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18,621 results for “development;”
Data and Material for 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'
<p>Data and Material supporting the paper 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'.</p>
What Do Developers Consider Magic literals? A Smalltalk Perspective
<p>Data for the paper: "What Do Developers Consider Magic literals? -- A Smalltalk Perspective"</p> <p>The file 'allLiteralData.csv' contains a classification of cerca 24K literals performed by 26 different participants (P1 to P26).</p> <p><strong>Data</strong></p> <p>Columns are:</p> <ul> <li>literal -- the value of the literal (eg: "1")</li> <li>kind -- the type among: <em>Integer</em>, <em>String</em>, <em>Symbol</em>, <em>Boolean</em>, <em>Array</em>, <em>UndefinedObject</em> (=nil), <em>Float</em>, and <em>Character</em></li> <li>astNode -- the AST Role of the literal among: <em>Argument</em> (of a message), <em>Operand</em> (of an operation), argument of a <em>Pragma</em>, value <em>Returned</em>, value <em>Assigned</em> to a variable, <em>Receiver</em> of a message, member of a <em>Sequence</em> (ie more or less an expressionStatement with the literal as expression), member of an <em>array</em>.</li> <li>specialArg -- whether the literal appears in one of the specific context that are linked to language conventions: "<em>loopFrom1</em>" (literal "1" is start of a loop), "<em>increment1</em>" (literal "1" used as increment or decrement), "<em>streamOutput</em>" (string or character literal output on a stream), "<em>nameArg</em>" (string literal argument of a "name:" message), "<em>other</em>" (none of the previous cases)</li> <li>status -- whether the literal was classified "<em>Magic</em>", "<em>NotMagic</em>" (acceptable), or "<em>Undecided</em>"</li> <li>idInMethod -- the position of the literal in the list of all literals that its parent method contains (whether it is the 1st, 2nd, ... of its parent method). Will always be >=1 and <= "<em>nbLiteralsInMethod</em>" (see below)</li> <li>isConstantMethod -- whether the parent method of the literal only returns it (which is the convention in Pharo to implement constant)</li> <li>method -- name of the class and method containing the literal</li> <li>specialMethod - whether the parent method is a "<em>test</em>" method, an "<em>example</em>" method, or a "regular" method (ie not <em>test</em> nor <em>example</em>)</li> <li>commentLength -- length (in character) of possible comments in the parent method</li> <li>nbLiteralsInMethod -- total number of literal in the parent method (related to "<em>idInMethod</em>")</li> <li>project -- name of the system where the literal was found, among: <em>Morphic</em>, <em>Parser</em>, <em>Pharo</em>, <em>Polymath</em>, <em>Roassal</em>, <em>Seaside</em>, and <em>VMMaker</em>.</li> <li>expId -- batch number in the project where that literal was classified by a participant ("<em>expert</em>").</li> <li>expert -- code of the participant that classified the literal</li> <li>expertise -- self declared participant's knowledge of the system. One of: "<em>low</em>", "<em>medium</em>", or "<em>high</em>"</li> </ul> <p><strong>Keys</strong></p> <p>The tuple (<em>idInMethod</em>, <em>method</em>) uniquely identifies a literal.</p> <p>The tuple (<em>project</em>, <em>expId</em>) uniquely identifies a batch of 50 literals classified by a participant.</p> <p>A batch contains 50 literals and the same literal may appear in different batches (intended redundancy).</p> <p><strong>Heuristic validation</strong></p> <p>The various hypotheses formulated in the paper are validated following the rules below:</p> <p><em>Heuristic 1 </em>(impact of participant expertise) -- counts all literals Magic or not ("<em>status</em>" column) for each value of column "<em>expertise</em>"</p> <p><em>Heuristic 2</em> (self describing literals) -- for all self describing literals, count number of "<em>status</em>"= "<em>Magic</em>" or not. Self describing literals are:</p> <ul> <li>booleans, "type" = "Boolean"</li> <li>empty pointer, "type" = "UndefinedObject"</li> <li>empty arrays, "type" = "Array" and "literal" = "#()" or "literal" = "{}"</li> <li>strings, "type" = "String"</li> <li>symbols "type" = "Symbol"</li> </ul> <p><em>Heuristic 3 </em>(coding convention) -- for all literals in specified contexts ("<em>specialArg</em>" != "<em>other</em>"), count number of "<em>status</em>"= "<em>Magic</em>" or not.</p> <p><em>Heuristic 4</em> (assigned to a variable/returned by a method) -- for all literals directly assigned to a variable ("<em>astNode</em>" = "<em>assign</em>.") or directly returned by a method ("<em>astNode</em>" = "<em>return</em>"), count number of "<em>status</em>"= "<em>Magic</em>" or not.</p> <p><em>Heuristic 5</em> (pragma argument) -- for all literals argument of a pragma ("<em>astNode</em>" = "<em>pragma</em>"), count number of "<em>status</em>"= "<em>Magic</em>" or not.</p> <p><em>Heuristic 6 </em>(in Test or Example method) -- for all literals in a test method ("<em>specialMethod</em>" = "<em>test</em>") or example method ("<em>specialMethod</em>" = "<em>example</em>"), count number of "<em>status</em>"= "<em>Magic</em>" or not.</p>
Dataset: Systematic Mapping Study on the Development and Application of Sentiment Analysis Tools in Software Engineering
<p>Update: We updated the data set in March 2022 by adding newly published papers and by providing more insights on how we analyzed them. Details can be found in the file " SEnti-SMS.xlsx".</p> <p>----------</p> <p>Update: The updated version (-v2) contains the results of one more snowballing iteration and extracted information on the accuracy of the used methods.</p> <p>----------</p> <p>In 2020, we conducted a systematic literature review to explore the development and application of sentiment analysis tools in software engineering.</p> <p>Information on the execution of the SLR, its scope, the search string, etc. are presented in the paper linked below.</p> <p> </p> <p> </p>
Data from: Public human microbiome data are dominated by highly developed countries
<p>Supplementary tables and datasets associated with the <em>PLOS Biology </em>publication "Public human microbiome data are dominated by highly developed countries." See README.txt for a description of the files and the data fields they contain.</p> <p>Update, 8 Apr 2022: The "figures.md" file described in the readme was inadvertently left out of the initial upload. It has been added here.</p>
Maps of the Sustainable Development Goal (SDG) indicator 15.3.1 with its sub-indicators for the entire Amazon River Basin
<p>Maps of the SDG indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) together with its sub-indicators for the Amazon River Basin for the period 2001-2020. The sub-indicators are trajectory (or trend), state, and performance. The SDG indicator 15.3.1 was calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution and the 16-day MOD13Q1 NDVI and SoilGrids dataset were used as inputs. In addition, annualized maps of drought severity derived from SPI12, SPEI12, and scPDSI are added. </p> <p>A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution.</p> <p>Coding for the SDG indicator 15.3.1, trajectory, state, and performance.</p> <p>-32768 is ‘No data’</p> <p>-1 is ‘Degraded’</p> <p>0 is ‘Stable’’</p> <p>1 is ‘Improvement’</p> <p>Coding for the drought severity.</p> <p>From 0 (minimum drought severity) to 1 (maximum drought severity).</p>
Data from: Understanding the Influence of Check Dam and Season on Habitat Use to Develop Habitat Suitability Criteria for Overwintering Tadpoles of Nanorana spp.
<p>Dataset for the article: Understanding the Influence of Check Dam and Season on Habitat Use to Develop Habitat Suitability Criteria for Overwintering Tadpoles of <em>Nanorana</em> spp.</p> <p>See readme.txt for details.</p>
Predicted stress development during cure and subsequent cooling
<p>Predicted stress development during cure and subsequent cool-down to room temperature. Stress-time curves for a path dependent model and the visco-elastic VisCoR model.<em> </em></p>
The allocation of Chinese and Indian development finance in Nepal and its influence on local election results
<p>This realease contains the used datasets and calculations for my Bachelorthesis about Indian and Chinese allocation of Overall Development Assistance in Nepal.</p>
Raw data for the article "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping"
<p>Raw data used for the article "Gerber, Andreas, Markus Ulrich, Flurin X. Wäger, Marta Roca-Puigròs, João S.V. Gonçalves, and Patrick Wäger. 2021. "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping" <em>Sustainability</em> 13, no. 4: 1997. <a href="https://doi.org/10.3390/su13041997">https://doi.org/10.3390/su13041997</a>"</p> <p>The documents include the raw data (both as .csv and .xlsx files with the same content), as well as the publication (.pdf file). The data collection process and the data itself are described in the publication. The data is published as "supplementary material" on the publisher's homepage.</p>
Dataset of Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy
<p>The dataset includes search results used in article “Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy” biorXiv, 2022.01.17.476562, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. <a href="https://doi.org/10.1101/2022.01.17.476562">https://doi.org/10.1101/2022.01.17.476562</a></p> <p> </p> <p>Search Results Description: Please use Figure 1 from paper to trace the data made available and experimental group.</p> <p>The excel “raw data 2022-06-24” file contains the following data</p> <ol> <li>Body weight of each rabbit on a weekly basis</li> <li>Analysis of breeding parameters at mid-pregnancy</li> <li>Histological areas of each mammry tissue measured</li> <li>Optical density values obtained for biochemical leptin concentration determination</li> <li>Optical density values obtained for biochemical triglyceride concentration determination</li> <li>Optical density values obtained for biochemical glucose concentration determination</li> <li>Optical density values obtained for biochemical cholesterol concentration determination</li> <li>RT-qPCR results (Ct) from QuantStudio export for milk protein analysis</li> <li>RT-qPCR results (Ct) from QuantStudio export for lipid metabolism analysisen</li> </ol> <p>“Statistical analysis.doc” contained the description of the statistics used in Excel and the description of the linear mixed model analysis , the reference of the script available by the CRAN project is also include.The R scipt file for using the linear mixed model in R added with the "data-croissance-analysis" file.</p>
How Developers Review Tests in GitHub?
<p>This dataset contains 330 code reviews with 40 tips, 16 request categories, 8 response categories, and 13 Pull Request and comment features. This dataset's column titles are:</p> <p>Project Owner Project Repo Project URI Project Language Pull Request URI Pull Request ID Pull Request Author Pull Request Merge Commit Hash Review comment URI Review comment ID Review comment Text Solving Commit Hash Solving Commit URI Validation Request Category Tips Response Category Test Case Unspecified Test Method Fix SUT Optional Test Improve Test Refactor Test Test Class Fix Test Test Branch Test Statement Achieve Specific Coverage Goal ML Model Test Prevent Exploit Disagreement Revert Test Removal Unit Test Code Snippet End-to-End Test Edge Case Expected Exception Filepath Set Up Parametric Negative Test Test Double Type Support Based On External Resource Integration Test Positive Test Avoid Wrong API Usage Increase Testability Rename Test Dependency Reproduce Issue More Specific Test Event Test Assert Message Fix Based On Test Boundary Test Regression Test Async Thread Consistency Test Move Test Compilation Check Extract Member Cache Modifier Empty Test File Merge Test Readability Sleep Invalid Test Remove Reflection</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</p>
Complete datasets and code for "Hungry or angry? Experimental evidence for the effects of food availability on two measures of stress in developing wild raptor nestlings"
<p><strong>Abstract</strong></p> <p>Food shortage challenges the development of nestlings; yet, to cope with this stressor, nestlings can induce stress responses to adjust metabolism or behaviour. Food shortage also enhances the antagonism between siblings, but it remains unclear whether the stress response induced by food shortage operates via the individual nutritional state or via the social environment experienced. In addition, the understanding of these processes is hindered by the fact that effects of food availability often co-vary with other environmental factors. We used a food supplementation experiment to test the effect of food availability on two complementary stress measures, feather corticosterone (CORTf) and Heterophil/Lymphocyte-ratio (H/L) in developing red kite (Milvus milvus) nestlings, a species with competitive brood hierarchy. By statistically controlling for the effect of food supplementation on the nestlings’ body condition, we disentangled the effects of food and ambient temperature on nestlings during development. Experimental food supplementation increased body condition, and both CORTf and H/L were reduced in nestlings of high body condition. Additionally, CORTf decreased with age in non-supplemented nestlings. H/L decreased with age in all nestlings and was lower in supplemented last-hatched nestlings compared to non-supplemented ones. Ambient temperature showed a negative effect on H/L. Our results indicate that food shortage increases the nestlings’ stress levels through both, a reduced food intake affecting nutritional state and the nestlings’ social environment. Thus, food availability in conjunction with ambient temperature shape between- and within nest differences in stress load, which may have carry-over effects on behaviour and performance in further life-history stages.</p>
A Practical Tool-Chain for the Development of Coordination Scenarios - Graphical Modeler, DSL, Code Generators and Automaton-Based Simulator
<p>The Peer Model is a modeling tool for coordination based on blackboard-based collaboration. </p> <p>The tool-chain consists of a modeler, translator and simulator.</p> <p>Its goal is to help developers of distributed and concurrent coordination software better understand algorithms and identify deficiencies from the beginning.</p> <p><br> </p>
Dataset of Open-Source Software Developers Labeled by their Experience Level and Associated with their Software Metrics
<p>This dataset contains 703 anonymized developers extracted from 17 open-source projects from GitHub. Projects were chosen because they use:</p> <ul> <li>the Java programming language</li> <li>the <a href="https://spring.io/projects/spring-framework">Spring framework</a></li> <li><a href="https://maven.apache.org/">Maven</a> / <a href="https://gradle.org/">Gradle</a> build tools</li> </ul> <p>For all these developers, 23 software metrics were calculated for each project to which they contribute. These metrics are either calculated by analyzing the source code or relative to project management metadata. Each of these developers then have been manually annotated. To do this, developers have been searched for in professionnal social media such as:</p> <ul> <li><a href="https://www.linkedin.com/">Linkedin</a></li> <li><a href="https://twitter.com/">Twitter</a></li> <li><a href="https://github.com/">Github</a></li> </ul> <p><strong>This dataset is published in the following journal article: </strong></p> <p><strong>Dataset of Open-Source Software Developers Labeled by their Experience Level in the Project and their Associated Software Metrics, Q. Perez, C. Urtado and </strong><strong>S. Vauttier, Data In Brief, </strong></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S2352340922010459">https://www.sciencedirect.com/science/article/pii/S2352340922010459</a></p>
Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams
<p>DNA were derived from fin tissue samples taken from individual trout captured from Little Jacks Creek, Big Jacks Creek , and Duncan Creek of the Owyhee mountains and Keithly Creek and Upper Mann Creek in the Hitt mountains of Western Idaho, United States. Fin tissues were collected from individual trout from each stream during monthly sampling events in June through October 2020. </p> <p><em>DNA Extraction:</em> Extraction of DNA from caudal fin tissues were performed using Quick-DNA Miniprep Plus purification kits (Zymo Research Inc.©). Small sections of fin tissue (≤ 25 mg) were collected from each sample. This was mixed with a digesting solution comprised of ultra-pure water, solid tissue buffer (Zymo Research Inc.©) and proteinase K. All tissues were digested in sealed microcentrifuge tubes for at minimum 3 h at 55°C in a water bath. We then aliquoted 100 µL of digestion supernatant and combined with 200 µL of genomic binding buffer (Zymo Research Inc.©). DNA was eluted in 50, 75, and 100 µL of elution buffer to determine which volume provided sufficient DNA concentration for genotyping. After it was determined all quantities produced suitable concentrations, going forward, 50 µL of elution buffer used.</p> <p><em>Genotyping:</em> Following extraction, genotyping-in-thousands sequencing took place at the Hagerman National Fish Hatchery’s genetics research facility with the assistance of the Columbia River Intertribal Fish Commission (CRTFC). Genotyping protocols were as described in Campbell et al. (2015) and summarized below. First, samples were prepared for amplification via PCR by combining DNA extracts with a Qiagen Plus multiplex master mix and a species-specific pooled primer mix. This step added the Illumina sequencing primer sites to amplicons. Following the creation of the PCR cocktail, thermocycling was conducted for amplification. Amplified samples were then diluted 20-fold. Diluted samples were transferred to new 96-well PCR plates where two genetic indexes and barcodes provides a unique set of tagging primers to each well and plate. Tagged plates then underwent a second PCR step. After the second PCR, all DNA were transferred to Charm Biotech normalization plates where DNA was bound to wells, washed, and finally eluted. After normalization, all DNA was pooled together and a purification step using magnetized beads in two steps to selectively remove fragments of DNA that are both too large and too small for sequencing. Following purification, each plate was quantified via qPCR using Life Technologies QuantStudio 6 Flex Instrument (Life Technologies). Finally, sequencing was performed using an Illumina HiSeq 1500 instrument.</p> <p><strong>Ancillary peer-reviewed manuscripts:</strong><br> <em>Genotyping protocols</em><br> Campbell NR, Harmon SA, Narum SR. 2015. Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing. Mol Ecol Resour, 15: 855-867. https://doi.org/10.1111/1755-0998.12357<br> <em>SNP loci reference</em><br> Collins EE, Hargrove JS, Delomas TA, Narum SR. 2020. Distribution of genetic variation underlying adult migration timing in steelhead of the Columbia River basin. Ecology and Evolution, 10(17): 9486-9502. https://doi.org/10.1002/ece3.6641 </p> <p><strong>Data Use</strong>:<br> <em>License</em>: <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> <br> <em>Recommended Citation</em>: Wooding AP, Narum SR, Pradhan DS. 2022. Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7055582</p> <p>Funding for this project is provided by US National Science Foundation and Idaho EPSCoR through award: OIA-1757324 </p>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
MAL04 Territorial development scenarios for the Charente River basin and its coastal zone (France)
<p>This dataset includes the territorial development scenarios developed by the H2020 COASTAL project’s MAL #4 for the Charente River basin and its coastal zone. These scenarios were co-designed with local stakeholders to depict possible futures of the territory. “Towards a desirable future” represents the implementation of the business roadmap also designed in collaboration with stakeholders to achieve a desirable and sustainable future. “Improving current trends” describes the expected evolution of the territory if current efforts are maintained without significant innovation. “Towards a fragmented territory” illustrates a negative development of the territory, exacerbating current issues and inequalities. Each scenario consists in a narrative and in a set of values attributed to the decision variables of the MAL #4 system dynamics model. These values are converted into time-series to simulate the scenarios (cf. data_scenarios.xlsx in <a href="https://doi.org/10.5281/zenodo.7075123">https://doi.org/10.5281/zenodo.7075123</a>).</p>
Housing development in Yinchuan
<p><strong>Coordinador del Seminario:</strong> Carlos A. Navarrete Ulloa.<br> <strong>Expositor</strong>: Briseida Corzo Rivera</p> <p><strong>Comité Ejecutivo PRONACE-Vivienda</strong></p> <ul> <li>Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).</li> <li>Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.</li> <li>Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).</li> <li>Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</li> </ul> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:</p> <p>Meng Wang, Aleksandra Krstikj y Hisako Koura (2017) Study on the housing supply and public programs impacts on the housing development in Yinchuan City, Western China. Journal of Architecture and Planning, 82, 469-476. <a href="https://doi.org/10.3130/aija.82.469">https://doi.org/10.3130/aija.82.469</a></p>
Dataset for the publication: Development of a Mg/O ReaxFF Potential to describe the Passivation Processes in Magnesium-Ion Batteries
<p>This dataset contains all input and output files of the performed calculations, which results were published in the ChemSusChem article: 'Development of a Mg/O ReaxFF Potential to describe the Passivation Processes in Magnesium-Ion Batteries' (<a href="https://doi.org/10.1002/cssc.202201821">https://doi.org/10.1002/cssc.202201821</a>). A preprint of the publication is further available under: <a href="http://doi.org/10.26434/chemrxiv-2022-3chph">https://doi.org/10.26434/chemrxiv-2022-3chph</a>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.