The Rise of Data-Centric Research and Publication
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The document summarizes a presentation about making scientific data FAIR (Findable, Accessible, Interoperable, Reusable). It discusses the concept of FAIR data and several of the presenter's related projects. Examples are provided of using standards like ISA-Tab to structure metadata and make datasets interoperable. The presentation outlines the presenter's roles in data capture, publication, and standards development efforts to promote FAIR data principles. Scientific Data, a new journal for peer-reviewed data descriptions, is introduced as a way to make datasets more discoverable and reusable.
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The Rise of Data-Centric Research and Publication
- 1. Data Consultant, Honorary Academic Editor Associate Director, Principal Investigator The rise of the data-centric ! research and publication enterprises! Susanna-Assunta Sansone, PhD! ! RIKEN Yokohama, 25 June, 2014 http://www.slideshare.net/SusannaSansone
- 2. • About myself! o activities and interests! • FAIR data! o concept! o my related projects! • Scientific Data! o rationale! o Data Descriptors! o examples! Outline!
- 3. My areas of activity:! • Data capture and curation! • Data (nano)publication! • Data provenance ! • Open, community ontologies and standards! • Semantic web! • Software development! • Training! Communities I work with/for:! As part of:! • UK, European and international consortia! • Pre-competitive informatics public-private partnerships! • Standardization initiatives! with e.g.:!
- 4. Notes in Lab Books (information for humans) Spreadsheets andTables ( the compromise) Facts as RDF statements (information for machines) Notes and narrative! Spreadsheets and tables! Linked data and nanopublications! Enabling reproducible research and open science, driving science and discoveries ! Increase the level of annotation at the source, tracking provenance and using community standards
- 5. https://projects.ac/blog/five-top-reasons-to-protect-your-data-and-practise-safe-science/ Credit to:
- 6. A great start, but not enough! image by Greg Emmerich
- 7. § Researchers and bioinformaticians in both academic and commercial science, along with funding agencies and publishers, embrace the concept that both • DATA: entities of interest e.g., genes, metabolites, phenotypes and • METADATA: experimental steps e.g., provenance of study materials, technology and measurement types should be Findable, Accessible, Interoperable and Reusable Worldwide movement for FAIR data
- 8. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 8 sample characteristic(s) experimental design experimental variable(s) technology(s) measurement(s) protocols(s) data file(s) ......
- 9. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 9 • make annotation explicit and discoverable • structure the descriptions for consistency • ensure/regulate access • deposit and publish • etc…. § To make this dataset ‘FAIR’, one must have tools, standards and best practices to: • report sufficient details • capture all salient features of the experimental workflow
- 11. General-purpose, configurable format, designed to support: • description of the experimental metadata, making the annotation explicit and discoverable • provenance tracking • use community standards, such as minimal reporting guidelines and terminologies • designed to be converted to - a growing number of - other metadata formats, e.g. used by EBI repositories analysis ! method! script! Data file or ! record in a database!
- 13. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project ISA powers data collection, curation resources and repositories, e.g.:
- 14. Reporting standards and data interoperability Including minimum information reporting requirements, or checklists to report the same core, essential information Including controlled vocabularies, taxonomies, thesauri, ontologies etc. to use the same word and refer to the same ‘thing’ Including conceptual model, conceptual schema from which an exchange format is derived to allow data to flow from one system to another Community-developed, standards are pivotal to structure, enrich the description and share datasets, facilitating understanding and reuse!
- 15. Growing number of reporting standards + 130 + 150 + 303 Source:BioPortal Databases, ! annotation,! curation ! tools ! implementing ! standards! miame! MIAPA! MIRIAM! MIQAS! MIX! MIGEN! CIMR! MIAPE! MIASE! MIQE! MISFISHIE….! REMARK! CONSORT! MAGE-Tab! GCDML! SRAxml! SOFT! FASTA! DICOM! MzML! SBRML! SEDML…! GELML! ISA-Tab! CML! MITAB! AAO! CHEBI! OBI! PATO! ENVO! MOD! BTO! IDO…! TEDDY! PRO! XAO! DO VO! Source:BioSharing Source:BioSharing
- 16. Which standards and database can we use/recommend I work in the field of cell migration research, which one are applicable to me? I us cell migration in translational research, are there specific clinical standards?
- 18. Registering and cataloging is just step one; the next one are: • Develop assessment criteria for usability and popularity of standards • Associate standards to data policies and databases • Assemble journal and funder policies re data storage • Make fully cross-searchable • Intended goal: help stakeholders make informed decisions
- 19. • About myself! o activities and interests! • FAIR data! o concept! o my related projects! • Scientific Data! o rationale! o Data Descriptors! o examples! Outline!
- 20. FAIR data - roles and responsibilities • Data has to become an integral part of the scholarly communications! • Responsibilities lie across several stakeholder groups: researchers, data centers, librarians, funding agencies and publishers! • But publishers occupy a “leverage point” in this process!
- 21. Human Genome 2001 62 Pages, 150 Authors, 49 Figure, 27 tables Encode Project 2012 30 papers, 3 Journals Journal publishing - changing landscape !
- 22. Helping you publish, discover and reuse research data Visit nature.com/scientificdata Email scientificdata@nature.com Tweet @ScientificData Supported by:! Honorary Academic Editor Susanna-Assunta Sansone, PhD Managing Editor Andrew L Hufton, PhD Editorial Curator Victoria Newman Advisory Panel and Editorial Board including senior researchers, funders, librarians and curators
- 23. ! ! ! Launched on May 27th, 2014 A new online-only publication for descriptions of scientifically valuable datasets in the life, environmental and biomedical sciences, but not limited to these! Credit for sharing your data Focused on reuse and reproducibility Peer reviewed, curated Promoting Community Data Repositories Open Access
- 24. ! ! ! Experimental metadata or! structured component! (in-house curated, machine- readable formats)! Data Descriptor: narrative and structure! Article or ! narrative component! (PDF and HTML)!
- 25. Data Descriptor: narrative! Sections:! • Title! • Abstract! • Background & Summary! • Methods! • Technical Validation! • Data Records! • Usage Notes ! • Figures & Tables ! • References! • Data Citations! ! 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! In traditional publications this information is not provided in a sufficiently detailed manner However this information is essential for understanding, reusing, and reproducing datasets
- 26. Data Descriptor: narrative! Sections:! • Title! • Abstract! • Background & Summary! • Methods! • Technical Validation! • Data Records! • Usage Notes ! • Figures & Tables ! • References! • Data Citations! ! 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! Joint Declaration of Data Citation Principles by the Data Citation Synthesis Group, incl.: - CODATA - Research Data Alliance (RDA), - Force11
- 27. In-house curation team:! • 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 will release a technical specification:! analysis ! method! script! Data file or ! record in a database! Data Descriptor: structure (CC0)!
- 28. Export to various formats (ISA_tab, RDF, etc) Linking between research papers, Data Descriptors, and data records Making data discoverable !
- 29. 2 4 3 10 4 1 4 3 4 DNA and protein sequence Functional genomics Genetic association and genome variation Metagenomics Molecular interactions Organism- or disease-specific Proteomics Taxonomy and species diversity Traces and sequencing reads “Omics” is emphasized among basic life- sciences repositories • We currently recognize over 50 public data repositories! • We have integrated systems with both:! ! ! Helping authors find the right place for the data!
- 30. 1. Broadly support and recognition within their scientific community ! 2. Ensure long-term persistence and preservation of datasets in their published form ! 3. Provide expert curation ! 4. Implement relevant, community-endorsed reporting requirements ! 5. Provide for confidential review of submitted datasets ! 6. Provide stable identifiers for submitted datasets ! 7. Allow public access to data without unnecessary restrictions ! 30 Data repositories - criteria!
- 31. Data: the primary datasets will reside in public repositories. Partnering with FigShare and Dryad, which are both CC0! Data Descriptor - structured component (ISA-Tab): as NPG has already done with its existing Linked Data Portal, the metadata about data descriptors in Scientific Data will be CC0! Data Descriptor - narrative component: describing the methodology of data generation/collection and processing will be licensed under either of the following, by author choice: Big data | CSE 2014 3 Open Access - APC supported!
- 32. ! ! ! ! ! ! ! ! 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! Relation with traditional articles - content and time! BEFORE: get your data to the community as soon as possible (see NPG pre-publication policy) AT THE SAME TIME: publish your Data Descriptor(s) alongside research article(s) AFTER: expand on your research articles, adding further information for reuse of the data
- 33. • Neuroscience, ecology, epidemiology, environmental science, functional genomics, metabolomics, toxicology! • New datasets and previously published data sets! • Datasets in figshare, OpenfMRI, GEO, GenomeRNAi, ArrayExpress and MetaboLights ! • Code deposited in figshare and GitHub! • Individual datasets, compendium and citizen science! • First dataset part of a collection ! • Academic and industry authors! 33 Current content is diverse – bimonthly releases !
- 34. Hanke: Neuroscience ! ! ! ! ! ! ! ! ! ! Code in GitHub New Dataset Data in OpenfMRI Source code in GitHub Big Data
- 35. Stefano: Stem Cells! Associated Nature Article Data - figshare - NCBI GEO Integrated figshare data viewer
- 36. Kirwan: MS metabolomics! richer ISA-Tab New Dataset Data in EBI MetaboLights
- 37. Yu: RNA-Seq transcriptomics (part of the SEQC collection)! Associated Nature Communications article Data in NCBI GEO ``````!
- 38. Baud: genomes and phenomes! Associated Nature Genetics article Data: - EBI ArrayExpress - figshare ``````!
- 39. Baud: genomes and phenomes! Associated Nature Genetics article Data: - EBI ArrayExpress - figshare ``````! ``````!
- 40. Evaluation is not be based on the perceived impact or novelty of the findings! • Experimental Rigour and Technical Data Quality! o Were the data produced in a rigorous and methodologically sound manner?! o Was the technical quality of the data supported convincingly with technical validation experiments and statistical analyses of data quality or error, as needed?! o Are the depth, coverage, size, and/or completeness of these data sufficient for the types of applications or research questions outlined by the authors?! • Completeness of the Description! o Are the methods and any data-processing steps described in sufficient detail to allow others to reproduce these steps?! o Did the authors provide all the information needed for others to reuse this dataset or integrate it with other data?! o Is this Data Descriptor, in combination with any repository metadata, consistent with relevant minimum information or reporting standards?! • Integrity of the Data Files and Repository Record! o Have you confirmed that the data files deposited by the authors are complete and match the descriptions in the Data Descriptor?! o Have these data files been deposited in the most appropriate available data repository?! Peer review process focused on quality and reuse!
- 41. • Do you run a data resource we should recognize?! o See on our website the list of criteria databases should meet!! • Are you interested in facilitating submission to us? ! o See our ISA-Tab specification on the website! - you can implement and export in this format from your authoring/curation tool, or from your database!! • Do you want to submit Data Descriptor(s)?! o Check suitability by sending a pre-submission enquire, we accept:! - Submissions in the life, environmental and biomedical sciences; but not limited to! - Experimental, observational and computational datasets! - Individual datasets, curated aggregations, and collections! - Unpublished data and follow-up, with additional information for wider reuse, e.g.:! ü a fuller, more in-depth look at the data processing steps, supported by additional data files and code from each step! ü additional tutorial-like information for scientists interested in reusing or integrating the data with their own! Interested in collaborating and/or submitting?!