DataCite

Background: While open datasets are adopting FAIR principles to improve their discovery and use, restricted data—those only accessible via request or application—have fallen behind. Metadata is not an inherent characteristic of restricted data, which limits its ability to be found and used. To better understand discoverability and accessibility of restricted data, this study reviewed restricted health data sources to determine how they describe their datasets and access procedures, what descriptive commonalities exist across data sources, and to what extent the commonalities we found can be accommodated within existing metadata schemas. Methods: This study extracted dataset and access information provided by a sample of 48 restricted data sources, identified commonalities across these data sources to develop possible metadata elements for restricted data, and mapped these metadata elements to existing metadata schemas (e.g., DataCite) to evaluate how well they accommodate information supplied by restricted data sources. Results: Restricted data sources describe their datasets (35 commonalities) and access procedures (27 commonalities) in similar ways. Dataset descriptions aligned with existing metadata schemas, with the DDI-Lifecycle and -Codebook schemas receiving 91.4% and 85.7% exact matches respectively with the dataset elements we identified. Access procedures did not align with metadata available in existing schemas. Discussion: While descriptive dataset metadata for restricted data sources will make their data more findable, the accessibility of these datasets could be significantly improved by structured metadata capturing data access information. Presently, metadata schemas do not accommodate the level of detail restricted data sources provide about access procedures and requirements.

The Implementing FAIR Workflows Project aims to leverage existing persistent identifier infrastructure, research tools, and platforms to build a proof of concept research workflow for neuroscience research that is FAIR on inception. The project will provide an exemplar workflow to enable and encourage the wider neuroscience community to adopt FAIR practices. Making research outputs FAIR and traceable can be accomplished by applying the correct persistent identifiers (PIDs) and creating rich and complete metadata. However, metadata schemas for PIDs, such as the DataCite metadata schema, are kept discipline-agnostic to ensure wide applicability across domains and resource types. Domain-specific metadata serves research communities with increased discoverability by providing more granular and more relevant facets of data and requires a different, integrated solution. As part of the effort to develop domain-specific metadata to make outputs FAIR by adopting semantic standardization and compliance with research community recommendations, the project team has invested in defining and developing a metadata template for cognitive neuroscience studies with human subjects, using the CEDAR Workbench. This document outlines the background, setup, tools used, the process for metadata definition, template building, testing, and community feedback. The template in its current form is presented, along with an overview of the challenges encountered while implementing the template. Future steps entail plans to improve the domain-specific metadata specifications and further evaluate domain ontology building and adoption. We have also identified areas that need improvement, including ease of use of the template interface, and approach to consensus building in the community. Those key future steps will keep the template relevant and useful to the community long term.

Die vorliegende Präsentation gibt einen Ausblick auf die Ergebnisse der “Umfrage zum Bedarf und Nutzung von Organisations-IDs an Hochschulen und außeruniversitären Forschungseinrichtungen in Deutschland” sowie einen Ausblick auf PIDs in weiteren Kontexten im Rahmen des Projekts ORCID DE.

<p>This report is a milestone of the FAIRsFAIR project. The document describes the work that is being done to improve re3data registry to enable researchers identify FAIR enabling repositories to find relevant datasets or deposit their research data. We are taking input from multiple efforts such as CoreTrustSeal plus FAIR alignment of repository practice, FAIR data repositories features and FAIR Object Assessment within FAIRsFAIR to identify specific properties within re3data schema that can be used or extended to capture criteria for FAIR enabling repositories. In parallel, we are also developing APIs to provide access to the re3data metadata and a search application to discover FAIR enabling repositories. The outcome of this is an improved description of repositories, hence enhanced metadata. This work will set the foundation for streamlining assessment and certification through improved organizational and data collection metadata.</p>

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Credit for Data Reuse Is Driven by Making Data FAIR: The PARSEC Project ApproachAuthors Shelley Stall iD Alison Specht iD Margaret O’Brien iD Anne Cambon-Thomsen Laurence Mabile iD Yasuhiro Murayama Pedro Luiz Pizzigatti Corrêa iD Helen Glaves Helena Cousijn Eric Olson iDSee all authors Shelley StalliDCorresponding AuthorAmerican Geophysical UnioniDhttps://orcid.org/0000-0003-2926-8353view email addressThe email was not providedcopy email addressAlison SpechtiDUniversity of QueenslandiDhttps://orcid.org/0000-0002-2623-0854view email addressThe email was not providedcopy email addressMargaret O’BrieniDUniversity of California Santa BarbaraiDhttps://orcid.org/0000-0002-1693-8322view email addressThe email was not providedcopy email addressAnne Cambon-ThomsenUniversity Paul Sabatier Toulouse IIIview email addressThe email was not providedcopy email addressLaurence MabileiDUniversity Paul Sabatier Toulouse IIIiDhttps://orcid.org/0000-0002-7724-1721view email addressThe email was not providedcopy email addressYasuhiro MurayamaNational Institute of Information and Communications Technologyview email addressThe email was not providedcopy email addressPedro Luiz Pizzigatti CorrêaiDUniversity of São PauloiDhttps://orcid.org/0000-0002-8743-4244view email addressThe email was not providedcopy email addressHelen GlavesBritish Geological Surveyview email addressThe email was not providedcopy email addressHelena CousijnDataCiteview email addressThe email was not providedcopy email addressEric OlsoniDCenter for Open ScienceiDhttps://orcid.org/0000-0002-5989-8244view email addressThe email was not providedcopy email address

This interim report describes the approach taken to creating training materials in the FREYA project and provides an overview of the materials created and links to them. Up to this point in the project the team has migrated and update the FREYA Knowledge Hub to a new location on pidforum.org. FREYA has also collaborated with a wide range of stakeholders, including other European projects, to broaden the reach of the training materials and co-organise events which appeal to a broader audience, including co-organising several joint events and creating a guide on personal identifiers. The report also discusses the successes and challenges encountered in creating training materials thus far. In the next six months, prior to submission of the final report, the project will evaluate the training materials created and refine them where possible to better suit users’ needs.The sustainability of the Knowledge Hub content will also be addressed. In addition, with the ongoing development of the PID Graph, further materials will be created to support early adopters and all events will be evaluated to inform and improve future events.

A comprehensive survey of the landscape of persistent identifiers across many disciplines is presented, with assessments of maturity of different PID types and conclusions for the future. [Revised version]

The connections between scholarly resources generated by persistent identifiers (PIDs) and associated metadata form a graph: the PID Graph [Fenner & Aryani (2019)]. We developed this PID Graph concept in the EC-funded FREYA project, and have identified important use cases and technical requirements. In May, DataCite introduced a GraphQL API to standardize and simplify how users can contribute to and consume the PID Graph [Fenner (2019b)].

Today we are announcing our first new functionality of 2019, a much improved search for DataCite DOIs and metadata. While the DataCite Search user interface has not changed, changes under the hood bring many important improvements and are our biggest changes to search since 2012. Faster Indexing Newly registered (and tagged findable) DOIs now appear in the DataCite Search index within a few minutes, compared with the previous up to 12 hour lag.