Planning Cooperative Data Curation Services

Presented at Open Repositories 2011, Austin, Texas, June 8-11

Institutional Readiness for Data Stewardship: Findings and Recommendations from the Research Data Assessment

This report was written by members of the Georgia Tech Library's Research Data Project Team. The report is based, in part, on survey data collected by this group. The survey data are located in SMARTech and can be found at http://hdl.handle.net/1853/48198.The potentials and possibilities afforded by managing, preserving, and sharing digital research data have been lauded by funding agencies, universities, and researchers alike. As federal funding agencies require data management plans and data sharing, questions around how to ensure that research data are managed and shared have come to the fore. Academic institutions and libraries are particularly interested in these issues, recognizing the need to support researchers in their work with research data. Accordingly, the Georgia Tech Library began investigating the research data practices and needs at Georgia Tech by conducting a campus-wide research data assessment. The assessment, which included a survey, interviews, analysis of data management plans submitted with NSF grants, and data archiving case studies, revealed a number of noteworthy trends, which are detailed more in the full findings of the report. The major findings of the assessment were: 1. Data management plans are still a frustrating burden for most researchers. 2. Georgia Tech researchers lack the guidelines, resources, standards, and policies to properly care for their research data. 3. A disconnect exists between the expectations of Principal Investigators and Graduate Assistants. 4. Researchers recognize the importance of documentation and metadata, but few capture this information adequately. 5. Sharing data with collaborators outside Georgia Tech is challenging. 6. Researchers are willing to share their data, but the conditions under which they are willing to do so vary widely. 7. Researchers rarely plan for the the final disposition of their research data. 8. Very few researchers deposit data into repositories. Based on these findings, we make the following six recommendations: 1. Enhance institutional ability to support data archiving 2. Establish a campus Research Data Stewardship Group 3. Develop a formal data stewardship marketing plan 4. Create a repository of Georgia Tech data management plans 5. Provide data management training, especially for graduate students 6. Create and update the necessary and appropriate institutional policies The challenges of caring for research data are many and constantly evolving, and Georgia Tech will need to adapt to the needs of their community. These recommendations are but a starting point for developing the institutional capacity to steward research data, but they provide important insight into the framework needed to properly care for institutional digital data

Building a Collaborative Curation Framework: Working Towards Sustainable Digital Stewardship

This presentation was given at the virtual 2021 Digital Library Federation Forum on November 2, 2021.This presentation will discuss lessons learned from an academic research library’s endeavor to reconsider curation work holistically – across siloed content types, processes, systems, and departments. Georgia Tech team members will explore insights from our efforts working with Artefactual Systems to reimagine and sustain digital stewardship work across existing organizational silos

Data Management Plans as a Research Tool

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112237/1/bult1720410510.pd

Reviewing Data Management Plans: Practical Experience for New Service Providers Workshop

This presentation was given as part of a workshop at the Southeast Data Librarian Symposium on October 12, 2022.Many academic librarians begin their work in research data support by offering reviews of Data Management Plans (DMPs, also called Data Management and Sharing Plans/DMSPs), which are required components of many grant proposals. After a researcher drafts a DMP, they may want someone to review it, assess its fit with best practices, and give feedback. Reviewing DMPs means evaluating and offering advice for improvement. But how does a library get started with reviewing DMPs? This workshop is tailored for new data librarians and subject librarians starting in data, who want to provide a DMP or DMSP support toolbox. The panel portion will compare real-world practices on how to provide DMP reviews for existing drafts created by researchers in different institutional settings. Next, presenters will compare different approaches, such as contrasting the DART Rubric (“DMPs as A Research Tool”) for in-depth National Science Foundation (NSF) reviews versus the Caltech NSF checklist for fast reviews; discussing how FASEB’s NIH DMSP contest rubric differs from the DART rubric; and summarizing how funder notes in DMPTool can be used for reviewing DMPs from various funders. This discussion will help new DMP evaluators think about how the process might change, and not change, for different funders’ DMPs. Finally, everyone will have guided practice in using the DART rubric to evaluate a simple research proposal and sketch out feedback for improvements in the DMP. At the end, the whole class will be better prepared to evaluate DMPs and offer researcher feedback on how to improve their Plan to make their research data FAIR

Using Data Management Plans to Explore Variability in Research Data Management Practices Across Domains

This paper describes an investigation into how researchers in different fields are interpreting and responding to the U.S. National Science Foundation’s data management plan (DMP) requirement. As documents written by the researchers themselves, DMPs can provide insight into researchers’ understanding of the potential value of their data to others; the environment in which their data are developed and prepared; and their willingness and ability to ensure the data are available to others now and in the long-term. With support from the Institute of Museum and Library Services, the authors conducted a content analysis of DMPs generated at their respective institutions using a shared rubric. By developing and testing a rubric designed to understand and evaluate the content of DMPs, the authors intend to develop a more complete understanding, at a larger scale, of how researchers plan for managing, sharing, and archiving their data.

NSF Data Management Plans as a Repository Research Tool

Poster presented at the 2016 International Open Repositories Conference, Dublin, Ireland.This project was made possible in part by the Institute of Museum & Library Services grant number LG-07-13-032

Data Management Plans as a Research Tool

Research data has gained wider acceptance as important scholarly products in and of themselves, and funding agencies, such as the National Science Foundation (NSF), have introduced requirements for Data Management Plans (DMPs) and data archiving. Accordingly, academic and research libraries are devoting significant consideration, effort, and resources toward expanding their role to include Research Data Management (RDM) services. RDM services can include training in data management best practices, consultations for writing DMPs, and support for various data management components, such as creating metadata or choosing appropriate data repositories. Libraries are stepping up to the challenge to create, implement, market, and assess new RDM services that will meet the demands of their community. The DMPs that researchers submit with grant proposals are a rich source of data about a university’s researchers and their knowledge, capabilities, and needs. Analysis of DMPs can provide valuable insight into the kinds of data researchers are generating and how they intend to manage those data. In light of such potential value, collaborators from multiple large, public, universities developed an analytic rubric to evaluate NSF DMPs. The rubric was designed to be a research tool for academic librarians, to enable librarians to analyze a large body of DMPs from their institution for the purposes of better understanding the practices of the local community. Awareness of local practices is fundamental to providing RDM services that are tailored to the diverse needs of an institution’s faculty and students. For example, if researchers routinely obligate certain Library services in their DMPs, the Library has a better idea of how they should allocate limited resources for the highest impact. Whether a library is developing RDM services from scratch or looking to improve current RDM services, using the rubric to analyze DMPs will equip the library with the information they need to best support their community. A secondary purpose is for a researcher (and librarians as consultants) to critique an individual DMP before it is submitted alongside a grant application, thus avoiding submitting a plan with missing or limited content. Although tools - such as the DMPTool or DMP Online - help with the creation of a DMP, there is no standardized tool to aid with the evaluation of the quality of a DMP. Further, nothing has been developed to enable large-scale evaluation of DMPs for research purposes. We expect our rubric to fill this need. Our poster will describe the rubric we have created, detail how we developed the tool, and explain how it can be used by academic librarians to analyze NSF data management plans. We will highlight examples from the rubric itself and show how it has been used in our initial testing. Participation in the poster session will allow us to engage directly with academic and research librarians, both to demonstrate the rubric in person as needed, and to solicit valuable feedback on the tool and its utility to the academic library community

Using assessment of NSF data management plans to enable evidence-based evolution of research data services

Funding agencies increasingly require a data management plan (DMP) with funding proposals, which describe how data generated in the proposed work will be managed, preserved and shared. Data management plans are a rich source of information about an institution’s researchers and their research data management (RDM) knowledge, capabilities, and needs. Structured review of DMPs could identify gaps and weaknesses in faculty understanding and application of data management concepts and practices, and identify barriers in applying best practices. As such, the assessment of DMPs can uncover important insights about local RDM practices and aptitudes, which can then inform the development of RDM services. We have created an analytic rubric for assessing DMPs that is intended to equip academic and research librarians with a tool that will both facilitate and standardize the review of NSF data management plans. Our rubric allows librarians to utilize DMPs as a research tool that can inform decisions about which research data services they should provide. This tool enables librarians who may have no direct experience in applied research or RDM to become better informed about researchers’ data practices and how library services can support them. Using the rubric, we have assessed several hundred NSF DMPs from successful proposals at five research-intensive institutions. We will share results of our analyses, and demonstrate how the assessment of DMPs can be used to evaluate how well current data services meet the needs of faculty or highlight areas where services may need to grow or evolve