12 Mar 2021 Top resources on FAIR principles, summed up in an understandable the promises and aims of the 2019 NIH Strategic Plan for Data Science.

However, as this report argues, the FAIR principles do not just apply to data but to other digital objects including outputs of research. Additionally, making digital objects FAIR requires a change in practices and the implementation of technologies and infrastructures.

Without applying appropriate data exchange standards with domain-relevant content standards and accessible rich metadata that uses applicable terminologies, interoperability is burdened by the need for transformation and/or mapping. 2019-06-29 · 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. 2019-04-08 · •Optimize data storage and security •Connect NIH data systems Modernized Data Ecosystem •Modernize data repository ecosystem •Support storage and sharing of individual datasets • Better integrate clinical and observational data into biomedical data science IIIID) National Institutes of Health Data Management, Analytics, and Tools The FAIR data principles What is FAIR data? The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015. 2016-03-15 · There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly Many in the data science community are familiar with the FAIR principles—a set of principles to make data findable, accessible, interoperable, and reusable.

Fair data principles nih

The term FAIR was launched at a Lorentz workshop in 2014, the resulting FAIR principles were published in 2016. The data principles known as FAIR improve drug studies and are integral to the new NIEHS Informatics and Information Technology roadmap. 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. NIH Clinical Center researchers published seven main principles to guide the conduct of ethical research: Social and clinical value; Scientific validity; Fair subject selection; Favorable risk-benefit ratio; Independent review; Informed consent; Respect for potential and enrolled subjects; Social and clinical value The NIH [National Institutes of Health] Policy for Data Management and Sharing, which was updated recently, directs researchers to preserve and share data from research it funds by placing it in appropriate repositories. Such repositories should meet FAIR Principles. Findable data can be discovered and identified using standard mechanisms.

Staff from the National Institutes of Health (NIH) worked with others in HHS to revise and service on panels such as Institutional Review Boards or Data and Safety reference to public prices or other reasonable measures of fair ma

Reward colleagues who share 10. Boost Data Science Best practices, tools and tips for integrating FAIR data principles into your daily work. NIH Deliverables Work supporting the NIH Data Commons Pilot Phase Consortium and Common Fund Data Ecosystem (CFDE).

NIH Clinical Center researchers published seven main principles to guide the conduct of ethical research: Social and clinical value; Scientific validity; Fair subject selection; Favorable risk-benefit ratio; Independent review; Informed consent; Respect for potential and enrolled subjects; Social and clinical value

Without applying appropriate data exchange standards with domain-relevant content standards and accessible rich metadata that uses applicable terminologies, interoperability is burdened by the need for transformation and/or mapping. Applying FAIR Principles to Improve Data Searchability of Emergency Department Datasets: A Case Study for HCUP-SEDD In this case study, the distribution of datasets from HCUP-SEDD was made more FAIR through the development of a search tool, EDCat. EDCat will be evaluated and developed further to include datasets from other sources. Many in the data science community are familiar with the FAIR principles—a set of principles to make data findable, accessible, interoperable, and reusable.

The FAIR principles are designed to support knowledge discovery and innovation both by humans and machines, support data and knowledge integration, promote sharing and reuse of data, be applied across multiple disciplines and help data and metadata to be ‘machine readable’, support new discoveries through the harvest and analysis of multiple datasets and outputs.
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In comments regarding NIH plans on data science, AMIA urged the NIH to commit to FAIR data principles and require the recipients of NIH grants to also adopt the principles as a condition of funding. FAIR is an acronym for Findable, Accessible, Interoperable and Reusable. The NIH published its draft Data Science Strategic Plan in early March. It includes five areas of data science: Data Infrastructure; Modernized Data Ecosystem; Data Management, Analytics and Tools; Workforce Development; and

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The scientific community has proposed the findable, accessible, interoperable, and reusable (FAIR) data principles to address this issue. Objective: The objective of this case study was to develop a system for improving the FAIRness of Healthcare Cost and Utilization Project's State Emergency Department Databases (HCUP's SEDD) within the context of data catalog availability.

Find and analyze SARS-CoV-2 sequence data, and related data. 2020-06-08 Speaking virtually from London to a group of more than 120 NIH employees at a recent NIH Data Science Town Hall sponsored by the Office of Data Science Strategy, Dr. Mark Hahnel said, “To get the most out of science, research data needs to be as open as possible, as closed as necessary.” Realizing the value of the FAIR principles will require a combination of scientific, technical, social, legal, and ethical advances for the production, sharing, discovery, assessment, and reuse of data.