Data collected from major Canadian and international research data repositories cover data storage, preservation, metadata, interchange, data file types, and other standard features used in the retention and sharing of research data. The outputs of this project primarily aim to assist in the establishment of recommended minimum requirements for a Canadian research data infrastructure. The committee also aims to further develop guidelines and criteria for the assessment and selection o f repositories for deposit of Canadian research data by researchers, data managers, librarians, archivists etc.

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

Collected in this dataset are the slideset and abstract for a presentation on Toward a Reproducible Research Data Repository by the depositar team at International Symposium on Data Science 2023 (DSWS 2023), hosted by the Science Council of Japan in Tokyo on December 13-15, 2023. The conference was organized by the Joint Support-Center for Data Science Research (DS), Research Organization of Information and Systems (ROIS) and the Committee of International Collaborations on Data Science, Science Council of Japan. The conference programme is also included as a reference.

Title

Toward a Reproducible Research Data Repository

Author(s)

Cheng-Jen Lee, Chia-Hsun Ally Wang, Ming-Syuan Ho, and Tyng-Ruey Chuang

Affiliation of presenter

Institute of Information Science, Academia Sinica, Taiwan

Summary of Abstract

The depositar (https://data.depositar.io/) is a research data repository at Academia Sinica (Taiwan) open to researhers worldwide for the deposit, discovery, and reuse of datasets. The depositar software itself is open source and builds on top of CKAN. CKAN, an open source project initiated by the Open Knowledge Foundation and sustained by an active user community, is a leading data management system for building data hubs and portals. In addition to CKAN's out-of-the-box features such as JSON data API and in-browser preview of uploaded data, we have added several features to the depositar, including sourcing from Wikidata as dataset keywords, a citation snippet for datasets, in-browser Shapefile preview, and a persistent identifier system based on ARK (Archival Resource Keys). At the same time, the depositar team faces an increasing demand for interactive computing (e.g. Jupyter Notebook) which facilitates not just data analysis, but also for the replication and demonstration of scientific studies. Recently, we have provided a JupyterHub service (a multi-tenancy JupyterLab) to some of the depositar's users. However, it still requires users to first download the data files (or copy the URLs of the files) from the depositar, then upload the data files (or paste the URLs) to the Jupyter notebooks for analysis. Furthermore, a JupyterHub deployed on a single server is limited by its processing power which may lower the service level to the users. To address the above issues, we are integrating the BinderHub into the depositar. BinderHub (https://binderhub.readthedocs.io/) is a kubernetes-based service that allows users to create interactive computing environments from code repositories. Once the integration is completed, users will be able to launch Jupyter Notebooks to perform data analysis and vsualization without leaving the depositar by clicking the BinderHub buttons on the datasets. In this presentation, we will first make a brief introduction to the depositar and BinderHub along with their relationship, then we will share our experiences in incorporating interactive computation in a data repository. We shall also evaluate the possibility of integrating the depositar with other automation frameworks (e.g. the Snakemake workflow management system) in order to enable users to reproduce data analysis.

Keywords

BinderHub, CKAN, Data Repositories, Interactive Computing, Reproducible Research

Project portal for publishing, citing, sharing and discovering research data. Software, protocols, and community connections for creating research data repositories that automate professional archival practices, guarantee long term preservation, and enable researchers to share, retain control of, and receive web visibility and formal academic citations for their data contributions. Researchers, data authors, publishers, data distributors, and affiliated institutions all receive appropriate credit. Hosts multiple dataverses. Each dataverse contains studies or collections of studies, and each study contains cataloging information that describes the data plus the actual data files and complementary files. Data related to social sciences, health, medicine, humanities or other sciences with an emphasis in human behavior are uploaded to the IQSS Dataverse Network (Harvard). You can create your own dataverse for free and start adding studies for your data files and complementary material (documents, software, etc). You may install your own Dataverse Network for your University or organization.

The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

The open access graph dataset that shows the connections between Dryad, CERN, ANDS and other international data repositories to publications and grants across multiple research data infrastructures. The graph dataset was created using the Research Graph data model and the Research Data Switchboard (RD-Switchboard), a collaborative project by the Research Data Alliance DDRI Working Group (DDRI WG) with the aim to discover and connect the related research datasets based on publication co-authorship or jointly funded grants.

Data relating to the publication. Sharing research data and scholarship is of national importance because of the increased focus on maximizing return on the U.S. government's investment in research programs. Recent government policy changes have directly affected the management and accessibility of publically funded research. On January 18, 2011, the National Science Foundation, a U.S. agency that supports research and education in nonmedical fields, required that data management plans be submitted with all grant proposals. On February 22, 2013, the U.S. President's Office of Science and Technology Policy extended a similar requirement for all federal agencies with research and development budgets of more than $100 million. These requirements illustrate the need for further coordination and management of data as scholarship with traditional publications. Purdue University Libraries and its Joint Transportation Research Program (JTRP) collaborated to develop a comprehensive work flow that links technical report production with the management and publication of associated data. This paper illustrates early initiatives to integrate discrete data publications with traditional scholarly publications by leveraging new and existing repository platforms and services. The authors review government policies, past data-sharing practices, early pilot initiatives, and work flow integration between Purdue's data repository, the traditional press, and institutional repository. Through the adoption of these work flows, the authors propose best practices for integrating data publishing and dissemination into the research process. The implementation of this model has the potential to assist researchers in meeting the requirements of federal funding agencies, while reducing redundancy, ensuring integrity, expanding accessibility, and increasing the return on research investment.

The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

This set groups together: Version 1 of "A Review of Options for the Development of Research Data Management Technical Infrastructure at the University of Sheffield." published at figshare.http://dx.doi.org/10.6084/m9.figshare.1092561 together with the "Executive Summary" published at figshare.http://dx.doi.org/10.6084/m9.figshare.1092562 and "Addendum 1" published at figshare. http://dx.doi.org/10.6084/m9.figshare.1130884

An updated Version 2, "Research Data Management Technical Infrastructure: A Review of Options for Development at the University of Sheffield" is published at figshare.http://dx.doi.org/10.6084/m9.figshare.1202230

This report reviews options available for the development of a technical infrastructure to support research data management (RDM) at the University of Sheffield. RDM, its situation within academic research and recent drivers towards change are defined. The processes involved in RDM and the elements of the supporting technical infrastructure are examined. The local context, of RDM technical infrastructure at the University of Sheffield and collaborating institutions, is explored. The report briefly describes the eighty most commonly used components of RDM technical infrastructure at UK HEIs. The report describes evaluations, reviews and comparisons of these components, gives examples of established RDM services and highlights the recent projects at UK HEIs which were involved in developing these services. This report focuses on the outcomes of projects at UK HEIs funded by the JISC ‘Managing Research Data’ programmes 2009-11 and 2011-2013. Generally the infrastructure architectures examined have been developed in response to the functional requirements derived from researcher workflows. Finally, recommendations for suitable technical infrastructure components are proposed.

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.

Clinical Data Repository Platforms Market Outlook

According to our latest research, the Global Clinical Data Repository Platforms market size was valued at $2.3 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 14.2% during 2024–2033. The primary driver for this remarkable growth is the escalating need for integrated, interoperable healthcare data systems that enable real-time access to patient information, support evidence-based medical decisions, and enhance overall healthcare outcomes globally. As healthcare providers and research organizations increasingly recognize the value of centralized data management, clinical data repository platforms are becoming indispensable for streamlining operations, improving patient care, and accelerating medical research.

Regional Outlook

North America currently dominates the clinical data repository platforms market, accounting for approximately 41% of the global market share in 2024. This region’s leadership is underpinned by the presence of advanced healthcare IT infrastructure, high adoption rates of digital health solutions, and favorable regulatory frameworks such as the Health Information Technology for Economic and Clinical Health (HITECH) Act. The United States, in particular, boasts a mature market with significant investments from both public and private sectors, fostering innovation and deployment of sophisticated data repository platforms. Additionally, the region benefits from a concentration of leading technology vendors, research organizations, and pharmaceutical companies, further accelerating the integration of clinical data repositories into routine healthcare and research workflows.

In contrast, the Asia Pacific region is witnessing the fastest growth, projected to record a CAGR of 17.8% from 2024 to 2033. This rapid expansion is driven by burgeoning healthcare infrastructure investments, government-led digital health initiatives, and increasing awareness about the benefits of data-driven healthcare management. Countries such as China, India, and Singapore are at the forefront, leveraging policy reforms and public-private partnerships to modernize their healthcare ecosystems. The rising prevalence of chronic diseases, coupled with a growing middle-class population seeking quality healthcare, has created significant demand for clinical data repository platforms. Furthermore, international collaborations and technology transfers are enabling local stakeholders to adopt best-in-class solutions, thereby accelerating market penetration across the region.

Emerging economies in Latin America, and Middle East & Africa are gradually embracing clinical data repository platforms, albeit at a slower pace. Key challenges include limited healthcare IT budgets, fragmented healthcare delivery systems, and a shortage of skilled IT professionals. However, localized demand for improved patient care, compliance with international healthcare standards, and efforts to digitize health records are creating new opportunities for market players. Policy interventions aimed at enhancing healthcare accessibility and data security are expected to facilitate gradual adoption, particularly in urban centers and among private healthcare providers. Despite these hurdles, the untapped potential in these regions makes them attractive targets for future market expansion and strategic investments.

Report Scope

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 14-Dec-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute. The dataset forms part of the outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023, and was funded with £13,913.85 of Research England monies held internally by the University of Sheffield as part of their ‘Enhancing Research Cultures’ scheme 2022-2023. The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2023. This includes due concern for participant anonymity and data management. ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made from reuse. It has been deposited under a CC-BY-NC license. Overall, this dataset comprises: 1 x Workshop transcript - in .docx file format which can be opened with Microsoft Word, Google Doc, or an open-source equivalent. The workshop took place on 18-Jul-2023 at the Wave Building, University of Sheffield. All five attendees have read and approved a portion of transcripts containing their own discussion. All workshop attendees have had an opportunity to retract details should they wish to do so. All workshop attendees have chosen whether to be pseudonymised or named directly. The pseudonym or real name can be used to identify individual participant responses in the qualitative coding held within accompanying dataset from the same project - Survey Responses: Hanchard M and San Roman Pineda I (2023) Fostering cultures of open qualitative research: Dataset 1 – Survey Responses. The University of Sheffield. DOI: 10.15131/shef.data.23567250.v1. Interviews: Hanchard M and San Roman Pineda I (2023) Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts. The University of Sheffield. DOI: 10.15131/shef.data.23567223.v2. As a limitation, the audio recording of the workshop session that this transcript is based upon is missing a section (due to a recording error) and may contain errors/inaccuracies (due to poor audio conditions within the workshop room). Every effort has been taken to correct these, including participants themselves reviewing their discussion/quotes, but the transcript may still contain minor inaccuracies, typos, and/or other errors in the text - as is noted on the transcript itself. The project was undertaken by two staff: Co-investigator: Dr. Itzel San Roman Pineda (Postdoctoral Research Assistant) ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk Labelled as ‘Researcher 1’ throughout all project datasets. Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard (Research Associate) ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk iHuman Institute, Social Research Institutes, Faculty of Social Science Labelled as ‘Researcher 2’ throughout all project datasets.

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

This study, inspired by Tony Hey’s “The Data Deluge”, was carried out to discover whether the Institutional Repository is a suitable body to manage curation of and access to the source data created at the institution. This study surveyed research scientists at the White Rose Universities to discover their practices of and attitudes to research data storage, sharing and publication. Key role players in the research data management process were interviewed to investigate possible options for a data infrastructure, the capabilities of WRRO, the establishment of a discipline-based repository and issues of metadata assignment.

Unified Data Repository Market Outlook

According to our latest research, the Global Unified Data Repository market size was valued at $4.2 billion in 2024 and is projected to reach $14.7 billion by 2033, expanding at a robust CAGR of 14.6% during 2024–2033. This remarkable growth trajectory is primarily driven by the exponential increase in enterprise data volumes, coupled with the rising need for holistic data management and governance across diverse industries. Organizations worldwide are prioritizing unified data repository solutions to break down data silos, enhance data accessibility, and ensure regulatory compliance, thereby fostering a data-driven culture that underpins strategic decision-making and operational efficiency. The convergence of cloud technologies, AI-driven analytics, and stringent data privacy regulations is further accelerating the adoption of unified data repository platforms, making them indispensable for modern enterprises navigating the complexities of digital transformation.

Regional Outlook

North America currently commands the largest share of the global unified data repository market, accounting for over 38% of total revenue in 2024. This dominance can be attributed to the region’s mature technological ecosystem, widespread adoption of advanced IT infrastructure, and a strong emphasis on data-driven business strategies. Major economies such as the United States and Canada are at the forefront, with significant investments in cloud computing, artificial intelligence, and cybersecurity, which directly support the deployment of unified data repository solutions. Additionally, the presence of leading technology vendors and a highly skilled workforce further bolster the North American market. Regulatory frameworks such as the CCPA and HIPAA in the United States also necessitate robust data governance and management practices, thereby driving sustained demand for unified data repository platforms among enterprises seeking compliance and risk mitigation.

The Asia Pacific region is projected to experience the fastest growth in the unified data repository market, boasting a remarkable CAGR of 17.3% from 2024 to 2033. This surge is fueled by rapid digitalization, burgeoning cloud adoption, and the proliferation of SMEs across countries like China, India, Japan, and South Korea. Governments in the region are actively promoting digital transformation initiatives, smart city projects, and data localization policies, which are compelling organizations to invest in unified data management solutions. Moreover, the increasing penetration of mobile devices, IoT, and e-commerce platforms is generating vast amounts of structured and unstructured data, necessitating scalable and secure data repository systems. Venture capital investments and strategic partnerships between global and regional technology firms are further accelerating market expansion in Asia Pacific, positioning it as a pivotal growth engine for the industry.

Emerging economies in Latin America and Middle East & Africa are gradually embracing unified data repository solutions, albeit at a slower pace due to infrastructural and regulatory challenges. In these regions, adoption is primarily concentrated among large enterprises and government agencies seeking to modernize legacy systems and improve data governance. However, issues such as limited IT budgets, inadequate digital infrastructure, and a shortage of skilled professionals pose significant hurdles to widespread implementation. Despite these challenges, localized demand is growing in sectors like BFSI, healthcare, and retail, driven by increasing awareness of the benefits of unified data management and compliance with evolving data protection laws. As digital transformation accelerates and regulatory frameworks mature, these regions are expected to witness steady growth, presenting untapped opportunities for market players willing to address local nuances and invest in capacity building.

Report Scope

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Tool for helping people identify and locate online repositories of research data. Users and bibliographers create and curate records that describe data repositories that users can search. * What repositories are appropriate for a researcher to submit his or her data to? * How do users find appropriate data repositories and discover datasets that meet their needs? * How can librarians help patrons locate and integrate data into their research or learning? Databib attempts to address these needs for the research community, including: * data users * data producers * publishers and professional societies * librarians * research funding agencies Are you familiar with a data repository that isn''t included in Databib? Please consider submitting a new record. You can suggest a repository for us to catalog by simply entering its title, URL, authority, and a subject for it... and we''ll do the rest!

This repository contains the collected resources submitted to and created by the NIST Collaborative Research Cycle (CRC) Data and Metrics Archive. The NIST Collaborative Research Cycle (CRC) is an ongoing effort to benchmark, compare, and investigate deidentification technologies. The program asks the research community to deidentify a compact and interesting dataset called the NIST Diverse Communities Data Excerpts, demographic data from communities across the U.S. sourced from the American Community Survey. This repository contains all of the submitted deidentified data instances each accompanied by a detailed abstract describing how the deidentified data were generated. We conduct an extensive standardized evaluation of each deidentified instance using a host of fidelity, utility, and privacy metrics, using out tool, SDNist. We?ve packaged the data, abstracts, and evaluation results into a human- and machine-readable archive.

These data and code support work presented at the ER&L 2023 conference on March 6, 2023 in Austin, TX, USA.Presentation description:Datasets and non-traditional research outputs are a growing use case for institutional repositories. We quantify how much FAIRer institutional repository outputs are and examine other benefits like reuse metrics and Altmetrics. Librarians can use this to quantify the value of their role and their institution's investment in the repository.Methods and file description:Metadata was harvested through Figshare's API (https://docs.figshare.com). Records publicly shared on figshare.com by researchers affiliated with a US based institution were found through figshare.com profiles registered using an email using an institution domain. This information is not public and the affiliation information was removed from the ANALYSIS-DATASET. All other information is publicly available through the API. The compressed file called 'additional-datasets.zip' contains the author, categories, funder, and file information for the researcher records and institution records. The Jupyter Notebook harvests metadata, creates datasets, produces figures used in the presentation, and creates the dataset used for the negative binomial analysis in R. The data-for-R.R file performs the negative binomial analysis which shows relative differences in several metrics for the groups of records.Altmetric data presented at the conference are not public and not included in this dataset.