“The Harvard Library Research Data Management Program connects members of the Harvard community to services and resources that span the research data lifecycle, to help ensure that Harvard’s multi-disciplinary research data is findable, accessible, interoperable, and reusable (FAIR)….”
“The Harvard Library Research Data Management Program connects members of the Harvard community to services and resources that span the research data lifecycle, to help ensure that Harvard’s multi-disciplinary research data is findable, accessible, interoperable, and reusable (FAIR).
Reporting to the Harvard Library Research Data Management Program Manager, the Data Services Librarian will work with partners and stakeholder within the Harvard Library, and across the university, to provide data curation services, training, and expertise to members of the Harvard community….”
“Minister of State for Training, Skills, Innovation, Research & Development, John Halligan, has launched Ireland’s National Framework on the Transition to an Open Research Environment.
Prepared by the National Open Research Forum (NORF), the framework was a response to developments in open research, both in the EU and internationally.
Open research refers to the movement towards more transparent, collaborative, accessible and efficient research.
The frameworks objective is to enhance the integrity, public trust and excellence in research across all disciplines. Its principles are to support access to research funded by the Irish government, improve the free flow of information across research communities, and boost transparency, accountability and public awareness of the results of publicly funded research. This is aligned with European Commission policy that has devloped in this area. It makes recommendations on a range of topics, including open access to research data, the preservation and reuse of scientific information, skills and competencies and incentives and rewards….”
“Minister John Halligan has launched Ireland’s National Framework on the Transition to an Open Research Environment….
The National Framework is a key deliverable of the National Open Research Forum (NORF), which was set up in 2017 to bring together key members of the research community to drive Ireland’s open research agenda as set out in Innovation 2020, Ireland’s research and development, science and technology strategy.
Patricia Clarke of the Health Research Board and co-chair of the NORF said: “The National Framework is a clear statement of intent by the Irish research community to take practical steps to embed open research in Ireland….
The framework is aligned with emerging European Union policy and includes principles on: open access to publications; enabling FAIR research data; underpinning infrastructures for access to and preservation of research; development of skills and competencies, and incentives and rewards for open research within research evaluation processes.
The framework will open up access to publicly funded research in Ireland and support research excellence across all disciplines. Open Research will be a requirement of the next EU Framework Programme, Horizon Europe, and Irish researchers and institutions need to be ready….”
“FAIR research data encompasses the way to create, store and publish research data in a way that they are findable, accessible, interoperable and reusable. In order to be FAIR, research data published should meet certain criteria described by the FAIR principles. Despite this, many research performing organisations and infrastructures are still reluctant to apply the FAIR principles and share their datasets due to real or perceived costs, including time investment and money.
To answer such concerns, this report formulates 36 policy recommendations on cost-effective funding and business models to make the model of FAIR data sustainable. It provides evidence to decision makers on setting up short and long-term actions pertinent to the practical implementation of FAIR principles….”
- Implementation of FAIR (findable, accessible, interoperable, and reusable) offers significant return on investment (ROI) but requires major changes in research culture, incentives, and substantial funding, and implementation is hindered by the need to coordinate across European Union’s member states.
- FAIR is constituted by data objects and a wider technical and data ecosystem.
- Publishers’ role is broad but prescribed in this report – although there may be business opportunities.
- While the continued validity of non?open data is acknowledged, the report recognizes that ROI is maximized where data are both FAIR and open….”
Abstract: Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance. The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives. For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively. For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society’s standard. For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.
“Based on the FAIR Data Principles, two questionnaires were created. The first (hereafter #Q1 – see Appendix #1) targeted repository managers and/or librarians and consisted of 40 questions. The second (hereafter #Q2 – see Appendix #2) targeted technical staff responsible for repository development and maintenance and consisted of 25 questions.
Members of LIBER’s Research Data Management (RDM) Working Group circulated the questionnaires between December 2018 and February 2019. Responses were collected from managers and/or librarians of 29 repositories for the first (#Q1) questionnaire.
In addition, technical staff responsible for the development and maintenance of 14 repositories (Table 1) responded to the second (#Q2) questionnaire. In 11 cases, repositories filled out both #Q1 and #Q2.
In this report, the responses for both questionnaires have been merged and analyzed to gain a comprehensive picture about FAIRness at the level of repositories and their data….”
“The CODATA 2019 Conference will be held on 19-20 September 2019 in Beijing, China. This year’s conference theme is: Towards next-generation data-driven science: policies, practices and platforms.
The conference will follow a high-level workshop, 17-18 September 2019, on ‘Implementing Open Research Data Policy and Practice’ that will examine such challenges in China and elsewhere in the light of the emergence of data policies and in particular the China State Council’s Notice on ‘Measures for Managing Scientific Data’.
Science globally is being transformed by new digital technologies. At the same time addressing the major global challenges of the age requires the analysis of vast quantities of heterogeneous data from multiple sources. In response, many countries, regions and scientific domains have developed Research Infrastructures to assist with the management, stewardship and analysis. These developments have been stimulated by Open Science policies and practices, both those developed by funders and those that have emerged from communities. The FAIR principles and supporting practices seek to accelerate this process and unlock the potential of analysis at scale with machines. This conference provides a significant opportunity to survey and examine these developments from a global perspective.”