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Managing Big Data - Berlin, July 9-10, 201.
Susanna-Assunta Sansone is a data consultant and honorary academic editor who works on several projects related to making data FAIR (Findable, Accessible, Interoperable, Reusable). She is the associate director of Scientific Data, a peer-reviewed journal focused on publishing data descriptors to describe and provide access to scientifically valuable datasets. The goal of Scientific Data is to help promote open science and data reuse by publishing structured metadata and narratives about datasets alongside traditional research articles.
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Managing Big Data - Berlin, July 9-10, 201.
- 1. Data Consultant, Honorary AcademicEditor Associate Director, Principal Investigator The rise of the data-centric ! research and publication enterprises! Susanna-Assunta Sansone, PhD! ! uk.linkedin.com/in/sasansone! @biosharing! @isatools! @scientificdata! ! 'Managing Big Data - Setting the standards for analyzing and integrating big data’, Berlin, July 9-10, 2014 http://www.slideshare.net/SusannaSansone
- 2. • About myself! o activities and interests! • Be FAIR to your data! o concept! o my related projects! • The Scientific Data exemplar! o rationale! o Data Descriptors! Outline!
- 3. My areas ofactivity:! • 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 LabBooks (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.
- 6. A great start,but not enough! image by Greg Emmerich http://discovery.urlibraries.org/ http://www.theguardian.com/higher-education-network/blog/2014/jun/26
- 7. Findable, Accessible, Interoperable,Reusable! Worldwide movement for FAIR data
- 8. In all fairness,no much data is FAIR!
- 9. But it isnot just about technology…!
- 10. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 1 0 …breath and depth of the content! …is pivotal!
- 11. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 11 sample characteristic(s)! experimental design! experimental variable(s)! technology(s)! measurement(s)! protocols(s)! data file(s)! ......!
- 12. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 1 2 • 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 standards, tools and best practices to: • report sufficient details • capture all salient features of the experimental workflow
- 13. A community mobilizationto develop standards, e.g.: § Structural and operational differences • organization types (open, close to members, society, WG etc.) • standards development (how to formulate, conduct and maintain) • adoption, uptake, outreach (link to journals, funders and commercial sector) • funds (sponsors, memberships, grants, volunteering) de jure de facto grass-roots groups standard organizations Nanotechnology Working Group
- 14. A community mobilizationto develop standards, e.g.: § Structural and operational differences • organization types (open, close to members, society, WG etc.) • standards development (how to formulate, conduct and maintain) • adoption, uptake, outreach (link to journals, funders and commercial sector) • funds (sponsors, memberships, grants, volunteering) de jure de facto grass-roots groups standard organizations Nanotechnology Working Group
- 15. Focus on reportingor content standards 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!
- 16. 16 Technologically-delineated views of theworld ! Biologically-delineated views of the world! Generic features ( common core )! - description of source biomaterial! - experimental design components! Arrays! Scanning! Arrays & Scanning! Columns! Gels! MS! MS! FTIR! NMR! Columns! transcriptomics proteomics metabolomics plant biology epidemiology microbiology Fragmentation, duplications and gaps To compare and integrate data we need interoperable standards
- 17. 17 Arrays! Scanning! Arrays & Scanning! Columns! Gels! MS!MS! FTIR! NMR! Columns! transcriptomics proteomics metabolomics Synergistic examples exist, but more are needed!
- 18. Growing number ofreporting 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
- 19. Navigating the seaof standards is not trivial! The relationship among popular standard formats for pathway information! BioPAX and PSI-MI are designed for data exchange to and from databases and pathway and network data integration. SBML and CellML are designed to support mathematical simulations of biological systems and SBGN represents pathway diagrams. ! CREDIT: Demir, et al., The BioPAX community standard for pathway data sharing, 2010.
- 20. Which standards anddatabase 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?
- 22. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 2 2
- 23. Registering and catalogingis 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
- 25. General-purpose, configurable format, designedto 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!
- 27. The International Conferenceon 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.:
- 28. eTRIKS – europeanTranslational Information and Knowledge management Services Consortium of academic (Imperial College, CNRS, Un of Luxemburg) and pharmas (Janssen, Merck, AZ, Lilly, Lundbeck, Pfizer, Roche, Sanofi, Bayer, GSK) building a sustainable, open translational research informatics platform • Nature Publishing Group‘s Scientific Data • BioMedCentral and BGI‘s GigaScience • F1000 Research • Oxford University Press Susanna-Assunta Sansone, Philippe Rocca-Serra, Alejandra Gonzalez-Beltran, Eamonn Maguire, Milo Thurston; Oxford alumni: Annapaola Santarsiero, Pavlos Georgiou + my previous team when at EBI (2001 – 2010)!
- 29. • About myself! o activities and interests! • Be FAIR to your data! o concept! o my related projects! • The Scientific Data exemplar! o rationale! o Data Descriptors! Outline!
- 30. 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!
- 31. Human Genome 2001 62Pages, 150 Authors, 49 Figure, 27 tables Encode Project 2012 30 papers, 3 Journals Journal publishing - changing landscape !
- 32. ! ! ! Launched on May27th, 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 Supported by:!
- 33. ! ! ! Experimental metadata or! structuredcomponent! (in-house curated, machine- readable formats)! Article or ! narrative component! (PDF and HTML)! Data Descriptor: narrative and structure!
- 34. ! ! ! ! ! ! ! ! Scientific hypotheses:! Synthesis! Analysis! Conclusions! Methods andtechnical 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! 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 Relation with traditional articles - content and time!
- 35. Export to variousformats (ISA_tab, RDF, etc) Linking between research papers, Data Descriptors, and data records Making data discoverable ! We currently recognize over 50 public data repositories! !
- 36. Evaluation is notbe 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!
- 37. • Neuroscience, ecology,epidemiology, environmental science, functional genomics, metabolomics, toxicology! • New datasets and previously published data sets! o a fuller, more in-depth look at the data processing steps, supported by additional data files and code from each step! o additional tutorial-like information for scientists interested in reusing or integrating the data with their own! • Datasets in figshare and domain specific databases! • Code deposited in figshare and GitHub! • Individual datasets, curated aggregation and citizen science! • First dataset part of a collection ! • Academic and industry authors! 37 Current content is diverse – bimonthly releases !
- 38. • Do yourun 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 enable submission?!
- 39. Helping you publish,discover and reuse research data Visit nature.com/scientificdata Email [email protected] 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