Category: Data Curation, Open Data, and Research Data Management

Ge Peng et al. have published "Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project" in the Data Science Journal.

Here's an excerpt:

Assessing the stewardship maturity of individual datasets is an essential part of ensuring and improving the way datasets are documented, preserved, and disseminated to users. It is a critical step towards meeting U.S. federal regulations, organizational requirements, and user needs. However, it is challenging to do so consistently and quantifiably. The Data Stewardship Maturity Matrix (DSMM), developed jointly by NOAA's National Centers for Environmental Information (NCEI) and the Cooperative Institute for Climate and Satellites–North Carolina (CICS-NC), provides a uniform framework for consistently rating stewardship maturity of individual datasets in nine key components: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. So far, the DSMM has been applied to over 800 individual datasets that are archived and/or managed by NCEI, in support of the NOAA's OneStop Data Discovery and Access Framework Project. As a part of the OneStop-ready process, tools, implementation guidance, workflows, and best practices are developed to assist the application of the DSMM and described in this paper. The DSMM ratings are also consistently captured in the ISO standard-based dataset-level quality metadata and citable quality descriptive information documents, which serve as interoperable quality information to both machine and human end-users. These DSMM implementation and integration workflows and best practices could be adopted by other data management and stewardship projects or adapted for applications of other maturity assessment models.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

Philip Espinola Coombs et al. have published "Skills, Standards, and Sapp Nelson's Matrix: Evaluating Research Data Management Workshop Offerings" in the Journal of eScience Librarianship.

Here's an excerpt:

Here's an excerpt from the announcement:

Objective: To evaluate library workshops on their coverage of data management topics.

Methods: We used a modified version of Sapp Nelson’s Competency Matrix for Data Management Skills, a matrix of learning goals organized by data management competency and complexity level, against which we compared our educational materials: slide decks and worksheets. We examined each of the educational materials against the 333 learning objectives in our modified version of the Matrix to determine which of the learning objectives applied.

Conclusions: We found it necessary to change certain elements of the Matrix’s structure to increase its clarity and functionality: reinterpreting the behaviors, shifting the organization from the three domains of Bloom’s taxonomy to increasing complexity solely within the cognitive domain, as well as creating a comprehensive identifier schema. We appreciated the Matrix for its specificity of learning objectives, its organizational structure, the comprehensive range of competencies included, and its ease of use. On the whole, the Matrix is a useful instrument for the assessment of data management programming.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

Jessica Van Der Volgen and Shirley Zhao have published "Building A National Research Data Management Course for Health Information Professionals" in the Journal of eScience Librarianship.

Here's an excerpt:

Background: In August 2017 the National Network of Libraries of Medicine Training Office (NTO) was awarded an administrative supplement from the National Library of Medicine (NLM) to create training for librarians in biomedical and health research data management (RDM). The primary goal of the training was to enable information professionals to initiate or enhance RDM at their institutions.

Case Presentation: An eight-week online course was developed to address key concepts in RDM. Each module was organized around measurable learning objectives using existing subject resources, such as readings, tutorials, and videos. Within each module, an expert in the field co-facilitated relevant discussions, created and graded a practical assignment, and answered questions. Thirty-eight participants were selected for this initial cohort. Mentors were assigned to each participant for guidance in completing a required project action plan to further their RDM goals at their institution. The course was evaluated through pre- and post-tests and an online questionnaire.

Results: Thirty participants successfully completed the online course work and project, and gathered at the National Institutes of Health for a Capstone Summit. Students demonstrated improved knowledge of RDM concepts between the pre- and post-tests. Most students also self-reported increased skill and confidence. Practical assignments with individual feedback from experienced data librarians were the most valued aspect of the course. Time to complete each module was underestimated.

Conclusions: The initial offering of this training program improved the RDM skills and knowledge of participants and enabled students to add or enhance services at their institutions. Further investigations are necessary to determine the longer-term impact on the individuals and their libraries. While many of the participants will need additional training to become part of the data-ready workforce of health information professionals, completing this training is an important step in their professional development.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

Gretchen Greene, Raymond Plante, and Robert Hanisch have published "Building Open Access to Research (OAR) Data Infrastructure at NIST" in Data Science Journal.

Here's an excerpt:

As a National Metrology Institute (NMI), the USA National Institute of Standards and Technology (NIST) scientists, engineers and technology experts conduct research across a full spectrum of physical science domains. NIST is a non-regulatory agency within the U.S. Department of Commerce with a mission to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life. NIST research results in the production and distribution of standard reference materials, calibration services, and datasets. These are generated from a wide range of complex laboratory instrumentation, expert analyses, and calibration processes. In response to a government open data policy, and in collaboration with the broader research community, NIST has developed a federated Open Access to Research (OAR) scientific data infrastructure aligned with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Through the OAR initiatives, NIST's Material Measurement Laboratory Office of Data and Informatics (ODI) recently released a new scientific data discovery portal and public data repository. These science-oriented applications provide dissemination and public access for data from across the broad spectrum of NIST research disciplines, including chemistry, biology, materials science (such as crystallography, nanomaterials, etc.), physics, disaster resilience, cyberinfrastructure, communications, forensics, and others. NIST’s public data consist of carefully curated Standard Reference Data, legacy high valued data, and new research data publications. The repository is thus evolving both in content and features as the nature of research progresses. Implementation of the OAR infrastructure is key to NIST’s role in sharing high integrity reproducible research for measurement science in a rapidly changing world.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

Christine L. Borgman has published "The Lives and After Lives of Data" in the Harvard Data Science Review.

Here's an excerpt:

The most elusive term in data science is 'data.' While often treated as objects to be computed upon, data is a theory-laden concept with a long history. Data exist within knowledge infrastructures that govern how they are created, managed, and interpreted. By comparing models of data life cycles, implicit assumptions about data become apparent. In linear models, data pass through stages from beginning to end of life, which suggest that data can be recreated as needed. Cyclical models, in which data flow in a virtuous circle of uses and reuses, are better suited for irreplaceable observational data that may retain value indefinitely. In astronomy, for example, observations from one generation of telescopes may become calibration and modeling data for the next generation, whether digital sky surveys or glass plates. The value and reusability of data can be enhanced through investments in knowledge infrastructures, especially digital curation and preservation. Determining what data to keep, why, how, and for how long, is the challenge of our day.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap