United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Overview This directory was developed to provide discovery information for anyone looking for publicly accessible repositories that house geological materials in the U.S. and Canada. In addition, this resource is intended to be a tool to facilitate a community of practice. The need for the directory was identified during planning for and follow-up from a drill core repository webinar series in Spring 2020 for public repository curators and staff in the U.S. and Canada hosted by the Minnesota Geological Survey and the Minnesota Department of Natural Resources. Additional supporting sponsors included the U.S. Geological Survey National Geological and Geophysical Data Preservation Program and the Association of American State Geologists Data Preservation Committee. The 10-part webinar series provided overviews of state, provincial, territorial, and national repositories that house drill core, other geoscience materials, and data. When the series concluded a small working group of the participants continued to meet to facilitate the development and production of a directory of repositories that maintain publicly-accessible geological materials throughout the U.S. and Canada. The group used previous directory efforts described in the next section, Summary of Historical Repository Directory Compilation Efforts, as guides for content during development. The working group prepared and compiled responses from a call for repository information and characterization. This directory is planned to be a living resource for the geoscience community with updates every other year to accommodate changes. The updates will facilitated through versioned updates of this data release. Summary of Historical Repository Directory Compilation Efforts 1957 – Sample and Core Repositories of the United States, Alaska, and Canada. Published by AAPG. Committee on Preservation of Samples and Cores. 13 members from industry, academia, and government. 1977 – Well-Sample and Core Repositories of the Unites States and Canada, C.K. Fisher; M.P. Krupa, USGS Open file report 77-567.USGS wanted to update the original index. Includes a map showing core repositories by “State” “University” “Commercial” and “Federal”. Also includes a “Brief Statement of Requirements for the Preservation of Subsurface Material and Data” and referral to state regulations for details on preserved materials. 1984 - Nonprofit Sample and Core Repositories Open to the Public in the United States – USGS Circular 942. James Schmoker, Thomas Michalski, Patricia Worl. The survey was conducted by a questionnaire mailed to repository curators. Information on additions, corrections, and deletions to earlier (1957,1977) directories from state geologists, each state office of the Water Resources Division of the U.S. Geological Survey, additional government agencies and colleagues were also used. 1997 - The National Directory of Geoscience Data Repositories, edited by Nicholas H. Claudy – American Geologic Institute. To prepare the directory, questionnaires were mailed to state geologists, more than 60 geological societies, private-sector data centers selected from oil and gas directories, and to the membership committee of the American Association of Petroleum Geologists, one of AGI's member societies. The directory contains 124 repository listings, organized alphabetically by state. 2002 – National Research Council 2002. Geoscience Data and Collections: National resources in Peril. Washington, D.C.: The National Academies Press 2005 – The National Geological and Geophysical Data Preservation Program (NGGDPP) of the United States Geological Survey (USGS) was established by The Energy Policy Act of 2005, and reauthorized in the Consolidated Appropriations Act, 2021, “to preserve and expose the Nation’s geoscience collections (samples, logs, maps, data) to promote their discovery and use for research and resource development”. The Program provides “technical and financial assistance to state geological surveys and U.S. Department of the Interior (DOI) bureaus” to archive “geological, geophysical, and engineering data, maps, photographs, samples, and other physical specimens”. Metadata records describing the preserved assets are cataloged in the National Digital Catalog (NDC). References American Association of Petroleum Geologists, 1957, Sample and core repositories of the United States, Alaska, and Canada: American Association of Petroleum Geologists, Committee on Preservation of Samples and Cores, 29 p. American Association of Petroleum Geologists, 2018, US Geological Sample and Data Repositories: American Association of Petroleum Geologists, Preservation of Geoscience Data Committee, Unpublished, (Contact: AAPG Preservation of Geoscience Data Committee) American Geological Institute, 1997, National Geoscience Data Repository System, Phase II. Final report, January 30, 1995--January 28, 1997. United States. https://doi.org/10.2172/598388 American Geological Institute, 1997, National Directory of Geoscience Data Repositories, Claudy, N. H., (ed.), 91pp. Claudy N., Stevens D., 1997, AGI Publishes first edition of national directory of geoscience data repositories. American Geological Institute Spotlight, https://www.agiweb.org/news/datarep2.html Consolidated Appropriations Act, 2021 (Public Law 116-260, Sec.7002) Davidson, E. D., Jr., 1981, A look at core and sample libraries: Bureau of Economic Geology, The University of Texas at Austin, 4 p. and Appendix. Deep Carbon Observatory (DCO) Data Portal, Scientific Collections, https://info.deepcarbon.net/vivo/scientific-collections; Keyword Search: sample repository, https://info.deepcarbon.net/vivo/scientific-collections?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22sample%20repository%20%22%2C%22default_operator%22%3A%22OR%22%7D%7D%2C%22sort%22%3A%5B%7B%22_score%22%3A%7B%22order%22%3A%22asc%22%7D%7D%5D%2C%22from%22%3A0%2C%22size%22%3A200%7D: Accessed September 29, 2021 Fisher, C. K., and Krupa, M. P., 1977, Well-sample and core repositories of the United States and Canada: U.S. Geological Survey Open-File Report 77-567, 73 p. https://doi.org/10.3133/ofr77567 Fogwill, W.D., 1985, Drill Core Collection and Storage Systems in Canada, Manitoba Energy & Mines. https://www.ngsc-cptgs.com/files/PGJSpecialReport_1985_V03b.pdf Goff, S., and Heiken, G., eds., 1982, Workshop on core and sample curation for the National Continental Scientific Drilling Program: Los Alamos National Laboratory, May 5-6, 1981, LA-9308-C, 31 p. https://www.osti.gov/servlets/purl/5235532 Lonsdale, J. T., 1953, On the preservation of well samples and cores: Oklahoma City Geological Society Shale Shaker, v. 3, no. 7, p. 4. National Geological and Geophysical Data Preservation Program. https://www.usgs.gov/core-science-systems/national-geological-and-geophysical-data-preservation-program National Research Council. 2002. Geoscience Data and Collections: National Resources in Peril. Washington, DC: The National Academies Press, 107 p. https://doi.org/10.17226/10348 Pow, J. R., 1969, Core and sample storage in western Canada: Bulletin of Canadian Petroleum Geology, v. 17, no. 4, p. 362-369. DOI: 10.35767/gscpgbull.17.4.362 Ramdeen, S., 2015. Preservation challenges for geological data at state geological surveys, GeoResJ 6 (2015) 213-220, https://doi.org/10.1016/j.grj.2015.04.002 Schmoker, J. W., Michalski, T. C., and Worl, P. B., 1984, Nonprofit sample and core repositories of the United States: U.S. Geological Survey Circular 942. https://doi.org/10.3133/cir942 Schmoker, J. W., Michalski, T. C., and Worl, P. B., 1984, Addresses, telephone numbers, and brief descriptions of publicly available, nonprofit sample and core repositories of the United States: U.S. Geological Survey Open-File Report 84-333, 13 p. (Superseded by USGS Circular 942) https://doi.org/10.3133/ofr84333 The Energy Policy Act of 2005 (Public Law 109-58, Sec. 351) The National Digital Catalog (NDC). https://www.usgs.gov/core-science-systems/national-geological-and-geophysical-data-preservation-program/national-digital U.S. Bureau of Mines, 1978, CORES Operations Manual: Bureau of Mines Core Repository System: U.S. Bureau of Mines Information Circular IC 8784, 118 p. https://digital.library.unt.edu/ark:/67531/metadc170848/

Data and code for paper: "Understanding Research Data Repositories as Infrastructures" (2021). This study discusses the properties of research data repositories and analyzes metadata about 2,646 entries in the Registry of Research Data Repositories (r3data.org) to identify which of the characteristics attributed to infrastructures they exhibit. The results reveal how research data repositories function as information infrastructure for members of the scientific community and contribute to the small body of literature that examines data repositories through a socio-technical lens.

This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.

Please cite this data collection as: von der Heyde, M. (2019). Data from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493

Further information is given in the corresponding data paper: von der Heyde, M. (2019). International Open Data Repository Survey: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643450

Contact

Swiss National Science Foundation (SNSF)

Open Research Data Group

E-mail: ord@snf.ch

swissuniversities

Program "Scientific Information"

Gabi Schneider

E-Mail: isci@swissuniversities.ch

Link Function: information

This dataset tracks the updates made on the dataset "Open Reading Frame Finder (ORF Finder)" as a repository for previous versions of the data and metadata.

Link Function: information

This dataset is part of Project MILDRED, Development Project of Research Data Infrastructure at University of Helsinki. The project started on April 29, 2016. Project aim is to provide University of Helsinki with state-of-the-art research data management service infrastructure. To gain knowledge about researchers' data storage and preservation practices in 2016, an e-survey was sent to the UH research staff about 1) what data repositories they use for depositing their research data; 2) what reasons they had for not depositing data and 3) what alternative storage devices and repository services they used for their data.The dataset consists of e-survey report master file and analysis of the original master file. The files have been anonymized. A readme.rtf file is included to provide full project and data level documentation.

Software to view the USGS core archive. Browse around the world. Zoom in and learn about the core including the date it was collected, core length, the curator, ect.

The number in brackets denotes the total number of available datasets (OAI-DC standard) at the time of download (October/November 2019).

Observational & model data from BIRA-IASB Data repository Datasets (observations & models) created by BIRA-IASB teams can be found via our data repository tool. The multi-criteria search engine to (the metadata of) these datasets will provide you, in the context of universal open data and thanks to the DOI system, with interesting matches, explanatory comparisons,... re3data.org - Registry of Research Data Repositories Our repository tool is on its turn referenced in re3data, a global registry of research data repositories that covers research data repositories from different academic disciplines.

This dataset supports the working paper, "Repository optimisation & techniques to improve discoverability and web impact : an evaluation", currently under review for publication and available as a preprint at: https://doi.org/10.17868/65389/.

• Macgregor, G. (2018). Repository optimisation techniques to improve discoverability and web impact: an evaluation. (pp. 1-13). Glasgow: University of Strathclyde [Strathprints repository]. Available: https://doi.org/10.17868/65389/

The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing seven data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and Google Scholar, and usage statistics from IRStats2. All data relate to the EPrints repository, Strathprints, based at the University of Strathclyde.

This data set accompanies the text at doi 10.5281/zenodo.3732273. // Correspondence: JH: info@africarxiv.org, SK: sk111@soas.ac.uk

Visual Map: https://kumu.io/access2perspectives/african-digital-research-repositories Dataset: https://tinyurl.com/African-Research-Repositories Archived at https://info.africarxiv.org/african-digital-research-repositories/ Submission form: https://forms.gle/CnyGPmBxN59nWVB38

Licensing: Text and Visual Map – CC-BY-SA 4.0 // Dataset – CC0 (Public Domain) // The licensing of each database is determined by the database itself

Preprint doi: 10.5281/zenodo.3732273.
Data set doi: 10.5281/zenodo.3732172 // available in different formats (pdf, xls, ods, csv)

AfricarXiv in collaboration with the International African Institute (IAI) presents an interactive map of African digital research literature repositories. This drew from IAI’s earlier work from 2016 onwards to identify and list Africa-based institutional repositories that focused on identifying repositories based in African university libraries. Our earlier resources are available at https://www.internationalafricaninstitute.org/repositories.

The interactive map extends the work of the IAI to include organizational, governmental, and international repositories. It also maps the interactions between research repositories. In this dataset, we focus on institutional repositories for scholarly works, as defined by Wikipedia contributors (March 2020).

Objective

The map of African digital repositories was created as a resource to be used in activities addressing the following aims:

Improving the discoverability of African research and publications

Enhance the interoperability of existing and emerging African repositories

Identify ways through which digital scholarly search engines can enhance the discoverability of African research

We promote the dissemination of research-based knowledge from African repositories as part of a bigger landscape that also includes online journals, research data repositories, and scholarly publishers to enhance the interconnectivity and accessibility of such repositories across and beyond the African continent and to contribute to a more granular understanding of the continent’s scholarly resources.

Data archiving and maintenance

The map and corresponding dataset are hosted on the AfricArXiv website under ‘Resources’ at https://info.africarxiv.org/african-digital-research-repositories/. The listing is not exhaustive and therefore we encourage any repositories relevant for the African continent not listed here to the submission form at https://forms.gle/CnyGPmBxN59nWVB38, or to notify the International African Institute (email sk111@soas.ac.uk). Both AfricArXiv and IAI will continue to maintain the list of repositories as a resource for African researchers and other stakeholders including international African studies communities.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

This dataset tracks the updates made on the dataset "Health Plan Finder Data" as a repository for previous versions of the data and metadata.

This repository was created to store, organize, and share data collected for the Eastern Kentucky Project, focusing on hydrological research in the region. It serves as a centralized platform to manage data efficiently and facilitate collaboration among researchers and stakeholders involved in the project.

The repository primarily contains data from level loggers, which are crucial for monitoring and recording water levels, temperature, and other hydrological parameters over time. The collected data has been carefully extracted, processed, and stored in Excel files to ensure compatibility with various analysis tools. This structured format enables easy access and seamless integration into research workflows.

In addition to providing secure storage, the repository is designed to support efficient data sharing, transparency, and interdisciplinary collaboration. By offering a well-organized dataset, it enables researchers to analyze and build upon existing data, promoting high-quality research outputs. The repository ultimately aims to advance understanding and inform decision-making in water resource management for Eastern Kentucky.

Dataset links to the Digital Collections of Colorado, DSpace Repository. From the homepage, you can search the 1240 datasets hosted there, or browse using a list of filters on the right. DSpace is a digital service that collects, preserves, and distributes digital material. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/ShortgrassSteppe_eaa_2015_March_19_1220

Link Function: information

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. Contents:

metadata.zip: The dataset metadata and analysis results as CSV files. scripts-and-logs.zip: Scripts and logs of the dataset creation. LICENSE: The Open Data Commons Attribution License (ODC-By) v1.0 text. README.md: This document. redistributable-repositiories.zip: Shallow copies of the head state of all redistributable repositories with an IaC program. This artifact is part of the ProTI Infrastructure as Code testing project: https://proti-iac.github.io. Metadata The dataset's metadata comprises three tabular CSV files containing metadata about all analyzed repositories, IaC programs, and testing source code files. repositories.csv:

ID (integer): GitHub repository ID url (string): GitHub repository URL downloaded (boolean): Whether cloning the repository succeeded name (string): Repository name description (string): Repository description licenses (string, list of strings): Repository licenses redistributable (boolean): Whether the repository's licenses permit redistribution created (string, date & time): Time of the repository's creation updated (string, date & time): Time of the last update to the repository pushed (string, date & time): Time of the last push to the repository fork (boolean): Whether the repository is a fork forks (integer): Number of forks archive (boolean): Whether the repository is archived programs (string, list of strings): Project file path of each IaC program in the repository programs.csv:

ID (string): Project file path of the IaC program repository (integer): GitHub repository ID of the repository containing the IaC program directory (string): Path of the directory containing the IaC program's project file solution (string, enum): PL-IaC solution of the IaC program ("AWS CDK", "CDKTF", "Pulumi") language (string, enum): Programming language of the IaC program (enum values: "csharp", "go", "haskell", "java", "javascript", "python", "typescript", "yaml") name (string): IaC program name description (string): IaC program description runtime (string): Runtime string of the IaC program 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") tests (string, list of strings): File paths of IaC program's tests testing-files.csv:

file (string): Testing file path language (string, enum): Programming language of the testing file (enum values: "csharp", "go", "java", "javascript", "python", "typescript") 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") 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(") program (string): Project file path of the testing file's IaC program Dataset Creation 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:

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. A shallow copy of the head of all repositories was downloaded using download-repositories.py (documented below). Using analysis.ipynb, the repositories were analyzed for the programs' metadata, including the used programming languages and licenses. 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. Searching Repositories The repositories are searched through search-repositories.py and saved in a CSV file. The script takes these arguments in the following order:

Github access token. Name of the CSV output file. Filename to search for. File extensions to search for, separated by commas. Min file size for the search (for all files: 0). Max file size for the search or * for unlimited (for all files: *). 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. https://www.pulumi.com/docs/intro/concepts/project/ 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. https://docs.aws.amazon.com/cdk/v2/guide/cli.html 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. https://www.terraform.io/cdktf/create-and-deploy/project-setup Limitations The script uses the GitHub code search API and inherits its limitations:

Only forks with more stars than the parent repository are included. Only the repositories' default branches are considered. Only files smaller than 384 KB are searchable. Only repositories with fewer than 500,000 files are considered. Only repositories that have had activity or have been returned in search results in the last year are considered. More details: https://docs.github.com/en/search-github/searching-on-github/searching-code 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: https://stackoverflow.com/questions/45382069/search-for-code-in-github-using-graphql-v4-api Downloading Repositories 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:

Name of the repositories CSV files generated through search-repositories.py, separated by commas. Output directory to download the repositories to. Name of the CSV output file. 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.

Machine-readable metadata available from landing pages for datasets facilitate data citation by enabling easy integration with reference managers and other tools used in a data citation workflow. Embedding these metadata using the schema.org standard with the JSON-LD is emerging as the community standard. This dataset is a listing of data repositories that have implemented this approach or are in the progress of doing so.

This is the first version of this dataset and was generated via community consultation. We expect to update this dataset, as an increasing number of data repositories adopt this approach, and we hope to see this information added to registries of data repositories such as re3data and FAIRsharing.

In addition to the listing of data repositories we provide information of the schema.org properties supported by these data repositories, focussing on the required and recommended properties from the "Data Citation Roadmap for Scholarly Data Repositories".