal with specific instances of RIs and take for granted the label of ‘research infrastructure’ without attempting to elaborate on its definition.

Several works analyzed the establishment of entities, which are today included among research infrastructures, such as CERN or EURATOM. However, they were usually subsumed under concepts such as ‘big science or ‘mega-science’, i.e., with the idea that some forms of scientific inquiry required a large scale of investments (in terms of funding, personnel, and political process), which required coordinated action at the country and/or international level. This original idea of ‘big science’ (and related criteria of scale and uniqueness) is still present in many political definitions of Ris, but hardly fits the current usage of the term.

Figure 1: Publications in Scopus using the word ‘research infrastructure’

Retrieved publications are distributed over all research areas, with the largest numbers in computer science (19%), engineering (13%), social sciences (10%), medicine (8%), physics and astronomy (7%). While the term ‘big science’ was mostly used for facilities in natural sciences and engineering, the term ‘research infrastructure’ has therefore become widespread in all subject domains. Given the differences between scientific domains in how research is conducted and in the type of facilities required, this is expected to translate into high heterogeneity of the entities labelled as RIs.

A fine-grained view of the concepts associated with the RI term is provided by the analysis of the words used in the title and abstract of the publications, grouped by co-occurrences. In this analysis, neighboring words in the map occur together frequently in the publications, and, accordingly, clusters of words identify specific sets of meanings associated with RIs.

Four main clusters can be identified (Figure 2); their main feature is to be mostly associated with specific disciplinary contexts, suggesting indeed that RI definitions are largely discipline-specific. More specifically, we distinguish between:

A research data cluster (blue), where the focus is on repositories, data architecture, ontologies and open data. Expectedly, this cluster is also associated with social sciences and humanities.

An IT cluster (yellow), including advanced computing facilities, but also software platforms and testbeds.

A cluster dealing with facilities and research instruments in physics, engineering and environmental sciences , such as telescopes, and accelerators (green); expectedly, this cluster includes also the sentence ‘large research infrastructure’, as well as the terms associated with European policies such as ESFRI.

A health-related cluster (red), which can be broadly divided into two dimensions: on the one hand, clinical medicine such as clinical trials and patients’ data, and on the other hand (basic and translational) medical research, such as biobanks.

Figure 2: Words associated with research infrastructures

A cursory look at the most cited papers in this sample shows that they deal with the presentation of examples of entities labelled as ‘research infrastructure’ without questioning the RI definition itself. To provide some examples from the most cited papers, these include the US XSEDE/ACCESS computing infrastructure , the open-source and collaborative online platform for computational metabolomics ( W4M ); a review paper on biobanking for biomedical research, the Global Earth Observation System digital infrastructure ( GEOSS ); the Human Brain Project , and the Australian Industrial Ecology Virtual Laboratory ( IELab ).

4.2. RI definition in research policy

In its Regulation (EU) 2021/695 establishing Horizon Europe, the ninth EU Framework Programme for Research and Innovation, RIs are defined as “facilities that provide resources and services for the research communities to conduct research and foster innovation in their fields, including the associated human resources, major equipment or sets of instruments; knowledge-related facilities such as collections, archives or scientific data infrastructures; computing systems, communication networks and any other infrastructure of a unique nature and open to external users, essential to achieve excellence in R&I; they may, where relevant, be used beyond research, for example for education or public services and they may be single sited, virtual or distributed.”

This definition considers several aspects linked to RIs, such as their purpose (“achieve excellence in RI”, “use beyond research”), their form (single-sited, virtual, or distributed), and some wide categories and examples. Although this definition serves as a reference for EU Member States, the set of national definitions collected within the InRoad project shows variations across national contexts.

For example, the Austrian, French, Dutch, and Spanish definitions, among others, state that, to be considered as such, RIs must offer “unique capabilities”. Some countries defined specific threshold values, e.g., at least € 50 million in construction costs and ten years of service life in Germany, the range of € 3-14 million for the construction and/or implementation of RIs in Denmark, or the minimum of five years of service life in the Netherlands. In Germany, the use of RIs is regulated based on scientific quality standards, while in Sweden, RIs must be easily accessible to researchers, industry, and other stakeholders (InRoad 2018).

Differences can also be found in the RIs’ target groups. While most countries largely consider researchers as their main target groups, some countries emphasized the industry relevance of the RIs. This includes the UK, which in its last roadmap to date, sees RIs as enablers for the development, demonstration, and delivery of new “innovative processes, products, and services” (UKRI 2020). The evaluation of Spanish potential RIs takes into account the potential industrial return and cooperation with other types of stakeholders.

Table 1 shows an overview of aspects covered in the EU and national definitions of RIs.

5. Preliminary conclusions

Through this preliminary analysis, we identified two usages of the term ‘research infrastructure’.

Within the scientific sector, the term RI is generically used for entities or facilities, or tools shared by a research community to organize joint research activities. The nature of these entities varies between scientific fields, but they have in common two basic ideas: the existence of research communities sharing them and the fact that what is shared is not just research ideas or people, but some kind of material (or electronic) artefact. In practice, it might be sometimes difficult to distinguish between research cooperation and RIs.

In policy, the term RI is used in the research policy process (at the national and international level) to designate entities that are awarded a certain label and, by this, are facilitated in the search for research funding from different sources. In such a perspective, RIs are a tool in the governance and funding of research, which allows for prioritizing scientific programs and structuring research communities around a specific stream of resources. By its nature, the subset of RIs in this second meaning is much smaller than in the first; and some labelled RIs do not fully correspond to the first definition as they are more collections of independent activities than shared facilities.

The linguistic analysis of the policy documents will further test our preliminary findings and possibly reinforce the identification of core elements for defining RIs.

Open science practices

In this paper, we used data from Scopus for the quantitative analysis along with a body of publicly available documents a body of policy documents from ESFRI, the InRoad project ( https://www.inroad.eu/ ), supranational entities (European Commission, OECD) and national RI roadmaps in Europe.

Author contributions  

BL conceived the original idea, conducted the literature review and developed the theoretical framework with support from MC. BL prepared datasets and carried out the analysis of the use of the RI concept across disciplines. MC compiled the official sources and carried out the analysis of RI definitions in research policy with BL's advice. BL and MC contributed equally to the interpretation of results.

Competing interests

Not applicable.

Funding information

Anthony, L. & Baker, P. (2015). ProtAnt: A tool for analysing the prototypicality of texts. International Journal of Corpus Linguistics , Volume 20, Issue 3, 273 – 292.

Bolliger, I. & Hallonsten, O. (2020). The introduction of ESFRI and the rise of national research infrastructure roadmaps in Europe. In K. Cramer & O. Hallonsten (Eds.) Big Science and Research Infrastructures in Europe Edward Elgar Publishing.

Bonaccorsi, A. (2008). Search Regimes and the Industrial Dynamics of Science. Minerva , 46(3), 285-315.

Cramer, K. & Hallonsten, O. (Eds.) (2020). Big Science and Research Infrastructures in Europe, Edward Elgar Publishing.

Elzinga, A. (2012). Features of the current science policy regime: viewed in historical perspective. Science and Public Policy , 39(4), 416-428.

ESFRI (2017). Sustainable European Research Infrastructures, a call for action . EC Publications Office, Luxembourg.

Franssen, T. (2020). Research infrastructure funding as a tool for science governance in the humanities: A country case study of the Netherlands. In K. Cramer & O. Hallonsten (Eds.) Big Science and Research Infrastructures in Europe, Edward Elgar Publishing.

GSO (2019). Framework for Global Research Infrastructures . GSO.

Hallonsten, O. (2020). Research infrastructures in Europe: The hype and the field. European Review, 28(4) , 617-635.

Hallonsten, O. & Cramer, K. (2020). Big science and research infrastructures in Europe: Conclusions and outlook. In K. Cramer & O. Hallonsten (Eds.), Big Science and Research Infrastructures in Europe, Edward Elgar Publishing.

InRoad (2018). Good practices and common trends of national research infrastructure roadmapping procedures and evaluation mechanisms.

Lepori B., Jongbloed B., & Hicks D. (2023). Handbook of Public Research Funding . Edward Elgar Publishing Ltd., Cheltenham.

OECD (2010). Establishing Large International Research Infrastructures: Issues and Options. OECD, Paris.

Papon, P. (2004). European scientific cooperation and research infrastructures: Past tendencies and future prospects. Minerva, 42(1) , 61-76.

Schoonenboom, J., & Johnson, R.B. (2017). How to Construct a Mixed Methods Research Design.  Köln Z Soziol,  69(2), 107–131.

Ulnicane, I. (2020). Ever-changing Big Science and Research Infrastructures: Evolving European Union policy. In K. Cramer & O. Hallonsten (Eds.), Big Science and Research Infrastructures in Europe, Edward Elgar Publishing.

Van Eck, N. & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2) , 523-538.

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• License: CC BY help
• Review type: Open Review
• Publication type: Conference Paper
• Submission date: 21 April 2023
• Conference: 27th International Conference on Science, Technology and Innovation Indicators (STI 2023)
• Publisher: International Conference on Science, Technology and Innovation Indicators

Lepori, B. & Cavallaro, M. (2023). What is a research Infrastructure? Commonalities and differences across science and policy, from mega-science to shared facilities [preprint]. 27th International Conference on Science, Technology and Innovation Indicators (STI 2023).

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Open Science and Research Infrastructures provide the foundation and pillars necessary to tackle global environmental issues

COVID has highlighted the importance of Open Science and research infrastructures to accelerate the impact of scientific research

COVID has been the dominant topic for more than two years, not just in our daily lives but also in science. There is no conference, paper, or article that does not set every topic in the context of the pandemic we live through.

Two positive outcomes have emerged from this crisis: the evidence that Open Science (OS) is critical to overcoming global crisis; and the relevance of research infrastructures (RIs) to accelerate scientific research quality, reproducibility, and impact.

The rapid sequencing of the virus, data sharing and tracking of viral strains lead to a fast and global response to COVID that was only possible because researchers, supported by a policy system promoting global and transnational collaboration, were willing to share their practices, methods, and findings across the planet in an Open Science paradigm.

It showed us that to swiftly tackle global issues, a global OS approach was the only way to follow. In the European Union, the RIs belonging to the ESFRI (1) Food & Health group (2), were rapidly mobilised to support research efforts, facilitating access to viral genomes and fast-tracking access to research platforms and analytical services.

What lessons can be drawn from Open Science?

With hundreds of millions of Euros spent on research and medical studies, it was possible to develop multiple vaccines in less than two years, demonstrating that global issues could be tackled using a scientific approach given sufficient financial means. So, if open RIs were so effective in helping tackle the pandemic, what lessons can be drawn from Open Science?

The institutionalisation of OS originated several decades ago as a policy to transform scientific practice to adapt to the changes, challenges, opportunities, and risks of the digital era and to increase the societal impact of science. To provide a collective understanding of its implications worldwide, the United Nations Educational, Scientific and Cultural Organization (UNESCO) adopted, in November 2021, a set of recommendations (3) for an international framework for OS policy and practice, with shared values, principles and standards. UNESCO defines Open Science as “an inclusive construct that combines various movements and practices aiming to make multilingual scientific knowledge openly available, accessible and reusable for everyone, to increase scientific collaborations and sharing of information for the benefits of science and society, and to open the processes of scientific knowledge creation, evaluation and communication to societal actors beyond the traditional scientific communit y.”

For the UN Organization, more transparent, collaborative, and inclusive scientific practices are key to reducing inequalities in access to scientific development. Engaging in OS is crucial to enable the world to respond to the current pressing global issues. Our planet is changing rapidly due to human activities, leading to rapid and severe climate change, and the ecosystem services we have relied on for decades, particularly in the ocean, are now no longer able to cope. We must now act collaboratively on a global scale, which brings a significant challenge on its own.

RIs have been identified by UNESCO as being important enablers of Open Science. European RIs on the ESFRI roadmap provide important virtual and physical support services to research communities, such as research vessels, collections, biobanks, platforms, data repositories and data analyses, both in Europe and beyond. ESFRI RIs (4) adhere to the EU open access policy, ensuring that anyone can access and use their facilities.

Built by and for research communities, which is crucial for their long-term sustainability, they develop protocols and scientific practices and aid them in becoming global standards in their field of research, thus ensuring reproducibility and quality. The FAIR (5) principles which have emerged from the data community is now embedded in RIs, safeguarding the traceability of material and data between them and contributing to building the European Open Science Cloud (EOSC), a shared environment for storing research digital objects (publications, data, software).

Improving scientific quality

OS is also important in raising the bar on scientific quality. It drives better and more reliable science for scientists, for peers, but also for policy and the public due to its openness and transparency. RIs that embrace this open approach are the cornerstone of this scientific revolution by providing sustainable, interoperable, coordinated, and standardised services to researchers supported by EU member states and national operators.

They contribute to defragmenting, sustaining and simplifying scientific practices and services and are key players in research integrity and science diplomacy. These factors are the facilitators of international scientific cooperation, enabling excellent science grounded in shared principles that ensure research across the planet is comparable, compatible and of benefit to all. It is here that the OS RIs become important because, in this environment, they can serve everyone: their repositories, resources, databases, and services become relevant to scientists on a geographical scale beyond the region in which they are located, and thus they contribute to capacity building on a global scale.

The European Marine Biological Resource Centre (EMBRC-ERIC) is a European RI for marine biological resources and biodiversity and an example of an OS RI. It operates as a European Research Infrastructure Consortium (ERIC), a legal entity granted and assessed by the European Commission. It has an open access policy and functions as a support structure for the marine biological and ecological research and innovation communities. EMBRC-ERIC promotes OS and open innovation, together with FAIR principles “from sampling to data”.

As part of its activities, EMBRC has established a DNA-based observatory, the European Marine Omics Biodiversity Observation Network (EMO BON) at 16 of its sites to strengthen biological observation within Europe. The initiative is built upon shared, simple (inclusive) protocols, and internationally recommended metadata standards. All protocols, data log sheets, and data management plans are open to encourage collaboration and uptake of the protocol by other observatories. All management aspects and DNA extraction and sequencing, as well as obtaining relevant sampling permits and access and benefit sharing (ABS) (6) agreements, have been centralised, allowing the ERIC to act on behalf of its member states to ensure the smooth operation and open access to the observatory, thus acting as a catalyst for collaboration in Europe and beyond.

Overall, it is clear that OS practices have been a real enabler of scientific collaboration by putting in place the necessary framework for excellent, reproducible, and comparable science. Embedding them in a global framework ensures scientific methods are sustained and shared.

The United Nations Decade of Ocean Science for Sustainable Development has provided a catalyst for achieving the necessary collaborations to establish such a global framework for ocean science. The UN Decade Programmes such as OBON (7), Marine Life 2030 (8) and Ocean Practices (9) bring together researchers, observatories, and marine stakeholders from across the planet to work together to achieve SDG14, one of the 17 goals of the UN 2030 Agenda for Sustainable Development aiming at conserving and sustainably using oceans, sea and marine resources. It is important that the RIs become hubs of capacity building, cooperation, and collaboration where scientists worldwide can work on an equal footing and are not reduced to mere data providers. As stated by the United Nations Sustainable Development Goals: no one should be left behind. With the UN Ocean Decade, ocean scientists become one world community.

Inclusivity is crucial

However, although OS has shown its worth for researchers and RIs provide the necessary framework to support them, it is crucial to be vigilant and remember the need for inclusiveness. RIs must be mindful of technological and financial discrepancies between different regions. As many of the world’s biggest challenges today are disproportionately impacting Least Developed Countries (LDCs), RIs must remain not only accessible and usable for their scientists but open to contributing to building services that meet their needs. RIs should, in this respect, become platforms where Global North’s current technological investments and Global South’s needs meet to develop knowledge further. To realise this, RIs must also foster regional collaborations and embrace the participation of stakeholders from other countries in their development.

By urging its 193 member countries to adopt a global model for the benefit of science and society, the UNESCO recommendation anticipates Open Science to go beyond open scientific data and free access to scientific publications but to be more inclusive, balanced, accessible, open to citizen science and embedded in cultural diversity. The recently established RIs in Europe now face the challenge of transforming their community-construct services to embrace this construct and, along with the historical research operators in public research, find new routes for Open Science to thrive and bring it to bear on the global environmental issues that we face today.

Written by Anne Emmanuelle Kervella and Nicolas Pade

• ESFRI stands for the European Strategy Forum on Research Infrastructures, supporting “a coherent and strategy-led approach to policy-making on research infrastructures in Europe, and (facilitating) multilateral initiatives leading to the better use and development of research infrastructures, at EU and international level”: https://www.esfri.eu/forum
• http://www.lifescience-ri.eu
• https://www.unesco.org/en/natural-sciences/open-science
• This article focuses predominantly on those RIs on the ESFRI roadmap. This distinction is made as they are particular constructs, with a broad range of missions, including scientific excellence, implementing SDGs and science diplomacy. 41 ESFRI RIs are operating in Europe, 22 are being set up: https://www.esfri.eu/ .
• FAIR stands for Findable, Accessible, Interoperable, and Reusable and is the framework for data stewardship in science.
• ABS stands for the framework for access to genetic resources and the fair and equitable sharing of benefits arising for their utilization as adopted by the Nagoya Protocol to the Convention on Biological Diversity: https://www.cbd.int/abs/
• http://www.obon-ocean.org/
• http://www.marinelife2030.org/
• http://www.oceandecade.org/actions/ocean-practices-for-the-decade/

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Sustainable Research Infrastructures? What Are They?

Are you a researcher? If you are, research infrastructures are probably essential to your career – whether you realise it or not. Basically, research infrastructures are the services, facilities and resources used during research. Research infrastructures can be both physical and digital. This can include, for example: major pieces of equipment or collections of equipment; libraries, both physical and virtual; collections of scientific data; and computing systems and networks. While such infrastructures are primarily used for research, they can also have a wider purpose, such as in education or public service.

The Variety of Research Infrastructures

You probably use several different research infrastructures, every day that you work. It is easy to take these facilities and services for granted .

Some research infrastructures are large and high-profile. One example is the European Organisation for Nuclear Research – perhaps better-known as CERN, the world’s largest particle physics facility. Another is the Infrafrontier Research Infrastructure, which aims to boost genome research into human disease. National supercomputers, like ARCHER in the UK, also form part of a community’s research infrastructure.

Other infrastructures are smaller or less visible, but no less vital. Some of these you might use every day. Biological databases like GenBank, publication databases such as PubMed, or even citation databases like Web of Science all form part of knowledge-based research infrastructure . Physical collections, such as in library or museum archives, are also important.

Some less well-known examples of research infrastructure might surprise you! Facilities such as research ships, satellites and collections of living organisms are more examples of research infrastructure.

Pros and Cons of Research Infrastructures

Research infrastructures are essential to the research community. Without these facilities and services, scientific progress would be far more difficult. However, research infrastructures are not without problems.

Some infrastructures work very well . One example is ORCID – the system which gives every researcher a unique ID, with which they can tag all their output. ORCID is praised by researchers as an example of an infrastructure that works well, benefiting both individual researchers and the scientific community.

Sometimes, though, there are problems with the development and use of infrastructures. At a recent workshop, researchers discussed some of these issues. One problem is that publicly-funded infrastructures may sometimes need to compete with one another, for example for funding or users. This could mean fragmented services, leading to duplication of effort: a waste of time and money.

In addition, some infrastructures struggle to attract long-term funding. While there is often interest in setting up new infrastructure – developing new software, for example – less value is placed on long-term maintenance of the service.

Joining together resources created in different countries , or by different teams, can be a challenge. For example, researchers in different countries may use different systems to store their data or publish their output. If infrastructures are incompatible, it can be difficult for researchers to access what they need.

Finally, from the point of view of a researcher, infrastructure is often designed without the end user in mind. This means that the people who create the infrastructure – governments, funders or libraries, for example – fail to consult the people who will actually be using the service. The result is that researchers discover that infrastructure does not meet their needs.

Why is Research Infrastructure Evolving?

Going forward, research infrastructure will become even more important. More international collaboration is taking place than ever before. It is vital that the structures are in place to allow these collaborations to reach their full potential.

These days, most new infrastructures are digital . One particular area of growth is in Open Science: the push for the results of scientific research to be freely accessible to all. For this to become a reality, we need new systems and databases.

In recent years, many new digital databases have been built to provide a gateway to Open Access research (i.e. research articles that are free to view). These include DOAJ and PMC. This is also an example of where better collaboration could aid the research community. Many of these databases overlap, while none cover all open access journals .

In some cases, research infrastructures also allow researchers around the world to share data far more quickly than in the past. Sometimes, the infrastructure itself is operated by paid staff or volunteers who are based in different parts of the world. The design needs to reflect this fact.

How Can We Create Sustainable Research Infrastructure?

For research infrastructures to survive, they must be sustainable. This does not only mean financially sustainable. Infrastructures must also attract the support of the scientific community. After all, it is the people who use an infrastructure who will ensure its survival.

From a researcher’s perspective, infrastructures should meet a number of criteria to be sustainable :

• They must be open: in how they are funded, how they work, what their aims are and who can access them. • They must collaborate. This means avoiding competition and duplication of effort. • They must be diverse. As researchers from countries such as China and India take on a more prominent role, infrastructures should adapt to recognise this fact. Infrastructures should be designed with an international user base in mind. • They must be adaptable. Sustainable infrastructures should take into consideration the needs of researchers working across different disciplines. • They must make use of the best technology available at the time. • They should support the principles of Open Science .

So, what can researchers do to help develop sustainable research infrastructure? The main thing to do is to contribute to discussions about this topic. This could be informally, through online forums. It could also be through participating in consultations on new infrastructure, or joining workshops or discussion groups.

In addition, when choosing which infrastructures to use for a project, researchers can think carefully about two things: which infrastructure meets their needs, and which is most sustainable. The more users sustainable infrastructures have, the more likely they are to survive.

Which research infrastructures do you use in your work? What improvements could be made to them? Let us know your thoughts and suggestions in the comments below.

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Dear Colleague Letter: NSF Support for Natural Hazards Engineering Research Infrastructure (NHERI) during FY 2026-FY 2035

August 13, 2024

Dear Colleagues:

The purpose of this Dear Colleague Letter (DCL) is to inform the natural hazards engineering research community of plans for U.S. National Science Foundation (NSF) support for operations of the Natural Hazards Engineering Research Infrastructure (NHERI) during fiscal year (FY) 2026-FY 2035.

Since 2015, NHERI has operated through NSF support as a national distributed, multi-user facility that provides the natural hazards engineering community with access to research infrastructure - earthquake and windstorm engineering testing facilities, cyberinfrastructure, computational modeling and simulation tools, and research data - coupled with education and community outreach activities to advance knowledge and innovation for the performance of the nation's civil infrastructure and communities under natural hazard events (earthquakes, windstorms, and the associated hazards of tsunamis and storm surge).

PAST AND CURRENT NSF SUPPORT

The NHERI portfolio is described at the NHERI web portal . NHERI was developed through two NSF program solicitations NSF 14-605 and NSF 15-598 . As the outcomes of these two solicitations, NSF supported eleven cooperative agreements for a Network Coordination Office (NCO), Cyberinfrastructure (CI), Computational Modeling and Simulation Center (SimCenter), and eight awards for earthquake and windstorm engineering experimental laboratory and field equipment facilities. These eleven cooperative agreements will expire on September 30, 2025, the end of FY 2025.

NSF-supported researchers have used these NHERI resources to advance fundamental knowledge and innovation on the performance of civil infrastructure under earthquake, tsunami, windstorm, storm surge, and climate change hazards. NHERI research has led to (1) resilient and sustainable materials and new structural systems for building design and structural rehabilitation; (2) new methods to predict and improve the performance of soil, underground infrastructure, and on soil-structure-interactions; (3) strategies for safeguarding coastal infrastructure; (4) open source computational, simulation, and workflow research and educational tools; (5) new experimental simulation techniques and instrumentation; and (6) systematic field data collection procedures that capture civil infrastructure performance site response information during post-disaster field reconnaissance investigations. Laboratory, field, simulation, and post-disaster reconnaissance data from NHERI research have been published in the certified NHERI Data Depot for data management, community sharing, and data reuse. The NCO has supported the NHERI Summer Institute for early career scholars, a NHERI-wide Graduate Student Council, and a NHERI-wide Research Experiences for Undergraduates program. NHERI awardees have organized Natural Hazards Summits in 2022 and 2024, NHERI Computational Symposia, NHERI Computational Academies, and workshops and bootcamps for uses of NHERI resources.

FUTURE NSF SUPPORT

Providing research infrastructure that can support new knowledge advancements, innovations, and workforce development for the resilience and sustainability of the nation’s civil infrastructure and communities under natural hazard events continues to be a high priority for NSF and the Directorate for Engineering. This DCL conveys the NSF plan for continued support of a visionary NHERI construct for FY 2026-FY 2035, with initial five-year cooperative agreements, starting on October 1, 2025, to be supported through the two funding mechanisms described below.

First, to provide continuity in NHERI operations to the natural hazards engineering community, this DCL conveys the NSF plan for potential cooperative agreement renewal of NHERI’s Network Coordination Office, Cyberinfastructure ( NHERI web portal ), Computational Modeling and Simulation Center, and Natural Hazard and Disaster Reconnaissance (RAPID) facility originally supported under NSF 14-605 and NSF 15-598 . NSF encourages the Recipients of these awards to discuss with the cognizant Program Officer the potential to submit an initial five-year renewal proposal for FY 2026-FY 2030. Renewal proposals are anticipated to be submitted by a target date of February 1, 2025 . If renewed for FY 2026-FY 2030, and then based on the Recipient’s satisfactory performance during that period and availability of funds, NSF would consider a second five-year proposal for FY 2031-FY 2035. NSF support will also be contingent upon the outcome of the external merit review of each five-year proposal.

Second, a forthcoming program solicitation is anticipated to be issued in 2024 by NSF’s Directorate for Engineering, Division of Civil, Mechanical and Manufacturing Innovation. The new program solicitation is expected to be for a competition to establish a new NHERI portfolio for exemplary operations of experimental and field equipment/instrumentation facilities to serve as national resources for NHERI research and education. These facilities will be expected to advance frontier science and engineering research focused on the impact of climate change, earthquake, tsunami, windstorm, storm surge, flooding, and fire/wildland-urban interface (WUI) hazards on the nation’s civil infrastructure. Current NSF-supported NHERI facilities, as well as other existing facilities that can bring new national resources to NHERI, would be eligible for this competition. Through this new solicitation, it is anticipated that support will be provided, for “multi-user ready” facilities that can provide fully operational experimental laboratory and/or field equipment/instrumentation, with unique, benchmarked capabilities not elsewhere available in the U.S., coupled with fully operational data acquisition system(s).

The planned solicitation is not intended to support the construction of a new facility or upgrade of an existing facility. Funding opportunities for equipment, instrumentation, and facility development are available through NSF programs such as Major Research Instrumentation , Mid-scale Research Infrastructure-1 , and Mid-scale Research Infrastructure-2 .

NSF does not intend to provide additional information beyond this DCL until the program solicitation is issued, as that will be the official issuance for this competition and take precedence over the information in this DCL. The anticipated due date for proposals submitted in response to this new program solicitation will be at least 90 days following the publication date. NSF will host an information webinar after the solicitation is issued.

NHERI is classified by NSF as a mid-scale facility, and therefore the NHERI components for the NCO, CI, SimCenter, and experimental facilities will be required to operate in accordance with the NSF Research Infrastructure Guide (RIG) , NSF 21-107, or its successor. The RIG is currently undergoing revision ( draft available ) to provide clarity of the guidance, with the updates to be finalized in spring 2025. More information about NSF-supported major and mid-scale facilities is available at the NSF Research Infrastructure Office website .

It is anticipated that all NHERI proposals for FY 2026-FY 2035 will be expected to build upon input gathered from the research community through workshop reports and community studies. These documents articulate the research needs and the consensus that continued support of a natural hazards engineering research infrastructure is critical for the research community to advance frontier engineering and science for understanding and mitigating the impacts of natural hazards on civil infrastructure and communities. These documents can include the NHERI Science Plan , U.S. National Science Foundation Natural Hazards Engineering Research Infrastructure (NHERI) Decadal Visioning Study 2026-2035 , Frontiers in Built Environment NHERI Series , The Role of Engineering to Address Climate Change: A Visioning Report , Engineering Materials for a Sustainable Future: A Visioning Report , AI Engineering: A Strategic Research Framework to Benefit Society , Strategic Plan for the National Earthquake Hazards Reduction Program, Fiscal Years 2022-2029 , and Strategic Plan for the National Windstorm Impact Reduction Program .

FURTHER INFORMATION

Program Contact: Questions or comments should be directed to Joy Pauschke, NHERI Program Director, 703-292-7024, [email protected] .

Susan S. Margulies Assistant Director, Directorate for Engineering (ENG)

Title: Integrated Research Infrastructure Architecture Blueprint Activity (Final Report 2023)

• National Science Foundation (NSF), Washington, DC (United States)
• Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
• USDOE Joint Genome Institute (JGI), Berkeley, CA (United States)
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• Argonne National Laboratory (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF)
• Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
• Argonne National Laboratory (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
• US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Advanced Scientific Computing Research (ASCR)

The complexity of scientific pursuits is increasing rapidly with aspects that require dynamic integration of experiment, observation, theory, modeling, simulation, visualization, machine learning (ML), artificial intelligence (AI), and analysis. Research projects across the Department of Energy (DOE) are increasingly data and compute intensive. Innovative research teams are accelerating the pace of discovery by using high-performance computational and data tools in their research workflows and leveraging multiple research infrastructures. Additionally, several recent high-level U.S. government reports underscore the necessity of a new advanced computing ecosystem for international competitiveness and national security. International competitors are moving forward with major research infrastructure integration efforts that seek to capture a competitive advantage in the global innovation race. Owing to its unparalleled constellation of world-class experimental and observational facilities and high-performance and extreme-scale computational, data, and networking infrastructure, DOE is positioned to be a global leader in this new era of integrated science. However, this new integration paradigm will demand continuing evolution to ensure the U.S. remains a global leader in research and innovation. The DOE Office of Science (SC) has seized on the strategic importance of integration and has adopted a vision for Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. To respond to the evolving computational requirements of research and the competitive international innovation landscape, experimental facilities could be connected with high performance computing resources for near real-time analysis, and resources should be provided for merging enormous and diverse data for AI/ML techniques and analysis.

Citation Formats

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Research Infrastructure Program

Important information about nsf’s implementation of the revised 2 cfr.

NSF Financial Assistance awards (grants and cooperative agreements) made on or after October 1, 2024, will be subject to the applicable set of award conditions, dated October 1, 2024, available on the NSF website . These terms and conditions are consistent with the revised guidance specified in the OMB Guidance for Federal Financial Assistance published in the Federal Register on April 22, 2024.

Important information for proposers

All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements. Submitting a proposal prior to a specified deadline does not negate this requirement.

Experimental infrastructure plays a central role in enabling transformative research and innovation at the frontiers of computing and discovery, and in providing unique learning opportunities for current and future generations of computing researchers and educators. Cognizant of the diversity of research infrastructure needs in the CISE community, this program provides support for the acquisition, enhancement, and operation of forefront computing research infrastructure and experimental facilities for all CISE research and education areas. Supported facilities range from instrumentation needed by a few projects to major experimental facilities for an entire department. Support is also provided to enhance the computational and human infrastructure in minority-serving institutions and to support the equipment needs of collaborative, distributed research projects. A goal for the coming year is to support a wider range of infrastructure needs, research projects, and institutions.

Research Infrastructure Program Staff

Program contacts

Funded as part of this program.

• Community Infrastructure for Research in Computer and Information Science and Engineering (CIRC)
• Global Environment for Networking Innovations (GENI)
• Major Research Instrumentation Program (MRI)

Organization(s)

• Directorate for Computer and Information Science and Engineering (CISE)
• Division of Computer and Network Systems (CISE/CNS)

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Bigelow lab receives $7 million for algae research, business development

The Boothbay lab will use the National Science Foundation grant to build the Maine Algal Research Infrastructure and Accelerator in a bid to unlock more ways macroalgae and its microscopic counterpart can be used and ultimately marketed.

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Researchers at Bigelow Laboratory for Ocean Sciences believe algae can make a big splash in the agricultural, aquaculture and pharmaceutical industries, and now they have the funding to help prove it.

Bigelow Laboratory has received $7 million in federal funding from the U.S. National Science Foundation to build the Maine Algal Research Infrastructure and Accelerator, which will focus on research, training, innovation and workforce development, the lab said.

The funding is part of a $38 million award given to researchers in Maine, Rhode Island, Mississippi, New Mexico and Puerto Rico as part of the Science Foundation’s Established Program to Stimulate Competitive Research. The program aims to enhance research facilities, expand networks, support workforce development and accelerate economic growth among 28 states and territories that have historically received less funding for scientific research.

Beth Orcutt, the vice president of research at Bigelow Laboratory for Ocean Sciences in Boothbay, says “there is so much potential for what algae can do to solve problems, and we wanted to double down on that.” She was photographed in March.   Shawn Patrick Ouellette/Staff Photographer

“We looked at that (program) and saw there was a unique niche that we could help the state fill,” said Beth Orcutt, vice president of research at Bigelow. “There is so much potential for what algae can do to solve problems, and we wanted to double down on that.”

Bigelow is a nonprofit, independent oceanography institute in Boothbay, exploring scientific topics that range from marine microbes to the large-scale processes that drive ecosystems and the health of the planet.

The lab is home to the National Center for Marine Algae and Microbiota – a distributor of marine algae for scientists – and has sold its cultures in 58 counties. Orcutt said it houses the world’s most genetically diverse collection of algae. The center also offers consulting, contracted research and intellectual property development. The commercialization helps the nonprofit laboratory fund additional research. Advertisement

Seaweed aquaculture is booming in Maine. The state has quickly become the largest U.S. producer of farmed kelp, harvesting just under 1 million pounds, or 453 wet tons, in 2022, up from just under 53,000 pounds, or 24 tons, in 2018.

Maine harvesters and manufacturers take the seaweed and turn it into veggie burgers, Korean-inspired seaweed salads and other food products.

But scientists are hoping to unlock more ways the macroalgae and its microscopic counterpart can be used and ultimately marketed.

Algae are somewhat mysterious – they produce tens of thousands of compounds with unknown functions and therefore unknown potential.

Maine seaweed farming conference promotes sustainable growth

“Understanding these compounds and their roles so that they can be used is at the core of this new effort led by Bigelow Laboratory,” said Michael Lowmas, a senior research scientist at Bigelow.

Some scientific studies have shown that the compounds have beneficial pharmaceutical properties used for killing cancers, bacteria and fungi, and reducing inflammation. But there’s little understanding about which of the compounds – or which mix of compounds – is having the effect, he said. Bigelow scientists hope the research can aid the pharmaceutical industry. Advertisement

Another algal compound creates a red pigment called astaxanthin that belongs to a group of chemicals called carotenoids. That red pigment is what makes salmon pink. In the wild, it’s part of their diet, but some farmed salmon are fed a synthetic form of the pigment to get their signature hue.

“What if we could come up with better ways of growing the algae in culture to make that pigment to feed to farmed fish to make them naturally pink like they would be if you caught them in the wild?” Orcutt said.

Bigelow researchers are also studying whether algae can be used in livestock feed to reduce methane emissions from burping cows and whether it can impact milk production.

It also has potential environmental uses. Some researchers believe it can be used to make biofuel. Algae are known carbon capturers, so Orcutt said there are ongoing studies about how it can help reduce carbon dioxide emissions.

“Really, the landscape is wide open,” she said.

Eel guts, salmon blood: Maine companies look to make the most of fish waste

Some companies are already exploring ways to use algae. Advertisement

Everything Seaweed, or EvSe, for example, hopes to use seaweed cellulose nanofibers as a replacement for PFAS coating on food packaging and eventually as a texturizer in skincare.

Ocean Organics processes seaweed to use in its plant fertilizer.

OPPORTUNITIES IN A BLUE ECONOMY

The rapid expansion of the so-called “blue economy” can provide numerous opportunities for other socially conscious start-up companies, Bigelow told the National Science Foundation.

“A fundamental challenge to advancing algae in the blue economy is identifying how to connect and support continuing interactions between research institutions, business training/creation entities and institutions of higher learning,” Bigelow said. “This challenge is particularly problematic in Maine, where these entities are spread over a large, sparsely populated and aging state. The Maine Algal Research Infrastructure and Accelerator (MARIA) project tackles this challenge.”

The accelerator will be run by Lowmas and Manoj Kamalanathan, also a senior research scientist. Advertisement

Sens. Susan Collins and Angus King said in a joint statement Wednesday that the grant will help Bigelow “harness the power of algae” by unlocking new potential for Maine’s agriculture, aquaculture and pharmaceutical industries.

“Maine is uniquely positioned to be a leader in the aquaculture and biochemistry industry and the addition of Bigelow Laboratory’s algal accelerator is another example of our cutting-edge innovation,” they said. “Not only will this research help drive new approaches to commercial algae use, but it will also open the door to workforce opportunities between local farmers, algal companies and our premier research institutions.”

Bigelow is collaborating with several institutions that also received some of the funding, including: Mount Desert Island Biological Laboratory, the University of New England, Colby College, Southern Maine Community College, Maine Center for Entrepreneurs, Gulf of Maine Ventures and the Maine Technology Institute.

Bigelow is in the midst of a $30 million expansion of its campus. The 25,000-square-foot addition will add sorely needed space for a rapidly growing team of scientists, as well as broaden its education, research and workforce development capabilities. It’s slated to open in spring 2025.

Related Headlines

$30 million project will expand footprint, capacity of Bigelow research center in East Boothbay

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Nonhuman Primate Evaluation and Analysis: Final Report

Nonhuman primates (NHPs) play a crucial role as animal models in biomedical research across many research areas. Due to their close physiological similarity to humans, NHP studies provide insights into human disease, cognitive and behavioral functions, aging processes, reproductive medicine, and more. Much of the research performed on NHPs is facilitated by the resources provided by the National Institutes of Health (NIH). A significant proportion of these NHP resources is supported by grants, cooperative agreements, or contracts managed by the Office of Research Infrastructure Programs (ORIP) within the NIH or by other NIH institutes, centers, or offices.

This NHP Evaluation and Analysis, initiated at the request of ORIP and the Office of AIDS Research, provides an overview of the demand and supply of NHPs in the United States. This study covers usage trends from fiscal years 2018 to 2022 and provides a forecast of NHP usage for calendar years 2024 to 2028. The findings from this study will assist NIH in refining management strategies for NIH-supported NHP research resources, which are essential to the pursuit of some of NIH’s high-priority research programs. Additionally, this study will assist the biomedical research community in planning and making decisions that will meet its research needs.

The analysis comprises four components:

• An identification of major available NHP service providers in the United States and their capabilities
• An analysis of historical NHP usage trends by NIH awardees and others from 2018 to 2022
• An assessment of the current NHP landscape and forecast of foreseeable NHP needs
• A survey of NIH-supported extramural and intramural NHP users to characterize current and foreseeable research needs

Past Reports

• Nonhuman Primate Evaluation and Analysis Part 1: Analysis of Future Demand and Supply
• Nonhuman Primate Evaluation and Analysis Part 2: Report of the Expert Panel Forum on Challenges in Assessing Nonhuman Primate Needs and Resources for Biomedical Research

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The U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) today announced the 18 winners of the American-Made Solar Photo Competition: Hit Me with Your Sun Shot . Professional and amateur photographers submitted awe-inspiring photos showcasing solar energy technologies, infrastructure, and research and development activities.  

"The remarkable photos we received spotlight the innovation and progress in solar technologies, as well as the transformative impact these advancements have had in communities," said Becca Jones-Albertus, SETO Director and Acting Deputy Assistant Secretary for Renewable Energy . “This artwork serves as a testament to the dedication and vision that have driven the solar industry forward." 

DOE received more than 450 submissions from 113 competitors. Judges from SETO and other DOE offices evaluated submissions based on their emotional appeal and impact, composition and content, technical quality, and originality. Based on judging scores, SETO selected two grand prize winners ($2,500 each) in addition to one winner ($1,000 each) and one runner-up ($500 each) for each of the eight photo categories.  

The winners are featured in a DOE Flickr album , and other eligible submissions will be posted soon. These photos are available to the public. If you intend to use the photos, please give credit to the photographer in your caption. 

Grand Prize Winners  

Ziaur chowdhury.

Rows of sleek solar panels stand tall amidst a vibrant field of wildflowers, capturing the sun's energy.

Brianna Bruce

A jackrabbit stands at attention just beyond the cover of a solar array near Hayward, California.

Solar and Weather Category Winner   

First place: dylan sontag.

Demonstrating snow shedding by adjusting trackers to a steeper angle, enabling faster site production recovery in Colorado.

Runner-up: David Penalva

A small plant thrives in the arid desert climate of Texas with solar trackers in the background.

Solar Workforce and Installation Category Winner   

First place: saman kouretchian.

Recent solar installation in Palm Desert, CA.

Runner-up: Craig Fritz

All hands on deck to clean the heliostats the morning before a critical test at the National Solar Thermal Test Facility at Sandia National Lab in Albuquerque, NM.

Concentrating Solar-thermal Power Category Winner   

First place: craig fritz.

The National Solar Thermal Test Facility arranged the heliostat field for the groundbreaking of the Gen 3 Particle Pilot Plant, with the original solar tower reflected in one of the facets at Sandia National Laboratories in Albuquerque, NM.

Runner-Up: Osdilieva Hinojosa

A CSP plant shines amongst the desert sky at the Ivanpah Solar Power Facility in San Bernardino County, CA. 

Research Processes, Solar Technologies in Detail, and Manufacturing Category Winner   

Dr. Angelique Montgomery places a perovskite module in a solar simulator at Sandia National Laboratories Photovoltaic System Evaluation Laboratory (PSEL) in Albuquerque, NM.

Runner-Up: John Freidah

Batch-processed perovskite solar cells and faster learning cycles promise to increase PV R&D productivity. 

Utility Scale, Commercial Solar, and Grid Integration Category Winner  

First place: michael slider.

Located in Riverside, CA and using American-made equipment, Intersect Power’s Oberon Facility generates 500 MWac, powers more than 200,000 homes, and includes 250 MW of storage.  

Runner-Up: Yung Chen

The 5 MW Orinda Photovoltaic Renewable Energy Project by East Bay Municipal Utility District,  located in the scenic hills next to the San Pablo Reservoir in Tilden Regional Park, CA.

Community Solar and Multifamily Housing Category Winner  

First place:  rachel gentile.

A series of solar arrays on an affordable multifamily housing development in Allston, MA.

Runner-Up: Rachel Gentile

Two school buses driving below the community solar array at the Discovery Museum in Acton, MA.

Residential Solar Category Winner  

First place: quincy biddle.

Solar panels on the rooftops of Massachusetts homes against a backdrop of cumulus clouds.

Runner-Up: Jacob Gross

Residential solar canopy, installed in Philadelphia, Pennsylvania. 

Agriculture and Solar, Solar in Nature, Solar and Wildlife Category Winner  

First place: chad alkire.

A bee circles around native pollinator plants used for vegetation management at E.W. Solar Farm in Kentucky.

Runner-Up: Nick de Vries

Resident flock of sheep at Silicon Ranch's Snipesville Ranch project in Jeff Davis County, Georgia. 

View all of the winners of this year’s competition and submissions to the 2017 competition .  

Learn more about the Solar Energy Technologies Office.