Browsing by Subject "Research data management"

Now showing 1 - 8 of 8
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    Building data services from the ground up: Strategies and resources
    (Journal of eScience Librarianship, 2014) Coates, Heather L.
    There is a scarcity of practical guidance for developing data services in an academic library. Data services, like many areas of research, require the expertise and resources of teams spanning many disciplines. While library professionals are embedded into the teaching activities of our institutions, fewer of us are embedded in activities occurring across the full research life cycle. The significant challenges of managing, preserving, and sharing data for reuse demand that we take a more active role. Providing support for funder data management plans is just one option in the data services landscape. Awareness of the institutional and library culture in which we operate places an emphasis on the importance of relationships. Understanding the various cultures in which our researchers operate is crucial for delivering data services that are relevant and utilized. The goal of this article is to guide data specialists through this landscape by providing key resources and strategies for developing locally relevant services and by pointing to active communities of librarians and researchers tackling the challenges associated with digital research data.
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    Building the Future of Research Together: Collaborating with a Clinical and Translational Science Award (CTSA)-Funded Translational Science Institute to Provide Data Management Training
    (2014-05-19) Coates, Heather L.
    Objectives: To explore potential collaborations between academic libraries and Clinical Translational Science Award (CTSA) - funded institutes with respect to data management training and support. Methods: The National Institutes of Health CTSAs have established a well-funded, crucial infrastructure supporting large-scale collaborative biomedical research. This infrastructure is also valuable for smaller, more localized research projects. While infrastructure and corresponding support is often available for large, well-funded projects, these services have generally not been extended to smaller projects. This is a missed opportunity on both accounts. Academic libraries providing data services can leverage CTSA-based resources, while CTSA-funded institutes can extend their reach beyond large biomedical projects to serve the long tail of research data. Results: A year-long series of conversations with the Indiana CTSI Data Management Team resulted in resource sharing, consensus building about key issues in data management, provision of expert feedback on a data management training curriculum, and several avenues for future collaborations. Conclusions: Data management training for graduate students and early career researchers is a vital area of need that would benefit from the combined infrastructure and expertise of translational science institutes and academic libraries. Such partnerships can leverage the instructional, preservation, and access expertise in academic libraries, along with the storage, security, and analytical expertise in translational science institutes to improve the management, protection, and access of valuable research data.
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    Data matters: how earth and environmental scientists determine data relevance and reusability
    (2019-05-01) Murillo, Angela
    Abstract Purpose – The purpose of this study is to examine the information needs of earth and environmental scientists regarding how they determine data reusability and relevance. Additionally, this study provides strategies for the development of data collections and recommendations for data management and curation for information professionals working alongside researchers. Design/methodology/approach – This study uses a multi-phase mixed-method approach. The test environment is the DataONE data repository. Phase 1 includes a qualitative and quantitative content analysis of deposited data. Phase 2 consists of a quasi-experiment think-aloud study. This paper reports mainly on Phase 2. Findings – This study identifies earth and environmental scientists’ information needs to determine data reusability. The findings include a need for information regarding research methods, instruments and data descriptions when determining data reusability, as well as a restructuring of data abstracts. Additional findings include reorganizing of the data record layout and data citation information. Research limitations/implications – While this study was limited to earth and environmental science data, the findings provide feedback for scientists in other disciplines, as earth and environmental science is a highly interdisciplinary scientific domain that pulls from many disciplines, including biology, ecology and geology, and additionally there has been a significant increase in interdisciplinary research in many scientific fields. Practical implications – The practical implications include concrete feedback to data librarians, data curators and repository managers, as well as other information professionals as to the information needs of scientists reusing data. The suggestions could be implemented to improve consultative practices when working alongside scientists regarding data deposition and data creation. These suggestions could improve policies for data repositories through direct feedback from scientists. These suggestions could be implemented to improve how data repositories are created and what should be considered mandatory information and secondary information to improve the reusability of data. Social implications – By examining the information needs of earth and environmental scientists reusing data, this study provides feedback that could change current practices in data deposition, which ultimately could improve the potentiality of data reuse. Originality/value – While there has been research conducted on data sharing and reuse, this study provides more detailed granularity regarding what information is needed to determine reusability. This study sets itself apart by not focusing on social motivators and demotivators, but by focusing on information provided in a data record.
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    Ensuring research integrity: The role of data management in current crises
    (Association of College & Research Libraries, 2014-12) Coates, Heather L.
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    Improving data management in academic research: Assessment results for a pilot lab
    (2014-05-19) Coates, Heather L.
    Common practices for data collection, storage, organization, documentation, sharing, re-use, and preservation are often suboptimal. Issues often arising from common data practices include data loss, corruption, poor data integrity, and an inability to demonstrate the provenance (i.e., the origin) of the data. Ineffective data management can result in data that are unusable for re-use and re-analysis. However, effective data management practices exist to support data integrity, interoperability, and re-use. These practices maximize the value and potential impact of any particular dataset. In light of the gap between common practice and known effective strategies, we developed an intensive lab curriculum to train students and research support staff in implementing these strategies. This lab addresses the lack of formal data management training available on our campus and targets key processes in the data life cycle, promoting strategies that facilitate generation of quality data appropriate for re-use.
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    Promoting sustainable research practices through effective data management curricula
    (2015-03-27) Coates, Heather L.; Muilenburg, Jenny; Whitmire, Amanda L.
    Managing research data effectively is critical to producing high quality datasets that support data preservation, sharing, reuse, and reproducible research. Academic librarians are increasingly playing a role in providing training and education in data management (DM) for faculty and students. While emerging data management curricula are converging on a common set of topics covered, expected learning outcomes, instructional materials, techniques and strategies still vary widely. This wide variability in DM instructional approaches largely reflects the similarly broad variety of audiences for the material, and the instructors offering it. The audience for DM instruction includes graduate students, faculty and research support staff from all disciplines, liaison librarians, data specialists and many others. Instructional methods range from online modules and coursework, workshops, and credit-bearing courses. There is no one-size-fits-all approach to teaching data management, so having a familiarity with the variety of teaching models and methods currently being used is very helpful in designing a teaching strategy that is targeted to your audience. Librarians from three public research universities will describe their developing DM teaching programs, including a credit-bearing graduate course, a workshop series for librarians, and a workshop series for graduate students, research support staff, and investigators. In support of establishing best practices for data management instruction, we will describe successes and challenges in delivery, retention, and customizing materials for particular audiences. We will also compare instructional design, activities, and assessment approaches to identify common, effective strategies across all three. We will invite the audience to guide the panel discussion through a series of group polls.
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    Teaching data literacy skills in a lab environment
    (2014-06-04) Coates, Heather L.
    Equipping researchers with the skills to effectively utilize data in the global data ecosystem requires proficiency with data literacies and electronic resource management. This is a valuable opportunity for libraries to leverage existing expertise and infrastructure to address a significant gap data literacy education. This session will describe a workshop for developing core skills in data literacy. In light of the significant gap between common practice and effective strategies emerging from specific research communities, we incorporated elements of a lab format to build proficiency with specific strategies. The lab format is traditionally used for training procedural skills in a controlled setting, which is also appropriate for teaching many daily data management practices. The focus of the curriculum is to teach data management strategies that support data quality, transparency, and re-use. Given the variety of data formats and types used in health and social sciences research, we adopted a skills-based approach that transcends particular domains or methodologies. Attendees applied selected strategies using a combination of their own research projects and a carefully defined case study to build proficiency.