Alasoo, Kaur

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Challenges of working with controlled access datasets in human geneticsKaur AlasooResearch Fellow in Bioinformatics9 April 2019I’m a proud research parasite.https://www.nejm.org/doi/full/10.1056/NEJMe1516564‘A second concern held by some is that a new class of research person will emerge — people who had nothing to do with the design and execution of the study but use another group’s data for their own ends, possibly stealing from the research productivity planned by the data gatherers, or even use the data to try to disprove what the original investigators had posited. There is concern among some front-line researchers that the system will be taken over by what some researchers have characterized as “research parasites.”’Molecular biology has a strong culture of open data sharing• Many open access databases• European Nucleotide Archive (ENA)• Short Read Archive (SRA)• Gene Expression Omnibus (GEO)• ArrayExpress • Submitting data is usually a pre-requisite for publicationhttps://elifesciences.org/articles/41673…but most human genetic data cannot be shared openly• Genetic data cannot be anonymised, it is always possible to re-identify individuals based on their genotype• 2.5/28 datasets have released their raw data publicly, the rest are “controlled access”• Data sharing policies depend on the consent obtained from participants, need to be evaluated manually by the Data Access Committee.• Some datasets can never be shared due to consent restrictions.Genetic data repositories• NCBI Database of Genotypes and Phenotypes (dbGaP) • Single application process• European Genome-phenome Archive (EGA)• Each data owner has their own Data Access Committee and application process.• NIMH Repository and Genomics Resources (NRGR)• Cover letter, biosketches, funding information, resourcesRegistered!Access!SOM!Dyke,!et%al.!Registered!Access:!A!“Triple?A”!Approach.!EJHG,%24!(12):!1676?80.%https://repositive.io/blog/post/how-to-successfully-apply-for-access-to-dbgaphttps://repositive.io/blog/post/watching-paint-dry-in-the-21st-century-or-applying-for-data-from-dbgaphttps://repositive.io/blog/post/accessing-dbgap-a-bureaucratic-oddesey-part-2Rejected data access requestsRejected data access requestsNew NIH policy comes into effect in May 2019Rejected data access requestsFrom the rejection letter: “Please note that NIH is updating its data management procedures under the NIH Genomic Data Sharing Policy to allow unrestricted access to GSR from most studies in NIH-designated data repositories (on/about May 1, 2019): https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/"New NIH policy comes into effect in May 2019www.libereurope.eu Ligue des Bibliothèques Européennes de RechercheAssociation of European Research Libraries 1 What is FAIR DATA?Data and supplementary materials have sufficiently rich metadata and a unique and persistent identifier.FINDABLEMetadata and data are understandable to humans and machines. Data is deposited in a trusted repository.ACCESSIBLEMetadata use a formal, accessible, shared, and broadly applicable language for knowledge representation.INTEROPERABLEData and collections have a clear usage licenses and provide accurate information on provenance.REUSABLEImplementing FAIR Data Principles:  The Role of Libraries1 What are the FAIR Data Principles?The FAIR Data Principles are a set of guiding principles in order to make data findable, accessible, interoperable and reusable (Wilkinson et al., 2016). These principles provide guidance for scientific data management and stewardship and are relevant to all stakeholders in the current digital ecosystem. They directly address data producers and data publishers to promote maximum use of research data. Research libraries can use the FAIR Data Principles as a framework for fostering and extending research data services. 2 Why is FAIR Data important?The advancement of digital science thrives on the timely sharing and accessibility of digital data. Accordingly, the need for development of infrastructures and services that enable a systemic change of science practices to Open Science is now strongly advocated by both research and funding organizations. The FAIR principles strengthen these developments. Consequently research institutions, funders and publishers have significantly stepped up their demands on research data management and opening up research data for reuse. In the European Commission’s Open Research Data Pilot the FAIR principles are applied in order to encourage funded researchers to ensure that their data is soundly managed and subsequently shared. GoodCould be better https://en.wikipedia.org/wiki/File:Implementing_FAIR_Data_Principles_-_The_Role_of_Libraries.pdfwww.libereurope.eu Ligue des Bibliothèques Européennes de RechercheAssociation of European Research Libraries 1 What is FAIR DATA?Data and supplementary materials have sufficiently rich metadata and a unique and persistent identifier.FINDABLEMetadata and data are understandable to humans and machines. Data is deposited in a trusted repository.ACCESSIBLEMetadata use a formal, accessible, shared, and broadly applicable language for knowledge representation.INTEROPERABLEData and collections have a clear usage licenses and provide accurate information on provenance.REUSABLEImplementing FAIR Data Principles:  The Role of Libraries1 What are the FAIR Data Principles?The FAIR Data Principles are a set of guiding principles in order to make data findable, accessible, interoperable and reusable (Wilkinson et al., 2016). These principles provide guidance for scientific data management and stewardship and are relevant to all stakeholders in the current digital ecosystem. They directly address data producers and data publishers to promote maximum use of research data. Research libraries can use the FAIR Data Principles as a framework for fostering and extending research data services. 2 Why is FAIR Data important?The advancement of digital science thrives on the timely sharing and accessibility of digital data. Accordingly, the need for development of infrastructures and services that enable a systemic change of science practices to Open Science is now strongly advocated by both research and funding organizations. The FAIR principles strengthen these developments. Consequently research institutions, funders and publishers have significantly stepped up their demands on research data management and opening up research data for reuse. In the European Commission’s Open Research Data Pilot the FAIR principles are applied in order to encourage funded researchers to ensure that their data is soundly managed and subsequently shared. GoodCould be better https://en.wikipedia.org/wiki/File:Implementing_FAIR_Data_Principles_-_The_Role_of_Libraries.pdfwww.libereurope.eu Ligue des Bibliothèques Européennes de RechercheAssociation of European Research Libraries 1 What is FAIR DATA?Data and supplementary materials have sufficiently rich metadata and a unique and persistent identifier.FINDABLEMetadata and data are understandable to humans and machines. Data is deposited in a trusted repository.ACCESSIBLEMetadata use a formal, accessible, shared, and broadly applicable language for knowledge representation.INTEROPERABLEData and collections have a clear usage licenses and provide accurate information on provenance.REUSABLEImplementing FAIR Data Principles:  The Role of Libraries1 What are the FAIR Data Principles?The FAIR Data Principles are a set of guiding principles in order to make data findable, accessible, interoperable and reusable (Wilkinson et al., 2016). These principles provide guidance for scientific data management and stewardship and are relevant to all stakeholders in the current digital ecosystem. They directly address data producers and data publishers to promote maximum use of research data. Research libraries can use the FAIR Data Principles as a framework for fostering and extending research data services. 2 Why is FAIR Data important?The advancement of digital science thrives on the timely sharing and accessibility of digital data. Accordingly, the need for development of infrastructures and services that enable a systemic change of science practices to Open Science is now strongly advocated by both research and funding organizations. The FAIR principles strengthen these developments. Consequently research institutions, funders and publishers have significantly stepped up their demands on research data management and opening up research data for reuse. In the European Commission’s Open Research Data Pilot the FAIR principles are applied in order to encourage funded researchers to ensure that their data is soundly managed and subsequently shared. GoodCould be better https://en.wikipedia.org/wiki/File:Implementing_FAIR_Data_Principles_-_The_Role_of_Libraries.pdfHow can we improve?Community metadata• Metadata can always be improved upon.• Devil’s advocate: not all datasets are worth to be extensively annotated by the data owners. Better to share poorly annotated data than not share at all.• Currently no good mechanism to link community-generated annotations and metadata to existing datasets.Data Use Ontologyhttps://www.ga4gh.org/news/data-use-ontology-approved-as-a-ga4gh-technical-standard/Registered accesswould allow for additional attestation statements attachingextra conditions of use for some datasets or for datafrom some providers. For example, Australian Genomicsis considering the model that researchers need to supplyproof of Human Research Ethics Committee (HREC)approval (HREC number, title, etc.), which can then beeasily verified by web search.In agreeing on these definitions of bona fide researcher/clinical care professional and level of security, cross-federation becomes possible (e.g., to enable European bonafide researchers to present queries to US Beacons at theregistered access level and vice versa). The current attri-butes chosen to define registered users in these categoriesare designed to cover most of the use cases. When excep-tions arise, there would have to be a very strong need for thenew definition to make everyone deploy it (and populatevalues to the existing users retrospectively). Importantly,such exceptions would only be considered valid if drivenby informed consent requirements or national laws.Although our model does not include a review andapproval of the user’s specific research or data use plans,we considered requesting abstracts of general planned datause in lay terms that would be published to enhance trans-parency. This may be reconsidered, especially to reinforceregistered users’ commitments to using data for appropriateresearch or clinical care purposes and to further theaims of public transparency. Another interesting suggestionregarding transparency was to request and publish linksto public researcher profiles for all registered researchers.AuthorizationIn agreeing on the proposed routes to registration, we haveeffectively delegated the authorization of registered usersfor the two categories described here to established pro-fessional employment, accreditation, or accomplishment.The data sharing environment is therefore assumed fromindividuals’ bona fides (including work practices and thesecurity aspect) along with the basic set of requirementsset out in the registration attestation. Along with effortsto automate registered access, this potentially limits theamount of manual authorization that will be required.Our pilot implementation of the first registration routefor academic researchers (their home institution confirmsthey are researchers) is the simplest in terms of liabilityfor the category of “bona fide researchers,” and thereforethe “safest” place to start. ELIXIR is piloting an approachFig. 1 The registered access policy model. The figure shows theauthentication and attestation requirements of the GA4GH registeredaccess policy model for the user categories of bona fide researcher andclinical care professional. The seven statements shown in quotationmarks form the attestation stage of the process1726 S. O. M. Dyke et al.Acknowledgements• Grant Office at University of Tartu• Eleri Vako• Taivo Raud

Challenges of working with controlled access datasets in human genetics

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Challenges of working with controlled access datasets in human genetics

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