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.

In the age of digital transformation, scientific and social interest for data and data products is constantly on the rise. The quantity as well as the variety of digital research data is increasing significantly. This raises the question about the governance of this data. For example, how to store the data so that it is presented transparently, freely accessible and subsequently available for re-use in the context of good scientific practice. Research data repositories provide solutions to these issues.

Considering the variety of repository software, it is sometimes difficult to identify a fitting solution for a specific use case. For this purpose a detailed analysis of existing software is needed. Presented table of requirements can serve as a starting point and decision-making guide for choosing the most suitable for your purposes repository software. This table is dealing as a supplementary material for the paper "How to choose a research data repository software? Experience report." (persistent identifier to the paper will be added as soon as paper is published).

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 New Depositor 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.

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.

PATRON is a human ethics approved program of research incorporating an enduring de-identified repository of Primary Care data facilitating research and knowledge generation. PATRON is a part of the 'Data for Decisions' initiative of the Department of General Practice, University of Melbourne. 'Data for Decisions' is a research initiative in partnership with general practices. It is an exciting undertaking that makes possible primary care research projects to increase knowledge and improve healthcare practices and policy. Principal Researcher: Jon EmeryData Custodian: Lena SanciData Steward: Douglas BoyleManager: Rachel CanawayMore information about Data for Decisions and utilising PATRON data is available from the Data for Decisions website.

These research datasets are the updated version of the conference poster "Research data repositories and their metadata: A comparative study," presented by Ms. Kavya Asok and Ms. Snigdha Dandpat in a Conference on Open and FAIR Data Ecosystem: Principles, Policies, and Platforms scheduled from 11th -13th September 2023, at IIC, New Delhi. The study describes the features of a select number of RDRs and analyzes their metadata practices.

This dataset aggregates information about 191 research data repositories that were shut down. The data collection was based on the registry of research data repositories re3data and a comprehensive content analysis of repository websites and related materials. Documented in the dataset are the period in which a repository was active, the risks resulting in its shutdown, and the repositories taking over custody of the data after.

Origin

This poster is for RDA P19 poster exhibition

Description

Although there is yet a holistic national level support in Taiwan on the pursue of excellence in research data management, however, a culture of research data management is starting to take shape. As a research data repository operating in Taiwan, we report in this poster our work in helping the advance of good research data practices in Taiwan.

The depositar is a general-purpose data repository open to all for the deposit, discovery, and reuse of research data. It has been in service since early 2018. Its development has been supported by Academia Sinica and, in part, by a grant from Taiwan’s Ministry of Science and Technology. In addition to developing and operating the repository, since early 2019 the depositar team has been active in advocating good research data practices in Taiwan. From the perspective of depositar, researchers in Taiwan will be more likely to share data—hence to deposit data to depositar or to any other data repositories—when their data is well managed and in a state ready to be reused and shared. The funding we receive from the Ministry of Science and Technology also has a focus on facilitating better research data management in Taiwan (though initially only applied to grants awarded in the area of sustainable development research).

For the last few years, the depositar team has been working to cultivate a culture of research data management in Taiwan. We hold co-learning workshops where domain experts share their practices in managing research data. We work closely with several research projects about implementing data management plans. Above all, we strive to produce and make available guidelines and toolkits on research data management and on using research data repositories. At the same time we constantly improve the functionalities of depositar in response to the feedback we received from our users and from the above activities.

This poster will report on these activities and the lessons we have learned. We will also reflect on the strategy aspects of advocating for good research data practices, especially in the settings of limited resources and/or missing policies.

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.

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

Inventory of Online Public Databases and Repositories Holding Agricultural Data in 2017

Metadata Updated: March 30, 2024

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 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 multidisciplinary 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 analyzed. 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 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 searched using 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 website 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 compi...

RADAR is a cross-disciplinary internet-based service for long-term and format-independent archiving and publishing of digital research data from scientific studies and projects. The focus is on data from disciplines that are not yet supported by specific research data management infrastructures. The repository aims to ensure access and long-term availability of deposited datasets according to FAIR criteriaWilkinson et al. 2016 for the benefit of the scientific community. Published datasets are retained for at least 25 years; for archived datasets, the retention period can be flexibly selected up to 15 years. The RADAR Cloud service was developed as a cooperation project funded by the DFG (2013-2016) and started operations in 2017. It is operated by FIZ Karlsruhe - Leibniz-Institute for Information Infrastructure.

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

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

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

Contact

Swiss National Science Foundation (SNSF)

Open Research Data Group

E-mail: ord@snf.ch

swissuniversities

Program "Scientific Information"

Gabi Schneider

E-Mail: isci@swissuniversities.ch

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

Please cite this data collection as: von der Heyde, M. (2019). Data and tools of the landscape and cost analysis of data repositories currently used by the Swiss research community. Retrieved from https://doi.org/10.5281/zenodo.2643495

Connected data papers are: von der Heyde, M. (2019). Open Data Landscape: Repository Usage of the Swiss Research Community: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643430 von der Heyde, M. (2019). International Open Data Repository Survey: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643450

Connected data sets are: von der Heyde, M. (2019). Data from the Swiss Open Data Repository Landscape survey. Retrieved from https://doi.org/10.5281/zenodo.2643487 von der Heyde, M. (2019). Data from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493

Contact

Swiss National Science Foundation (SNSF)

Open Research Data Group

E-mail: ord@snf.ch

swissuniversities

Program "Scientific Information"

Gabi Schneider

E-Mail: isci@swissuniversities.ch

Unified Data Repository Market Outlook

According to the latest research conducted in early 2025, the global Unified Data Repository Market size reached USD 8.3 billion in 2024, demonstrating robust momentum driven by the accelerating need for comprehensive data management solutions across diverse industries. The market is projected to expand at a CAGR of 13.5% from 2025 to 2033, ultimately attaining a forecasted value of USD 26.3 billion by the end of 2033. This remarkable growth is primarily fueled by the increasing complexity of enterprise data ecosystems, rising regulatory compliance demands, and the surge in digital transformation initiatives worldwide.

One of the primary growth factors propelling the Unified Data Repository Market is the exponential increase in data volumes generated by organizations. As businesses transition towards digital operations, the need for centralized, scalable, and easily accessible data repositories has become paramount. Enterprises are no longer dealing with siloed data sources; instead, they require unified platforms that can seamlessly integrate structured and unstructured data, ensuring real-time access and optimal data quality. The proliferation of IoT devices, cloud-based applications, and edge computing has further intensified the need for unified data repositories that can consolidate disparate data streams, enabling organizations to derive actionable insights and maintain a competitive edge.

Another significant driver is the growing emphasis on regulatory compliance and data governance. With stringent data privacy regulations such as GDPR, CCPA, and other region-specific mandates, organizations are under immense pressure to maintain transparency, ensure data lineage, and safeguard sensitive information. Unified data repositories offer a comprehensive framework that facilitates compliance by providing centralized control, robust audit trails, and granular access management. This is particularly critical for sectors like BFSI, healthcare, and government, where data breaches and non-compliance can result in substantial financial and reputational damage. The market is also witnessing increased investments in advanced analytics and artificial intelligence, which are further enhancing the capabilities of unified data repository solutions.

The rapid adoption of cloud technologies and the rise of hybrid IT environments are also contributing significantly to market growth. Organizations are increasingly leveraging cloud-based unified data repositories to achieve greater scalability, flexibility, and cost efficiency. Cloud deployment models enable seamless integration with existing IT infrastructure, support remote access, and facilitate real-time collaboration across geographically dispersed teams. Moreover, the shift towards cloud-native architectures is enabling vendors to offer innovative features such as automated data discovery, intelligent data cataloging, and self-service analytics, thereby expanding the addressable market and attracting a broader customer base.

In the context of the Unified Data Repository Market, the role of the Home Subscriber Server (HSS) is becoming increasingly significant, especially in the telecommunications sector. The HSS is a central database that contains subscriber-related information and plays a crucial role in managing user profiles, authentication, and mobility management. As telecom operators transition to 5G networks, the integration of HSS with unified data repositories is essential to ensure seamless data flow and real-time access to subscriber information. This integration enhances the ability of telecom companies to deliver personalized services, optimize network resources, and maintain high levels of customer satisfaction. The growing demand for efficient data management solutions in the telecom industry is driving the adoption of unified data repositories that incorporate HSS functionalities, enabling operators to streamline operations and improve service delivery.

From a regional perspective, North America continues to dominate the Unified Data Repository Market owing to its advanced technological landscape, high digital adoption rates, and significant investments in data-driven initiatives. However, the Asia Pacific region is poised for the fastest growth, driven by rapid digitalization, expanding enterprise IT infrastructure, and support

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

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.

Platform developed and operated by Stanford Medicine Research IT team for working with clinical data for research purposes. Permits collection and aggregation of all clinical data generated at Stanford for care purposes, and articulates formal approval process each research project must follow in order to obtain and work with this data for research purpose. Home of stride/web tools for Cohort Discovery and Chart Review.

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 23-Jun-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute. The dataset forms part of three outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023:

· Fostering cultures of open qualitative research: Dataset 1 – Survey Responses · Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts · Fostering cultures of open qualitative research: Dataset 3 – Coding Book

The project 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-2021. 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 form reuse. It has been deposited under a CC-BY-NC license. Overall, this dataset comprises:

· 15 x Interview transcripts - in .docx file format which can be opened with Microsoft Word, Google Doc, or an open-source equivalent.

All participants have read and approved their transcripts and have had an opportunity to retract details should they wish to do so.

Participants chose whether to be pseudonymised or named directly. The pseudonym can be used to identify individual participant responses in the qualitative coding held within the ‘Fostering cultures of open qualitative research: Dataset 3 – Coding Book’ files.

For recruitment, 14 x participants we selected based on their responses to the project survey., whilst one participant was recruited based on specific expertise.

· 1 x Participant sheet – in .csv format which may by opened with Microsoft Excel, Google Sheet, or an open-source equivalent.

The provides socio-demographic detail on each participant alongside their main field of research and career stage. It includes a RespondentID field/column which can be used to connect interview participants with their responses to the survey questions in the accompanying ‘Fostering cultures of open qualitative research: Dataset 1 – Survey Responses’ files.

The project was undertaken by two staff:

Co-investigator: Dr. Itzel San Roman Pineda ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk Postdoctoral Research Assistant Labelled as ‘Researcher 1’ throughout the dataset

Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk Research Associate iHuman Institute, Social Research Institutes, Faculty of Social Science Labelled as ‘Researcher 2’ throughout the dataset

These documents provide information about how the MacEwan University Data Repository is managed and provide guidance for prospective depositors.