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 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

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 central login account.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.

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.

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

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.

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.

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

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.

Slides and other material for the presentation, Choosing a Data Repository. Originally presented for CIHEB at University of Maryland, Baltimore

Sharing and preserving data are central to protecting the integrity of science. DataHub, a Research Computing endeavor, provides tools and services to meet scientific data challenges at Pacific Northwest National Laboratory (PNNL). DataHub helps researchers address the full data life cycle for their institutional projects and provides a path to creating findable, accessible, interoperable, and reusable (FAIR) data products. Although open science data is a crucial focus of DataHub’s core services, we are interested in working with evidence-based data throughout the PNNL research community.

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.

A set of materials prepared by Tyng-Ruey Chuang for the session Research Data Management and Planning in the Humanities organized by Profs. Shoichiro Hara and Tatsuki Sekino at the PNC 2024 Annual Conference and Joint Meetings, held at the Korea University, Seoul, South Korea, on August 29-31, 2024.

Collected in this dataset are the abstract (in text) and slideset (in PDF) of the presentation. The abstract is also appended below.

The Active Roles of Research Data Repositories in Cultural Landscape Documentation

Tyng-Ruey Chuang (trc@iis.sinica.edu.tw)

2024-06-05

Research data repositories have been used mainly for archiving data (e.g. supporting datasets are deposited when research articles are being published). Some data repositories, however, can also be used for active data management if certain functional requirements are met (e.g. the repository will allow for flexible project organization and controlled data access). For active data management, we mean the repositories are used for data aggregation, curation, and sharing during the entire research process.

In a team effort to document cultural landscapes, the project team members will necessarily collect existing datasets from diverse sources (e.g. old photos) as well as generate new documentary materials (e.g. on-site images). These materials are heterogeneous in nature. Some data collections are about historical records and sensor datasets, while the others may consist of soundtracks and videos, aerial and panorama images, documents and pictures, plain text journals, bibliographies, and so on. For rapid research exploration, therefore, it will be crucial to streamline the management and presentation of multiple data collections related by space and time.

At Academia Sinica, we have developed the depositar (https://data.depositar.io), a data repository open to all for the deposit, discovery, and reuse of research datasets. The depositar is a generalist repository suitable for active data management, and it can serve as a data hub for collaborative data collection. In this talk, we will present a few showcases in using the depositar for collaborative cultural landscape documentation.

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 RESOURCE IS NO LONGER IN SERVICE, documented January 13, 2022. Open access repository of original, unprocessed data underlying work published by Stowers researchers to allow the scientific community to validate and extend the findings made by Stowers researchers. For papers first submitted for publication after November 1, 2011, the Stowers Institute requires its members to deposit original data files into the Stowers Original Data Repository or to repositories maintained by third parties at the time of publication. Access to the Stowers Original Data Repository is free, but you will be asked to register before you can download data.

Data were gathered under NSF grants 1760894 & 1431234. Participants included 336 bioscience Ph.D. students from 53 research universities across the United States. Data include include yearly surveys and biweekly assessments sent to all participants who opted to participate and met criteria for remaining in the study each year, yearly scores on a sole-author submitted research paper writing sample graded by 2 trained experts and averaged across expert ratings, and yearly interviews conducted with a subset of participants. Retention and missing data handling are outlined in the documents below. Further information about methods regarding instrumentation, interview protocols, participation, and assessment of writing samples can be found in these materials. If there are pieces of these materials that need further clarification or detail, please let us know.

When using this data in any publication or other form of public communication, please cite this repository as specified in the Open Data Commons Attribution License, Sections 4.2.b and 4.2.c.

Instructions and guidance materials on how to prepare your research data for sharing and long-term access and how to deposit your research data in the University of Guelph Research Data Repositories including the Agri-environmental Research Data Repository and the University of Guelph Research Data Repository.How the Data Repositories work:Self-deposit with mediation data deposit service: Upon request, depositors are given dataset creator access to a collection in the Data Repositories allowing them to create new draft dataset records and submit their draft datasets for review/publication. Repository staff review all submitted datasets for alignment with repository policies and data deposit guidelines. Repository staff will work with depositors to make any required changes to the metadata, data files, and/or supplemental documentation to improve the FAIRness (findability, accessibility, interoperability, and reusability) of the dataset. When the dataset is ready, repository staff will publish the dataset on behalf of the depositor. How to start the deposit process: If you are interested in depositing data in the Data Repositories and/or have questions about preparing your data for deposit, please contact repository staff to start the process.

The Big Data Interagency Working Group (BD IWG) held a workshop, Measuring the Impact of Digital Repositories, on February 28 - March 1, 2017 in Arlington, VA. The aim of the workshop was to identify current assessment metrics, tools, and methodologies that are effective in measuring the impact of digital data repositories, and to identify the assessment issues, obstacles, and tools that require additional research and development (R&D). This workshop brought together leaders from academic, journal, government, and international data repository funders, users, and developers to discuss these issues...

The dataset contains five models with extracted elements for each model. The elements are manually labeled by researchers with construction/civil engineering backgrounds based on discussion and majority vote.

Anonymised data of the research data repository survey for the European Research Data Landscape study.