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What is Database Search?

Harvard Library licenses hundreds of online databases, giving you access to academic and news articles, books, journals, primary sources, streaming media, and much more.

The contents of these databases are only partially included in HOLLIS. To make sure you're really seeing everything, you need to search in multiple places. Use Database Search to identify and connect to the best databases for your topic.

In addition to digital content, you will find specialized search engines used in specific scholarly domains.

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Reference management. Clean and simple.

The top list of academic research databases

2. Web of Science

5. ieee xplore, 6. sciencedirect, 7. directory of open access journals (doaj), get the most out of your academic research database, frequently asked questions about academic research databases, related articles.

Whether you are writing a thesis , dissertation, or research paper it is a key task to survey prior literature and research findings. More likely than not, you will be looking for trusted resources, most likely peer-reviewed research articles.

Academic research databases make it easy to locate the literature you are looking for. We have compiled the top list of trusted academic resources to help you get started with your research:

Scopus is one of the two big commercial, bibliographic databases that cover scholarly literature from almost any discipline. Besides searching for research articles, Scopus also provides academic journal rankings, author profiles, and an h-index calculator .

• Coverage: 90.6 million core records
• References: N/A
• Discipline: Multidisciplinary
• Access options: Limited free preview, full access by institutional subscription only
• Provider: Elsevier

Web of Science also known as Web of Knowledge is the second big bibliographic database. Usually, academic institutions provide either access to Web of Science or Scopus on their campus network for free.

• Coverage: approx. 100 million items
• References: 1.4 billion
• Access options: institutional subscription only
• Provider: Clarivate (formerly Thomson Reuters)

PubMed is the number one resource for anyone looking for literature in medicine or biological sciences. PubMed stores abstracts and bibliographic details of more than 30 million papers and provides full text links to the publisher sites or links to the free PDF on PubMed Central (PMC) .

• Coverage: approx. 35 million items
• Discipline: Medicine and Biological Sciences
• Access options: free
• Provider: NIH

For education sciences, ERIC is the number one destination. ERIC stands for Education Resources Information Center, and is a database that specifically hosts education-related literature.

• Coverage: approx. 1.6 million items
• Discipline: Education
• Provider: U.S. Department of Education

IEEE Xplore is the leading academic database in the field of engineering and computer science. It's not only journal articles, but also conference papers, standards and books that can be search for.

• Coverage: approx. 6 million items
• Discipline: Engineering
• Provider: IEEE (Institute of Electrical and Electronics Engineers)

ScienceDirect is the gateway to the millions of academic articles published by Elsevier, 1.4 million of which are open access. Journals and books can be searched via a single interface.

• Coverage: approx. 19.5 million items

The DOAJ is an open-access academic database that can be accessed and searched for free.

• Coverage: over 8 million records
• Provider: DOAJ

JSTOR is another great resource to find research papers. Any article published before 1924 in the United States is available for free and JSTOR also offers scholarships for independent researchers.

• Coverage: more than 12 million items
• Provider: ITHAKA

Start using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with PubMed and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Scopus is one of the two big commercial, bibliographic databases that cover scholarly literature from almost any discipline. Beside searching for research articles, Scopus also provides academic journal rankings, author profiles, and an h-index calculator .

PubMed is the number one resource for anyone looking for literature in medicine or biological sciences. PubMed stores abstracts and bibliographic details of more than 30 million papers and provides full text links to the publisher sites or links to the free PDF on PubMed Central (PMC)

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How to find articles and databases: finding articles.

• Finding Articles
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Help finding articles

You can use UC Library Search on the Library homepage to search across various book and article databases simultaneously. To find the best resources for your topic, you might want to go directly to a specific database.

Find the best database(s) for your research topic:

General article databases  are a good place to start since they include both popular and scholarly journal titles covering numerous disciplines. Simply choose one of those databases and type in your keywords to begin to find articles.

Browse for databases by subject  (such as  Economics ,  Electrical Engineering , or  Art History ) if you want to dig deeper into resources covering a specific discipline. If you aren’t sure what subject to choose, look for the academic department that your class is listed under. Once you’ve chosen a subject, search for your topic in one or two of the recommended databases that are listed on the top of the subject list.

Browse for databases by type  if you want to find other kinds of formats, such as encyclopedias, newspapers, government information sources, statistics, maps, images and more.

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Peer Reviewed and Scholarly Articles

What are they? Peer-reviewed articles, also known as scholarly or refereed articles are papers that describe a research study. 

Why are peer-reviewed articles useful? They report on original research that have been reviewed by other experts before they are accepted for publication, so you can reasonably be assured that they contain valid information. 

How do you find them?  Many of the library's databases contain scholarly articles! You'll find more about searching databases below.

Watch: Peer Review in 3 Minutes

Why watch this video?

We are often told that scholarly and peer-reviewed sources are the most credible, but, it's sometimes hard to understand why they are credible and why we should trust these sources more than others. This video takes an in depth approach at explaining the peer review process. 

Hot Tip: Check out the Reading Scholarly Articles page for guidance on how to read and understand a scholarly article.

Using Library Databases

What Are Library Databases? 

Databases are similar to search engines but primarily search scholarly journals, magazines, newspapers and other sources. Some databases are subject specific while others are multi-disciplinary (searching across multiple fields and content types). 

You can view our most popularly used databases on the Library's Home Page , or view a list of all of our databases organized by subject or alphabetically at  U-M Library Databases .

Popular Multidisciplinary Databases

Many students use ProQuest , JSTOR , and Google Scholar for their initial search needs. These are multi-disciplinary and not subject-specific, and they can supply a very large number of  search results.

Subject-Specific Databases

Some popular subject-specific databases include PsycINFO for psychology and psychiatry related topics and  PubMed for health sciences topics. 

Why Should You Use Library Databases?

Unlike a Google search, the Library Databases will grant you access to high quality credible sources. 

The sources you'll find in library databases include:

• Scholarly journal articles
• Newspaper articles
• Theses & dissertations
• Empirical evidence

Database Filters & Limits Most databases have Filters/Limits. You can use these to narrow down your search to the specific dates, article type, or population that you are researching.

Here is an example of limits in a database, all databases look slightly different but most have these options:

Keywords and Starting a Search

What are Keywords?

• Natural language words that describe your topic 
• Allows for a more flexible search - looks for anywhere the words appear in the record
• Can lead to a broader search, but may yield irrelevant results

Keyword searching  is how we normally start a search. Pull out important words or phrases from your topic to find your keywords.

Tips for Searching with Keywords:

• Example: "climate change"
• Example:  "climate change" AND policy
• Example: comput* will return all words starting with four letters; computing, computer, compute, etc.  
• Example: wom?n will find both woman and women.

What are Subject Headings?

• Pre-defined "controlled vocabulary" that describe what an item is  about 
• Makes for a less flexible search - only the subject fields will be searched
• Targeted search; results are usually more relevant to the topic, but may miss some variations

Subject Terms and/or Headings are pre-defined terms that are used to describe the content of an item. These terms are a controlled vocabulary and function similarly to hashtags on social media. Look carefully at the results from your search. If you find an article that is relevant to the topic you want to write about, take a look at the subject headings. 

Hot Tip: Make a copy of this Google Doc to help you find and develop your topic's keywords.

More Database Recommendations

Need articles for your library research project, but not sure where to start? We recommend these top ten article databases for kicking off your research. If you can't find what you need searching in one of these top ten databases, browse the list of all library databases by subject (academic discipline) or title .

• U-M Library Articles Search This link opens in a new window Use Articles Search to locate scholarly and popular articles, as well as reference works and materials from open access archives.
• ABI/INFORM Global This link opens in a new window Indexes 3,000+ business-related periodicals (with full text for 2,000+), including Wall Street Journal.
• Academic OneFile This link opens in a new window Provides indexing for over 8,000 scholarly journals, industry periodicals, general interest magazines and newspapers.
• Access World News [NewsBank] This link opens in a new window Full text of 600+ U.S. newspapers and 260+ English-language newspapers from other countries worldwide.
• CQ Researcher This link opens in a new window Noted for its in-depth, unbiased coverage of health, social trends, criminal justice, international affairs, education, the environment, technology, and the economy.
• Gale Health and Wellness This link opens in a new window
• Humanities Abstracts (with Full Text) This link opens in a new window Covers 700 periodicals in art, film, journalism, linguistics, music, performing arts, philosophy, religion, history, literature, etc.
• JSTOR This link opens in a new window Full-text access to the archives of 2,600+ journals and 35,000+ books in the arts, humanities, social sciences and sciences.
• ProQuest Research Library This link opens in a new window Indexes over 5,000 journals and magazines, academic and popular, with full text included for over 3,600.
• PsycInfo (APA) This link opens in a new window Premier resource for surveying the literature of psychology and adjunct fields. Covers 1887-present. Produced by the APA.

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EBSCO provides free research databases covering a variety of subjects for students, researchers and librarians.

Exploring Race in Society

This free research database offers essential content covering important issues related to race in society today. Essays, articles, reports and other reliable sources provide an in-depth look at the history of race and provide critical context for learning more about topics associated with race, ethnicity, diversity and inclusiveness.

EBSCO Open Dissertations

EBSCO Open Dissertations is a collaboration between EBSCO and BiblioLabs to increase traffic and discoverability of ETD research. You can join the movement and add your theses and dissertations to the database, making them freely available to researchers everywhere.

GreenFILE is a free research database covering all aspects of human impact to the environment. Its collection of scholarly, government and general-interest titles includes content on global warming, green building, pollution, sustainable agriculture, renewable energy, recycling, and more. 

Library, Information Science and Technology Abstracts

Library, Information Science & Technology Abstracts (LISTA) is a free research database for library and information science studies. LISTA provides indexing and abstracting for hundreds of key journals, books, research reports. It is EBSCO's intention to provide access to this resource on a continual basis.

Teacher Reference Center

A complimentary research database for teachers,  Teacher Reference Center  (TRC) provides indexing and abstracts for more than 230 peer-reviewed journals.

European Views of the Americas: 1493 to 1750

European Views of the Americas: 1493 to 1750 is a free archive of indexed publications related to the Americas and written in Europe before 1750. It includes thousands of valuable primary source records covering the history of European exploration as well as portrayals of Native American peoples.

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MEDLINE is the National Library of Medicine's (NLM) premier bibliographic database that contains references to journal articles in life sciences, with a concentration on biomedicine. See the MEDLINE Overview page for more information about MEDLINE.

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Discover a digital archive of scholarly articles, spanning centuries of scientific research.

Learn how to find and read articles of interest to you.

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Challenges in database research for anesthetic neurotoxicity

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Nagai T, Yoda Y, Tokuda N, Takeshima Y, Hirose M, Shima M (2024) Japan Environment, Children’s Study (JECS) Group. Association between general anesthesia in early childhood and neurodevelopment up to 4 years of age: the Japan Environment and Children’s Study. J Anesth Online ahead of print

Walkden GJ, Pickering AE, Gill H. Assessing long-term neurodevelopmental outcome following general anesthesia in early childhood: challenges and opportunities. Anesth Analg. 2019;128:681–94.

Article   PubMed   PubMed Central   Google Scholar  

Liu X, Ji J, Zhao GQ. General anesthesia affecting on developing brain: evidence from animal to clinical research. J Anesth. 2020;34:765–72.

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• Published: 28 August 2024

A Global Database of Pitted Cones on Mars for Research on Martian Volcanism

• Zeyu Chen   ORCID: orcid.org/0000-0002-4114-0195 1 ,
• Bo Wu   ORCID: orcid.org/0000-0001-9530-3044 1 ,
• Sergey Krasilnikov 1 ,
• Wenjing Xun 1 ,
• Yuan Ma 1 ,
• Shuo Liu 1 &
• Zhaojin Li 1  

Scientific Data volume  11 , Article number:  942 ( 2024 ) Cite this article

Metrics details

• Geomorphology
• Volcanology

This study presents a global database of pitted cones on Mars. This database was created using an active deep-learning approach based on a global mosaic of Mars Context Camera (CTX) images, followed by extensive manual verification. The global database contains 250,547 pitted cones along with their central coordinates and diameters. 94.2% of the pitted cones have diameters ranging from 100 m to 1 km. Most of them are in the northern lowlands, while some are scattered in the southern highlands. Systematic evaluation of the extracted pitted cones is performed in Isidis Planitia, Acidalia Planitia, and Cerberus Palus on Mars. The results reveal that the precision ranges from 79.2% to 85.6%, and the recall rate ranges from 62.4% to 87.7%, indicating a varying among regions but satisfactory performance. Manual digitizing evaluation indicates that over 94.1% of manual labels by different annotators have a differentiation rate of 10%, showing promising precision and consistency. This global database can facilitate further research on the origin and evolution of pitted cones on Mars.

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Background & summary.

Mars volcanic activity is the process that continued until very recently 1 . On Earth, some volcanoes are related to plate tectonics and typically appear at plate boundaries and magma hotspots 2 . However, the absence or early cessation of plate tectonics on Mars has led to uncertainty regarding the origins of Martian volcanism. The factors triggering Martian volcanic activity, particularly the small but numerous features related to volcanism such as pitted cones found in Utopia Planitia 3 , 4 , 5 , Isidis Planitia 6 , and Acidalia Planitia 7 , 8 , remain unclear. Examining the global distribution of these features can yield insights into the potential mechanisms that triggered the emergence of pitted cones.

Investigating the global distribution of volcanism-related features on Mars is challenging owing to constraints in data coverage and algorithmic capabilities. Since its deployment in 2005, the Context Camera (CTX) onboard the Mars Reconnaissance Orbiter 9 , has collected images with a spatial resolution range from 5–12 m/pixel, covering approximately 99% of the Martian surface 10 . CTX images have been used to generate a global image mosaic of the same resolution through image-to-image registration 11 , which represents a valuable dataset for global analysis. Moreover, advanced deep-learning algorithms have proven adept at detecting geomorphological features from Mars images with favorable reliability and automation 12 , 13 , 14 , 15 , 16 . These algorithms can thus enable the generation of a global database of pitted cones on Mars and facilitate the investigation of their distribution characteristics.

This study presents a global database of Mars pitted cones, derived from the global CTX image mosaic using a deep-learning approach and extensive manual verification. The database includes 250,547 pitted cones along with their centroid coordinates and diameters, with many of these cones reported for the first time. This global database of pitted cones can facilitate research on the origin and evolution history of pitted cones in the northern hemisphere of Mars.

Morphologies of pitted cones and active learning process

Pitted cones on the Martian surface exhibit specific morphological characteristics, such as (1) round or elliptical craters on top of the cone and (2) clearly discernible rounded cone flanks with distinct boundaries. The proposed approach aims to extract accurate morphometric parameters and differentiate pitted cones from other features, such as domes, pingos, and mesas, which have distinct formation mechanisms 5 . In certain cases, impact craters cannot be clearly distinguished owing to suspicious tuff cones/rings and pseudocraters. The identification of these ambiguous features is approached conservatively unless sufficient evidence to justify assumptions related to tuff cones/rings and pseudocraters. Notably, giant volcanoes are beyond the scope of this research as they have been extensively studied 17 , 18 , 19 and are thus manually digitized and added as a separate part of the database. Unless specifically stated below, they are not included in the discussion.

The proposed active deep-learning approach for cone detection is based on the YOLO-V4 network 20 as it balances accuracy and efficiency of prediction. The detection performance is enhanced by leveraging manual annotation to dynamically join new labels to form the training dataset. Unlike fully supervised learning, active learning is semi-supervised, involving specific criteria prompting human annotators to judge the correctness of network predictions. The manually annotated labels are thus more representative and reliable, enabling the enhancement of the algorithm performance. As shown in Fig.  1 , the workflow begins with training the network using a small subset of labeled data from a larger set of unlabeled CTX images. The image size is 416 × 416 pixels to match the input requirements of the network. The trained network then adds labels to the unlabeled tiles and introduces them into the labeled dataset. High-confidence labels serve as training data for the next round. Detections in new regions and low-confidence labels are manually checked by human annotators. The manually identified correct detections are then added to the training dataset for the next round. These processes are repeated until the cones are globally checked and labeled. In the final step, an overall manual check is conducted to digitize undetected labels and generate attribute information for the labeled cones, including their area, center coordinates, and diameters.

Framework of the active deep learning approach for pitted-cone detection.

The initial training dataset, digitized by Wu et al . 21 , consists of 2,503 pitted cones in southern Utopia Planitia (110.0°E, 21 \(.0^\circ \) N). Each entry in the dataset includes an image of the pitted cone and an XML file that records the bounding box coordinates of the pitted cone. These images are extracted from the beta01 version of the Global CTX Mosaic of Mars ( https://murray-lab.caltech.edu/CTX/ ) 11 , with an image size of 416 × 416 pixels to match the input requirements of the network.

The active deep-learning process for detecting pitted cones on Mars involves several stages. The initial training dataset is from southern Utopia Planitia 21 . After training, the first stage prediction focuses on pitted cones in Utopia Planitia (113.0°E, 43 \(.0^\circ \) N), Isidis Planitia (87.0°E, 13 \(.0^\circ \) N), Cerberus Palus (148.0°E, 5.6°N), and several local regions in Acidalia Planitia (50.0°E, 21 \(.0^\circ \) N), and Amazonis Planitia (165.0°W, 22 \(.0^\circ \) N), as pitted cone morphologies in these regions are similar to the initial training samples correspond to southern Utopia Planitia. Subsequent stages gradually expand the identifiable regions and number of pitted cones, and the accuracy of detection improves with the expansion of the training dataset. During this process, new training samples that contain distinct features are incorporated, gradually decreasing the possible initial high biases resulting from the limited and region-specific training data sources. Although the biases may still exist due to uneven training numbers across regions, the proposed acting learning method can effectively reduce the possible biases, as observed during the training process and reported by previous similar research 22 . After several cycles, the training dataset is adequately comprehensive to represent most pitted-cone morphologies on Mars and can reasonably predict potential pitted cones in a global context.

The active deep-learning approach predicts bounding boxes of pitted cones on the CTX images. During the inference stage, geo-transformation parameters are calculated for each CTX image crop based on the original tiles. The bounding box coordinates are subsequently converted into planar geospatial coordinates according to the calculated geo-transformation parameters. The pitted cone diameter is estimated by averaging the height and width of the bounding boxes. A polygon shapefile is created by drawing circles using the center coordinate of the bounding boxes and averaged diameters.

The network used for pitted-cone detection is trained using a single NVIDIA RTX 3090 graphics processing unit on the TensorFlow 2.6.0 platform 23 . The learning rate is \(3\times {10}^{-4}\) , an order of magnitude lower than the normal setting. The batch size is 32, and default values are maintained for the other hyperparameters.

The process of human verification involves two steps. The first step involves a rapid assessment of ambiguous pitted cones by cropping and saving them with a labeled boundary in an image. These images are then manually marked as “correct,” “ambiguous,” or “incorrect.” False detections are removed and not checked again. The second step of inspection focuses on the other two types of images. This two-step inspection aims at promptly deleting obvious false detections and thoroughly analyzing the detections based on geological units and geomorphology. Certain areas such as fossae and the north polar region may include ambiguous objects such as mesas and fresh craters with high, intact rims, which may be challenging to distinguish if observed in only a single image. In addition, severely degraded and collapsed pitted cone candidates are conservatively digitized to ensure the accuracy of morphometric parameters.

Generation of the global database

The global CTX image mosaic used for pitted-cone detection covers 99% of the Martian surface and includes 3,960 tiles with a range of 4 \(^\circ \times \) 4°, spanning from 88°S to 88°N. In the active deep-learning approach for pitted-cone detection, because the input image size is limited, a multi-scale image pyramid of each tile is constructed to identify candidates with varying sizes. The size of CTX image tiles decreases by half as we build the pyramid, and the spatial resolution of the CTX image tiles is resampled from the original resolution of 5–12 m/pixel. The lowest layer closely matches the input size of 416 × 416 pixels, with a spatial resolution ranging from 160 to 384 m/pixel. During the inference, each pyramid layer from a CTX tile is clipped and fed into the model. Moving to the lower layers of the image pyramid, the spatial resolution decreases, enabling the model to detect pitted cones of varying sizes. The predicted results, which are rectangular bounding boxes of pitted cones, are converted to circles with diameters equivalent to the average values of the bounding boxes’ length and width. The area calculation is then performed using ArcGIS software, and the Hammer projection is applied to ensure consistent dimension metrics among all tiles. Finally, the diameter of each label is recalculated with an area identical to the circle under Hammer projection and is determined as the diameter of the pitted cone. This approach simplifies the diameter estimation. Finally, a comprehensive human examination of CTX images, same as the active learning process, is performed to remove false detections. Any pitted cones missed during the automated detection are manually added. The manual examination is limited to the CTX image tiles where the deep-learning network has detected pitted cones. Missed pitted cones are digitized using ArcGIS software and merged with the dataset created by the algorithm as the final product. While there might be some missed pitted cones in the unchecked CTX image tiles, their number should be negligible.Large shield volcanoes are manually digitized as they are few in number. Their boundaries are determined by referencing their geomorphologic features, topographic elevations, and geologic units 24 . Specifically, these polygenetic volcanoes are characterized by multiple eruptions and a complex resurfacing history, typically resulting in large and undistinctive volcanic regions. In the database, the boundary of the latest eruption is delineated based on stratigraphic evidence.

The database also includes volcanoes that have been previously reported. Dohm and Tanaka 17 examined volcanic and tectonic activities in the Thaumasia region. Xiao et al . 18 reported several Early Noachian volcanoes in the southern highlands. Ghatan and Head 19 mapped subglacial volcanoes in the south-polar region. Brož and Hauber 3 discovered several plausible tuff rings/cones on the southern Utopia Planitia margin. Scoria cones in Ulysses, Hydraotes, and Coprates were identified by Brož et al . 25 . These additional cones exhibit large and irregular shapes, rendering their detection difficult. In our database, the source of the cones is recorded in the attribute file.

Data Records

The global database and Python codes for data generation of pitted cones on Mars are publicly available at Zenodo 26 . The “database” file contains two shapefiles that is the proposed database. The “Code” file involves all program codes and corresponding network parameter files for prediction.

The pitted cone database 26 includes two separate files. “Global_Volcano_Database_1” contains giant volcanos (e.g., Alba, Elysium) and previously reported volcanos. “Global_Volcano_Database_2” includes all pitted cones detected by the proposed algorithm and manually added cones. The database attribute table includes several columns that record properties. “Cone_ID” is the unique number of each pitted cone. The number is given from north to south and from west to east directions. “Source” indicates the origin of the label 17 , 18 , 19 , 25 , 26 . A citation is provided if it is from other works. “Area” is the geodesic area of each pitted cone in the unit of square meters. “CenterX” and “CenterY” are the centroid coordinates of each pitted cone label under the global CTX mosaic coordinate system. Center_Lat and Center_Lon are the centroid coordinates of each pitted cone under the geographic coordinate system converted from “CenterX” and “CenterY”. The relationship between them is shown as follows:

where \(x\) in the horizontal coordinate (CenterX), \(y\) is the vertical coordinate (CenterY), \(r\) is the radius of Mars, \({\varphi }_{1}\) is the standard parallel, \({\lambda }_{0}\) is the central meridian of the coordinate system, and \({\varphi }_{0}\) is the central parallel of the coordinate system. All projection parameters are obtained from CTX image tiles. “Diameter” is from equivalent circles whose area is identical to the geodesic area provided in the attribute table.

The global database includes 250,547 pitted cones. As depicted in Fig.  2(a) , the latitudinal spread of these cones extends from 52° N to 74°S. The accompanying attribute file records essential details for each cone, including its identification number, source, area, centroid coordinates, and equivalent circular diameter. Nearly all (99.5%) of the pitted cones are found in the northern lowlands, particularly in the Utopia, Isidis, and Acidalia Planitia regions. Figure  2(b)–(d) show enlarged views of the pitted-cone distribution in these three regions.

Spatial distribution of Martian pitted cones generated by active deep-learning and manual verification. Yellow lines depict pitted cone boundaries. ( a ) Global distribution of pitted cones; Enlarged views of pitted cones in ( b ) Utopia Planitia; ( c ) Isidis Planitia; ( d ) Acidalia Planitia. The background is the global hillshaded Mars Orbiter Laser Altimeter (MOLA) digital elevation model 200 m V2. Observed gaps are regions where no CTX mosaic coverage is present.

Figure  3 shows the histogram of global pitted cone diameters. Despite the prevalence of giant shield volcanoes in volcanic provinces, the diamaters of the pitted cones range from 33.9 m to 30,567.9 m, with an average diameter of 445.8 m. The distribution is highly skewed and 98.8% of diameters are less than 1 km. 94.2% of pitted cone diameters are in the range of 100 m to 1 km, showing the dominant number.

Histogram of global pitted cone diameters. Diameter is derived from both automatically detected and manually labeled pitted cone labels. All pitted cones have been verified manually.

Figure  4 illustrates the global density map of pitted cones on Mars, generated by applying a moving window (50 km × 50 km) to count the number of emerging pitted cones within each window. Acidalia Planitia, Isidis Planitia, and Utopia Planitia exhibit notably higher cone densities. Cones with dense distributions are also observed in Amazonis Planitia, Alpheus Colles (59.0°E, 39 \(.0^\circ \) S), Cerberus Palus, Chryse Planitia (38.0° W, 28 \(.0^\circ \) N), Eridania Planitia (122.0°E, 38 \(.0^\circ \) S), Galaxias Colles (146.0°E, 37 \(.0^\circ \) N), Marineris (59.0°W, 14 \(.0^\circ \) S), and Phlegra Dorsa (176.0°E, 23 \(.0^\circ \) N), as well as late Amazonian regions such as Erebus Montes (175.0°W, 36 \(.0^\circ \) N) and Acheron Fossae (136.0°W, 37 \(.0^\circ \) N). The densest concentration of pitted cones in Isidis Planitia is observed in its northwest section (Fig.  2c ), adjacent to Syrtis Major. Furthermore, regions with lower concentrations of pitted cones exhibit lower densities.

Global density map of pitted cones on Mars. The base map is the global hillshaded MOLA digital elevation model 200 m V2.

Pitted cones in the densest regions imply distribution patterns that are similar to those derived from the dataset by Mills et al . 27 . In Acidalia Planitia, pitted cones are densely distributed, forming several clusters. One cluster forms an arc that begins near Chryse Planitia and extends along the boundary between Martian highlands and lowlands (Fig.  2d ). The west and central clusters lack distinct patterns and may have originated from the heart of Acidalia. Pitted cones in Chryse Planitia are more scattered and continued to those found in Marineris. The pitted cones in Utopia Planitia form a clear arc from the southwest to southeast, near Elysium Mons. Approximately four arc distributions (Fig.  2b ) can be discerned in the density map within this area. Some smaller groups of pitted cones can be found in the central east part of the basin, exhibiting morphological characteristics distinct from those in the south but more akin to those in Acidalia and Galaxia Colles.

Technical Validation

The performance of detecting pitted cones and deriving their morphometric parameters depends on both algorithmic and manual factors, given their integral roles in network training. The human contribution involves providing training data and conducting subsequent verifications, while the extensively trained algorithm detects and calculates the morphometric parameters. Although assessing the complete dataset presents challenges due to the dual involvement of these elements, an independent evaluation of each component is crucial for the reliability of the detection results.

The network performance is evaluated following standard metrics, similar to those used in the common objects in context (COCO) dataset 28 , which is widely used for vision tasks and contains 330,000 images of common objects on Earth. These metrics comprehensively evaluate the precision and recall rate under different IoU thresholds. The precision rate is defined as follows:

where TP and FP denote true positive and false positive, respectively. Similarly, the recall rate is defined as

where FN represents false negative. The average precision ( AP ) is the main evaluation metric for object detection. When predicting labels, a confidence score is output, representing the network’s likelihood of a correct prediction. The confidence threshold is applied, with predictions under the threshold filtered out. The AP evaluates the model performance under various confidence scores and is calculated as

where \(p(\widetilde{r})\) is the precision at a given recall \(r\) .

Three regions in Isidis Planitia, Acidalia Planitia, and Cerberus Palus are chosen as test data. The site in Isidis Planitia (86.0°E, 17.2°N) has a dimension of 587.7 km 2 , including 314 pitted cones with an average diameter of 461.2 m. Many of them are formed as pitted cone chains that jointly erupt along fissures. The second test site is in Acidalia Planitia (14.5°E, 38.5°N) with a dimension of 1094.7 km 2 . The site has 144 pitted cones, whose diameter is 586.5 m, which is generally higher than those in Isidis Planitia but less densely distributed. The last site is located in Cerberus Palus (146.6°E, 2.4°N). The region has smooth terrain and very small pitted cones. Pitted cones are significantly smaller than the other two sites, which have an average diameter of 106.6 m. The dimension of the region is 27.1 km 2 , including 73 pitted cones.

Table  1 presents the evaluation results of the test dataset composed of three sites. The performance is satisfying with the average AP of 42.1%, which is concurrent with other tasks 12 , but distinct among regions. AP 50 indicates that the average precision is 78.5% if a true positive is defined assuming an IoU threshold of 50% under various confidence thresholds. AP S , AP M , and AP L denote AP values for small, medium, and large objects defined by their pixel occupancy (less than 32 × 32 pixels, between 32 × 32 and 96 × 96 pixels, and more than 96 × 96 pixels, respectively). The performance of three-size objects is distinct among sites. For small pitted cones, performance in Isidis and Acidalia Planitia is poor but good in Cerberus Palus. In Isidis Planitia, the metric of middle and large-size objects is not distinct, but AP M in the other two regions is significantly better than AP L . Evaluation results indicate that the precision is not completely dependent on object size but varies in regions, implying that may be relevant to morphological features and image quality.

Figure  5 shows the results of pitted-cone detection in three representative regions by selecting a score threshold to filter low-quality detections, and Table  2 summarizes the evaluation metrics compared with manually digitized ground truth data. The average precision and recall values are 82.3% and 70.9%, respectively, representing satisfying performance. High precision but relatively low recall rate promises that algorithm-drawn labels are accurate, and missing undetected pitted cones are labeled manually to keep the overall high quality of the database. False detections are primarily observed at cone chains, where the network struggles to split pitted cones as individuals due to ambiguous conical shapes and boundaries. Moreover, the network performance in Acidalia Planitia is below the mean performance, likely attributable to the active deep-learning workflow first accumulating training samples from Utopia Planitia before progressively encompassing a global scope. During this process, the number of pitted cones in Acidalia Planitia identified as training samples may be disproportionately low due to its distinct morphology characteristics. Consequently, the trained network may exhibit marginally inferior performance in this region relative to other areas.

( a ) Evaluation results in Isidis Planitia (86.0°E, 17.2°N). The base map is Murray-Lab_CTX-Mosaic_beta01-tile-blended_E084_N16; ( b ) Evaluation results in Acidalia Planitia (14.5°E, 38.5°N), The base map is Murray-Lab_CTX-Mosaic_beta01-tile-blended_E-016_N36; ( c ) Evaluation results in Cerberus Palus (146.6°E, 2.4°N). The base map is Murray-Lab_CTX-Mosaic_beta01-tile-blended_E144_N00.

Comparisons among several previous works demonstrate the favorable performance of our method and derived dataset. Pieterek et al . 15 reported a success rate of pitted-cone detection of about 90% on their self-constructed dataset. Jiang et al . 13 proposed a mini single-shot multibox detector network and achieved an average precision ranging from 72.8% to 89.9%. Purohit et al . 28 released an expert-annotated dataset of Martian cones and evaluated several popular segmentation models. Their experiments indicate the variable average mAP among regions is between 11.55% to 34.2%. Mills et al . 29 presented a comparable pitted cone dataset generated using a deep learning model with human verification. The reported precision of 90% and a recall rate of 75% outperform many of the algorithms mentioned above. Additionally, the dataset contains extensive CTX image parameters and individual image patches for every record, facilitating further research. Our evaluation (Tables  1 and 2 ) shows an average performance comparable to previous works. However, the average recall rate of 70.9% of our method implies that there are about 72,909 manually added pitted cones in our dataset. Considering the model size, the trained network is satisfactory and significantly enhances the efficiency for building such a global dataset.

Additionally, the potential uncertainty introduced by human annotation is evaluated. A specific region in Isidis Planitia is selected, with four human annotators independently tasked with labeling 679 pitted cones within this region. The diameter and standard deviation of each pitted cone measured by the four annotators are calculated. The percentage error is determined by dividing the standard deviation of measured diameters by the mean value. As depicted in Fig.  6 , more than 94.1% of the annotator-labeled cones exhibit a differentiation rate of less than 10%, indicating the high precision of manual labeling. Errors exceeding 10% are predominantly associated with the indistinct edges of pitted-cone chains, suggesting that caution should be exercised when using the diameter information from these particular labels.

Evaluation of uncertainty associated with manual digitizing.

Code availability

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Acknowledgements

This work was supported by grants from the Research Grants Council of Hong Kong (RIF Project No: R5043-19, Project No: PolyU 15210520, Project No: PolyU 15215822). The authors would like to thank all those who worked on the archive of the datasets to make them publicly available.

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Zeyu Chen, Bo Wu, Sergey Krasilnikov, Wenjing Xun, Yuan Ma, Shuo Liu & Zhaojin Li

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Z.C.: Conceptualization, Methodology, Software, Validation, Writing- Original Draft. B.W.: Conceptualization, Methodology, Writing- Review & Editing, Supervision. S.K.: Methodology, Validation, Writing- Review & Editing. W.X.: Methodology, Validation. Y.M.: Methodology, Validation. S.L.: Methodology. Z.L.: Validation.

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Chen, Z., Wu, B., Krasilnikov, S. et al. A Global Database of Pitted Cones on Mars for Research on Martian Volcanism. Sci Data 11 , 942 (2024). https://doi.org/10.1038/s41597-024-03811-1

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DOI : https://doi.org/10.1038/s41597-024-03811-1

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AAS 4970 / ENGL 4010: Topics in African American Culture: African American Folklore, Fiction, and Film: Articles / Databases

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• African American Women Folklorists “He is the story that all Weak People Create to Compensate for their Weakness”: African American Women Writing Folklore in the Federal Writers’ Project" by Kathi King.
• Types and Distribution of Negro Folk-lore in America Anthropology master's thesis by Virginia Costroma Osborne-Marsh, 1920.
• Folklore: Rescuing Black America from its Erroneous and Stereotypical Depictions In Literature by Samantha Shields University of Texas master's thesis.
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• “I Got a Tree on My Back and a Haint in my House”: Sonic and Supernatural Wanderings through the Black Southern Landscape by Morgan Mitchell University of Texas master's thesis
• Conjure in African-American Society by Jeffrey Anderson University of Florida doctoral dissertation
• African American Folklore: Its Role in Reconstructing African American History Journal article by Tolagbe Ogunleye.

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Environmental Science: Water Research & Technology

Glycine-assisted phosphorus release and recovery from waste-activated sludge †.

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a School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China E-mail: [email protected]

b Key Laboratory of Pollution Control and Ecological Restoration in Industrial Clusters, Ministry of Education, Guangzhou 510006, China

c Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling, Guangzhou 510006, China

d Guangdong Society of Environmental Sciences, Guangzhou, China

This study reports a sustainable and green method for phosphorus (P) extraction and recovery from waste activated sludge (WAS) using glycine as a P-extraction agent. Glycine showed an extraordinary ability to induce P release from waste-activated sludge at a rate of 8.7 mg P per L per h without being consumed. The P-extraction rate was linearly related to the mixed liquor suspended solid concentration and was not affected by the temperature in the range of 25–35 °C. After extraction, the released P was recovered via calcium precipitation, resulting in high P-content (48%, as phosphate) products (dominated by amorphous calcium phosphate). An unparallel advantage of the method is the high recyclability of glycine. Repetitive experiments showed <10% glycine loss over four P-extraction–P-recovery–glycine-reuse cycles. Additionally, extremely low heavy metal contents were observed in the P-recovery products in comparison to the acid/alkali-assisted P extraction, indicating its environmental friendliness as a sustainable strategy for P recovery from WAS.

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Glycine-assisted phosphorus release and recovery from waste-activated sludge

S. Cen, Y. Zou, H. Chen, X. Deng, F. Huang, L. Chen, L. Li, T. Jin, C. Wei, L. Nengzi and G. Qiu, Environ. Sci.: Water Res. Technol. , 2024, Advance Article , DOI: 10.1039/D4EW00158C

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