Data Sharing- FAIR Principles and Resources
- Anuradha Mansinghka (Unlicensed)
In 2016, the ‘FAIR Guiding Principles for scientific data management and Stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasize the use of IT systems to access data because of the increasing volume and complexity of data.
The following section gives an in-depth explanation of the FAIR principles:
Findable
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatically discovering datasets and services, which is a critical component in the FAIRification process.
2. Accessible
Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorization.
3. Interoperable
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
4. Reusable
The ultimate goal of FAIR is to optimize the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.
These principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure.
There are a plethora of resources for understanding the FAIR data-sharing policies. Every resource entails an extensive FAQ section to answer questions regarding the principles. The following section enlists these resources:
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This website is a curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies. FAIRsharing is a comprehensive guide for consumers to discover, select and use these resources with confidence, and producers to make their resources more discoverable, more widely adopted and cited. Furthermore, it is essentially a repository of standard terminologies, guidelines, databases and digital assets, and the data sharing policies from international funding agencies, regulators, journals, and other organisations. | |
Similar to NIH, Go Fair is a resource repository on FAIR principles and data sharing. The resources include Documents from previous meetings, data management policies and know hows, workshops on data sharing and FAIR principles, glossary, and FAQs. | |
NIH has consolidated a list of questions on data sharing and management policies. From sharing of model organisms to genomic data findings, the website covers intel on every category. Researchers can visit the website to understand the nuances of data sharing and abide by the policies. | |
The Gates Foundation Open Access policy enables the unrestricted access and reuse of all peer-reviewed published research funded, in whole or in part, by the foundation, including any underlying data sets. It differs from FAIR data sharing policy in terms of unrestricted access to data. FAIR data does not necessarily have to be open. If your research is funded by Gates foundation, visit this website to understand the policy in detail. | |
OHDSI (Observational Health Data Sciences and Informatics) | The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. All the solutions on OHDSI are open-source. Specifically, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an open community data standard, designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence. A central component of the OMOP CDM is the OHDSI standardized vocabularies. The OHDSI vocabularies allow organization and standardization of medical terms to be used across the various clinical domains of the OMOP common data model and enable standardized analytics. This model caters to the interoperability of data from the FAIR principle. |
Johns Hopkins Precision Medicine Analytics Platform (PMAP) gives you data from multiple sources and a broad suite of analytical tools in an approved, secure, compliant environment. The Observational Medical Outcomes Partnership (OMOP) dataset on PMAP is a first-rank research resource representing the largest, most up to date, curated representation of patients receiving care at Johns Hopkins Medicine. The OMOP Common Data Model (CDM) enables the capture of patients information in the same way across departments, institutions, and research partners around the globe. Researchers can access not only Johns Hopkins data using PMAP, but can quickly scale to larger research studies since it uses standardized language and codes. The PMAP abides by the interoperability and accessbility of data from the FAIR principle. | |
Contacts | All the queries can be emailed at contact@fairsharing.org |
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