Overview to: BBSRC Oxford Doctoral Training Partnership - Dr Sansone - July 2014
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The document discusses the pivotal role of data management and curation in ensuring fair data practices within scientific research, emphasizing the need for clear standards and community resources. It outlines various initiatives and consortia focused on improving data accessibility, interoperability, and reusability. The importance of detailed data descriptors and the necessity for collaborative efforts across stakeholders in academia and industry to enhance data practices is highlighted.
Overview to: BBSRC Oxford Doctoral Training Partnership - Dr Sansone - July 2014
- 1. Data Consultant, Honorary Academic Editor Associate Director, Principal Investigator The rise of the data-centric ! research and publication enterprises! ! A journey into the whirlwind! Susanna-Assunta Sansone, PhD! ! uk.linkedin.com/in/sasansone! @biosharing! @isatools! @scientificdata! ! http://www.slideshare.net/SusannaSansone Oxford Interdisciplinary Bioscience Doctoral Training Partnership (DTP) – 31 July, 2014
- 2. Scope is to outline the strategies taken by the researchers to allow access to they research outputs
- 3. But it does not give you all info you need to know! Also because data sharing policies are unclear; a very common and loose text is: “Applicants should make use of existing, recognised standards for data collection and management, where these exist, and make data available through existing community resources or databases where possible”. But what constitutes a recognised standard or acceptable community resource?
- 4. • About myself! o activities and interests! • FAIR data! o concept! o my related projects! • Scientific Data! o rationale! o Data Descriptors! o examples! Outline!
- 5. Communities we work with/for:
- 6. Communities we work with/for: • Describe my experiments • Share info with my group • Store for query & access • Upload to analysis tool • Compare with similar data • etc…… • Support the needs of our researchers • Enhancing our existing tools; develop new components, or connect with other tools • Comply to community standards • etc…. • Drive data reusability agenda • Recommend tools, data resources and standards in data policies • Criteria for endorsement • Mapping the ecosystem of efforts • etc……
- 7. Key areas of activity: • Data capture and curation • Database development • Data (nano)publication • Data provenance • Open, community ontologies and standards • Semantic web • Social engineering • Software development • Training • Visualization (collaboration/ jointly with Prof Min Chen) Communities we work with/for:
- 8. Key areas of activity: • Data capture and curation • Database development • Data (nano)publication • Data provenance • Open, community ontologies and standards • Semantic web • Social engineering • Software development • Training • Visualization (collaboration/ jointly with Prof Min Chen) Communities we work with/for: As part of: • UK, European and international consortia • Pre-competitive informatics public-private partnerships • Standardization initiatives
- 9. http://www.flickr.com/photos/notbrucelee/8016189356/ CC BY Our activities are around and in support of data curation, management and publication and their pivotal roles in enabling FAIR data and research, driving science and discoveries
- 10. eTRIKS – european Translational 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)!
- 14. 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
- 15. § 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 Source: http://ebbailey.wordpress.com
- 16. In all fairness, no much data is FAIR!
- 17. In all fairness, no much data is FAIR!
- 18. But it is not just about technology…!
- 19. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 1 9 …breath and depth of the content! …is pivotal!
- 20. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 2 0 sample characteristic(s) experimental design experimental variable(s) technology(s) measurement(s) protocols(s) data file(s) ......
- 21. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 2 1 • 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 best practices, standards and tools to: • report sufficient details • capture all salient features of the experimental workflow
- 22. A community mobilization to 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
- 23. A community mobilization to develop standards, e.g.: de jure de facto grass-roots groups standard organizations Nanotechnology Working Group
- 24. Focus on reporting or 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 data and associated contextual metadata, facilitating understanding and reuse!
- 25. 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
- 26. 26 Technologically-delineated views of the world ! 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
- 27. Three EBI omics systems SubmissionAccessStorage Fragmentation of the databases and data, e.g.
- 28. Three EBI omics systems SubmissionAccessStorage Fragmentation of the databases and data, e.g. Proteomics
- 29. Three EBI omics systems SubmissionAccessStorage Fragmentation of the databases and data, e.g. Proteomics
- 30. Three EBI omics systems DIFFERENT Formats, terminologies and tools Submission DIFFERENT Download formats Access DIFFERENT - Core requirements represented - Representation of the studies and related samples - Curation practices Storage Fragmentation of the databases and data, e.g. Proteomics
- 31. How much do we know and which standards can we use
- 33. 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
- 34. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 3 4
- 35. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 3 5 Users can claim records and maintain them
- 36. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 3 6 Users can claim records and maintain them
- 37. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 3 7 Classify, links standards and visualize relations
- 38. Example 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.
- 39. The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 3 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 best practices, standards and tools to: • report sufficient details • capture all salient features of the experimental workflow
- 42. 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.:
- 43. 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!
- 45. Our industry needs a Disruptive Innovation. That Disruption...is Pistoia!
- 46. Oxford e-Research Centre (Sansone SA): Data Standards and Curation training
- 47. 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!
- 48. Journal publishing: the changing landscape!
- 49. Human Genome 2001 62 Pages, 150 Authors, 49 Figure, 27 tables Encode Project 2012 30 papers, 3 Journals Nature Publishing Group: the changing landscape!
- 50. ! ! ! 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 Supported by:!
- 51. ! ! ! Experimental metadata or! structured component! (in-house curated, machine- readable formats)! Article or ! narrative component! (PDF and HTML)! Data Descriptor: narrative and structure!
- 52. 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
- 53. 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!
- 54. 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
- 55. 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)!
- 56. Hanke: Neuroscience ! ! ! ! ! ! ! ! ! ! Code in GitHub New Dataset Data in OpenfMRI Source code in GitHub Big Data
- 57. Stefano: Stem Cells! Associated Nature Article Data - figshare - NCBI GEO Integrated figshare data viewer
- 58. ! ! ! ! ! ! ! ! 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! 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!
- 59. Export to various formats (ISA_tab, RDF, etc) Linking between research papers, Data Descriptors, and data records Making data discoverable ! We currently recognize over 50 public data repositories! !
- 60. 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!
- 61. • 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! 61 Current content is diverse – bimonthly releases !
- 62. Take home messages http://www.flickr.com/photos/12308429@N03/4957994485/ u Make sure your research outputs make an impact! u Open your research outputs, via the right channels to get cited and credited u Contribute to the reproducible research movement and to FAIR data
- 63. Take home messages u Uniquely identify yourself via ORCID u Share identified generic research outputs, e.g. FigShare u Share and deposit code, e.g. GitHub, Bitbucket http://www.flickr.com/photos/idiolector/289490834/
- 64. Take home messages u Learn about open standards in your area, via e.g. BioSharing u Select tools that implement relevant standards, e.g. ISA u Publish not just in traditional journals, but think Scientific Data http://www.flickr.com/photos/webhamster/2582189977/
- 65. Data Consultant, Honorary Academic Editor Associate Director, Principal Investigator The rise of the data-centric ! research and publication enterprises! ! A journey into the whirlwind! Susanna-Assunta Sansone, PhD! ! uk.linkedin.com/in/sasansone! @biosharing! @isatools! @scientificdata! ! http://www.slideshare.net/SusannaSansone Oxford Interdisciplinary Bioscience Doctoral Training Partnership (DTP) – 31 July, 2014