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Cancer is a fatal disease and one of the leading causes of death worldwide. The cure rate in cancer treatment remains low; hence, cancer treatment is gradually shifting toward personalized treatment. Artificial intelligence (AI) and radiomics have been recognized as one of the potential areas of research in personalized medicine in oncology. Several researchers have identified the capabilities of AI and radiomics to characterize phenotype and there by predict the outcome of treatment in oncology. Although AI and radiomics have shown promising initial results in diagnosis and treatment in oncology, these technologies are also facing challenges of standardization and scalability. In the last few years, researchers have been trying to develop a research infrastructure for federated machine learning that increases the usability of Big Data for clinical research. These research infrastructures are based on the findable, accessible, interoperable, and reusable (i.e., FAIR) data principles. The India-Dutch "big imaging data approach for oncology in a Netherlands India collaboration" (BIONIC) is a jointly funded initiative by the Dutch Research Council (NWO) and the Indian Ministry of Electronics and Information Technology (MeitY), aiming to introduce radiomic-based research into clinical environments using federated machine learning on geographically dispersed collections of FAIR data. This article described a prototype end-to-end research infrastructure implemented through the BIONIC partnership into a leading cancer care public hospital in India.

Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable.The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral.FAIR is evolving in some expected and some unexpected ways. FAIR “Reusability” sub-principle R1.3 states that "(meta)data should meet domain-relevant community standards," which predicts a proliferation of FAIR interpretations by individual communities as they select their preferred approach to FAIRness. Similarly, as expected, there is an active movement around the adaptation of the FAIR Principles to digital objects other than data (e.g., software and workflows), again with individual communities interpreting what FAIRness means in these expanded contexts. However, there have also been attempts to expand the FAIR Principles themselves in recent years, including features of digital objects beyond reusability, including popularity (reuse/citation), reproducibility, reliability, data quality, etc. All of this is occurring with no overall coordination or planning.A range of FAIR assessment metrics and tools have been designed that measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expectedbehaviours. While it is anticipated that communities will define domain-specific FAIR metrics and tests, it is desirable to avoid "gaming the system" and have broadly agreed-upon approaches to FAIRness that do not favour a specific implementation of technology.These observations suggest a growing need to align the different interpretations of the FAIR Principles. However, this whitepaper does not suggest that the FAIR Principles themselves require governance. Indeed, the document argues that the Principles should remain untouched. Specialised communities should extend/edit those Principles to adapt and make them more relevant to their community and their specific research outcome intended to be FAIR.This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners - and provides examples outlining specific stakeholders' (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits.This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable.

This study explores the possibility of opening a policy window for the adoption of the FAIR Guidelines— that data be Findable, Accessible, Interoperable, and Reusable (FAIR)—in Uganda's eHealth sector. Although the FAIR Guidelines were not mentioned in any of the policy documents relevant to Uganda's eHealth sector, the study found that 83% of the documents mentioned FAIR Equivalent efforts, such as the adoption of the National Identification Number (NIN) as a unique identifier in Uganda's national Electronic Health Management Information System (eHMIS) (findability), the planned/ongoing integration of various information systems (interoperability), and the alignment of various projects with international best practices/standards (reusability). A FAIR Equivalency Score (FE-Score), devised in this study as an aggregate score of the mention of the equivalent of FAIR facets in the policy documents, showed that the documents at the core of Uganda's digital health/eHealth policy have the highest score of all the documents analysed, indicating that there is a degree of alignment between Uganda's National eHealth Vision and the FAIR Guidelines. Therefore, it can be concluded that favourable conditions exist for the adoption and implementation of the FAIR Guidelines in Uganda's eHealth sector. Hence, it is recommended that the FAIR community adopt a capacity building strategy through organisations with a worldwide mandate, such as the World Health Organization, to promote the adoption of the FAIR Guidelines as part of international best practices.

This document is work in progress. A guide to make research outputs FAIR for image and microscopy researchers.

The long-term preservation of digital objects, and the means by which they can be ReUsed, are addressed by both the FAIR Data Principles (Findable, Accessible, Interoperable, ReUsable) and a number of standards bodies providing Trustworthy Digital Repository (TDR) certification, such as the CoreTrustSeal. Upon application to the CoreTrustSeal, repositories are assessed against 16 Requirements which cover organisational infrastructure, digital object management and technical provision. To adequately address these areas (and to assess them) both the repository and the CoreTrustSeal require an understanding of the repository’s designated community and the technical environment that it operates in. Work related to the European Open Science Cloud (EOSC), which aims to provide the research community with access to FAIR data and interoperable data services, has recognised the value of the CoreTrustSeal as a certification requirement for repositories in providing assurance that data and metadata become, and remain, FAIR over time. The EOSC has sought to align its practice, and the practice of participating institutions, with the CoreTrustSeal Requirements to ensure Trustworthy storage of FAIR data. Though many of the requirements listed in the CoreTrustSeal Extended Guidance address the FAIR Data Principles indirectly, there is currently no formal ‘FAIR Certification’ offered by the CoreTrustSeal or other TDR standards bodies such as ISO16363 and nestorSeal. The FAIRsFAIR project has developed the CoreTrustSeal+FAIRenabling Capability Maturity model (CTS+FAIR CapMat) as a first step in aligning the CoreTrustSeal Requirements and the FAIR Data Principles. A FAIR-Enabling Trustworthy Digital Repositories-Capability Maturity Self Assessment template has also been developed to allow repositories to ascertain their current levels of capability and maturity in relation to both the FAIR Data Principles and TDR best practices. The CTS+FAIR CapMat addresses a number of areas concerning ReUsability including the provision of Licence Information (R02), Data Integrity and Authenticity (R07) and Data ReUse (R14), alongside other Core requirements. Another output of the FAIRsFAIR project is the F-UJI tool, which is a web-based tool designed to assess the FAIRness of research data objects. Using the tests developed within F-UJI, the FAIR Data Assessment Toolset and Badging Scheme was also created, providing metrics and a series of Practical Tests. Repositories can use this toolset to assess the FAIRness of their practice and the data they preserve, in turn providing a publicly available means to increase the ReUsability of data, amongst other benefits. The above outcomes are underpinned by the mapping conducted in the FAIR Principles: Baseline Comments, which maps the FAIR Data Principles, RDA Indicators and the Metrics and Practical Tests developed within FAIRsFAIR, providing repositories with a further resource who wish to develop their FAIR practises. However, increasing the ReUsability of data across the digital preservation landscape is not only dependent on the availability of adequate methods, but also their uptake. This requires a community-wide commitment to develop best practices to increase the ReUse of data and community consensus on what these practices are. Once reached, the best practices can be incorporated within the requirements of the different TDR certification bodies such as the CoreTrustSeal. A possible way of achieving such a consensus would be through the creation of a network of FAIR-enabling TDRs, as proposed by FAIRsFAIR. Such a network could help ensure that specific and minimal measures are in place to provide evidence that the relevant designated community (as a subset of the wider community of users) are consulted on the necessary requirements to ensure the availability of data for ReUse., FAIRsFAIR "Fostering FAIR Data Practices In Europe" has received funding from the European Union's Horizon 2020 project call H2020-INFRAEOSC-2018-2020 Grant agreement 831558.

IASSIST 2022 Presentation by the Data Sharing for Demographic Research (DSDR) topical archive within ICPSR discussing how deposit options can enhance the FAIR principles

The CoreTrustSeal+FAIRenabling Capability Maturity (CapMat) model aligns the CoreTrustSeal Requirements with the FAIR Data Principles, allowing repositories to self-assess their practice and associated evidence with a view to their development and improvement. The FAIR Data Principles define the expectation that digital objects should be findable, accessible, interoperable and re-usable. Repositories provide the organisational context for enabling FAIR data. Trustworthy digital repositories (TDR), such as those certified to the CoreTrustSeal, offer long-term digital preservation services that can ensure digital assets remain FAIR over time. This text describes the alignment between the 15 FAIR Data Principles and the 16 CoreTrustSeal Requirements so that repositories can self assess their trustworthiness and FAIR enabling status together. The associated capability-maturity model measures the repository in terms of the policies and procedures used to deliver (meta)data services. FAIR enabling, trustworthy digital repositories are acknowledged as key nodes in the research data lifecycle and in networks of federated data infrastructures, including the European Open Science Cloud (EOSC). The community development of indicators, metrics and automated systems for assessing compliance with the FAIR Data Principles is ongoing and to date, no formal standard, process and governance structure is in place to certify FAIR objects or FAIR enabling repositories. The CoreTrustSeal+FAIRenabling CapMat self-assessment can be used immediately by repositories seeking to to identify current levels of capability and to plan for increased maturity. Applying this approach supports the readiness of a repository for formal CoreTrustSeal certification. Other types of data service can also use it to prepare for future assessments of FAIR enabling capability. Development of the CoreTrustSeal+FAIRenabling CapMat model within FAIRsFAIR has benefited from a range of internal perspectives including policy administration, data services, repository support, registry developments, and guidance and testing for object FAIRness. Iterations of the model have been improved through engaged and informed public feedback. This paper presents guidance for those applying the CoreTrustSeal+FAIRenabling CapMat model and provides detailed alignment of the CoreTrustSeal Requirements, FAIR Principles and Capability-Maturity levels. Concluding remarks reflect on the next steps for FAIRenabling TDRs, trustworthy data services and FAIR certification.

The CoreTrustSeal+FAIRenabling Capability Maturity (CapMat) model aligns the CoreTrustSeal Requirements with the FAIR Data Principles, allowing repositories to self-assess their practice and associated evidence with a view to their development and improvement. The FAIR Data Principles define the expectation that digital objects should be findable, accessible, interoperable and re-usable. Repositories provide the organisational context for enabling FAIR data. Trustworthy digital repositories (TDR), such as those certified to the CoreTrustSeal, offer long-term digital preservation services that can ensure digital assets remain FAIR over time. This text describes the alignment between the 15 FAIR Data Principles and the 16 CoreTrustSeal Requirements so that repositories can self assess their trustworthiness and FAIR enabling status together. The associated capability-maturity model measures the repository in terms of the policies and procedures used to deliver (meta)data services. FAIR enabling, trustworthy digital repositories are acknowledged as key nodes in the research data lifecycle and in networks of federated data infrastructures, including the European Open Science Cloud (EOSC). The community development of indicators, metrics and automated systems for assessing compliance with the FAIR Data Principles is ongoing and to date, no formal standard, process and governance structure is in place to certify FAIR objects or FAIR enabling repositories. The CoreTrustSeal+FAIRenabling CapMat self-assessment can be used immediately by repositories seeking to to identify current levels of capability and to plan for increased maturity. Applying this approach supports the readiness of a repository for formal CoreTrustSeal certification. Other types of data service can also use it to prepare for future assessments of FAIR enabling capability. Development of the CoreTrustSeal+FAIRenabling CapMat model within FAIRsFAIR has benefited from a range of internal perspectives including policy administration, data services, repository support, registry developments, and guidance and testing for object FAIRness. Iterations of the model have been improved through engaged and informed public feedback. This paper presents guidance for those applying the CoreTrustSeal+FAIRenabling CapMat model and provides detailed alignment of the CoreTrustSeal Requirements, FAIR Principles and Capability-Maturity levels. Concluding remarks reflect on the next steps for FAIRenabling TDRs, trustworthy data services and FAIR certification.

The FAIR (Findable, Accessible, Interoperable, Reproducible) Data Principles were formally conceptualised by the FORCE11 FAIR Data Publishing Group following the original Lorentz Workshop entitled Jointly Designing a Data FAIRport which was held in January, 2014. This spreadsheet provides a tabular comparison of five publicly available versions of the FAIR Data Principles which were published following their initial conceptualisation by the FORCE11's FAIR Data Publishing Group. The five publicly available versions of the FAIR Data Principles are contrasted with the FAIR Data Principles that are listed on the FORCE11 FAIR Data Publishing Group's webpage. See the 'Further notes' section in the spreadsheet���s cover page for further information relating to the provenance of the FORCE11 FAIR Data Principles; links to archived Images of the FORCE11 FAIR Data Publishing Group���s webpage are provided in the Footnotes. The wording of the Principles in all six versions of the FAIR Data Principles presented in this spreadsheet are current as of the date that this spreadsheet was published., Column headers for all six versions of the FAIR Data Principles have been hyperlinked to the relevant resource. All URLs were active at the time of publication. The authors are not responsible for the maintenance and/or content of said third-party URLs., {"references":["Dunning, A., de Smaele, M., & Boehmer, J. (2020). Are the FAIR Data Principles fair? International Journal of Digital Curation, 12(2), 177-195. https://doi.org/10.2218/ijdc.v12i2.567","FORCE11 (2022) The FAIR Data Principles, Available at: https://www.force11.org/group/fairgroup/fairprinciples [accessed 18-02-2022","Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18","Wikipedia (2022) FAIR Data, Available at: https://en.wikipedia.org/wiki/FAIR_data [accessed 18-02-2022]","GO FAIR (2022) FAIR Principles, Available at: https://www.go-fair.org/fair-principles/ [accessed 19-01-2022]","GO FAIR US (2022) FAIR Principles, Available at: https://gofair.us/principles/ [accessed 18-02-2022]","RDA FAIR Data Maturity Model Working Group (2020): FAIR Data Maturity Model. Specification and Guidelines. https://doi.org/10.15497/rda00050"]}

This is the technical report for the'Making it FAIR'report.was supported by the Arts and Humanities Research Council’s Towards a National Collection programme (TaNC) as part of UKRI’s call for COVID-19 projects. The project responded to challenges faced by smaller museums struggling to engage online with audiences during lockdown, and beyond. The difficulties faced by these smaller museums (and many larger ones too) mattered to AHRC’s aspirations for the digital humanities, because they would leave a huge amount of potential source material simply unavailable to researchers. In the team’s experience, too much museum activity relating to digitised collections was resulting in outputs that did not meet the FAIR data principles (data should be Findable, Accessible, Interoperable and Reusable). &nbsp

Making It FAIR was supported by the Arts and Humanities Research Council’s Towards a National Collection programme (TaNC) as part of UKRI’s call for COVID-19 projects. The project responded to challenges faced by smaller museums struggling to engage online with audiences during lockdown, and beyond. The difficulties faced by these smaller museums (and many larger ones too) mattered to AHRC’s aspirations for the digital humanities, because they would leave a huge amount of potential source material simply unavailable to researchers. In the team’s experience, too much museum activity relating to digitised collections was resulting in outputs that did not meet the FAIR data principles (data should be Findable, Accessible, Interoperable and Reusable). The project team drew on academic researchers, museum sector support organisations and commercial IT practitioners, each bringing different skills and perspectives to bear on both the action and research sides of the work. Project Partners included: University of York, Museum of London Archaeology, Culture24, Collections Trust, The Audience Agency, Intelligent Heritage and Knowledge Integration. Making it FAIR was framed as a research project wrapped around an action project. Between January and September 2021, the project team worked with a cohort of eight small museums as they navigated the challenges of staying connected with existing audiences, and reaching new audiences, through collections-focussed digital content (the Action Project). The cohort received training, mentoring and technical support to plan and carry out digital storytelling experiments. The Action Project methodology was built around the Let's Get Real collaborative action research approach developed by Culture24 over a number of previous projects, but adapted for delivery online in a time of home-working and social distancing. The Research Project consisted of a core collaborative action research study which included a socio-technical challenge: as the participants encountered difficulties along the way, the project team responded where possible and prototyped simple tools that demonstrate how a fully developed infrastructure might support the smallest and least resourced museums. The methodology concluded with a critical evaluation of the experiences of all involved, reflecting on the implications for Towards a National Collection and AHRC’s longer-term planning of research infrastructure. By considering a fully rounded picture of the digital problems faced by small museums, the project revealed insights into the scope and nature of the national infrastructure challenge, which may be missed with the current focus on well-resourced Independent Research Organisations(IROs) and resulted the following project and strategic recommendations. Version 2 includes updated DOI link for footnote 17:https://doi.org/10.5281/zenodo.5770760.

A tremendous number of new chemical entities are discarded in drug development because candidate drugs prove to be toxic, even when potential drugs show excellent efficacy. Broadly sharing toxicology knowledge would: • Lower the attrition rate in drug development • Significantly reduce animal testing • Accelerate the development of novel drugs. The IMI FAIRplus project demonstrated how the eTOX treasure trove of toxicology data could be made more Findable, Accessible, Interoperable and Reusable (FAIR) for drug development, arrived at lessons learned and developed a generic ‘recipe’ for the underlying ontology mapping.

Open Science, also der offene Zugang zu wissenschaftlichen Ergebnissen, ist eine politische Priorit��t der EU, die im aktuell anlaufenden Rahmenprogramm Horizon Europe umgesetzt werden soll. Die Erwartungen sind, dass Forschung damit dynamischer, transparenter und effizienter wird. Dies soll in allen Forschungsbereichen realisiert werden. Open Science wird somit auch in der gemeinsamen europ��ischen Luftfahrtforschung ihren Einzug halten ��� eine der wichtigsten Quellen f��r Forschung und Innovation in diesem prestigetr��chtigen und wirtschaftlich bedeutenden, international hart umk��mpften Industriesektor. Es stellt sich die Frage: Ist diese Idee genial, da sie der Luftfahrt neuen Schwung, neue Technologien und neue Gesch��ftsm��glichkeiten bringen wird? Oder stellt es im Gegenteil ein geradezu wahnsinniges Unterfangen dar, das den technologischen Vorsprung und damit die ausgezeichnete, hart erk��mpfte Position der europ��ischen Luftfahrtindustrie auf dem Weltmarkt gef��hrden wird?, Hamburg Aerospace Lecture Series --- Collection of Presentations --- http://www.AeroLectures.de

(Quantitative) structure-activity relationship ([Q]SAR) methodologies are widely applied to predict the (eco)toxicological effects of chemicals, and their use is envisaged in different regulatory frameworks for filling data gaps of untested substances. However, their application to the risk assessment of nanomaterials is still limited, also due to the scarcity of large and curated experimental datasets. Despite a great amount of nanosafety data having been produced over the last decade in international collaborative initiatives, their interpretation, integration and reuse has been hampered by several obstacles, such as poorly described (meta)data, non-standard terminology, lack of harmonized reporting formats and criteria. Recently, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles have been established to guide the scientific community in good data management and stewardship. The EU H2020 Gov4Nano project, together with other international projects and initiatives, is addressing the challenge of improving nanosafety data FAIRness, for maximizing their availability, understanding, exchange and ultimately their reuse. These efforts are largely supported by the creation of a common Nanosafety Data Interface, which connects a row of project-specific databases applying the eNanoMapper data model. A wide variety of experimental data relating to characterization and effects of nanomaterials are stored in the database; however, the methods, protocols and parameters driving their generation are not fully mature. This article reports the progress of an ongoing case study in the Gov4nano project on the reuse of in vitro Comet genotoxicity data, focusing on the issues and challenges encountered in their FAIRification through the eNanoMapper data model. The case study is part of an iterative process in which the FAIRification of data supports the understanding of the phenomena underlying their generation and, ultimately, improves their reusability.

FAIRsFAIR has asked projects and EOSC partners to share their experience and lessons learnt in this series of “FAIR Adoption Stories”. These stories focus on how projects have utilised FAIRsFAIR tools, services, recommendations and guidelines. This is the first story of the series, highlighting ExPaNDS project's journey in becoming more FAIR.

FAIRsFAIR has asked projects and EOSC partners to share their experience and lessons learnt in this series of “FAIR Adoption Stories”. These stories focus on how projects have utilised FAIRsFAIR tools, services, recommendations and guidelines. This is the first story of the series, highlighting ExPaNDS project's journey in becoming more FAIR.

This poster describes the development of a testbed for FAIR Digital Objects, consisting of an ecosystem of interacting services to demonstrate mandatory and optional FAIR use cases and to identify gaps in the specifications. Preprocessing data for research, like finding, accessing, unifying or converting, takes up to 80% of research time spans. The FAIR (Findability, Accessibility, Interoperability, Reproducibility) principles aim to support and facilitate the reuse of data, and are therefore tackling this problem. A FAIR Digital Object (FAIR DO) is one way to capsule research data resources of all kinds (raw data, metadata, software, ...) so they are following the FAIR principles. A FAIR DO ecosystem can be regarded as a set of services to enable the creation and use of such FAIR DOs. Besides basic functionality like PID management and PID record validation, it may also offer assistive services, i.e. for the automated building of a search index to allow reverse-searching of PIDs. To establish a FAIR DO ecosystem, it must reach a certain level of maturity in order to be used productively. To identify gaps in specifications and concepts during development and use, as well as to demonstrate necessary and optional use cases, we developed a testbed for FAIR use cases as a part of the HMC project, which is easy to set up and run on everyday computers. Currently, the testbed enables PID record management and validation using a PIT service implementation following the RDA PID Information Types (PIT) Working Group Recommendations and an externally hosted Data Type Registry following the RDA Data Type Registry Working Group Recommendations . It also features automated indexing of PID records (proof-of-concept), and provides an implementation of the Collection API specification, which was published by the corresponding RDA Research Data Collections Working Group . The most important gap identified, and the most difficult to close, is the design and specification of the profiles that determine the content of PID records. Software in use by researchers will need to use these contents to determine whether and to what extent it can use a FAIR Digital Object, so this gap needs to be strongly considered.The testbed development has been supported by the research program ‘Engineering Digital futures’ of the Helmholtz Association of German Research Centers and the Helmholtz Metadata Collaboration Platform.

Data are currently being used, and reused, in ecological research at an unprecedented rate. To ensure appropriate reuse however, we need to ask the question: "Are aggregated databases currently providing the right information to enable effective and unbiased reuse?" We investigate this question, with a focus on designs that purposefully favor the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those designs that have uneven inclusion probabilities or are stratified. We perform a simulation experiment by creating data sets with progressively more uneven inclusion probabilities and examine the resulting estimates of the average number of individuals per unit area (density). The effect of ignoring the survey design can be profound, with biases of up to 250% in density estimates when naive analytical methods are used. This density estimation bias is not reduced by adding more data. Fortunately, the estimation bias can be mitigated by using an appropriate estimator or an appropriate model that incorporates the design information. These are only available however, when essential information about the survey design is available: the sample location selection process (e.g., inclusion probabilities), and/or covariates used in their specification. The results suggest that such information must be stored and served with the data to support meaningful inference and data reuse.

Online presentation at the2nd Basel Sustainable Publishing Forum - The Global Transition to Open Access: Challenges and Solutions on October 27, 2020.

UBC Workshop 4: FAIR data and SPARQLing Genomics How far can you go to make your data FAIR? Presentation: This session will shortly introduce the FAIR data principles and the efforts to realise them. Showcasing the differences between FAIRness on data-set level vs. FAIRness on data-file level, the challenges and thresholds will be discussed. Lastly, one of the options to create FAIR data - namely producing linked data - will be illustrated with the example of RDF and SPARQL. Hands-On: The first exercise offers to play around the ARDC FAIR data assessment tool to understand how data from different domains differ in their FAIRness. The second and longer interactive session will provide the SPARQLing Genomics instance of the Center for Molecular Medicine to play around with SPARQL and get an idea how RDF works. This session is wrapped up with a discussion and Q&A section. In Detail: Session: FAIR data and SPARQLing Genomics Presentation: -Fair principles explained - FAIR data two ways - FAIR data applied - Fair enough? Interactive Exercises: - FAIR data assessment tool exploration - SPARQLing Genomics hands-on Session Plan, Thursday 19th December 2019: 15:00-16:00 Intro Ice-Breaker + Grouping Presentation Break FAIR data Exercise Exercise Discussion 16:00-17:00 Hands-on time for SPARQLing Genomics 17:00 Snack and Drinks, The PDF version depicts the original slide-set, PPTX and ODP for reusing slides.

Brief explanation of the FAIR principles.

Brief explanation of the FAIR principles.

The SHARC (SHAring Reward & Credit) interest group (IG) is an interdisciplinary group set up in the framework of RDA (Research Data Alliance) to improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Notably, one of the objectives is to promote data sharing activities in research assessment schemes at national and European levels. To this aim,the RDA-SHARC IG is developing assessment grids using criteria to establish if data are compliant to the FAIR principles (findable /accessible / interoperable / reusable). The grid is aiming to be extensive, generic and trans-disciplinary. It is meant to be used by evaluators to assess the quality of the sharing practice of the researcher/scientist over a given period, taking into account the means & support available over that period. The grid displays a mind-mapped tree-graph structure based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2016; Wilkinson et al., 2018; and E.U.Guidelines about FAIRness Data Management Plans). The criteria used are based on the work from FORCE 11*, and the Open Science Career Assessment Matrix designed by the EC Working group on Rewards under Open science. The criteria are organised in 5 clusters: ‘Motivations for sharing’; ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’. For each criterion, 4 graduations are proposed (‘Never / Not Assessable’; ‘If mandatory’; ‘Sometimes’; ‘Always’). Only one value must be selected per criterion. Evaluation should be done by cluster; the final overall assessment will be based on the sum of the number of each ticked value / total number of criteria in each cluster; the ‘motivations for sharing’ should be appreciated qualitatively in the final interpretation. The final goals are to develop a graduated assessment of the researcher FAIRness literacy and help identifying needs to build FAIRness guidelines to improve the sharing capacity of researchers.

Informationsmaterial zu den "FAIR Data Principles"., Diese Publikation wurde im Rahmen des Verbundprojekts "FDMentor" mit den Fördernummern 16FDM010 und 16FDM011 vom Bundesministerium für Bildung und Forschung gefördert.

Le poster a été déposé dans Zenodo; International audience; The SHARC (SHAring Reward & Credit) interest group (IG) is an interdisciplinary group set up in the framework of RDA (Research Data Alliance) to improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Notably, one of the objectives is to promote data sharing activities in research assessment schemes at national and European levels. To this aim, the RDA-SHARC IG is developing assessment grids using criteria to establish if data are compliant to the FAIR principles (findable /accessible / interoperable / reusable).The grid is aiming to be extensive, generic and trans-disciplinary. It is meant to be used by evaluators to assess the quality of the sharing practice of the researcher/scientist over a given period, taking into account the means & support available over that period. The grid displays a mind-mapped tree-graph structure based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2016; Wilkinson et al., 2018; and E.U.Guidelines about FAIRness Data Management Plans). The criteria used are based on the work from FORCE 11*, and the Open Science Career Assessment Matrix designed by the EC Working group on Rewards under Open science. The criteria are organised in 5 clusters: ‘Motivations for sharing’; ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’. For each criterion, 4 graduations are proposed (‘Never / Not Assessable’; ‘If mandatory’; ‘Sometimes’; ‘Always’). Only one value must be selected per criterion. Evaluation should be done by cluster; the final overall assessment will be based on the sum of the number of each ticked value / total number of criteria in each cluster; the ‘motivations for sharing’ should be appreciated qualitatively in the final interpretation. The final goals are to develop a graduated assessment of the researcher FAIRness literacy and help identifying needs to build FAIRness guidelines to improve the sharing capacity of researchers.

The increasing capacity of computing resources and complexity of the instruments used in Science in our days, resulting in a growing volume of data with different heterogeneous formats and methodologies, makes an appropriate Data Life Cycle management essential for addressing new multidisciplinary challenges. LifeWatch ESFRI, the Research Infrastructure for Biodiversity and Ecosystem Research in Europe, provides clear examples, like the global warming or species extinctions. In addition, the European Commission is promoting the reuse of data, findings, and developments, through the “European Open Science Cloud” (EOSC), a new framework to stimulate the interaction among researchers, developers, and infrastructures from different disciplines. Thanks to its dynamism and flexibility providing and connecting services, Cloud Computing emerges as the most promising technology solution to support this new framework. Starting from these pillars, Data Life Cycle management, the EOSC framework, and Cloud Computing, this thesis proposes complete solutions for the implementation of relevant LifeWatch Case Studies. They are based on the experience and developments related to different EU projects, including COOPEUS, EGI-Engage LifeWatch Competence Centre, and INDIGO-DataCloud. Going through the different stages of this new approach, integrating different protocols, standards, tools, methods and services oriented to ensure a “FAIR” data (Findable, Accessible, Interoperable, Reusable), this thesis presents different designs, developments, prototypes, and services in production, confirming Cloud Computing as an adequate technology to support these complex LifeWatch challenges.

A tremendous number of new chemical entities are discarded in drug development because candidate drugs prove to be toxic, even when potential drugs show excellent efficacy. Broadly sharing toxicology knowledge would: • Lower the attrition rate in drug development • Significantly reduce animal testing • Accelerate the development of novel drugs. The IMI FAIRplus project demonstrated how the eTOX treasure trove of toxicology data could be made more Findable, Accessible, Interoperable and Reusable (FAIR) for drug development, arrived at lessons learned and developed a generic ‘recipe’ for the underlying ontology mapping.

A tremendous number of new chemical entities are discarded in drug development because candidate drugs prove to be toxic, even when potential drugs show excellent efficacy. Broadly sharing toxicology knowledge would: • Lower the attrition rate in drug development • Significantly reduce animal testing • Accelerate the development of novel drugs. The IMI FAIRplus project demonstrated how the eTOX treasure trove of toxicology data could be made more Findable, Accessible, Interoperable and Reusable (FAIR) for drug development, arrived at lessons learned and developed a generic ‘recipe’ for the underlying ontology mapping.