Uploaded on:2019-11-07
Lehväslaiho, Heikki; Parland-von Essen, Jessica; Riungu-Kalliosaari, Leah; Behnke, Claudia; Laine, Heidi; Staiger, Christine; Koers, Hylke; LeFranc, Yann

As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The work package "WP2 FAIR Practices: Semantics, Interoperability, and Services" will produce three reports on FAIR requirements for persistence and interoperability to identify domain-specific standards and practices in use. These will review and document commonalities and possible gaps regarding semantic interoperability, and the use of metadata and persistent identifiers across infrastructures. They will also look into differences in terms of standards, vocabularies and ontologies. The collected information will be updated during the course of the project in cooperation with other tasks and EOSC projects. This survey was done to complement and validate the information from desk research for the first of these reports. It was aimed at data managers and data support experts. We hoped to get information about tools and services we might have missed, but also some reflections on the thinking around identifiers and ontologies and other semantic artefacts. The information was also collected to support preparing workshops on semantics and interoperability that are forthcoming in the project, as well as the work on software and services. The survey covers questions about metadata, use of persistent identifiers, use of semantic artefacts and handling research software. The survey was conducted as a joint effort with WP3, FAIR Policy and Practice and its open consultation, and was disseminated on the fairsfair.eu web pages, social media channels and via email lists. We received 66 answers during the period the survey was open, that is between 15 July to 2 October 2019.

DOI: 10.5281/zenodo.3518921

Uploaded on:2019-10-22
Coen, Gerard

Presentation given at the FAIRsFAIR Workshop on "FAIR Semantics" 22 Oct 2019. This presentation introduces people to the idea of 'Semantic Artefacts' and highlights a relationship between the complexity of formats in Tim Berners-Lee’s Five Star Open Data Model and the complexity of Knowledge Organization Systems (KOS) and other Semantic Artefacts such as Data Vocabularies, Code, Code Lists, Identifiers (PIDs), Standards (e.g. Unicode). It briefly highlights benefits, issues, and the challenges for sustainability of these resources.

DOI: 10.5281/zenodo.3549374

Uploaded on:2019-11-29
Lehväslaiho, Heikki; Parland-von Essen, Jessica; Behnke, Claudia; Laine, Heidi; Le Franc, Yann; Staiger, Christine

This document is the first iteration of three annual reports on the state of FAIR in European scientific data by the FAIRsFAIR project. The interpretation of the FAIR data principles and their implications for services are now under intense scrutiny across Europe with multiple possible outcomes. The report is based on studies of public information, especially EOSC infrastructure efforts, and on limited surveying and interviews. The focus has been on understanding the usage of persistent identifiers and semantic interoperability. This study highlights the rapidity of change in technical solutions and wide variation across scientific domains in the uptake. More efforts are needed to guide researchers in best practices. This report is the first of three of a kind to be produced by the FAIRsFAIR project. This deliverable reviews and documents commonalities and possible gaps regarding semantic interoperability, and the use of metadata and persistent identifiers across infrastructures. Since many landscaping, specification and “FAIRification” activities are ongoing in the EOSC projects and elsewhere, much new information will be added to the later versions. The authors hope to get feedback to enrich and adjust the observations and conclusions made in this document. FAIR Digital Objects are central to the realisation of FAIR data principles. These objects need to be accompanied by Persistent Identifiers (PIDs) and rich metadata as they sit in a wider FAIR ecosystem comprising of services and infrastructures for FAIR, including identifiers, standards and repositories. The details of the FAIR principles for data, the implementation and implications for services are neither defined nor settled yet. The first suggestions for a more specific definition of a FAIR Digital Object has only recently been presented and will be further tested within the FAIRsFAIR project. Implications of the FAIR data principles for services, repositories and software are being investigated in other FAIRsFAIR tasks. Thus, this report focuses on semantic interoperability as it is a prerequisite for linking and finding data, as well as on the identifiers, which can offer persistence but also need context sensitive solutions. We use the term semantic artefact to overcome the terminological diversity that ironically is a challenge in discussions on this important element of the architecture we need in order to enable semantic interoperability within a FAIR Ecosystem. Development and implementation of the FAIR data principles should be driven by researcher needs to achieve wide penetration and the potentially significant benefits of FAIR data. The differences within research domains are often bigger than between them. Enforcing standards comes with the risk of making gaps grow between mature and emerging research domains. Community adoption and trust are decisive factors. Enabling services for publishing crosswalks, mappings and semantic application profiles are needed. All these should be registered and published in machine readable formats. A challenge with PID and data type registries is having them to promote reuse of data rather than bulk creation of PIDs. To support interoperability, they should be considered semantic artefacts, curated and reused. The aim should be born-FAIR data, which requires integrated and user friendly solutions throughout the research process and data lifecycle. By publishing application profiles, preferably in a common registry and in a machine readable format, reuse of semantic artefacts can be promoted, thereby enabling interoperability. Also curated registries like the EOSC Hub, FAIRsharing and re3data.org are important resources for enabling implementation of the FAIR data principles.

DOI: 10.5281/zenodo.3518922

DOI: 10.5281/zenodo.3557380

Uploaded on:2019-11-21
Herterich, Patricia; Davidson, Joy; Grootveld, Marjan; Whyte, Angus; Molloy, Laura; Matthews, Brian; Kayumbi Kabeya, Gabin

As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. WP6 "FAIR Competence Centre" will establish a Competence Centre that will serve as a nexus between the FAIRsFAIR project and the communities, encouraging a 2-way communication to represent the community to solution providers. It will then have the opportunity to identify synergies across communities and promote harmonisation and coordination of efforts across communities, building on the progress of others. To get an overview of the current landscape of competence centres, we characterised 36 of them using broad concepts and categories for stakeholders and services to identify major characteristics and gaps. Types, partners, subject, target users, services, and resources have been characterised using controlled vocabulary; strength and gaps are free text fields where notes could be provided if aspects stood out for certain initiatives. A work in progress version of the spreadsheet is available at https://docs.google.com/spreadsheets/d/1oJ-bKAYBeDbkdx7lqThzK0KIGrFJlZKfdAu3sZR667w/edit?usp=sharing where additional competence centres can be syggested.

DOI: 10.5281/zenodo.3549791

DOI: 10.5281/zenodo.3549660

Uploaded on:2019-12-03
Davidson, Joy; Engelhardt, Claudia; Proudman, Vanessa; Stoy, Lennart; Whyte, Angus

As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The FAIRsFAIR project runs from March 2019-February 2022. Policies are a crucial component in the FAIR ecosystem. To this end, FAIRsFAIR Work Package 3 (WP3): FAIR Data Policy and Practice carried out an analysis of the current data policy landscape at various levels (national, funder, publisher, institutional) to provide a snapshot of the situation in 2019 and to identify policy elements that support or hinder FAIR data practice. To provide a comparative baseline for reviewing the data policies of various stakeholders, the priority and supporting actions presented in the Turning FAIR into Reality (TFiR) action plan were employed. To assess how well the policies of different stakeholders currently reflect TFiR’s action plan, we carried out desk research to characterise policies, undertook an analysis of responses to an open consultation, and conducted a small number of interviews. This report presents the findings of these landscape assessment activities and provides an evidence base for FAIRsFAIR to build upon as work begins to define a set of practical recommendations to support policy enhancement (D3.3). Landscaping activities have been a core activity during the initial stages of the FAIRsFAIR project and there has been close cooperation with colleagues in Work Package 3 carrying out the FAIR data policy and practice analyses; Work Package 2 on assessing FAIR requirements for interoperability and persistence; Work Package 6 on providing an overview of research communities’ needs for competence centres; and Work Package 7 on mapping RDM policies and support as well as FAIR education offerings in European HEIs. In particular, efforts were made to avoid duplication of effort across the three open consultation and survey instruments developed to assess the current landscape and to define a consistent approach to presenting our findings.

DOI: 10.5281/zenodo.3558172