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45 results for “msr”
Mining API Interactions to Analyze SoftwareRevisions for the Evolution of Energy Consumption (MSR'2021 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our MSR'2021 paper <em>Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption</em>.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file <em>msr_2021_dataset.csv</em> and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>sampleNr - a number identifying the group this sample relates to</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>durationAdjusted - duration after alignment between method trace and energy profile</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>`package-names` - per package uAPI profile</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>The files <em>joule_anova_posthoc_result.csv</em> and <em>uAPI_anova_posthoc_result.csv</em> contain the results of the ANOVA and Tukey HSD posthoc analysis to determine accuracy and F1-score of the presented approach.</p> <p> </p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
Extracted MSR GitHub Repository URLs
<p>This dataset contains text files of <a href="https://github.com">GitHub</a> URLs pointing to hosted git repositories.</p> <p>These URLs come from mining software repository (MSR) datasets. URLs are built by taking the repository owner's name (OWNER) and it's name (REPO) and appending them to https://github.com/. There is one URL per line. <em>URLs have not been tested for their current availibility</em>. An example URL format is provided below:</p> <pre><code>https://github.com/OWNER/REPO</code></pre> <p> Current URLs are from the following datasets:</p> <ul> <li>libraies.io January 12th, 2020 dataset <ul> <li>Jeremy Katz, "Libraries.io Open Source Repository and Dependency Metadata". Zenodo, Jan. 12, 2020. doi: 10.5281/zenodo.3626071.</li> </ul> </li> <li>RepoReapers/reaper dataset <ul> <li>Munaiah, N., Kroh, S., Cabrey, C. et al. Curating GitHub for engineered software projects. Empir Software Eng 22, 3219–3253 (2017). https://doi.org/10.1007/s10664-017-9512-6</li> </ul> </li> <li>GH Torrent dataset <ul> <li>G. Gousios, “The GHTorent dataset and tool suite,” in <em>Proceedings of the 10th Working Conference on Mining Software Repositories</em>, San Francisco, CA, USA, May 2013, pp. 233–236.</li> </ul> </li> </ul>
MSR Licenses
<p>For the purposes of replicating the license study on free software JavaScript ecosystem projects, all results generated in the study, including the script code used to perform the experiments and data analysis, as well as the data generated by the tool used are available here. .</p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
MSR SIMULATION WITH CGEMS: SALT AND FISSION PRODUCT EVAPORATION
<p>Conference proceedings: 10th Europen Review Meeting on Severe Accident Research, ERMSAR 2022</p>
MSR Simulation With cGEMS: Fission Product Release And Aerosol Formation
<p>The release of fission products and fuel materials from a molten salt fast reactor fuel in hypothetical accident conditions was investigated. The molten salt fast reactor in this investigation features a fast neutron spectrum, operating in the thorium cycle, and it uses LiF-ThF4-UF4 as a fuel salt. A coupling between the severe accident code MELCOR and thermodynamical equilibrium solver GEMS, the so-called cGEMS, with the updated HERACLES database was used in the modeling work. The work was carried out in the frame of the EU SAMOSAFER project. At the beginning of the simulation, the fuel salt is assumed to be drained from the reactor to the bottom of a confinement building. The containment atmosphere is nitrogen. The fission products and salt materials are heated by the decay heat, and due to heating, they are evaporated from the surface of a molten salt pool. The chemical system in this investigation included the following elements: Li, F, Th, U, Zr, Np, Pu, Sr, Ba, La, Ce, and Nd. In addition to the release of radioactive materials from the fuel salt, the formation of aerosols and the vapor phase species in the modeled confinement were determined.</p>
Numerical methodology for design and evaluation of natural circulation systems for MSR applications - Dataset
<p>Dataset associated to the 2022 ANS Annual meeting conference paper "Numerical methodology for design and evaluation of natural circulation systems for MSR applications".</p>
Dataset for the paper "Googling for Software Development: What Developers Search For and What They Find?", MSR 2021.
<p>This is the dataset for the paper "Googling for Software Development: What Developers Search For and What They Find?" submitted to the Mining Software Repositories Conference (MSR), 2021.</p> <p>This dataset has three data:</p> <ul> <li><strong>Search queries</strong>: contains the search queries to compute RQ1, RQ2, RQ3, and RQ4.</li> <li><strong>Search results RQ5</strong>: contains the search results to compute RQ5 (files starting with "search-results-rq5").</li> <li><strong>Search results RQ6</strong>: contains the search results to compute RQ6 (files starting with "search-results-rq6").</li> </ul> <p>The dataset "Search results RQ6" has 8 columns: </p> <ol> <li>same_top10: whether the top 10 links are exactly the same (0 or 1)</li> <li>same_top1: whether the top 1 links are exactly the same (0 or 1)</li> <li>inter_ratio_top5: the intersection of links in the top 5</li> <li>inter_ratio_top10: the intersection of links in the top 10</li> <li>original_query: the original queries</li> <li>original_search_resuls: the top 10 links returned for the original queries</li> <li>modified_query: the modified queries</li> <li>modified_search_resuls: the top 10 links returned for the modified queries</li> </ol> <p>Example (word swap, context)</p> <ul> <li>Original query: "java string replaceall case insensitive"</li> <li>Modified query: "string replaceall case insensitive java"</li> <li>Single row example: "0","1.0","0.8","0.9","java string replaceall case insensitive","['https://stackoverflow.com/questions/5054995/how-to-replace-case-insensitive-literal-substrings-in-java', ...]","string replaceall case insensitive java","['https://stackoverflow.com/questions/5054995/how-to-replace-case-insensitive-literal-substrings-in-java', ...]"</li> </ul>
Characterizing high quality test methods, MSR'22
<p>Replication Kit for "Characterizing high quality test methods" paper.</p> <p>Number of records: 15970;</p> <p>Number of columns: 50;</p> <p>Columns list:</p> <ul> <li>Id;</li> <li>Project;</li> <li>Class;</li> <li>Method;</li> <li>Killed;</li> <li>Survived;</li> <li>TimedOut;</li> <li>Total;</li> <li>Score;</li> <li>WhileCount;</li> <li>ConditionCount;</li> <li>RedundantCount;</li> <li>AssertCount;</li> <li>IfCount;</li> <li>ExceptionCount;</li> <li>ForeachCount;</li> <li>PrintCount;</li> <li>SwitchCount;</li> <li>MysteryCount;</li> <li>ForCount;</li> <li>VerboseCount;</li> <li>ResourceOptimismCount;</li> <li>ThreadSleepCount;</li> <li>SensitiveCount;</li> <li>MagicNumberCount;</li> <li>Assertion Roulette;</li> <li>Mystery Guest;</li> <li>Sleepy Test;</li> <li>Unknown Test;</li> <li>Redundant Assertion;</li> <li>Dependent Test;</li> <li>Magic Number Test;</li> <li>Conditional Test Logic;</li> <li>EmptyTest;</li> <li>General Fixture;</li> <li>Sensitive Equality;</li> <li>Verbose Test;</li> <li>IgnoredTest;</li> <li>Resource Optimism;</li> <li>Duplicate Assert;</li> <li>Exception Catching Throwing;</li> <li>Print Statement;</li> <li>SLOC;</li> <li>Complexity;</li> <li>ModifyingCommits;</li> <li>Contributors;</li> <li>MaxExperienced;</li> <li>MinExperienced;</li> <li>MeanExperience;</li> <li>MedianExperience;</li> <li>StdevExperience</li> </ul> <p>Format: CSV</p> <p>Attributes per category:</p> <p>Size & Complexity: number of lines of code, cyclomatic complexity, loop count and conditional count;</p> <p>Exception: exception count and resource optimism;</p> <p>Contributors: Developers' expertise and number of contributors;</p> <p>Changes: Number of modifications;</p> <p>Quality: Number of asserts, mystery guest count, magic number count, print count, sleep count,<br> sensitive count</p> <p>Test Smells: Assertion Roulette, Mystery Guest, Sleepy Test, Unknown Test, Redundant Assertion, Dependent Test, Magic Number Test, Conditional Test Logic, EmptyTest, General Fixture, Sensitive Equality, Verbose Test, IgnoredTest, Resource Optimism, Duplicate Assert, Exception Catching Throwing, Print Statement; </p>
Estimating Usage Of Open Source Projects - Flutter Telemetry Case Study - MSR' 24
<p>This dataset (CSV) was assembled to support analysis within a case study that will be published in the proceedings of the <a href="https://conf.researchr.org/home/msr-2024">Mining Software Repositories</a> conference (MSR ‘24) April 14-15 2024: <a title="Estimating Usage Of Open Source Projects" href="https://doi.org/10.1145/3643991.3645066">Estimating Usage Of Open Source Projects.</a></p> <p>This case study explored whether publicly available metrics could serve as proxies to estimate usage of an open source project. Using the <a href="https://flutter.dev/">Flutter</a> project as our case study, we collected monthly proxy metrics from GitHub, StackOverflow and Slack to compare with Flutter’s monthly active user count over the same time period: January 2018 through February 2021.</p> <p>All metrics correspond to the last day of the month and/or represent aggregate activity in that month. Data from GitHub shows aggregate activity counts across the entire <a href="https://github.com/flutter">Flutter GitHub organization</a> (up to 33 repositories). </p> <p>Our specific metrics include:</p> <div> <table> <tbody> <tr> <td> <p>Source</p> </td> <td> <p>Metric</p> </td> <td> <p>Aggregation method</p> </td> <td> <p>Details</p> </td> </tr> <tr> <td> <p>Flutter</p> </td> <td> <p>Monthly Active Users (MAU)</p> </td> <td> <p>Google internal tooling</p> </td> <td> <p>Flutter users active in the last 30 days, collected on the last day of each month</p> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>PullRequest Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> <p>As Google governs changes to this code base, we excluded known Google employees in code change related metrics </p> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Issues Created in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Issue Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Fork events in month</p> </td> <td> <p><a href="http://gharchive.org">gharchive.org</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Fork cumulative count at the end of month</p> </td> <td> <p><a href="http://gharchive.org">gharchive.org</a></p> </td> <td> </td> </tr> <tr> <td> <p>StackOverflow</p> </td> <td> <p>Question Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>StackOverflow</p> </td> <td> <p>Questions in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>Slack</p> </td> <td> <p>Claimed (cumulative) members at the end of the month</p> </td> <td> <p>fluttercommunity.slack</p> </td> <td> </td> </tr> <tr> <td> <p>Slack</p> </td> <td> <p>Cumulative messages at the end of the month</p> </td> <td> <p>fluttercommunity.slack</p> </td> <td> </td> </tr> </tbody> </table> </div> <p>Tools: </p> <ul> <li> <p>We used an instance of <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a> hosted by <a href="https://bitergia.com/">Bitergia</a> to aggregate GitHub PullRequest Authors, GitHub Issue Authors, GitHub Issues, across all repositories under the Flutter Organization, and StackOverflow Question Authors, and StackOverflow Questions for questions that mention Flutter. We used the <a href="http://github.com/chaoss/grimoirelab-sortinghat">Sorting Hat</a> of feature GrimoireLab to identify Google employees in this sample.</p> </li> </ul> <ul> <li> <p><a href="http://gharchive.org">GHArchive</a> via <a href="https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code">BigQuery</a> was used to count GitHub Star and Fork events across all repositories under the Flutter Organization</p> </li> <li> <p>We pulled Slack activity directly from <a href="http://fluttercommunity.slack.com">Flutter's slack channel </a>dashboard</p> </li> </ul>
MSR Dataset 2025
<p>The following Datasets are for the submission of MSR 2025 dataset track</p> <p>1) Bug report metadata dataset</p> <p>2) Contributor Information Dataset</p> <p>3) Bug reports and Comments Dataset</p>
Data showcase papers published in the Mining Software Repositories (MSR) conference
<p>Data regarding data showcase papers published in the Mining Software Repositories (MSR) conference.</p> <p>The following data files are included.</p> <p>citation-table.csv: SWEBOK areas of citing studies<br> citations.bib: Bibliographic details of citing studies<br> citing_dp_dois_citations.txt: Citations of citing studies<br> data_papers.bib: MSR data papers<br> dp_dois_citations.txt: Citations of data papers<br> false_citations.bib: Citing studies that don't actuall use data papers<br> msr-all: Bibliographic details of all MSR papers<br> ndp_dois_citations.txt: Citations of non-data papers<br> ndp_rand_dois_citations.txt: Citations of a randomly chose non-data paper weighted sample<br> self-citations.csv: Data papers citations by their authors</p> <p> </p>
SATD Data MSR 2025
<p>### Description of the dataset.</p> <p>There are 49 files for the 49 projects. The first line (header) shows the metrics' names (columns are separated by tab). For each metric measurement, there can be multiple values because we captured the complete evolution history of each method. </p> <p>McCabe values 5#6#7 means at the introduction (when the method was first pushed), the McCabe was 5. Then after a change, it became 6, and then 7 (latest). <br>But for change values (such as ChangeDates), the first value is always 0. This means that the method was introduced that day. The first value is 0 for other change indicators as well, because the method was introduced, not modified. </p> <p>ChangeAtMethodAge --- How old was the method when the change happened?</p> <p>TangledWMoveandFileRename ---how many methods were modified in that commit, without method move and file rename? We have used this information, because if a method is moved in a bug fix commit, but without any content change, then that method should not be responsible for bugs. </p> <p>Buggycommiit --- 1 means, it was a bug fix commit according to our accurate keyword-based approach. If it is 1, and the number of TangledWMoveandFileRename is also one, this method version is definitely buggy. This approach was used for the high-precision dataset. </p> <p>RiskyCommit ---A method is buggy if the value is 1. This dataset was used for the high-recall dataset. </p> <p> </p> <h3>Code</h3> <p>The necessary code to generate the results is shared here: https://github.com/shaifulcse/SATD-MSR-2025</p>
Muon Scattering Radiography (MSR) measurements on blocks of ice in laboratory, and on simulated snowpack
<p>Experimental setup (scenario 5):</p> <p>Muon data used in this work has been collected with our muon detection system. This muon monitoring system is currently in use for both scientific and industrial purposes <a href="https://www.zotero.org/google-docs/?broken=RG5rWA">(Martínez-Ruiz del Árbol et al., 2022)</a>. The particle detectors are composed of four Multi-Wire Proportional Chambers (MWPC) and each chamber has two layers with 224 detection wires, all of them separated by 4 mm. The two layers form a two-dimensional grid of wires which covers an area of 89.6 x 89.6 cm and detects the positions where muons cross it.</p> <p>When a muon event is identified, our system detects four points located in the horizontal two-dimensional grids, two points before the particle goes through the target and another two points after the particle traverses it. With this data, way-in and way-out trajectories can be reconstructed, and muon deviations calculated. Specifically, in the numerical analysis of this work, we utilised the projection of muon deviations in two planes perpendicular to the detection wires.</p> <p>Simulation setup (scenarios 1 to 4):</p> <p>The snowpack was simulated using a one-dimensional snow model forced by surface meteorological data. We have used the SNOWPACK model <a href="https://www.zotero.org/google-docs/?EqYNAT">(Bartelt & Lehning, 2002</a><a href="https://www.zotero.org/google-docs/?LUKxAu">)</a> to realistically simulate the behaviour of the snowpack along two seasons, 2015/2016 (1_Modelling) and 2016/2017 (2_Testing). SNOWPACK was forced by the ERA5-Land surface reanalysis <a href="https://www.zotero.org/google-docs/?QCLBxK">(Muñoz-Sabater et al., 2021)</a>. The simulations were performed in the Pyrenees, using the ERA5-Land cell whose centroid falls closer to the Monte Perdido massif (42.7°N, -0.1°E), at an elevation of 2041m asl.</p> <p>We coupled the SNOWPACK simulations with a full MSR simulation setup that uses the Cosmic RaY generator <a href="https://www.zotero.org/google-docs/?oSIPTu">(Hagmann et al., 2012)</a> to reproduce the atmosphere muon flux and GEANT4 <a href="https://www.zotero.org/google-docs/?3ZDRDN">(Agostinelli et al., 2003)</a> to simulate the muon scattering caused by the snowpack. GEANT4 is a state-of-the-art software designed and maintained at CERN to simulate the interactions of particles and matter in high-energy and nuclear physics. Our simulation framework contains a model of our experimental setup including the muon detectors and their response. This framework has been successfully applied to multiple industrial problems, for instance, to steel-made pipe wear <a href="https://www.zotero.org/google-docs/?F8nYbS">(Martínez-Ruiz del Árbol et al., 2018)</a>. Similar simulation frameworks are typically used to research applications of muography <a href="https://www.zotero.org/google-docs/?115QeU">(Mori et al., 2017)</a>.</p> <p>We expanded the one-dimensional snowpack geometry to a 1m² snow column, assuming homogeneous snow layers in the longitude and latitude dimensions. Then, we propagated and measured muons penetrating the whole snow column, virtually reproducing the detection process using GEANT4. We collected muon deviations and their Root-mean-square (RMS) value for different accumulations of snow during the two simulated seasons.</p>
Data showcase papers published in the Mining Software Repositories (MSR) conference (v2.2)
<p>Data regarding data showcase papers published in the Mining Software Repositories (MSR) conference.</p> <p>The following data files are included.</p> <ul> <li>citing_dp_dois_citations.txt: Strong and weak citations of (strong and weak) citation papers</li> <li>data_paper_clustering.csv: The clustering process of MSR data papers</li> <li>data_paper_clusters.csv: Clusters of MSR data papers</li> <li>data_papers.bib: Bibliographic details of MSR data papers, along with their assigned clusters (field 'cluster') and strong citations (field 'usedby')</li> <li>dp_dois_citations.txt: Strong and weak citations of MSR data papers</li> <li>msr-all: Bibliographic details of all MSR (data and non-data) papers</li> <li>ndp_dois_citations.txt: Strong and weak citations of MSR non-data papers</li> <li>ndp_rand_dois_citations.txt: Strong and weak citations of a randomly chosen MSR non-data paper weighted sample</li> <li>self-citations.txt: Strong citations of MSR data papers by their authors</li> <li>strong_citation_classification.csv: The classification process of strong citation papers according to the SWEBOK knowledge areas</li> <li>strong_citation_fields.csv: SWEBOK knowledge areas of strong citation papers</li> <li>strong_citations.bib: Bibliographic details of strong citation papers</li> <li>survey_questionnaire.pdf: The final survey questionnaire</li> <li>survey_responses.csv: Anonymized responses of the final survey questionnaire (Email addresses have been excluded for privacy reasons.)</li> <li>weak_citations_notes.bib: Weak citations of MSR data papers and the use they make</li> </ul>
48271 MSR Hercules Farnese
This is a 3D model of an ancient marble statue in the Musée Saint-Raymond in Toulouse. The statue depicts the demigod and hero Hercules. This small statue follows a sculptural type known as "the Farnese Hercules type," named after the most famous example, the Farnese Hercules in Naples (see [70132 NAP Hercules Farnese](https://skfb.ly/6RnSN)). Here, Hercules rests after many of his miraculous labors. He leans on his club and the Nemean Lion skin, taken from his first labor. **Bibliography**: [Musée Saint-Raymond](https://saintraymond.toulouse.fr/Musee-Saint-Raymond-Toulouse-archaeological-museum_a1081.html) # Ancient World 3D This model posting is part of Ancient World 3D, a project that provides curated 3D open access content for Classical Studies. Each model has an etched catalog# and [3D Printable frame](https://skfb.ly/6RFHE) for building a library. The [original model was posted by Musée Saint-Raymond](http://mmf.io/o/48271). This entry was composed by Riley Smith (Dr. Elizabeth Thill, advisor). Source: Objaverse 1.0 / Sketchfab
Reproduction Package for MSR 2024 Article `P3: A Dataset of Partial Program Fixes'
Open the record for dataset details and reuse information.
Bistatic HF Observations of CODAR Radars from CARL and MSR sites
<p>03-09-2022: More to written with a description of the data. </p>
39874 MSR Hercules Geryon
This is a 3D model of an ancient marble relief in the Musée Saint-Raymond, Toulouse. This relief from the 3rd c. CE depicts Hercules's sixth labor, Stealing Geryon's Cattle. Hercules is shown killing Geryon to bring his cattle to King Eurystheus. This piece was part of a larger series of twelve works, with only nine surviving to this day. This is the largest relief in the collection. **Bibliography**: [Musée Saint-Raymond](https://bit.ly/2YG6iLa), [Perseus: Geryon](https://bit.ly/3b59Nxi) # Ancient World 3D This model posting is part of Ancient World 3D, a website that provides curated 3D open access content for Classical Studies. Each model has an etched catalog# and [3D Printable frame](https://skfb.ly/6SHKw) for building a library. The [original model was posted by Musée Saint-Raymond](https://bit.ly/2zXhTer). This entry was composed by Chloe Antonio and Rachel Moore (Dr. Elizabeth Thill, advisor). Source: Objaverse 1.0 / Sketchfab
Supplementary material 1 from: Estupiñán RA, Ferrari SF, Gonçalves EC, Barbosa MSR, Vallinoto M, Schneider MPC (2016) Evaluating the diversity of Neotropical anurans using DNA barcodes. ZooKeys 637: 89-106. https://doi.org/10.3897/zookeys.637.8637
Data on the specimens examined in the present study : Explanation note: Please note that some of the sequences used in the study are incompletely referenced in the GenBank barcode database because they lack some data and we are unable to rectify this because the samples were collected too long ago (1980s or before) for the missing data to be found.
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.