High quality data publications: drives and needs - Sansone, BDebate, 12 Nov 2014
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High quality data publications: drives and needs - Sansone, BDebate, 12 Nov 2014
- 1. ! High quality data publications:! drives and needs! ! Susanna-Assunta Sansone, PhD! ! @biosharing! @isatools! @scientificdata! ! B-DEBATE: Big Data in Biomedicine. Challenges and Opportunities, 12 Nov, 2014 Data Consultant, Honorary Academic Editor Associate Director, Principal Investigator
- 2. Credit to: https://projects.ac/blog/five-top-reasons-to-protect-your-data-and-practise-safe-science/
- 3. Plagued by selective reporting of data and methods • Over 50% of completed studies in biomedicine do not appear in the published literature! ! • 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”!
- 4. Incentivizing individual contributor to share data • Big science efforts! o data is often better organized, reported and shared! • Small independent efforts, yielding a rich variety of specialty data sets! o Most of these data (such as null findings) is unpublished! o These dark data hold a potential wealth of knowledge!
- 5. From made reproducible to born reproducible “Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results”
- 6. Worldwide movement for FAIR data http://bd2k.nih.gov/workshops.html#ADDS
- 7. Publishers occupy a leverage point Because of importance of formal publications in the academic ! incentive structure!
- 8. Role of publishers as “agents of change” Serve as the implementation and/or enforcement arm at the point of publication!
- 9. 2013 Credit to: Iain Hrynaszkiewicz
- 10. Data/reproducibility at NPG Wang et al, Nature, 2013 doi:10.1038/nature12730 • Figure source data o putting data behind figures/graphs
- 11. Data/reproducibility at NPG • Figure source data o putting data behind figures/graphs • Data citation o tackling both styling and format; monitoring community developments, such the Data Citation Synthesis Group • Code reproducibility o peer review, availability and reuse • NPG’s Linked Data release – CC0 • A new data journal
- 12. Role of data papers and data journals • Incentive, credit for sharing! • Peer review focus! • Value of data vs. analysis! • Discoverability and reusability!
- 13. market research (2011) • What do researchers want from a data publications? o 96% - increased visibility and discovery o 95% - increased usability of their research data o 93% - credit mechanism for deposit of data o 80% - peer review of content/datasets Respondent characteristics 387 respondents (329 active researchers Physics (24%) Earth and environmental science (21%) Biology (20%) Chemistry (19%) Others (16%)
- 14. ! ! ! Helping you publish, discover and reuse research data Credit for sharing your data Focused on reuse and reproducibility Peer reviewed, curated Promoting community data and code repositories Open Access • Currently covering life, natural and environmental sciences! • Big and small data! o power of small data are in their aggregation and integration with other datasets! • New and previously published individual datasets, curated collections and citizen science! o a fuller, more in-depth look at the data processing steps, additional data files, codes etc! o tutorial-like information for scientists interested in reusing or integrating the data with their own!
- 15. Introducing a new content type: Data Descriptor Methods and technical analyses supporting the quality of the measurements:! What did I do to generate the data?! How was the data processed?! Where is the data?! Who did what when! How can the data be used or reused?! Designed to make data more discoverable, interpretable and reusable
- 16. Relation with traditional article - content ! ! ! ! ! ! ! ! Scientific hypotheses:! Synthesis! Analysis! Conclusions! Methods and technical analyses supporting the quality of the measurements:! What did I do to generate the data?! How was the data processed?! Where is the data?! Who did what when! How can the data be used or reused?!
- 17. Relation with traditional article - time Publish Data! AFTER: expand on your research articles, adding further information for reuse of the data AT THE SAME TIME: publish your Data Descriptor(s) alongside research article(s) OR BEFORE !
- 18. Share your data, get credited and cited ! ! ! ! ! ! ! ! ! Code in GitHub ! ! ! ! ! ! ! ! ! Data in OpenfMRI
- 19. Data Descriptor: narrative and structure ! ! ! Experimental metadata or ! structured component! (in-house curated, machine-readable formats)! Article or ! narrative component! (PDF and HTML) !
- 20. Data Descriptor: narrative Focus on data reuse! Detailed descriptions of the methods and technical analyses supporting the quality of the measurements.! Does not contain tests of new scientific hypotheses! Sections:! • Title! • Abstract! • Background & Summary! • Methods! • Technical Validation! • Data Records! • Usage Notes ! • Figures & Tables ! • References! • Data Citations! ! Joint Declaration of Data Citation Principles by the Data Citation Synthesis Group
- 21. Focus on data reuse! Detailed descriptions of the methods and technical analyses supporting the quality of the measurements.! Does not contain tests of new scientific hypotheses! Sections:! • Title! • Abstract! • Background & Summary! In traditional publications this • Methods! • Technical Validation! • Data Records! • Usage Notes ! • Figures & Tables ! • References! • Data Citations! information is not provided in a sufficiently detailed manner However this information is essential for understanding, reusing, and reproducing datasets ! Data Descriptor: narrative
- 22. Data Descriptor: structure (CC0) In-house editorial curator:! • assists users to submit the structured content via simple templates and an internal authoring tool! • performs value-added semantic annotation of the experimental metadata! For advanced users/service providers willing to export ISA-Tab for direct submission, we have released a technical specification:! Data file or ! record in a database! analysis ! method! script!
- 23. Adding value to research articles and data records Research papers Descriptors Data Data records We currently recognize over 60 public data repositories! !
- 24. Citation of and link to data files and databases
- 25. Peer review process focused on quality and reuse! Evaluation is not be based on the perceived impact ! or novelty of the findings or size of the data! ! • Experimental rigour and technical data quality! o Methodologically sound! o Technical validation experiments and statistical analyses! o Depth, coverage, size, and/or completeness of data sufficient for the types of applications! • Completeness of the description! o Sufficient details to allow others to reproduce the results, reuse or integrate it with other data! o Compliance with relevant minimum information or reporting standards! • Integrity of the data files and repository record! o Data files match the descriptions in the Data Descriptor! o Deposited in the most appropriate available databases!
- 26. Progressively refine guidance to 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!
- 27. Mapping the landscape of standards and databases
- 28. PI: Lucila Ohno-Machado, UCSD biocaddie.org
- 29. PI: Mark Musen, Stanford metadatacenter.org
- 30. Acknowledgements! Visit nature.com/scientificdata Email scientificdata@nature.com Tweet @ScientificData Honorary Academic Editor Susanna-Assunta Sansone, PhD Managing Editor Andrew L Hufton, PhD Editorial Curator Varsha Khodiyar Publisher Iain Hrynaszkiewicz Advisory Panel and Editorial Board including senior researchers, funders, librarians and curators and our Advisory Boards and Collaborators Philippe Rocca-Serra, PhD Alejandra Gonzalez-Beltran, PhD Eamonn Maguire Milo Thurston, PhD Funds: