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FIGURE 2 in Dates of publication of the Zoology parts of the Report of the Scientific Results of the Voyage of H.M.S. Challenger During the Years 1873-76
FIGURE 2. The title-page of the fourth volume of the Zoology of the Report on the Scientific Results of the H.M.S. Challenger During the Years 1873–76. Image from a work no longer in copyright held by the library of the Woods Hole Oceanographic Institute and digitised under the Biodiversity Heritage Library initiative (see text for details). Note, the first line "(Provisional Title)" was removed from the title page of this and successive volumes (see Fig. 1).
FIGURE 1 in Dates of publication of the Zoology parts of the Report of the Scientific Results of the Voyage of H.M.S. Challenger During the Years 1873-76
FIGURE 1. The title-page of the first volume of the Zoology of the Report on the Scientific Results of the H.M.S. Challenger During the Years 1873–76. Image from a work no longer in copyright held by the library of the Woods Hole Oceanographic Institute and digitised under the Biodiversity Heritage Library initiative (see text for details). Note, the first line "(Provisional Title)" was removed from the title page for volume IV and successive volumes (see Fig. 2).
Malaria disease and grading system dataset from public hospitals reflecting complicated and uncomplicated conditions
<p>Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy.</p>
Data and code for the publication "Evaluating the effectiveness of the MicroPlastic Sediment Separator (MPSS)"
<p><strong>Authors</strong></p><p>Prume, Julia A.; Laermanns, Hannes; Löder, Martin G. J.; Laforsch, Christian; Bogner, Christina; Koch, Martin</p><p> </p><p><strong>Background</strong></p><p>The data set contains all data analysed in the paper with the title "Evaluating the effectiveness of the MicroPlastic Sediment Separator (MPSS)", published in the journal Microplastics & Nanoplastics (https://doi.org/10.1186/s43591-023-00073-3).</p><p> </p><p><strong>Description</strong></p><p>The spreadsheet <i>MPSS_data.xlsx</i> contains all data analysed in this work, organised in 11 sheets.</p><p>The R notebook <i>2023-11-09_MPSS_data_analysis.Rmd</i> contains the code to read, process, analyse, and plot the data for both the main text and the Supplementary Information.</p><p>The zipped folder <i>Figures</i> contains all plots for the main text and the Supplementary Information.</p><p>The file <i>EXTRACT_2023-06-05_Q9-Blank-2-1000.0_000001.0.dpt</i> contains data for the spectrum plotted in Supplementary Figure S1, the file <i>EXTRACT_2023-05-12-MPSS-Testpartikel.0_000000.0.dpt</i> contains data for the spectrum plotted in Supplementary Figure S17.</p><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>J.A.P. gratefully acknowledges the support of the German Federal Ministry for Education and Research (BMBF) for the Citizen Lab for Microplastics (project 01BF1705) and the Hessian Ministry of Higher Education, Research and the Arts (HMWK) for the project Entwicklung eines Modells zur Simulation von Stoffströmen im Bereich Mikroplastik, LOEWE-Exploration. H.L., M.G.J.L., C.L., and C.B. acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956 SFB 1357. Open access funding provided by the Open Access Publishing Fund of Philipps-Universität Marburg with support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation).</p>
Data and Code for Indirect Savings from Public Procurement Centralization
<p>The data and code in this repository reproduce tables and figures for Indirect Savings from Public Procurement Centralization by Clarissa Lotti, Arieda Muço, Giancarlo Spagnolo, and Tommaso Valletti.</p><p> </p>
GitHub Public Pull Request Comments
<p>Over 13 MILLION pull request comments</p><p>Dataset used for the master's thesis "LLMs for Code Comment Consistency." Covers the languages Go, Java, JavaScript, TypeScripp, and Python. All data is mined from permissively-licensed GitHub public projects with at least 25 stars and 25 pull requests submitted at the time of access.</p><p> </p><p>This dataset pertains specifically to **pull request comments that are made on files.** In other words, every comment in this dataset is linked to a specific file in a pull request.</p><p> </p><p>### What can I do with this data?</p><p>Anything you want, of course, but here are some starter ideas:</p><p>- Sentiment analysis of comments, is there a correlation between number of contributions and positivity of reviews?</p><p>- Pull request comment generation: can we automatically make code review comments?</p><p>- PR text mining: can we mine out examples of a specific type of comment? (in my project, this was comments about function documentation)</p><p> </p><p>The mining code is publicly accessible.</p><p> </p><p>Each file is a JSON object where each key is a Github repository, and each value is a pull request comment in that repository.</p>
GitHub Public Repository Metadata
<p>Metadata (i.e. no code) of all public repositories with 5+ stars on GitHub</p><p> </p><p>This dataset is obtained from the Github API and contains only public repository-level metadata.<br>It may be useful for anyone interested in studying the Github ecosystem. It contains approximately 3.1 million entries.</p><p>The Github API Terms of Service apply.</p><blockquote><p>You may not use this dataset for spamming purposes, including for the purposes of selling GitHub users' personal information, such as to recruiters, headhunters, and job boards.</p></blockquote><p><br>The data format is a JSON array of entries, an example of which is given below.</p><p><i>Redacted for anonymity.</i></p>
Public Repository: Aquino (2023) Front Netw Physiol
<p>This GitLab project contains data and MATLAB code to reproduce the analyses and figures published in</p><p>Aquino, M. R. C., Liddy, J. J., Napoli, C. D., Fonseca, S. T., van Emmerik, R. E. A., Busa, M. A. (2023). Changes to balance dynamics following a high-intensity run are associated with future injury occurrence in recreational runners. Fron Netw Physiol, 3(#), #. https://doi.org/10.3389/fnetp.2023.1227861.</p><p>Note: The alphanumeric code appended to the end of the folder name is the commit hash.</p>
PIPr: A Dataset of Public Infrastructure as Code Programs
<p>Programming Languages Infrastructure as Code (PL-IaC) enables IaC programs written in general-purpose programming languages like Python and TypeScript. The currently available PL-IaC solutions are Pulumi and the Cloud Development Kits (CDKs) of Amazon Web Services (AWS) and Terraform. This dataset provides metadata and initial analyses of all public GitHub repositories in August 2022 with an IaC program, including their programming languages, applied testing techniques, and licenses. Further, we provide a shallow copy of the head state of those 7104 repositories whose licenses permit redistribution. The dataset is available under the Open Data Commons Attribution License (ODC-By) v1.0.</p><p>Contents:</p><ul><li>metadata.zip: The dataset metadata and analysis results as CSV files.</li><li>scripts-and-logs.zip: Scripts and logs of the dataset creation.</li><li>LICENSE: The Open Data Commons Attribution License (ODC-By) v1.0 text.</li><li>README.md: This document.</li><li>redistributable-repositiories.zip: Shallow copies of the head state of all redistributable repositories with an IaC program.</li></ul><p>This artifact is part of the ProTI Infrastructure as Code testing project: <a href="https://proti-iac.github.io/">https://proti-iac.github.io</a>.</p><h2><strong>Metadata</strong></h2><p>The dataset's metadata comprises three tabular CSV files containing metadata about all analyzed repositories, IaC programs, and testing source code files.</p><p>repositories.csv:</p><ul><li>ID (integer): GitHub repository ID</li><li>url (string): GitHub repository URL</li><li>downloaded (boolean): Whether cloning the repository succeeded</li><li>name (string): Repository name</li><li>description (string): Repository description</li><li>licenses (string, list of strings): Repository licenses</li><li>redistributable (boolean): Whether the repository's licenses permit redistribution</li><li>created (string, date & time): Time of the repository's creation</li><li>updated (string, date & time): Time of the last update to the repository</li><li>pushed (string, date & time): Time of the last push to the repository</li><li>fork (boolean): Whether the repository is a fork</li><li>forks (integer): Number of forks</li><li>archive (boolean): Whether the repository is archived</li><li>programs (string, list of strings): Project file path of each IaC program in the repository</li></ul><p>programs.csv:</p><ul><li>ID (string): Project file path of the IaC program</li><li>repository (integer): GitHub repository ID of the repository containing the IaC program</li><li>directory (string): Path of the directory containing the IaC program's project file</li><li>solution (string, enum): PL-IaC solution of the IaC program ("AWS CDK", "CDKTF", "Pulumi")</li><li>language (string, enum): Programming language of the IaC program (enum values: "csharp", "go", "haskell", "java", "javascript", "python", "typescript", "yaml")</li><li>name (string): IaC program name</li><li>description (string): IaC program description</li><li>runtime (string): Runtime string of the IaC program</li><li>testing (string, list of enum): Testing techniques of the IaC program (enum values: "awscdk", "awscdk_assert", "awscdk_snapshot", "cdktf", "cdktf_snapshot", "cdktf_tf", "pulumi_crossguard", "pulumi_integration", "pulumi_unit", "pulumi_unit_mocking")</li><li>tests (string, list of strings): File paths of IaC program's tests</li></ul><p>testing-files.csv:</p><ul><li>file (string): Testing file path</li><li>language (string, enum): Programming language of the testing file (enum values: "csharp", "go", "java", "javascript", "python", "typescript")</li><li>techniques (string, list of enum): Testing techniques used in the testing file (enum values: "awscdk", "awscdk_assert", "awscdk_snapshot", "cdktf", "cdktf_snapshot", "cdktf_tf", "pulumi_crossguard", "pulumi_integration", "pulumi_unit", "pulumi_unit_mocking")</li><li>keywords (string, list of enum): Keywords found in the testing file (enum values: "/go/auto", "/testing/integration", "@AfterAll", "@BeforeAll", "@Test", "@aws-cdk", "@aws-cdk/assert", "@pulumi.runtime.test", "@pulumi/", "@pulumi/policy", "@pulumi/pulumi/automation", "Amazon.CDK", "Amazon.CDK.Assertions", "Assertions_", "HashiCorp.Cdktf", "IMocks", "Moq", "NUnit", "PolicyPack(", "ProgramTest", "Pulumi", "Pulumi.Automation", "PulumiTest", "ResourceValidationArgs", "ResourceValidationPolicy", "SnapshotTest()", "StackValidationPolicy", "Testing", "Testing_ToBeValidTerraform(", "ToBeValidTerraform(", "Verifier.Verify(", "WithMocks(", "[Fact]", "[TestClass]", "[TestFixture]", "[TestMethod]", "[Test]", "afterAll(", "assertions", "automation", "aws-cdk-lib", "aws-cdk-lib/assert", "aws_cdk", "aws_cdk.assertions", "awscdk", "beforeAll(", "cdktf", "com.pulumi", "def test_", "describe(", "github.com/aws/aws-cdk-go/awscdk", "github.com/hashicorp/terraform-cdk-go/cdktf", "github.com/pulumi/pulumi", "integration", "junit", "pulumi", "pulumi.runtime.setMocks(", "pulumi.runtime.set_mocks(", "pulumi_policy", "pytest", "setMocks(", "set_mocks(", "snapshot", "software.amazon.awscdk.assertions", "stretchr", "test(", "testing", "toBeValidTerraform(", "toMatchInlineSnapshot(", "toMatchSnapshot(", "to_be_valid_terraform(", "unittest", "withMocks(")</li><li>program (string): Project file path of the testing file's IaC program</li></ul><h2><strong>Dataset Creation</strong></h2><p>scripts-and-logs.zip contains all scripts and logs of the creation of this dataset. In it, executions/executions.log documents the commands that generated this dataset in detail. On a high level, the dataset was created as follows:</p><ol><li>A list of all repositories with a PL-IaC program configuration file was created using search-repositories.py (documented below). The execution took two weeks due to the non-deterministic nature of GitHub's REST API, causing excessive retries.</li><li>A shallow copy of the head of all repositories was downloaded using download-repositories.py (documented below).</li><li>Using analysis.ipynb, the repositories were analyzed for the programs' metadata, including the used programming languages and licenses.</li><li>Based on the analysis, all repositories with at least one IaC program and a redistributable license were packaged into redistributable-repositiories.zip, excluding any node_modules and .git directories.</li></ol><h2><strong>Searching Repositories</strong></h2><p>The repositories are searched through search-repositories.py and saved in a CSV file. The script takes these arguments in the following order:</p><ol><li>Github access token.</li><li>Name of the CSV output file.</li><li>Filename to search for.</li><li>File extensions to search for, separated by commas.</li><li>Min file size for the search (for all files: 0).</li><li>Max file size for the search or * for unlimited (for all files: *).</li></ol><p>Pulumi projects have a Pulumi.yaml or Pulumi.yml (case-sensitive file name) file in their root folder, i.e., (3) is Pulumi and (4) is yml,yaml. <a href="https://www.pulumi.com/docs/intro/concepts/project/">https://www.pulumi.com/docs/intro/concepts/project/</a></p><p>AWS CDK projects have a cdk.json (case-sensitive file name) file in their root folder, i.e., (3) is cdk and (4) is json. <a href="https://docs.aws.amazon.com/cdk/v2/guide/cli.html">https://docs.aws.amazon.com/cdk/v2/guide/cli.html</a></p><p>CDK for Terraform (CDKTF) projects have a cdktf.json (case-sensitive file name) file in their root folder, i.e., (3) is cdktf and (4) is json. <a href="https://www.terraform.io/cdktf/create-and-deploy/project-setup">https://www.terraform.io/cdktf/create-and-deploy/project-setup</a></p><h3><strong>Limitations</strong></h3><p>The script uses the GitHub code search API and inherits its limitations:</p><ul><li>Only forks with more stars than the parent repository are included.</li><li>Only the repositories' default branches are considered.</li><li>Only files smaller than 384 KB are searchable.</li><li>Only repositories with fewer than 500,000 files are considered.</li><li>Only repositories that have had activity or have been returned in search results in the last year are considered.</li></ul><p>More details: <a href="https://docs.github.com/en/search-github/searching-on-github/searching-code">https://docs.github.com/en/search-github/searching-on-github/searching-code</a></p><p>The results of the GitHub code search API are not stable. However, the generally more robust GraphQL API does not support searching for files in repositories: <a href="https://stackoverflow.com/questions/45382069/search-for-code-in-github-using-graphql-v4-api">https://stackoverflow.com/questions/45382069/search-for-code-in-github-using-graphql-v4-api</a></p><h2><strong>Downloading Repositories</strong></h2><p>download-repositories.py downloads all repositories in CSV files generated through search-respositories.py and generates an overview CSV file of the downloads. The script takes these arguments in the following order:</p><ol><li>Name of the repositories CSV files generated through search-repositories.py, separated by commas.</li><li>Output directory to download the repositories to.</li><li>Name of the CSV output file.</li></ol><p>The script only downloads a shallow recursive copy of the HEAD of the repo, i.e., only the main branch's most recent state, including submodules, without the rest of the git history. Each repository is downloaded to a subfolder named by the repository's ID.</p>
Table S1: Publication bias and heterogeneity test results among studies in overall and subgroup analyses
Open the record for dataset details and reuse information.
Origianl data for publication: Anchoring of Metal Complexes on Au25 Nanocluster for Enhanced Photocoupled Electrocatalytic CO2 Reduction
<p>The file contains the original data for the publication: Anchoring of Metal Complexes on Au25 Nanocluster for Enhanced Photocoupled Electrocatalytic CO2 Reduction.</p><p>Angew. Chem. Int. Ed. 2023. doi.org/10.1002/anie.202316649.</p><p>The data is grouped acording to the figures in the manuscript and the supporting information.</p><p> </p>
Dataset for publication "The effect of formation swimming on tailbeat and breathing frequencies in killer whales"
<p>Data collected by the Center for Whale Research under federal permit provided by National Marine Fisheries Service (permit number 21238). Observation of the Southern Resident killer whale population (<i>Orcinus orca</i>) with aerial drone-based videos recorded during the summer season in 2019 and 2021. Observations conducted during formation swimming sequences in which the whales displayed a directional and regular cruising swimming pattern (i.e., without sharp changes in direction, chasing prey, social interactions, or surface-active behaviors) to estimate their relative energetic expenditure by estimating the breathing frequency and tailbeat frequency of each individual depending on its positioning relative to the other whales.</p>
FIGURE 4 in Single syntypes of Somatochlora exuberata Bartenev, 1910 (Odonata: Corduliidae), discovered in both the Milwaukee Public Museum and the University of Michigan Museum of Zoology, U.S.A., with designation of the lectotype
FIGURE 4. Specimen No. 39690 from "Amasia / Asia Minor" received in exchange from Kenneth J. Morton and belonging to the same general series as the type-series of Calopteryx amasina Bartenev, 1912. Scale bar 1 cm.
FIGURE 3 in Single syntypes of Somatochlora exuberata Bartenev, 1910 (Odonata: Corduliidae), discovered in both the Milwaukee Public Museum and the University of Michigan Museum of Zoology, U.S.A., with designation of the lectotype
FIGURE 3. Syntype of S. exuberata from Gazimurskie Kavykuchi village, Gazimurskiy District, Zabaykal'skaya Oblast', 24 VI 1909 (according to the Julian Calendar), preserved in UMMZ and its labels. This specimen has been designated as the lectotype of Somatochlora exuberata Bartenev, 1910. Scale bar 1 cm.
FIGURE 2 in Single syntypes of Somatochlora exuberata Bartenev, 1910 (Odonata: Corduliidae), discovered in both the Milwaukee Public Museum and the University of Michigan Museum of Zoology, U.S.A., with designation of the lectotype
FIGURE 2. Syntype of S. exuberata from Gazimurskie Kavykuchi village, Gazimurskiy District, Zabaykal'skaya Oblast', 24 VI 1909 (according to the Julian Calendar), preserved in MPM, and its labels. Scale bar 1 cm.
Data for publication "On the flow behaviour of unconfined dual porosity aquifers with sloping base"
<p>Data used in the paper </p> <p>"On the flow behaviour of unconfined dual porosity aquifers with sloping base", </p> <p>by Konstantinos N. Moutsopoulos, John N. E. Papaspyros, Antonis D. Koussis, Frédérick Delay, and Marwan Fahs, </p> <p>submitted to Advances in Water Research.</p>
Ferrocene Appended Porphyrin Based Bipolar Electrode Material for High Performance Energy Storage -Data for publication
<p>All combined datasets</p>
Supplemental Data for the Journal Article Entitled Accelerating FEM-based Corrosion Predictions using Machine Learning submitted for publication to the Journal of the Electrochemical Society.
<p>This repository contains the Supplemental Data for the Journal Article Entitled Accelerating FEM-based Corrosion Predictions using Machine Learning submitted for publication to the Journal of the Electrochemical Society.</p> <p>Authors:</p> <p>David Montes de Oca Zapiain 1, Demitri Maestas 1, Matthew Roop 1,2, Philip Noel 1, Michael Melia 1, Ryan Katona 1</p> <p>1 Sandia National Laboratories, Albuquerque, NM 87185, USA <br>2 University of New Mexico, Albuquerque, NM 87131, USA</p> <p>Each folder contains a ReadMe.txt describing the files and their organization within each folder. </p> <p><br>Acknowledgements:<br>Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc.,<br>for the U.S. Department of Energy National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article<br>do not necessarily represent the views of the U.S. Department of Energy or the United States Government. SAND No: SAND2023-14419O</p>
Data for the CBM Algorithm associated with the WASPAA23 publication.
<p>Data associated with the publication "Marmoret, Axel, Jérémy E. Cohen, and Frédéric Bimbot. "Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis." <em>2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)</em>. IEEE, 2023." https://arxiv.org/abs/2210.15356.</p> <p>Initially released on https://gitlab.inria.fr/amarmore/autosimilarity_segmentation/-/tree/WASPAA23, but moved because the git folder was too big.</p> <p>If you are willing to reproduce experiments, you should download these files and place them in the "data" folder, at root.</p>
Data supporting the publication "Observation of Landau levels and chiral edge states in photonic crystals through pseudomagnetic fields induced by synthetic strain"
<p>Data supporting the publication "Observation of Landau levels and chiral edge states in photonic crystals through pseudomagnetic fields induced by synthetic strain".</p> <p>Version 2: updated Figure 1c1</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.