A Destination for DRUM Dataset Deposits: Creating the UMD Data Collection

As publisher policies and funding agencies increasingly require or encourage research data be made open for wider access and review, improving data collection and curation practices in institutional repositories has become commensurately necessary to support the needs of researchers and the goals of open scholarship. Since the Digital Repository at the University of Maryland’s (DRUM) launch in 2005, it has been utilized in archiving research datasets produced by UMD researchers. However, the repository’s general self-submission workflow lacks mechanisms to consistently collect essential identifying metadata as well as other metadata necessary for best practices in research data archiving. The UMD Data Collection was created in the summer of 2021 to address such issues. This poster outlines the design and implementation of the collection and its customized workflow to better enable future curation, management, and discovery of research datasets archived in UMD's institutional repository

Data Repositories: The Answer that Actually Came with a Question

Data repositories: the answer that actually came with a question. Funders, journal publishers, and disciplinary societies recognize the benefits of long-term access to valuable data that could validate results, increase scholarly democracy, or possibly lead to future discoveries. With this in mind, a majority of research now being done in academia is subject to data sharing requirements that the underlying data be publicly accessible, citable, and persevered. As many subject-based data repositories help make this happen, particularly for computing-intensive disciplines with shared infrastructure, such as high-energy physics or real-time climate monitoring, who will manage the long-tail of smaller or multi-disciplinary research data? Our institutional repositories (IR) could be the answer. With a few key policy decisions, and robust review and curation procedures, libraries are well-positioned to help researchers comply with mandates to share and archive their data. Whether you use Hydra, DSpace, Fedora, E-prints, or Digital Commons, this talk will outline important issues to consider as you build new capacity with existing IR infrastructure or a custom data repository, including staffing, curation procedures, and metadata and documentation requirements. Finally it will explore the results and faculty response to launching the Data Repository for the University of Minnesota in 2015, which is based on the Libraries’ existing IR service. Our data submission process, curation procedures, faculty usage, and lessons learned will be placed in context of our broader data management and curation program. Lisa Johnston is Research Data Management/Curation Lead and Co-Director of the University Digital Conservancy, University of Minnesota

Peer Review of Research Data Submissions to ScholarsArchive@OSU: How can we improve the curation of research datasets to enhance reusability?

Objective: Best practices such as the FAIR Principles (Findability, Accessibility, Interoperability, Reusability) were developed to ensure that published datasets are reusable. While we employ best practices in the curation of datasets, we want to learn how domain experts view the reusability of datasets in our institutional repository, ScholarsArchive@OSU. Curation workflows are designed by data curators based on their own recommendations, but research data is extremely specialized, and such workflows are rarely evaluated by researchers. In this project we used peer-review by domain experts to evaluate the reusability of the datasets in our institutional repository, with the goal of informing our curation methods and ensure that the limited resources of our library are maximizing the reusability of research data. Methods: We asked all researchers who have datasets submitted in Oregon State University’s repository to refer us to domain experts who could review the reusability of their data sets. Two data curators who are non-experts also reviewed the same datasets. We gave both groups review guidelines based on the guidelines of several journals. Eleven domain experts and two data curators reviewed eight datasets. The review included the quality of the repository record, the quality of the documentation, and the quality of the data. We then compared the comments given by the two groups. Results: Domain experts and non-expert data curators largely converged on similar scores for reviewed datasets, but the focus of critique by domain experts was somewhat divergent. A few broad issues common across reviews were: insufficient documentation, the use of links to journal articles in the place of documentation, and concerns about duplication of effort in creating documentation and metadata. Reviews also reflected the background and skills of the reviewer. Domain experts expressed a lack of expertise in data curation practices and data curators expressed their lack of expertise in the research domain. Conclusions: The results of this investigation could help guide future research data curation activities and align domain expert and data curator expectations for reusability of datasets. We recommend further exploration of these common issues and additional domain expert peer-review project to further refine and align expectations for research data reusability. The substance of this article is based upon a panel presentation at RDAP Summit 2019

Curating E-Mails; A life-cycle approach to the management and preservation of e-mail messages

E-mail forms the backbone of communications in many modern institutions and organisations and is a valuable type of organisational, cultural, and historical record. Successful management and preservation of valuable e-mail messages and collections is therefore vital if organisational accountability is to be achieved and historical or cultural memory retained for the future. This requires attention by all stakeholders across the entire life-cycle of the e-mail records. This instalment of the Digital Curation Manual reports on the several issues involved in managing and curating e-mail messages for both current and future use. Although there is no 'one-size-fits-all' solution, this instalment outlines a generic framework for e-mail curation and preservation, provides a summary of current approaches, and addresses the technical, organisational and cultural challenges to successful e-mail management and longer-term curation.

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Integrated Findings to Develop Research Data Services

This article reports on the integrated findings of an exploratory sequential mixed methods research design aimed to understand data management behaviors and challenges of faculty at the University of Vermont (UVM) in order to develop relevant research data services. The exploratory sequential mixed methods design is characterized by an initial qualitative phase of data collection and analysis, followed by a phase of quantitative data collection and analysis, with a final phase of integration or linking of data from the two separate strands of data. A joint display was used to integrate data focused on the three primary research questions: How do faculty at UVM manage their research data, in particular how do they share and preserve data in the long-term?; What challenges or barriers do UVM faculty face in effectively managing their research data?; and What institutional data management support or services are UVM faculty interested in? As a result of the analysis, this study suggests four major areas of research data services for UVM to address: infrastructure, metadata, data analysis and statistical support, and informational research data services. The implementation of these potential areas of research data services is underscored by the need for cross-campus collaboration and support

Digital Curation Bibliography: Preservation and Stewardship of Scholarly Works

In a rapidly changing technological environment, the difficult task of ensuring long-term access to digital information is increasingly important. The Digital Curation Bibliography: Preservation and Stewardship of Scholarly Works presents over 650 English-language articles, books, and technical reports that are useful in understanding digital curation and preservation. This selective bibliography covers digital curation and preservation copyright issues, digital formats (e.g., data, media, and e-journals), metadata, models and policies, national and international efforts, projects and institutional implementations, research studies, services, strategies, and digital repository concerns. Most sources have been published from 2000 through 2011; however, a limited number of key sources published prior to 2000 are also included. The bibliography includes links to freely available versions of included works. It is under a Creative Commons Attribution-NonCommercial 3.0 Unported License. Cite as: Bailey, Charles W., Jr. Digital Curation Bibliography: Preservation and Stewardship of Scholarly Works. Houston: Digital Scholarship, 2012

Digital Curation Bibliography: Preservation and Stewardship of Scholarly Works

In a rapidly changing technological environment, the difficult task of ensuring long-term access to digital information is increasingly important. The Digital Curation Bibliography: Preservation and Stewardship of Scholarly Works presents over 650 English-language articles, books, and technical reports that are useful in understanding digital curation and preservation. This selective bibliography covers digital curation and preservation copyright issues, digital formats (e.g., data, media, and e-journals), metadata, models and policies, national and international efforts, projects and institutional implementations, research studies, services, strategies, and digital repository concerns. Most sources have been published from 2000 through 2011; however, a limited number of key sources published prior to 2000 are also included. The bibliography includes links to freely available versions of included works. It is under a Creative Commons Attribution-NonCommercial 3.0 Unported License. Cite as: Bailey, Charles W., Jr. Digital Curation Bibliography: Preservation and Stewardship of Scholarly Works. Houston: Digital Scholarship, 2012

Development of a pilot data management infrastructure for biomedical researchers at University of Manchester – approach, findings, challenges and outlook of the MaDAM Project

Management and curation of digital data has been becoming ever more important in a higher education and research environment characterised by large and complex data, demand for more interdisciplinary and collaborative work, extended funder requirements and use of e-infrastructures to facilitate new research methods and paradigms. This paper presents the approach, technical infrastructure, findings, challenges and outlook (including future development within the successor project, MiSS) of the ‘MaDAM: Pilot data management infrastructure for biomedical researchers at University of Manchester’ project funded under the infrastructure strand of the JISC Managing Research Data (JISCMRD) programme. MaDAM developed a pilot research data management solution at the University of Manchester based on biomedical researchers’ requirements, which includes technical and governance components with the flexibility to meet future needs across multiple research groups and disciplines

Digital Preservation Services : State of the Art Analysis

Research report funded by the DC-NET project.An overview of the state of the art in service provision for digital preservation and curation. Its focus is on the areas where bridging the gaps is needed between e-Infrastructures and efficient and forward-looking digital preservation services. Based on a desktop study and a rapid analysis of some 190 currently available tools and services for digital preservation, the deliverable provides a high-level view on the range of instruments currently on offer to support various functions within a preservation system.European Commission, FP7peer-reviewe

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Findings from the Quantitative Phase

This article reports on the second quantitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional support and services. The study aims to understand data management activities and challenges of faculty at the University of Vermont (UVM), a higher research activity Research University, in order to develop appropriate research data services (RDS). Data was collected via a survey, built on themes from the initial qualitative data analysis from the first phase of this study. The survey was distributed to a nonrandom census sample of full-time UVM faculty and researchers (P=1,190); from this population, a total of 319 participants completed the survey for a 26.8% response rate. The survey collected information on five dimensions of data management: data management activities; data management plans; data management challenges; data management support; and attitudes and behaviors towards data management planning. Frequencies, cross tabulations, and chi-square tests of independence were calculated using demographic variables including gender, rank, college, and discipline. Results from the analysis provide a snapshot of research data management activities at UVM, including types of data collected, use of metadata, short- and long-term storage of data, and data sharing practices. The survey identified key challenges to data management, including data description (metadata) and sharing data with others; this latter challenge is particular impacted by confidentiality issues and lack of time, personnel, and infrastructure to make data available. Faculty also provided insight to RDS that they think UVM should support, as well as RDS they were personally interested in. Data from this study will be integrated with data from the first qualitative phase of the research project and analyzed for meta-inferences to help determine future research data services at UVM