licensing obligation
The Norwegian Association of Higher Education Institutions (UHR) has created a short dictionary (termbase). In this dictionary, you will find translations of more than 2000 administrative terms from the two written languages in Norway to English, and vice versa.
CESSDA ERIC (the Consortium of European Social Science Data Archives European Infrastructure Consortium) provides an expert tour guide on data management . The guide aims to help researchers make their data findable, understandable, sustainably accessible, and reusable.
Wilkinson, M.D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A.,...Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data , 3 , Article 160018. https://doi.org/10.1038/sdata.2016.18
PhD on Track
These examples of data management plans (DMPs) were provided by University of Minnesota researchers. They feature different elements. One is concise and the other is detailed. One utilizes secondary data, while the other collects primary data. Both have explicit plans for how the data is handled through the life cycle of the project.
All data to be used in the proposed study will be obtained from XXXXXX; only completely de-identified data will be obtained. No new data collection is planned. The pre-analysis data obtained from the XXX should be requested from the XXX directly. Below is the contact information provided with the funding opportunity announcement (PAR_XXX).
Types of data : Appendix # contains the specific variable list that will be used in the proposed study. The data specification including the size, file format, number of files, data dictionary and codebook will be documented upon receipt of the data from the XXX. Any newly created variables from the process of data management and analyses will be updated to the data specification.
Data use for others : The post-analysis data may be useful for researchers who plan to conduct a study in WTC related injuries and personal economic status and quality of life change. The Injury Exposure Index that will be created from this project will also be useful for causal analysis between WTC exposure and injuries among WTC general responders.
Data limitations for secondary use : While the data involve human subjects, only completely de-identified data will be available and used in the proposed study. Secondary data use is not expected to be limited, given the permission obtained to use the data from the XXX, through the data use agreement (Appendix #).
Data preparation for transformations, preservation and sharing : The pre-analysis data will be delivered in Stata format. The post-analysis data will also be stored in Stata format. If requested, other data formats, including comma-separated-values (CSV), Excel, SAS, R, and SPSS can be transformed.
Metadata documentation : The Data Use Log will document all data-related activities. The proposed study investigators will have access to a highly secured network drive controlled by the University of Minnesota that requires logging of any data use. For specific data management activities, Stata “log” function will record all activities and store in relevant designated folders. Standard file naming convention will be used with a format: “WTCINJ_[six letter of data indication]_mmddyy_[initial of personnel]”.
Data sharing agreement : Data sharing will require two steps of permission. 1) data use agreement from the XXXXXX for pre-analysis data use, and 2) data use agreement from the Principal Investigator, Dr. XXX XXX ([email protected] and 612-xxx-xxxx) for post-analysis data use.
Data repository/sharing/archiving : A long-term data sharing and preservation plan will be used to store and make publicly accessible the data beyond the life of the project. The data will be deposited into the Data Repository for the University of Minnesota (DRUM), http://hdl.handle.net/11299/166578. This University Libraries’ hosted institutional data repository is an open access platform for dissemination and archiving of university research data. Date files in DRUM are written to an Isilon storage system with two copies, one local to each of the two geographically separated University of Minnesota Data Centers. The local Isilon cluster stores the data in such a way that the data can survive the loss of any two disks or any one node of the cluster. Within two hours of the initial write, data replication to the 2nd Isilon cluster commences. The 2nd cluster employs the same protections as the local cluster, and both verify with a checksum procedure that data has not altered on write. In addition, DRUM provides long-term preservation of digital data files for at least 10 years using services such as migration (limited format types), secure backup, bit-level checksums, and maintains a persistent DOIs for data sets, facilitating data citations. In accordance to DRUM policies, the de-identified data will be accompanied by the appropriate documentation, metadata, and code to facilitate reuse and provide the potential for interoperability with similar data sets.
Expected timeline : Preparation for data sharing will begin with completion of planned publications and anticipated data release date will be six months prior.
Back to top
Types of data to be collected and shared The following quantitative and qualitative data (for which we have participant consent to share in de-identified form) will be collected as part of the project and will be available for sharing in raw or aggregate form. Specifically, any individual level data will be de-identified before sharing. Demographic data may only be shared at an aggregated level as needed to maintain confidentiality.
Student-level data including
Procedures for managing and for maintaining the confidentiality of the data to be shared
The following procedures will be used to maintain data confidentiality (for managing confidentiality of qualitative data, we will follow additional guidelines ).
Roles and responsibilities of project or institutional staff in the management and retention of research data
Key personnel on the project (PIs XXXXX and XXXXX; Co-Investigator XXXXX) will be the data stewards while the data are “active” (i.e., during data collection, coding, analysis, and publication phases of the project), and will be responsible for documenting and managing the data throughout this time. Additional project personnel (cost analyst, project coordinators, and graduate research assistants at each site) will receive human subjects and data management training at their institutions, and will also be responsible for adhering to the data management plan described above.
Project PIs will develop study-specific protocols and will train all project staff who handle data to follow these protocols. Protocols will include guidelines for managing confidentiality of data (described above), as well as protocols for naming, organizing, and sharing files and entering and downloading data. For example, we will establish file naming conventions and hierarchies for file and folder organization, as well as conventions for versioning files. We will also develop a directory that lists all types of data and where they are stored and entered. As described above, we will create a log to track data entry and downloads for analysis. We will designate one project staff member (e.g., UMN project coordinator) to ensure that these protocols are followed and documentation is maintained. This person will work closely with Co-Investigator XXXXX, who will oversee primary data analysis activities.
At the end of the grant and publication processes, the data will be archived and shared (see Access below) and the University of Minnesota Libraries will serve as the steward of the de-identified, archived dataset from that point forward.
Expected schedule for data access
The complete dataset is expected to be accessible after the study and all related publications are completed, and will remain accessible for at least 10 years after the data are made available publicly. The PIs and Co-Investigator acknowledge that each annual report must contain information about data accessibility, and that the timeframe of data accessibility will be reviewed as part of the annual progress reviews and revised as necessary for each publication.
Format of the final dataset
The format of the final dataset to be available for public access is as follows: De-identified raw paper data (e.g., student pre/posttest data) will be scanned into pdf files. Raw data collected electronically (e.g., via survey tools, field notes) will be available in MS Excel spreadsheets or pdf files. Raw data from audio/video files will be in .wav format. Audio/video materials and field notes from observations/interviews will also be transcribed and coded onto paper forms and scanned into pdf files. The final database will be in a .csv file that can be exported into MS Excel, SAS, SPSS, or ASCII files.
Dataset documentation to be provided
The final data file to be shared will include (a) raw item-level data (where applicable to recreate analyses) with appropriate variable and value labels, (b) all computed variables created during setup and scoring, and (c) all scale scores for the demographic, behavioral, and assessment data. These data will be the de-identified and individual- or aggregate-level data used for the final and published analyses.
Dataset documentation will consist of electronic codebooks documenting the following information: (a) a description of the research questions, methodology, and sample, (b) a description of each specific data source (e.g., measures, observation protocols), and (c) a description of the raw data and derived variables, including variable lists and definitions.
To aid in final dataset documentation, throughout the project, we will maintain a log of when, where, and how data were collected, decisions related to methods, coding, and analysis, statistical analyses, software and instruments used, where data and corresponding documentation are stored, and future research ideas and plans.
Method of data access
Final peer-reviewed publications resulting from the study/grant will be accompanied by the dataset used at the time of publication, during and after the grant period. A long-term data sharing and preservation plan will be used to store and make publicly accessible the data beyond the life of the project. The data will be deposited into the Data Repository for the University of Minnesota (DRUM), http://hdl.handle.net/11299/166578 . This University Libraries’ hosted institutional data repository is an open access platform for dissemination and archiving of university research data. Date files in DRUM are written to an Isilon storage system with two copies, one local to each of the two geographically separated University of Minnesota Data Centers. The local Isilon cluster stores the data in such a way that the data can survive the loss of any two disks or any one node of the cluster. Within two hours of the initial write, data replication to the 2nd Isilon cluster commences. The 2nd cluster employs the same protections as the local cluster, and both verify with a checksum procedure that data has not altered on write. In addition, DRUM provides long-term preservation of digital data files for at least 10 years using services such as migration (limited format types), secure backup, bit-level checksums, and maintains persistent DOIs for datasets, facilitating data citations. In accordance to DRUM policies, the de-identified data will be accompanied by the appropriate documentation, metadata, and code to facilitate reuse and provide the potential for interoperability with similar datasets.
The main benefit of DRUM is whatever is shared through this repository is public; however, a completely open system is not optimal if any of the data could be identifying (e.g., certain types of demographic data). We will work with the University of MN Library System to determine if DRUM is the best option. Another option available to the University of MN, ICPSR ( https://www.icpsr.umich.edu/icpsrweb/ ), would allow us to share data at different levels. Through ICPSR, data are available to researchers at member institutions of ICPSR rather than publicly. ICPSR allows for various mediated forms of sharing, where people interested in getting less de-identified individual level would sign data use agreements before receiving the data, or would need to use special software to access it directly from ICPSR rather than downloading it, for security proposes. ICPSR is a good option for sensitive or other kinds of data that are difficult to de-identify, but is not as open as DRUM. We expect that data for this project will be de-identifiable to a level that we can use DRUM, but will consider ICPSR as an option if needed.
Data agreement
No specific data sharing agreement will be needed if we use DRUM; however, DRUM does have a general end-user access policy ( conservancy.umn.edu/pages/drum/policies/#end-user-access-policy ). If we go with a less open access system such as ICPSR, we will work with ICPSR and the Un-funded Research Agreements (UFRA) coordinator at the University of Minnesota to develop necessary data sharing agreements.
Circumstances preventing data sharing
The data for this study fall under multiple statutes for confidentiality including multiple IRB requirements for confidentiality and FERPA. If it is not possible to meet all of the requirements of these agencies, data will not be shared.
For example, at the two sites where data will be collected, both universities (University of Minnesota and University of Missouri) and school districts have specific requirements for data confidentiality that will be described in consent forms. Participants will be informed of procedures used to maintain data confidentiality and that only de-identified data will be shared publicly. Some demographic data may not be sharable at the individual level and thus would only be provided in aggregate form.
When we collect audio/video data, participants will sign a release form that provides options to have data shared with project personnel only and/or for sharing purposes. We will not share audio/video data from people who do not consent to share it, and we will not publicly share any data that could identify an individual (these parameters will be specified in our IRB-approved informed consent forms). De-identifying is also required for FERPA data. The level of de-identification needed to meet these requirements is extensive, so it may not be possible to share all raw data exactly as collected in order to protect privacy of participants and maintain confidentiality of data.
All first year post graduate researchers should complete a data management plan for their research and include it as part of their first three month review. There is also a Blackboard course Data Management Plans for Doctoral Students - mandatory for all new doctoral students - to introduce you to research data management and help you complete the plan. Log into Blackboard using your university username and password.
A data management plan or DMP is a living document that helps you consider how you will organise your data, files, research notes and other supporting documentation throughout the length of the project. The aim is to help you find these easily, keep them safe and have sufficient documentation to be able to re-use throughout your research and beyond.
You will need to complete a preliminary data management plan in your first three months, along with your Academic Needs Analysis. Your DMP will continue to develop as your research progresses and you will need to update and review your DMP at every progression review. ( Code of Practice for Research Degree Candidature and Supervision, )
All researchers will have data. Data can be broadly defined as 'Material intended for analysis'. This covers many forms and formats, and is not just about digital data.
For example,
Art History - high resolution reproductions of photographs, notebook describing context
English literature - research notes on text, textual analysis
Engineering - experimental measurements on the physical properties of liquid metals
The University also has a definition for “Research Data” in its Research Data Management Policy that you should consider.
A PhD DMP template and guidance on how to complete your Data Management Plan is available ( see below ). All new doctoral students should complete the Data Management Plans for Doctoral Students module on Blackboard. Contact us if you need further information or have feedback via [email protected]
Guidance on depositing your research data at the end of your doctorate can be found on the Thesis Data Deposit guide. Please also see our depositing research data videos at https://library.soton.ac.uk/researchdata/datasetvideos
What are data management plans? A data management plan is a document that describes:
Your data management plan should be written specifically for the research that you will be doing. Our template is a guide to help you identify the key areas that you need to consider, but not all sections will apply to everyone. You may need to seek further guidance from your supervisor, colleagues in your department or other sources on best practice in your discipline. We provide some details of guidance available in our training section and on our general research data management pages.
Each of the tabs looks at the different topics that can be included in a data management plan. You can move through the tabs in any order.
Describing your Project
At the start of your data management plan (DMP) it is useful to include some basic information about the research you are planning to do. This may already exist in other documents in more detail, but for the purposes of the DMP try to summarise in as few sentences as possible.
What policies will apply?
It is important that you think about who is funding your research and whether there are any requirements that you need to meet. Are you funded by a UK Research Council? What policies do they have on research data - see Funder Guidance . What does our University Research Data Management policy and Code for Conduct for Research state is required?
Does the type of data you will be creating, using, collecting mean that you have to meet certain legal conditions? Will you be collecting any form of personal data, (see ICO Personal Data Definition ), special category data (see ICS Special Category definition ) or is it commercially sensitive? For example, if you are involved in population health and clinical studies research data and records minimum retention could be 20-25 years for certain types of data - see the MRC Retention framework for research data and records for further details.
Do you need Ethics Approval?
Anyone who is dealing with human subjects or cultural heritage (see University policies ) will require to obtain ethics approval and this must be done prior to collecting any data. Your DMP should inform what you say in your ethics application about how you will collect, store and re-use your data. It is important that your DMP and your ethics application are in agreement and you provide your participants with the correct information. Once you receive your ethics approval, review your data management plan and update as necessary.
Reviewing your Data Management Plan
A DMP should be a living document and should be updated as your research develops. It should be reviewed on a regular basis and good practice would encourage that the dates of review are included in the plan itself. Use of a version table in any document can be helpful.
What data will be created?
In your data management plan you need to provide some detail about the material you will be collecting to support your research. This should cover how you will collect notes, supporting documentation and bibliographic management as well as your primary data. Will all your data be held electronically or will you require to maintain a print notebook to collect your observations?
Are you using Secondary Data?
Not everyone has to collect their own data, it may already have been collected and made available. This data is known as secondary data. Some secondary data are freely available, but other data are released with terms and conditions that you need to meet. In some cases this may influence where you can store and analyse the data. You need to be aware of this as you plan the work you intend to do.
How are you collecting or creating your data?
How you collect or gather the material for your research will influence what you need to do to manage them. The way you do this may alter as your research progresses and you should update your plan as required. Will you be collecting data by observing, note-taking in an archive, carrying out experiments or a mixture of these?
How much data are you likely to have?
Knowing how much data you might create is important as it will dictate where you can store your data and whether you need to ask for additional storage from iSolutions. It is unlikely that you can say exactly what volume of data you might create, but you will have an idea of individual file sizes. If you will be working with word, excel documents and a reference management software library then you are likely to be dealing with megabytes or gigabytes of data. If you will be collecting high resolution images then you may end up needing to store terabytes. Estimate as early as possible and if you think you may need additional space you should discuss this with your supervisor.
What formats will you be using?
A crucial factor in being able to share data is that it is in an open format or collected using disciplinary standard software that allow export to open formats. Consider how open the format of your data will be when selecting the software, instruments, word processing packages that you use. See the Data formats section in Introducing Research Data Part III for points to consider.
Who will own the data?
If you have been sponsored by a research council, government, industry or commercial body the agreement you signed may cover ownership of the data that you create. Being aware of this early is useful as it will influence what you are able to do when you come to writing papers, sharing and depositing your data when your finish. It may also impact on where you can store your data.
How will you make your data findable?
Using standards to capture the essential metadata is a good way to help create data that will be easy to find. It will also make preparing for deposit in the future more straightforward. The Research Data Alliance has a helpful list of disciplinary metadata and use case examples. You can make reference to these in your plan once you know what will be most appropriate to use.
Where will you store the data during your PhD?
Where you store your data will depend on things such as the type and size of data you are collecting. Certain types of data, such as personal , special category data (formerly referred to as sensitive data) or commercially confidential data, will require to be stored more securely than others. This type of data generally requires to be stored on University network drives that have additional protection and not on personal computers or cloud storage (for example, Office 365, One Drive). Where you are collecting less sensitive data your choice of storage is wider. For all storage it should in a location with good back-up procedures in place. Consult iSolutions knowledge base for further information.
How will you name your files and folders?
It can be helpful to think about creating a procedure on how you will name your files. This is a basic step where it is useful to consider how easy it will be to interpret the name in the future. Abbreviations can be good, but ask yourself how someone else might understand the file name should you need to share it with them. What would make it easy to know what each file contains? While it is possible to have quite longer file names this can cause problems when you zip files.
How will you tell one version of a file from another?
How will you be able to tell whether you are dealing with the latest version of a file? How will you manage major versus minor changes? What if you want to return to an earlier version? Use the data management plan to investigate what would be the optimum method for you and establish a good procedure from the beginning. Generally the use of 'draft', 'latest' or 'final' should be avoided. Instead consider using the data (YYYY-MM-DD) or a version number, for example, v.1.0 where the nominal value increases with major changes and decimal for minor ones. Adding a version table at the end of a document can also be helpful.
How can you share your data?
To make data accessible is not about doing something at the end of the project, but needs to be planned for from the beginning. During your research you are likely to have colleagues or collaborators who will need to be able to access the data - how will you do this? Will you need a collaborative space and if so what can you use? Does it need to be is a protected location with restricted access due to the type of data you are using? By establishing good procedures on documentation, metadata collection, file-naming and using disciplinary standards this will assist you throughout your research, as well as helping at the end.
How do you handle personal, sensitive or commercially confidential data?
If the data you are collecting contains personal , special category data (formerly referred to as sensitive data) or commercially confidential data then sharing or transferring the files needs to be carried out in a way that does not make the data vulnerable. Data should be anonymised or pseudo-anonymised as early as possible after collection, seek disciplinary guidance prior to collection.
The medium of transfer must be secure and where necessary encryption should be used. You may want to consider one of the following:
There may be other software available and you should check if there is a standard in your discipline.
Transferring data via USB or external drives is not recommended, but where required these should be encrypted. Avoid using email to send files and instead use our University SafeSend service. This offers transfer of files up to 50GB and your files can be encrypted by ticking "Encrypt every file" when creating a new drop-off - see ' How secure is SaveSend'
What data do you need to keep and what do you need to destroy?
Not all the data from a project needs to be kept and the data you collect should be reviewed regularly. The Digital Curation Centre (2014) guide ' Five steps to decide what data to keep: a checklist for appraising research data v.1 ' may help you to decide what to retain. It is important that you retain or discard data in line with your ethics approval.
You also need to consider what data needs to be destroyed, how you will mark the data for destruction and when this needs to happen. Destroying paper based records is relatively easy through our confidential waste system. Destroying digital data is less so as it may need to be done so that it cannot be forensically recovered. Guidance on destroying your data is available or contact iSolutions for advice.
Why do you need to consider the long-term storage now?
At the end of your PhD you will be encouraged to share your data as openly as possible, and as closed as necessary. To do this safely consider what you need to do to enable your data to be accessible in the future. Knowing where the best place to store your data may inform what you need to plan for in its creation or collection. Are you aware of any disciplinary data repositories that hold similar data? Examples are:
Investigate what requirements these repositories have on formats, documentation etc and incorporate these into your plan. Otherwise you should plan to deposit in the University Institutional Repository .
There are currently no costs for depositing most dataset in our Institutional Repository unless the data requires specialist archive storage or is in excess of 1TB. External repositories may have charges for depositing data.
Who will be creating the archive?
Generally as a PhD the job of drawing together your data into a dataset ready for deposit will fall to you as the researcher. It is not the responsibility of your supervisor, although they may be able to advise on what needs to be done. If you are part of a larger project there may be someone designated to curate the project data. For further assistance contact [email protected] .
How long should the data be kept?
This will depend on a number of factors. Your funder may have a policy that requires the data to be held for a minimum of 10 years from last use. If you are working in certain medical areas the data may need to be held for 25 years. There may be some restrictions on how long you can retain personal data relating to Data Protection Act 2018 (GDPR). Significant data that has been given a persistent identifier (DOI) will be kept permanently.
What documentation or additional information needs to accompany the data?
Keeping a record of what changes you have made, when data was collected, where data was collected from, observations, definitions of what has been collected are all crucial to allowing data to be used safely and with integrity. How do you plan to do this? How will you make sure that you can match up your notes with the files they refer to? Some programming languages such as Python and R allow you to make notes in the files about what you are doing which is really helpful. Where this is not an option then you will need to develop your own method to make sure that processes applied to the data are recorded and available to you to refer back to later. Creating a register of your files by type using an excel spreadsheet may be worth considering, but it should be manageable and importantly kept up-to-date.
In order for data to be reusable it requires data provenance. Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures.
What restrictions will need to apply?
Not all data can be made openly available. Some data may only be shared once a data sharing agreement has been signed, while other data may not be suitable for sharing. Funding councils encourage all data to be as open as possible and as closed as necessary. Where will your data fit with this? What agreements do you need to be able to share your data?
When can data be made available?
Data can be deposited in our Institutional Repository and kept as an 'entry in progress' until it is ready for publication.
Not all data needs to be made immediately available at the end of your PhD. It is possible to add an embargo to give yourself some additional time to find funding to continue your work and re-use your own data. See Regulations on embargoes.
However, it is not always necessary for you to wait until the end of your PhD before depositing data. If you write a conference or journal paper it is likely that you will be asked to make the underpinning data available.
How will you keep your data safe?
What would happen if your files became corrupted or your laptop was stolen, would you be able to restore them? What would happen if someone was able to access your data without your knowledge or approval? If you are holding personal or special category data (formerly referred to as sensitive data) and these became public this would be a data breach with potentially serious consequences.
Dr Fitzgerald Loss of seven years of Ebola research
Consider carefully the impact to you and your research if these were to happen and what procedures you may need to put into place to reduce the risk of these happening.
How will you back up your data?
Good housing keeping of your data is important and this includes doing regular back ups of your data. University storage is backed up regularly but it is important to have your own 'back up' folders, kept separately from your working files. Back up should be done on as regular a basis as required. This can be defined by the length of time you are prepared to repeat work lost. You may need to back up daily, weekly or monthly depending on the nature of your research.
As well as establishing a process for backing up your files, you should check the process of restoring your files. You will need to check that the files restore correctly. Having good documentation on what your files contain, what transformations or analysis has been carried out will be invaluable for this process.
How can you safely destroy data?
Destroying data, especially personal , special category data (formerly referred to as sensitive data) or commercially confidential data , is not as straightforward as just deleting the file. Further action is required otherwise the data could be recovered. Please read our guidance on destruction of data and GDPR regulations .
An important part of research data management is that your plan is implemented and part of your everyday good research practice. The plan should be a living document and reflect your practice. You may find that some parts become redundant or that there is a better way to carry out a process so your plan should be updated. As a PhD researcher it is likely that you will be the person responsible for implementing the plan. If your research is part of a wider research project there may be someone in the team who has been given the role and you should discuss your data management plan with them.
Having written your plan consider what actions do you need to take in order to carry it out? What further information do you need to find? Investigate what training or briefing sessions are available via PGR Manager. If you want to enhance your data analysis skills check out material on Linked in Learning
Over time we will add plans to this section as we get permission to share them.
Courses offered by the University:
Data Management Plans for Doctoral Students - mandatory course on for all new doctoral students. Log into Blackboard using your university username and password.
Data Management Plan: Q&A Clinic - as a follow up to the compulsary online course, the Library is running twice weekly clinics to answer your DMP queries. Book PGR Development Hub .
Data Management Plan: Why Plan? 45 minute briefing. A Panopto recording of this course is available
Research Data Management: What you need to know from the start . 45 minute briefing. Book via Gradbook
Research Data Management Workshop .180 minute workshop Book via Gradbook
The template below has been provided to assist you in writing your data management plan. Not all sections will be relevant, but you should consider carefully each section.
When the time comes to deposit your data, follow the advice in our Thesis Data Deposit guide .
Email us on: [email protected]
Who's Who in the Research Engagement Team
research support.
Research skills.
Welcome to this module, where we will cover all the main aspects of looking after your research data, including:
Data can take many forms: not only spreadsheets, but also images, interview recordings and transcripts, old texts, survey results, protocols... the list goes on.
Lack of planning at the start of a project can cause problems (and much more work!) later on. Think of data management as a time investment to make sure that the data you collect is used effectively and remains usable over time.
Watch this video by the NYU Health Sciences Library as an example of poor data management and take some brief notes on any mistakes you spot. When you’re done, compare your notes with our answers underneath.
What did this researcher do wrong?
Here are all the mistakes we spotted: -he did not consider how others may want to reuse his data -he did not share the data in a repository -he was not aware of his funder and publisher requirements -he did not have multiple backups -he did not keep the data in a safe place -data on a USB stick is easy to lose -he did not use a safe way to share data (the post could have been lost) -he did not save the data in a common format -he did not save instructions on how to open the data -he did not plan for long-term preservation -he did not give variables intuitive names -he did not save metadata on what the variable names mean -he relied on knowledge found only in the brain of one person, rather than writing metadata
Keeping your data safe and up to date
Ensuring your data are safe is crucial to any research project. A good storage and backup strategy will help prevent potential data loss. Explore this scenario to see if your choices align with good research practice. Click on the link below to begin
Note: scenario opens in new window. Please view the scenario in full-screen. Return to this window to continue with the module, or if you wish to restart the scenario
Data storage and backup - why bother?
Organising data
Once you are sure that your data is safe from accidental loss, you should be thinking about how to organise it. Are your computer files ‘an amorphous plethora of objects’? In this video by the University of Edinburgh Data Library, Professor Jeff Haywood talks about his experiences of organising data.
If you want to read more about organising your data, including folder structures and file naming, there is a detailed guide on the Cambridge data website.
If you are at the start of a project, spend some time now preparing an organisational structure for your data. Create all the folders you are likely to need and a few named placeholders for files you will create. If you would like some feedback on it, email me .
Follow Martha in our scenario and help her make the best choices!
Take a look a this video of Cambridge researchers talking about their experience of sharing data.
So what does it mean in practice to share your data? All you have to do is upload your dataset and information about it on a repository, either a subject-specific one, an institutional one like Apollo, or a general one. The repository then lets people find and download the data. Find out more in the video below.
Useful resources related to the video:
If your research data is of a personal or sensitive nature, you must make sure you understand and respect the additional requirements associated with managing it. If possible, get in touch with your department’s ethics committee, or your industrial sponsor to check what they expect of you. Additional help can be sought from the Research Data team , the Research Integrity team , and the Information Compliance Office .
What are personal and sensitive data?
Personal data is data relating to a living individual, which allows the individual to be identified from the information itself or from the information plus any other information held by the 'data controller' (or from information available in the public domain). The University of Cambridge as a whole is the data controller. Sensitive data is personal data about: racial or ethnic origin, political opinions, religious beliefs, Trade Union membership, physical and mental health, sexual life, or criminal offences and court proceedings about these.
What are the legal requirements for data protection?
The The EU General Data Protection Regulation (GDPR), coupled with the UK Data Protection Act 2018 (DPA 2018) gives individuals certain rights and imposes obligations on those who record and use personal information to be open about how information is used and to follow eight data protection principles. Personal data must be: processed fairly, lawfully and transparently; obtained for specified, explicit and lawful purposes; adequate, relevant and not excessive; accurate and, where necessary, kept up-to-date; not kept for longer than necessary; processed in accordance with the subject's rights; kept secure; not transferred abroad without adequate protection
How should I store my sensitive or confidential data?
You should limit physical access to sensitive data or encrypt it (speak with your local IT/Computing Officer or the University Information Services Help Desk for help in doing this). To avoid accidentally compromising the data at some future date, you should always store information about the data's sensitivity and any available information on participants' consent or use agreements from your data provider with the data itself (i.e. put information about lawful and ethical data use in your data documentation or metadata description).
Data supporting my research is personal or sensitive. How do I share these data?
There can be a potential conflict between abiding by data protection legislation and ethical guidelines, whilst at the same time fulfilling funder's and individual's requirements to make research results available. Consult your ethics committee before deciding to share participants’ data. Your plans for research data processing, storage and sharing should be considered at the start of each project and reflected in both your data management plan and consent form. For example, you can inform your participants that anonymised data will be shared via the University of Cambridge data repository. There is good guidance on consent forms at the UK Data Archive (www.ukdataservice.ac.uk). The UK Data Archive also provides a sample consent form. Your Department’s Ethics Committee may also provide sample consent forms.
If you would like to learn more about personal and sensitive data and do some practical exercises on identifying these data types, the University of Cambridge offers short 30-mins long online courses on personal and sensitive data .
You should also consider whether your data is commercially sensitive: do you or a sponsor plan to profit from the research in the future? There should be a collaboration agreement in place from the start to clarify the terms of any commercial collaboration. The Research Operations Office can help with this. If you are working with both public funders and commercial partners, clarify early what data can be shared and what can’t, so you can make this clear to all parties.
Throughout this module we have seen how important it is to plan the way you will manage your data right at the start of a project. A Data Management Plan (DMP) is a document that captures that process.
To end this module and pull together everything you have learnt, we recommend you write your own DMP for a project you are about to start or have recently started. Use these instuctions as a guide.
© Cambridge University Libraries | Accessibility | Privacy policy | Log into LibApps
Library Services
Learn more about using the research data lifecycle to inform your data management planning
What is a data management plan, why are data management plans useful, before you get started, dmp training and review service, ucl research data policy, what are research data at ucl.
According to the UCL Research Data policy , data are: “facts, observations or experiences on which an argument or theory is constructed or tested. Data may be numerical, descriptive, aural or visual. Data may be raw, abstracted or analysed, experimental or observational. Data include but are not limited to: laboratory notebooks; field notebooks; questionnaires; texts; audio files; video files; models; photographs; test responses”.
There are three kinds of data:
The research data lifecycle models the different phases of the research process - from planning and preparation through to archiving and sharing - making your research and outputs discoverable to the wider research community and members of the public. There are four phases:
A Data Management Plan (DMP) describes your planned and/or actioned data management and sharing activities. It is generally 1-3 pages in length and should cover the four phases of the research data lifecycle. It is generally written at the start of a research project and should be revisted at different stages of the project and updated where necessary. DMPs may be published in the UCL Research Data Repository and assigned a DOI.
When writing your plan, remember to check if any funder's policies and requirements apply to your rseearch. A range of how-to guides are also available to assist you in writing your plan.
Download our Data Management Plan template (MS Word)
Guidance is provided as comments in the margins.
In addition to often being a prerequisite to receiving certain grants, DMPs are useful for:
Here are a few tips to help you start writing a DMP:
The RDM team offers both face-to-face and online training courses on how to write a data management plan. Using the UCL DMP template, attendees have the opportnity to write a data management plan which they can take away with them and use as a basis for a more detailed plan of their data management and sharing activities.
For more help and advice, contact your Research Data Support Officers who can also review drafted UCL Data Management Plans if you send them in advance of submission (allow 1 to 2 weeks at least before your submission deadline).
The UCL Research Data Policy describes UCL's expectations relating to data management and sharing within the wider Open Science context.
DMPonline , a free tool created by the DCC, provides a framework for creating your Data Management Plan. UCL guidance is now incorporated into DMPonline; see our further guidance on using the tool.
In this section
What is research data management .
Research data management refers to how you will look after the data you collect or generate during your research. It covers activities such as planning for your data management needs at the start of your project, organising , storing and securing data during your project and ensuring long-term preservation , data sharing and reuse at the end of your project.
Data management is increasingly recognised as an essential part of good research practice. Responsibly managed data is important for research integrity, transparency and open science. Many funders now expect data that supports published findings or has potential for reuse in future research to be made publicly available with as few restrictions as possible whenever legal and ethical restrictions allow .
The benefits of research data management include:
Research data are any materials that you collect or generate during your research project that can be used to support or verify your research findings . The UKRI Concordat on Open Research Data defines research data as ‘… the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form (e.g. print, digital, or physical)'. Research data can be generated or collected for different purposes and through different processes:
Having a clear understanding of the types of data you will collect or generate will help you make informed decisions about managing your data effectively.
Contact the Research Data Management team by booking a 1-2-1 consultation or send us an email at [email protected] .
What does my funder require.
Find out what your funder requires in relation to research data management
Find out what your publisher requires in relation to research data management
Read the Imperial College London research data management policy
PS – This is just the start…
We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.
Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.
Find the perfect research topic.
How To Choose A Research Topic Step-By-Step Tutorial With Examples + Free Topic...
A comprehensive list of automation and robotics-related research topics. Includes free access to a webinar and research topic evaluator.
Research Topics & Ideas: Sociology 50 Topic Ideas To Kickstart Your Research...
A comprehensive list of public health-related research topics. Includes free access to a webinar and research topic evaluator.
Research Topics & Ideas: Neuroscience 50 Topic Ideas To Kickstart Your Research...
📄 FREE TEMPLATES
Research Topic Ideation
Proposal Writing
Literature Review
Methodology & Analysis
Academic Writing
Referencing & Citing
Apps, Tools & Tricks
The Grad Coach Podcast
I have to submit dissertation. can I get any help
Your email address will not be published. Required fields are marked *
Save my name, email, and website in this browser for the next time I comment.
Submit Comment
This chapter examines issues related to quantitative and qualitative data including data collection, data management, data processing, data preparation and data analysis; as well as data storage and security in relation to HIPAA and other security requirements. The selection of appropriate statistical procedures including descriptive and inferential statistics is reviewed, as are the requirements and strategies for the collection and analysis of qualitative data including data coding and theme identification.
Sign in with a library card.
Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:
Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.
Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.
If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.
Enter your library card number to sign in. If you cannot sign in, please contact your librarian.
Society member access to a journal is achieved in one of the following ways:
Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:
If you do not have a society account or have forgotten your username or password, please contact your society.
Some societies use Oxford Academic personal accounts to provide access to their members. See below.
A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.
Some societies use Oxford Academic personal accounts to provide access to their members.
Click the account icon in the top right to:
Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.
For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.
Our books are available by subscription or purchase to libraries and institutions.
Month: | Total Views: |
---|---|
October 2022 | 11 |
November 2022 | 2 |
December 2022 | 2 |
January 2023 | 3 |
February 2023 | 4 |
March 2023 | 3 |
April 2023 | 11 |
June 2023 | 3 |
July 2023 | 3 |
August 2023 | 4 |
September 2023 | 4 |
November 2023 | 2 |
December 2023 | 2 |
January 2024 | 1 |
March 2024 | 1 |
April 2024 | 3 |
May 2024 | 13 |
June 2024 | 4 |
July 2024 | 2 |
August 2024 | 1 |
September 2024 | 12 |
Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide
Sign In or Create an Account
This PDF is available to Subscribers Only
For full access to this pdf, sign in to an existing account, or purchase an annual subscription.
Schärer, denise (2021).
Tiivistelmä, samankaltainen aineisto.
Näytetään aineisto, joilla on samankaltaisia nimekkeitä, tekijöitä tai asiasanoja.
Selaa kokoelmaa, henkilökunnalle.
OATD.org provides open access graduate theses and dissertations published around the world. Metadata (information about the theses) comes from over 1100 colleges, universities, and research institutions. OATD currently indexes 6,654,285 theses and dissertations.
IMAGES
VIDEO
COMMENTS
At many institutions, research IT support. Foundational Practices of Research Data Management 9. and information security offices are available to help researchers think through these decisions and build an appropriately secure and feasible research workflow. Practice 8: Close out the project.
A common concern when starting a dissertation or research project is collecting enough data. This tends to be a concern whether you are collecting primary data (data you generate yourself from experiments, questionnaires, interviews, field work) or secondary data (data generated by other people, such as previous research findings, government reports, business figures).
general, and ecient techniques for dealing with uncertainty in the context of data management systems. This thesis makes advances in the field of uncertain data management by presenting ecient techniques for managing and integrating uncertain data. Section 1.1 introduces some motivating applications, Section 1.2 provides an overview of the ...
The Research Data Management Team will provide support for any students, supervisors or assessors that are in need. Submitting your digital thesis and depositing your data. If you have created data that is connected to your thesis and the data is in a format separate to the thesis file itself, we recommend that you deposit it in the data ...
Data management refers to the systematic collection, processing, storing and description of research data. Students are encouraged to learn about data management early in their studies, because good data management skills are beneficial to study progress and to adopting suitable data management practices during the thesis-writing process.
Definition and Scope of Data Analysis in the Context of a Dissertation. Data analysis in a dissertation involves systematically applying statistical or logical techniques to describe and evaluate data. This process transforms raw data into meaningful information, enabling researchers to draw conclusions and support their hypotheses.
Data Cleaning. Data cleaning refers to the process of improving the quality of your data by checking that your dataset does not contain data entry errors and that it is set up appropriately for analysis. The data cleaning step should not be skipped and should be done before conducting any analysis. Running descriptive statistics, including ...
develop a formalism for reasoning about human-powered data processing, and use this formalism to design: (a) a toolbox of basic data processing algorithms, optimized for cost, latency, and accuracy, and (b) practical data management systems and applications that
Practicing data management principles during thesis work brings benefits in the professional world. Therefore, it is worth familiarizing oneself with these practices while working on a thesis. If there is a desire to share or reuse the data after completing the thesis, the data must be of high quality and well-managed. In such cases, the life ...
Research data management is a complex issue, but if done correctly from the start it could save you a lot of time and hassle at the end of the project, when preparing your data for a publication or writing up your thesis. Research data takes many forms, ranging from measurements, numbers and images to documents and publications.
Introduction. Research Data Management (RDM) is a burgeoning field of research (Tenopir et al., Citation 2011; Zhang and Eichmann-Kalwara, Citation 2019) and RDM skills are increasingly required across all disciplines (Borghi et al., Citation 2021) as researchers take on more responsibilities to meet the demand for open and reusable data.Higman et al. (Citation 2019, p.
Abstract. Databases and related fields such as Information Retrieval, Data Mining and Knowledge Management offer many topics of interest for dissertation research. Specific areas include, for ...
PlanGuide to writing aResearch M. nagement PlanThis guide was created by FAIRmat. Cite it as "FAIRmat, Guide to Writing a Research Data. Management Plan", version 1.0, 25 March, 2023.This work is licensed under the Creative Commons A. DOI: 10.5281/zenodo.7936477.
Data management. If your PhD contains research data, you will have to think about how to deal with those data. In this section, you will learn about. principles for data management. data management plans. how to store and archive your data. how to provide good and sufficient metadata. how to structure data files.
Examples of data management plans. These examples of data management plans (DMPs) were provided by University of Minnesota researchers. They feature different elements. One is concise and the other is detailed. One utilizes secondary data, while the other collects primary data. Both have explicit plans for how the data is handled through the ...
All new doctoral students should complete the Data Management Plans for Doctoral Students module on Blackboard. Contact us if you need further information or have feedback via [email protected]. Guidance on depositing your research data at the end of your doctorate can be found on the Thesis Data Deposit guide.
Research Data Management. Welcome to this module, where we will cover all the main aspects of looking after your research data, including: how to store and backup up data. how to organise data. what to do with protected data (personal or commercially sensitive) why sharing data is important and how to do it. writing Data Management Plans.
A Data Management Plan (DMP) describes your planned and/or actioned data management and sharing activities. It is generally 1-3 pages in length and should cover the four phases of the research data lifecycle. It is generally written at the start of a research project and should be revisted at different stages of the project and updated where ...
The benefits of research data management include: Reduces the risk of data loss. Makes it easier to find and understand data. Helps make data authentic, accurate and reliable. Improves research integrity and reproducibility. Facilitates data sharing and reuse. Enables compliance with funder and publisher policies.
I f you're just starting out exploring data science-related topics for your dissertation, thesis or research project, you've come to the right place. In this post, we'll help kickstart your research by providing a hearty list of data science and analytics-related research ideas, including examples from recent studies.. PS - This is just the start…
Abstract. This chapter examines issues related to quantitative and qualitative data including data collection, data management, data processing, data preparation and data analysis; as well as data storage and security in relation to HIPAA and other security requirements. The selection of appropriate statistical procedures including descriptive ...
This thesis aims to examine Master Data Management (MDM) and its implementation pro-cess, to identify challenges, opportunities and ongoing discussions. The purpose of this re-search is to point out and formulate key success factors, and to create an MDM implemen-tation process framework. The framework provides suggestions, regarding ...
Freely accessible to the public via the Internet. Subjects: Dissertations and Theses. Watson Library. 1425 Jayhawk Blvd. Lawrence, KS 66045. Contact Us. 785-864-8983. Libraries website feedback.