156 research outputs found
MappingTables_ISO19115-1ToCodemeta.csv
No full textThis Table describes a mapping from ISO 19115-1 and 19115-3 to the codemeta vocabulary for software citation. The mapping is from Habermann, 2019, Mapping ISO 19115-1 geographic metadata standards to codemeta, PeerJ.
The columns are: Schema,Property,Type,Description,ISO 19115-1,ISO 19115-3</p
Figure 1 from: Habermann T (2020) Metadata 2020 Metadata Evaluation Projects. Research Ideas and Outcomes 6: e54176. https://doi.org/10.3897/rio.6.e54176
No full textFigure 1 Metadata dialects, recommendations, and communities
Figure 3 from: Habermann T (2020) Metadata 2020 Metadata Evaluation Projects. Research Ideas and Outcomes 6: e54176. https://doi.org/10.3897/rio.6.e54176
No full textFigure 3 Schematic diagram of Metadata 2020 Project 6: Metadata Evaluation and Guidance
Think Globally, Act Locally: The Importance of Elevating Data Repository Metadata to the Global Infrastructure
No full textInconsistent and incomplete applications of metadata standards and unsatisfactory approaches to connecting repository holdings across the global research infrastructure inhibit data discovery and reusability. The Realities of Academic Data Sharing (RADS) Initiative has found that institutions and researchers create and have access to the most complete metadata, but that valuable metadata found in these local institutional repositories (IRs) are not making their way into global data infrastructure such as DataCite or Crossref. This panel examines the local to global spectrum of metadata completeness, including the challenges of obtaining quality metadata at a local level, specifically at Cornell University, and the loss of metadata during the transfer processes from IRs into global data infrastructure. The metadata completeness increases over time, as users reuse data and contribute to the metadata. As metadata improves and grows, users find and develop connections within data not previously visible to them. By feeding local IR metadata into the global data infrastructure, the global infrastructure starts giving back in the form of these connections. We believe that this information will be helpful in coordinating metadata better and more effectively across data repositories and creating more robust interoperability and reusability between and among IRs.Taylor, Shawna; Wright, Sarah; Narlock, Mikala R.; Habermann, Ted. (2022). Think Globally, Act Locally: The Importance of Elevating Data Repository Metadata to the Global Infrastructure. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/228001
Persistent identification of instruments
No full textInstruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements
Metadata 2020 Metadata Evaluation Projects
No full textMetadata 2020: a cross-community collaboration that advocates richer, connected, reusable, and open metadata for all research outputs to advance scholarly pursuits for the benefit of society. A group of volunteers working together trying to encourage and facilitate progress towards this challenging goal. Management guru Peter Druker famously said "If you can't measure it, you can't improve it". With that in mind, several Metadata 2020 projects examined approaches to metadata evaluation and connections between evaluation and guidance. Accomplishing this progress across the broad expanse of the Metadata 2020 landscape requires connecting metadata dialects and community recommendations and analysis of multiple metadata corpora. This paper describes one framework for approaching that task and some potential examples
INFORMATE Project - CHORUS Report Summaries - 20231106
No full text<p>These data provide a summary of the All, Author Affiliation, and Dataset Reports generated by the <a href="https://dashboard.chorusaccess.org/">CHORUS Dashboard</a> for three agencies: the U.S. National Science Foundation, U.S. Geological Survey, and the U.S. Agency for International Development. The reports summarized here was collected on November 6-7, 2023 as part of the INFORMATE Project funded by NSF.</p><p>The columns are:</p><p>Column Definition</p><p>agency The funding agency [NSF, USGS, or USAID]</p><p>date. The date of data retrieval (YYYYMMDD)</p><p>report. The report [all, authors, datasets]</p><p>Property Name of the column in the input file</p><p>count Number of values (rows) of the property</p><p>unique Number of unique values of the property</p><p>top Most common value of the property</p><p>freq Number of occurrences (frequency) of the most common value</p><p>Count % The percentage of rows that include the property</p>
Affiliation Homogeneity Index
No full textThe nature of affiliations in DataCite repositories varies significantly... Can we predict ease of ROR adoption? This presentation describes an index for measuring affiliation homogeneity and shows some examples.
Some DataCite Repositories have very homogeneous affiliations and adopting RORs for these repositories is relatively easy… If affiliations are there…!
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Metadata 2020@FORCE18
No full textMetadata2020 is a group of volunteers from many communities that are coming together to try to improve scholarly communications by improving the metadata that is the lifeblood of the system. Metadata 2020 started by convening researchers, funders, librarians, service providers, data publishers and repositories, and journal publishers to identify metadata challenges. Six projects that emerged from that discussion are described along with a diagram that shows how metadata needs and incentives identified by researchers flow through the system to motivate improvements that address those needs through better systems and guidance.
After the projects were described workshop participants got together to play The Metadata Game as a tool for opening up channels of communication across the metadata life cycle. Ten teams collaborated to build complete metadata repositories based on the FAIR principles.</p
LTER Sites: Evaluate, Analyze, Report
No full text<p>This package contains the Jupyter Notebooks that drive the python script, XSL, and associated scripts and files that create the collections of LTER sites metadata, evaluate them and analyze them for recommendation completeness. These yearly collections are comprised of the most recent version of any metadata record uploaded that year. We do this to try study how a community serves the information needs of its users over time. We choose LTER because they have many data providers and have done a lot of work to provide the community with guidance and tools over the past 14 years. Does this focus on recommendation guidance create a consistent documentation of datasets? When a community is given the tools to evaluate their metadata with community best practices, does the consistency improve? Does the recommendation completeness improve? What shape do the changing information needs make?</p>
<p>Unless contents were previously licensed, dataset files are shared under a Creative Commons Non-Commercial Attribution Share-Alike 4.0 International License.</p>
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