Big data, small data, data papers - short statement for "BDebate on Biomedicine 2014"

Big data, small data, data papers - short statement for "BDebate on Biomedicine 2014"

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  ! What isBig Data in Biomedicine?! Data Types to be considered! ! Susanna-Assunta Sansone, PhD! ! @biosharing! @isatools! @scientificdata! ! B-DEBATE: Big Data in Biomedicine. Challenges and Opportunities, 11 Nov, 2014 Data Consultant, Honorary Academic Editor Associate Director, Principal Investigator
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  Let’s not forgetthe long tail of research data • Big science efforts represent only a small proportion! o often featuring homogenous and well-organized data! ! • There is a large proportion of small independent research efforts! o a rich variety of specialty data sets!
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  Let’s not forgetthe long tail of research data • Small independent research efforts fall in the long-tail of the distribution! o Most of this (such as as siloed databases, null findings) is unpublished! o These dark data hold a potential wealth of knowledge!
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  Plagued by selectivereporting of data and methods • Over 50% of completed studies in biomedicine do not appear in the published literature! ! • Instead reside in file drawers personal and hard drives! ! • Often because results do not conform to author's hypotheses! “Only half the health-related studies funded by the European Union between 1998 and 2006 - an expenditure of €6 billion - led to identifiable reports”!
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  Role of datapapers and data journals • Incentive, credit for sharing! o Big and small data! o Unpublished data! o Long tail of data! o Curated aggregation ! • Peer review focus! • Value of data vs. analysis! • Discoverability and reusability! o Complementing community databases! • Narrative/context!
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  Role of datapapers and data journals • The power of “small data” are in their aggregation and integration with other datasets! • There is value in all well-curated, validated and reusable data – big and small!
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  Adding value toresearch articles and data records Research articles Descriptors Data Data records
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  Adding value toresearch articles and data records Research articles Descriptors Data Data records Credit for sharing your data Focused on reuse and reproducibility Peer reviewed, curated Open Access Promoting community data and code repositories
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  Progressively refine guidanceto authors and reviewers ~ 156 ~ 70 ~ 334 Source: BioPortal Databases ! implementing ! standards! miame! MIAPA! MIRIAM! MIX!MIQAS! MIGEN! MIAPE! CIMR! MIASE! REMARK! MIQE! CONSORT! MISFISHIE….! MAGE-Tab! GCDML! SRAxml! SOFT! FASTA! DICOM! MzML! SBRML! CML! GELML! SEDML…! MITAB! ISA-Tab! AAO! CHEBI! OBI! PATO! ENVO! MOD! TEDDY! BTO! IDO…! XAO! PRO! DO VO!
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  Mapping the landscapeof standards and databases
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  Mapping the landscapeof standards and databases
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  Help stakeholders tomake informed decisions Researchers, developers and curators lack support and guidance on how to best navigate and select content standards, understand their maturity, or find databases that implement them; Funders, journals and librarians do not have enough information to make informed decisions on which content standards or database to recommended in policies, or funded or implemented
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  Summarizing • Selectivereporting of data and methods is still an issue • Let’s not forget the potential value of the long-tail of data • Data papers and journals can provide incentive and credit to share more data - big and small • Content standards do help - but the current wealth of options is an obstacle