ST - DHS Public Access Database: Consistent with the 2013 OSTP Memorandum and the 2022 update, “Increasing Access to the Results of Federally Funded Scientific Research,” directed all agencies with greater than $100 million in R&D expenditures each year to prepare a plan for improving the public’s access to the results of federally funded research, specifically peer-reviewed scholarly publications and digital data. In response to the memorandum, DHS developed a DHS Public Access Plan, and intends to make available to the public digitally formatted scientific data that support the conclusions in peer-reviewed scholarly publications that are the results of DHS R&D funding. This data repository site with a customized DHS Storefront allows DHS to post releasable scientific digital data from peer-reviewed publications resulting from DHS-funded research. The data repository is configured to allow DHS users (and publishers acting on behalf of these users) to deposit data sets into the repository, making them available to the general public.

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

Data and variable key for Dunham, Dotsch, Clark, & Stepanova, "The development of White-Asian categorization: Contributions from skin color and other physiognomic cues"

The NSF Public Access Repository contains an initial collection of journal publications and the final accepted version of the peer-reviewed manuscript or the version of record. To do this, NSF draws upon services provided by the publisher community including the Clearinghouse of Open Research for the United States, CrossRef, and International Standard Serial Number. When clicking on a Digital Object Identifier number, you will be taken to an external site maintained by the publisher. Some full text articles may not be available without a charge during the embargo, or administrative interval. Some links on this page may take you to non-federal websites. Their policies may differ from this website.

The attached data sets provides an overview of the landscape of research data repositories in 2015. They are based on an analysis of the re3data - registry of research data repositories from December 2015.

The Bear Lake Data Repository (BLDR) is an active archive, containing a growing compilation of biological, chemical, and physical datasets collected from Bear Lake and its surrounding watershed. The datasets herein have been digitized from historical records and reports, extracted from papers and theses, and obtained from public and private entities, including the United States Geological Survey, PacifiCorp, and, inter alia, Ecosystems Research Institute.

Contributions are welcome. The BLDR accepts biological, chemical, or physical datasets obtained at Bear Lake, irrespective of funding source. There is no submission size limit at present—workarounds will be found if submissions exceed Hydroshare limits (20 GB). Contributions are published with an open access license and will serve many use cases. The current repository steward, Bear Lake Watch, will advise on submissions and make accepted contributions available promptly.

Metadata files are provided for each dataset, however, contact with original contributor(s) is encouraged for questions and additional details prior to data usage. The BLDR and its contributors shall not be liable for any damages resulting from misinterpretation or misuse of the data or metadata.

This dataset provides guidance materials and templates to help you prepare your research datasets for deposit in the U of G Research Data Repositories.Please refer to the U of G Research Data Repositories LibGuide for detailed information about the U of G Research Data Repositories including additional resources for preparing datasets for deposit. The library offers a self-deposit with curation service. The deposit workflow is as follows:Create your repository account.If you are a first-time depositor, complete the U of G Research Data Repositories Dataset Deposit Intake Form.Activate your Data Repositories account by logging in with your U of G username and password.Once your account is created, contact us to set up your dataset creator access to your home department’s collection in the Data Repositories.Note: If you already have a Data Repositories account and dataset creator access, you can log in and begin a new deposit to your home department’s collection right away.Prepare your dataset.Assemble your dataset following the Dataset Deposit Guidelines. Use the README file template to capture data documentation.Create a draft dataset record.Log in to the Data Repositories and create a draft dataset record following the instructions in the Dataset Submission Guide.Submit your draft dataset for review.Dataset review.Data Repositories staff will review (also referred to as curate) your dataset for alignment with the Dataset Deposit Guidelines using a standard curation workflow.The curator will collaborate with you to enhance the dataset.Public release.Once ready, the dataset curator will make the dataset publicly available in the Data Repositories, with appropriate file access controls. Support: If you have any questions about preparing and depositing your dataset, please make a Publishing and Author Support Request.

Research Data Repositories Market Outlook

According to our latest research, the global research data repositories market size reached USD 4.12 billion in 2024, driven by the surging demand for secure, accessible, and scalable data management solutions across academic, government, and corporate sectors. The market is projected to expand at a robust CAGR of 8.7% from 2025 to 2033, reaching a forecasted value of USD 8.65 billion by 2033. This impressive growth trajectory is primarily attributed to the increasing emphasis on open science, data transparency, and regulatory compliance, which are compelling organizations to invest in advanced research data repository solutions.

One of the primary growth factors driving the research data repositories market is the global shift towards open data policies and mandates by funding agencies and governments. The proliferation of open-access initiatives, such as the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, has significantly increased the need for robust data repositories that can support data sharing, reproducibility, and long-term preservation. As research outputs become more data-intensive and collaborative, the ability to store, manage, and disseminate large datasets efficiently has become a strategic imperative for research institutions and organizations worldwide. This trend is further reinforced by the growing recognition of data as a critical asset in scientific discovery, innovation, and policy-making.

Another major driver is the rapid digital transformation occurring across academia, government, and the corporate sector. Organizations are increasingly leveraging cloud-based research data repositories to overcome traditional storage limitations, enhance data security, and streamline workflows. The adoption of advanced technologies such as artificial intelligence, machine learning, and blockchain within these repositories is also enhancing data curation, metadata management, and access control. This technological evolution is enabling researchers and organizations to extract greater value from their data assets while ensuring compliance with evolving data governance standards and privacy regulations, such as GDPR and HIPAA.

The expansion of interdisciplinary and international research collaborations is also fueling the demand for scalable and interoperable research data repositories. As research projects become more complex and involve multiple stakeholders across different geographies, there is a growing need for standardized platforms that facilitate seamless data exchange and integration. This is particularly evident in domains such as health sciences, environmental research, and social sciences, where data sharing and cross-institutional collaboration are essential for addressing global challenges. Furthermore, the increasing availability of funding for research infrastructure development, particularly in emerging economies, is creating new opportunities for market growth.

From a regional perspective, North America currently dominates the research data repositories market, owing to its advanced research ecosystem, strong government support, and the presence of leading technology providers. Europe follows closely, driven by stringent data protection regulations and a vibrant academic landscape. The Asia Pacific region is expected to witness the fastest growth over the forecast period, supported by significant investments in research infrastructure, rising adoption of digital technologies, and increasing participation in global research initiatives. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a smaller base, as governments and institutions in these regions ramp up their efforts to enhance research capacity and data management capabilities.

Type Analysis

The research data repositories market is segmented by type into institutional repositories, disciplinary repositories, generalist repositories, and others. Institutional repositories form the backbone of most academic and research organizations, serving as centralized platforms for storing, managing, and disseminating research outputs generated by faculty, students, and staff. These repositories are increasingly being adopted as part of open access and research data management policies, enabling institutions to showcase their research impact, comply with funder mandates, and facilitate knowledge sharing. The growing emphasis o

Data sharing for submitted manuscript. Full citation information will be shared upon publication.Title: Open but Hidden: Open Access gaps in the National Science Foundation funded publications as posted online to the NSF Public Access Repository.AbstractObjectives: In August of 2022, the U.S. federal government released new guidelines for making publicly funded research open and available. This study looks at the availability of National Science Foundation (NSF) funded research within the designated Public Access Repository (PAR) from two research intensive (R1) universities as required under the previous 2016 policy to evaluate the current state of compliance before new guidelines go into effect.Methods: The project team searched the NSF PAR for records published between 2017 and 2021 from two institutions. Records were reviewed to determine if the PAR held a deposited copy or provided a link out to the publisher held version(s). Where only a publisher linkout was provided, links were evaluated for the availability of an open access version.Results: A total of 841 unique records were identified. Of these 42% had a deposited PDF version as required by the NSF 2016 Public Access Policy. The remaining 58% relied exclusively on a publisher-held version. However, 45% of the provided publisher links directed to paywall versions. Additionally, 24% of records required users to have specialized knowledge of the CHORUS initiative in order to navigate from the initial paywall prompt to a publicly available version.Conclusions: Despite having a public access mandate since 2016, NSF compliance rates remain low. It seems unlikely that the additional guidelines introduced under the 2022 memo, meant to further drive public access to federal research, will increase compliance without additional dedication to oversight and/or imposed consequences for non-compliance.

This document describes data collected from the Main Collection of the Web of Science database. Records of published studies addressing the intersection of Open Science and data repository were searched up to January 15th, 2024, and the final dataset was comprised of 545 records for bibliometric analysis.

Harvard Dataverse => Digital Library - Projects & Theses - Prof. Dr. Scholz ----- Introduction and background information to "Digital Library - Projects & Theses - Prof. Dr. Scholz". The URL of the dataverse: http://dataverse.harvard.edu/dataverse/LibraryProfScholz The URL of this (introduction) dataset: http://doi.org/10.7910/DVN/R33RS9 YOU MAY HAVE BEEN DIRECTED HERE, BECAUSE THE CALLING PAGE HAS NO OTHER ENTRY POINT (with DOI) INTO THIS DATAVERSE. Click on the title of this page to reach the start page of the dataverse! Introduction to the Data in this Dataverse This dataverse is about: Aircraft Design Flight Mechanics Aircraft Systems This dataverse contains research data and software produced by students for their projects and theses on above topics. Get linked to all other resources from their reports using the URN from the German National Library (DNB) as given in each dataset under "Metadata": https://nbn-resolving.org/html/urn:nbn:de:gbv:18302-aeroJJJJ-MM-DD.01x Alternative sites that store the data given in this dataverse are: http://library.ProfScholz.de and https://archive.org/details/@profscholz Open an "item". Under "DOWNLOAD OPTIONS" select the file (as far as available) called "ZIP" to download DataXxxx.zip. Alternatively, go to "SHOW ALL"; In the new window select next to DataXxxx.zip click "View Contents" or select URL next to "Data-list". Download single file from DataXxxx.zip. Data Publishing Data publishing means publishing of research data for (re)use by others. It consists of preparing single files or a dataset containing several files for access in the WWW. This practice is part of the open science movement. There is consensus about the benefits resulting from Open Data - especially in connection with Open Access publishing. It is important to link the publication (e.g. thesis) with the underlying data and vice versa. General (not disciplinary) and free data repositories are: Harvard Dataverse (this one!) figshare (emphasis: multi media) Zenodo (emphasis: results from EU research, mainly text) Mendeley Data (emphasis: data associated with journal articles) To find data repositories use http://re3data.org Read more on https://en.wikipedia.org/wiki/Data_publishing

Digital Repository for Open Access to University of Luxembourg publications. ORBilu was officially launched on the 22nd April 2013. The acronym ORBi stands for "Open Repository and Bibliography". It also expresses the Latin word "orbi" ("for the world") and signals the will of the University to make its academic research available to everyone, without barriers, be they legal, financial or technical. By keeping the ORBi name and adding “lu”, the University of Luxembourg wants to show its appreciation for the work done by the University of Liège but also clearly indicate that this is a version adapted to the UL context. The API format is described at https://www.openarchives.org/pmh/.

Horizon 2020 programme supports access to and reuse of research data generated by Horizon 2020 projects through the Open Research Data Pilot (ORDP). To support the validation of scientific results, the pilot focuses on providing access to data needed to validate the scientific results. There are several types of such data, e.g. machine learning data sets, models, measurements, statistical results of experiments, survey outcomes, etc. This deliverable summarizes the data that are expected to be collected in the course of the project and where and how they are stored. The aspect of providing open access to research data (as required by the European Commission’s Open Research Data Pilot, https://www.openaire.eu/what-is-the-open-research-data-pilot) is addressed in Section 3. Finally, in Section 4 we describe the data sets that were or are expected to be generated within the TRINITY projects and made freely available.

General descriptionThis dataset contains some markers of Open Science in the publications of the Chemical Biology Consortium Sweden (CBCS) between 2010 and July 2023. The sample of CBCS publications during this period consists of 188 articles. Every publication was visited manually at its DOI URL to answer the following questions.1. Is the research article an Open Access publication?2. Does the research article have a Creative Common license or a similar license?3. Does the research article contain a data availability statement?4. Did the authors submit data of their study to a repository such as EMBL, Genbank, Protein Data Bank PDB, Cambridge Crystallographic Data Centre CCDC, Dryad or a similar repository?5. Does the research article contain supplementary data?6. Do the supplementary data have a persistent identifier that makes them citable as a defined research output?VariablesThe data were compiled in a Microsoft Excel 365 document that includes the following variables.1. DOI URL of research article2. Year of publication3. Research article published with Open Access4. License for research article5. Data availability statement in article6. Supplementary data added to article7. Persistent identifier for supplementary data8. Authors submitted data to NCBI or EMBL or PDB or Dryad or CCDCVisualizationParts of the data were visualized in two figures as bar diagrams using Microsoft Excel 365. The first figure displays the number of publications during a year, the number of publications that is published with open access and the number of publications that contain a data availability statement (Figure 1). The second figure shows the number of publication sper year and how many publications contain supplementary data. This figure also shows how many of the supplementary datasets have a persistent identifier (Figure 2).File formats and softwareThe file formats used in this dataset are:.csv (Text file).docx (Microsoft Word 365 file).jpg (JPEG image file).pdf/A (Portable Document Format for archiving).png (Portable Network Graphics image file).pptx (Microsoft Power Point 365 file).txt (Text file).xlsx (Microsoft Excel 365 file)All files can be opened with Microsoft Office 365 and work likely also with the older versions Office 2019 and 2016. MD5 checksumsHere is a list of all files of this dataset and of their MD5 checksums.1. Readme.txt (MD5: 795f171be340c13d78ba8608dafb3e76)2. Manifest.txt (MD5: 46787888019a87bb9d897effdf719b71)3. Materials_and_methods.docx (MD5: 0eedaebf5c88982896bd1e0fe57849c2),4. Materials_and_methods.pdf (MD5: d314bf2bdff866f827741d7a746f063b),5. Materials_and_methods.txt (MD5: 26e7319de89285fc5c1a503d0b01d08a),6. CBCS_publications_until_date_2023_07_05.xlsx (MD5: 532fec0bd177844ac0410b98de13ca7c),7. CBCS_publications_until_date_2023_07_05.csv (MD5: 2580410623f79959c488fdfefe8b4c7b),8. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.xlsx (MD5: 9c67dd84a6b56a45e1f50a28419930e5),9. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.csv (MD5: fb3ac69476bfc57a8adc734b4d48ea2b),10. Aggregated_data_from_CBCS_publications_until_2023_07_05.xlsx (MD5: 6b6cbf3b9617fa8960ff15834869f793),11. Aggregated_data_from_CBCS_publications_until_2023_07_05.csv (MD5: b2b8dd36ba86629ed455ae5ad2489d6e),12. Figure_1_CBCS_publications_until_2023_07_05_Open_Access_and_data_availablitiy_statement.xlsx (MD5: 9c0422cf1bbd63ac0709324cb128410e),13. Figure_1.pptx (MD5: 55a1d12b2a9a81dca4bb7f333002f7fe),14. Image_of_figure_1.jpg (MD5: 5179f69297fbbf2eaaf7b641784617d7),15. Image_of_figure_1.png (MD5: 8ec94efc07417d69115200529b359698),16. Figure_2_CBCS_publications_until_2023_07_05_supplementary_data_and_PID_for_supplementary_data.xlsx (MD5: f5f0d6e4218e390169c7409870227a0a),17. Figure_2.pptx (MD5: 0fd4c622dc0474549df88cf37d0e9d72),18. Image_of_figure_2.jpg (MD5: c6c68b63b7320597b239316a1c15e00d),19. Image_of_figure_2.png (MD5: 24413cc7d292f468bec0ac60cbaa7809)

This is a CSV file containing a listing of the top 60 articles published in the Journal of Digital Scholarship in the Humanities (JDSH, preciously LLC), as exported from the Altmetric Explorer tool on11 April 2017. The sheet has been manually annotated adding columns to indicate each article entry's corresponding License (column E) and Access Type (column F). License and Access Type data was crosschecked manually by accessing each article online individually. The file also contains data obtained from the Open Access Button API. The article DOIs as obtained from the Altmetric Explorer were run through the Open Access Button API on 15 May 2017 in order to discover if any of the published articles had open versions available. Any resulting links when available, were added to column O. Columns O and P also include additional information, when available, about the type of content available via the Open Access Button. Joe McArthur from the Open Access Button ran the first initial search for open surrogates of this dataset through the Open Access Button API. Ernesto Priego then manually crosschecked each entry and limited the final dataset to the top 60 articles (of 82). Please note that the Altmetric data for the JDSH is likely to have changed by now, though not too significantly. Altmetric scores have not been included in this file but the order of the entries correspond to the order in the data initially exported from the Altmetric Explorer (from most mentions to fiewer mentions, with a minimum of 1 mention). This dataset is part of the author and collaborator's ongoing research on open access and institutional repository uptake in the digital humanities. The data included in this file allows users to quickly quantify the number of JDSH articles published with open licenses, number of currently 'free', paywalled or open access articles. The data shared here also allows users to see which of the articles and/or their metadata (according to the Open Access Button API) have been deposited in institutional repositories. The data presented is the result of the specific methods employed to obtain the data. In this sense this data represents as much a testing of the technologies employed as of the actual articles' licensing and open availability. This means that data in columns L-P reflect the data available through the Open Access Button API at the moment of collection. It is perfectly possible that 'open surrogates' of the articles listed are available elsewhere through other methods. As indicated above data in columns E-F was obtained and added manually. Article DOI's were accessed manually from a computer browser outside/without access to university library networks, as the intention was to verify if any of the articles were available to the general public without university library network/subscription credentials.This deposit is part of a work in progress and is shared openly to document ongoing work and to encourage further discussion and analyses.

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/

The open science movement produces vast quantities of openly published data connected to journal articles, creating an enormous resource for educators to engage students in current topics and analyses. However, educators face challenges using these materials to meet course objectives. I present a case study using open science (published articles and their corresponding datasets) and open educational practices in a capstone course. While engaging in current topics of conservation, students trace connections in the research process, learn statistical analyses, and recreate analyses using the programming language R. I assessed the presence of best practices in open articles and datasets, examined student selection in the open grading policy, surveyed students on their perceived learning gains, and conducted a thematic analysis on student reflections. First, articles and datasets met just over half of the assessed fairness practices, but this increased with the publication date. There was a..., Article and dataset fairness To assess the utility of open articles and their datasets as an educational tool in an undergraduate academic setting, I measured the congruence of each pair to a set of best practices and guiding principles. I assessed ten guiding principles and best practices (Table 1), where each category was scored ‘1’ or ‘0’ based on whether it met that criteria, with a total possible score of ten. Open grading policies Students were allowed to specify the percentage weight for each assessment category in the course, including 1) six coding exercises (Exercises), 2) one lead exercise (Lead Exercise), 3) fourteen annotation assignments of readings (Annotations), 4) one final project (Final Project), 5) five discussion board posts and a statement of learning reflection (Discussion), and 6) attendance and participation (Participation). I examined if assessment categories (independent variable) were weighted (dependent variable) differently by students using an analysis of ..., , # Data for: Integrating open education practices with data analysis of open science in an undergraduate course

Author: Marja H Bakermans Affiliation: Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609 USA ORCID: https://orcid.org/0000-0002-4879-7771 Institutional IRB approval: IRB-24–0314

Data and file overview

The full dataset file called OEPandOSdata (.xlsx extension) contains 8 files. Below are descriptions of the name and contents of each file. NA = not applicable or no data available

1. BestPracticesData.csv
   • Description: Data to assess the adherence of articles and datasets to open science best practices.
   • Column headers and descriptions:
     • Article: articles used in the study, numbered randomly
     • F1: Findable, Data are assigned a unique and persistent doi
     • F2: Findable, Metadata includes an identifier of data
     • F3: Findable, Data are registered in a searchable database
     • A1: ...

The purpose of the national research data repository HARDMIN (Hellenic Academic Research Data Management Initiative) is to collect all research data generated by Greek researchers and academics. The repository aims to address the critical need for the secure storage and publication of research data from the Greek scientific community, to increase transparency in research, to enable reuse by interested researchers worldwide, to accelerate the digital transformation of the research field in our country, and to adopt competitive practices in research proposals and scientific communication. All researchers from Greek Universities are connected to the repository with their credentials and can easily upload their research data. Special teams of editors, either within an academic unit (e.g., laboratory head) or at an institutional level (e.g., Library staff), can make your data public, which will have permanent identifiers, the ability to link to your unique ORCiD identifier, and coupling with your published work, e.g., with a scientific journal article. For special cases, access can be controlled and provided upon request. HARDMIN has been developed with the open-source software CKAN and, together with HELIX, constitutes the national digital scientific infrastructure (eInfrastructure) software for providing catalog and repository services for scientific data, part of the infrastructure network for Open Science. The repository will have the ability to connect to existing repositories and retrieve the corresponding data from already existing collections. The repository is accessible at https://hardmin.heal-link.gr, and interested researchers can contact their local Library for more details. Currently, the repository is operating on a pilot basis to resolve technical details. Translated from Greek Original Text: Σκοπός της λειτουργίας του εθνικού αποθετηρίου ερευνητικών δεδομένων HARDMIN (Hellenic Academic Research Data Management Initiative)είναι η συγκέντρωση του συνόλου των ερευνητικών δεδομένων που δημιουργούνται από Έλληνες ερευνητές και ακαδημαϊκούς. Το αποθετήριο έρχεται να καλύψει την καίρια ανάγκη ασφαλούς φύλαξης και δημοσίευσης ερευνητικών δεδομένων της ελληνικής επιστημονικής κοινότητας για την αύξηση της διαφάνειας στην έρευνα, τη δυνατότητα επαναχρησιμοποίησης από τους ενδιαφερόμενους ερευνητές ανά τον κόσμο, της επιτάχυνσής του ψηφιακού μετασχηματισμού του ερευνητικού πεδίου στη χώρα μας και την υιοθέτηση ανταγωνιστικών πρακτικών στον στίβο των ερευνητικών προτάσεων και της επιστημονικής επικοινώνησης. Στο αποθετήριο συνδέονται όλοι οι ερευνητές των ελληνικών Πανεπιστημίων με τα διαπιστευτήριά τους και μπορούν να αναρτήσουν με ευκολία τα ερευνητικά τους δεδομένα. Ειδικές ομάδες συντακτών, είτε εντός μιας ακαδημαϊκής μονάδας (π.χ. υπεύθυνος εργαστηρίου), είτε σε ι δρυματικό επίπεδο (π.χ. προσωπικό Βιβλιοθήκης), μπορούν να καταστήσουν δημόσια τα δεδομένα σας, τα οποία θα διαθέτουν μόνιμα αναγνωριστικά, δυνατότητες διασύνδεσης με το μοναδικό σας αναγνωριστικό ORCiD και σύζευξης με το δημοσιευμένο σας έργο, π.χ. με ένα άρθρο επιστημονικού περιοδικού. Για ειδικές περιπτώσεις, η πρόσβαση μπορεί να είναι ελεγχόμενη και να παρέχεται κατόπιν αιτήματος. Το HARDMIN έχει αναπτυχθεί με το ανοικτό λογισμικό CKAN και αποτελεί, μαζί με το HELIX την εθνική ψηφιακή επιστημονική υποδομή (eInfrastructure) λογισμικού για την παροχή υπηρεσιών καταλόγου και αποθετηρίου επιστημονικών δεδομένων, μέρος του πλέγματος υποδομών για την Ανοικτή Επιστήμη. Το αποθετήριο θα διαθέτει τη δυνατότητα σύνδεσης με τα υφιστάμενα αποθετήρια και άντλησης των αντίστοιχων δεδομένων από ήδη υπάρχουσες συλλογές. Το αποθετήριο είναι προσβάσιμο από τη διεύθυνση https://hardmin.heal-link.gr, ενώ οι ενδιαφερόμενοι ερευνητές μπορούν να επικοινωνούν με την οικεία Βιβλιοθήκη τους για περισσότερες λεπτομέρειες. Αυτή τη στιγμή, το αποθετήριο λειτουργεί πιλοτικά για τη διευθέτηση τεχνικών λεπτομερειών.

A comprehensive versioned dataset of the repositories and relevant related metadata about public projects hosted on GitHub related to the 2019 Novel Coronavirus and associated COVID-19 disease.

GitHub had received a number of enquiries from researchers and the community surrounding open collaboration on projects on the platform related to the disease COVID-19 caused by the SARS-CoV-2 virus. Many projects, ordered by star count, can be found using the covid-19 topic on GitHub, however, discovery of other important projects is difficult due to differences in the way users self identify their work.

to hear about it so that we can help ensure it becomes more prominently featured. Please open a PR against the file USER_SUBMISSIONS.md with a link to your research. We are especially interested in highlighting the most promising and impactful projects in need of community help and support.

Open data Open source is bigger than any company or community. The dataset is released under CC0-1.0 for anyone to use and learn from.

There are two main sets of files, released via TSV and json formats for public consumption in the directory data/. A comprehensive data dictionary that explains the contents of these files is here. The files are sorted in descending order by the count of distinct contributors at the time of extract.

The files have been versioned based on a weekly snapshot of identified repositories from the week of 2020-01-20 onward.

We will update this repository with new data files on a monthly basis, generally on the first Tuesday of a month. We will revisit this each month and provide an update on continuing this commitment.

Identification methodology Rather than relying on any one GitHub topic to identify potential COVID-19 related projects, the data set is produced using a more comprehensive set of search criteria to identify projects likely to be COVID-19 related. Note: This has the potential to include a small number of false positives however we figured we were better to cast a wide net and allow consumers of the data to perform additional cleaning if they desire. Furthermore, since this data is versioned based on the week the repo was initially created, there may exist data that are included for repos that were originally public that have been made private and are currently inaccessible.

The following parts of public metadata are currently being used to identify public projects (those licensed and not) as COVID-19 related: The repo's description The name of the repo The topics associated with the repo The organization bio description where that exists Search terms against these metadata include variations of: covid, coronavirus, ncov and sars-cov-2

Use of a persistent identifier for access to journal articles (the DOI) is now almost universal amongst researchers. It directs to the journal landing page where the human has to then take over navigation (or payment). Recently, the deposition of data into open access repositories and the resulting assignment of a data-DOI to the data or fileset has started to be increasingly adopted, and in the near future probably mandated by funders. Unfortunately, mechanisms for the retrieval and application of the data from such sources are still inherited from those developed for journal articles. We argue these mechanisms are not fit for (data) purpose. In these three demonstrations, we show how existing standards can be used to automate the data retrieval process, starting purely from the DOI assigned to the objects. The first of these utilises the 10320/loc method (see doi:10.1021/ci500302p) which is flexible and efficient, but is not supported by the DataCite registry. The next two schemes were developed to achieve such interoperability, the first using the DataCite Media API and the second exploiting added metadata such as relatedMetadataScheme = ORE to use the repository ORE resource map. We have embedded these methods into a Javascript-based data viewing demonstrator (JSmol), which is designed to display molecular information. Handlers for other types of data could be readily incorporated, and the system could also be exploited for data-mining. Examples of recently published journal articles which use such data-DOI handling will be cited.