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Open Access Week - Oxford, 20-24 Oct 2014

This document summarizes Susanna-Assunta Sansone's presentation on open access and open data at Nature Publishing Group. Some key points discussed include: - The benefits of open data including reducing errors/fraud and increasing return on investment in research. However, barriers also exist such as lack of incentives and standards. - Recent initiatives at NPG to improve data/reproducibility such as requiring data behind figures and expanding methods sections. - The role of data journals in increasing credit/visibility for shared data and promoting standards/best practices. - Market research found researchers want increased visibility, usability, and credit for sharing their data.

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Consultant, Honorary Academic Editor Associate Director, Principal Investigator ! Open access and open data at ! Nature Publishing Group: ! better data = better science! ! Susanna-Assunta Sansone, PhD! ! ! @biosharing! @isatools! @scientificdata! ! Open Access Week at Oxford, 20-24 October, 2014 http://www.slideshare.net/SusannaSansone
Credit to: https://projects.ac/blog/five-top-reasons-to-protect-your-data-and-practise-safe-science/
A community mobilization http://discovery.urlibraries.org/ image by Greg Emmerich http://www.theguardian.com/higher-education-network/blog/2014/jun/26 https://okfn.org
Open access is not enough on its own http://www.theguardian.com/higher-education-network/blog/2014/jun/26 If your research has been funded by the taxpayer, there's a good chance you'll be encouraged to publish your results on an open access basis….. This final article makes publicly available the hypotheses, interpretations and conclusions of your research. But what about the data that led you to those results and conclusions?
Also open data is not always enough http://www.theguardian.com/higher-education-network/blog/2014/jun/26 So data that is in theory open and free to access! • may still be hard to get hold of! • it may not have been stored or cited in the appropriate manner! • it may not be interoperable with related data because it is not formatted appropriately; or! • it may not be reusable because it may not contain enough information for others to understand it!
Benefits and barriers to data sharing Credit to: Iain Hrynaszkiewicz Benefits! Barriers! • Reduction of error and fraud! • Increased return on investment in research! • Compliance with funder and journal mandates! • Reduce duplication and bias! • Reproduction/validation of research! • Testing additional hypotheses! • Use for teaching! • Integration with other data sets! • Increased citations ! • Concerns over inappropriate reuse! • Limited time/resources! • Costs associated with data sharing! • Human privacy concerns! • Unclear ownership of data/ authority to release data! • Lack of academic incentives/ recognition! • Lack of repositories or lack of awareness of repositories! • Protecting commercially sensitive information !
Movement for FAIR data in life and medical sciences http://bd2k.nih.gov/workshops.html#ADDS
The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 8 • make annotation explicit and discoverable • structure the descriptions for consistency • ensure/regulate access • deposit and publish • etc…. § To make any dataset ‘FAIR’, one must have standards, tools and best practices to: • report sufficient details • capture all salient features of the experimental workflow
The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 9 …breadth and depth ! of the experimental context! …is pivotal!
sample characteristic(s)! The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 1 0 experimental design! experimental variable(s)! technology(s)! measurement(s)! protocols(s)! data file(s)! ......!
Doing my fair share of work Working with and for: Increase the level of annotation at the source, tracking provenance and using community standards Notes and narrative! Spreadsheets and tables! Linked data and nanopublications! Notes in Lab Books (information for humans) Spreadsheets and Tables ( the compromise) Facts as RDF statements (information for machines)
Because, in all fairness, no much data is FAIR! 12
Because, in all fairness, no much data is FAIR!
Role of publishers as “agents of change” • 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! • Publishers occupy a leverage point in this process!
Publishers and data/reproducibility • Policies on access (to data, code, reagents etc.)! o Supporting funder & community needs! • Format and amount of content! o Methodological details, supplementary info, data integration and links to repositories! • Licensing for reuse! • Incentives to share! o Data citations! o Data journals and articles! • Quality assurance through peer review! Credit to: Iain Hrynaszkiewicz
Data/reproducibility at NPG Some important events! ! • 1996: Bermuda Principles! o prepublication of DNA sequence data! • 1998: Structural data! o accession codes required by Nature & Science! • 2002: MIAME community standards! • microarray data deposition public repositories required! • 2007: Methods sections! o Limitations for the online version removed! • 2009: Ioannidis et al. Nat Gen 41, 2, 149 ! Credit to: Veronique Kiermer
2013 Credit to: Iain Hrynaszkiewicz
Data/reproducibility at NPG Some important recent events 2013-2014 • Figure source data o putting data behind figures/graphs o rolled out at Nature and progressively across all other Nature branded titles Wang et al, Nature, 2013 doi:10.1038/nature12730
Data/reproducibility at NPG Some important recent events 2013-2014 • Figure source data o putting data behind figures/graphs o rolled out at Nature and progressively across all other Nature branded titles • Extended data o expandable text and extra figures; rolled out at Nature
Data/reproducibility at NPG Some important recent events 2013-2014 • Figure source data o putting data behind figures/graphs o rolled out at Nature and progressively across all other Nature branded titles • Extended data o expandable text and extra figures; rolled out at Nature • Data citation o tackling both styling and format; monitoring community developments, such the Data Citation Synthesis Group o to be rolled out across all Nature branded titles and Scientific Data • Code reproducibility o peer review, availability and reuse • NPG’s Linked Data release – CC0 • A new data publication platform:
Nature Publishing Group: the changing landscape Human Genome 2001 62 Pages, 150 Authors, 49 Figure, 27 tables Encode Project 2012 30 papers, 3 Journals
The role of data journals/articles • Credit! • Unpublished data! • Peer review focus! • Value of data vs. analysis! • Discoverability! • Reusability! • Narrative/context! • “Intelligently open data”! Credit to: Iain Hrynaszkiewicz
Data journals everywhere? Credit to: Iain Hrynaszkiewicz
market research (2011) • Scope of survey! o How much data researchers produce, in what format and what they do with it! o Perceived availability of public repositories! o Perceptions of the Scientific Data concept! o Level/nature of data journal peer review! • Respondent characteristics! o 387 respondents (329 active researchers)! o Physics (24%), Earth and environmental science (21%), Biology (20%) Chemistry (19%) Others (16%)! Credit to: Iain Hrynaszkiewicz
market research (2011) • Key survey data o 60% share their data with their colleagues o 50% look at other researchers’ datasets at least once a month o very few respondents produce more than 1TB of data per year; the majority produce less than 1G o 45% unaware of a repository for some of their data o 90% reacted positively to the concept of Scientific Data o 80% believed Scientific Data would increase data deposition o what do researchers want from a data publication? 96% - increased visibility and discovery 95% - increased usability of their research data 93% - credit mechanism for deposit of data 80% - peer review of content/datasets Credit to: Iain Hrynaszkiewicz
• Get Credit for Sharing Your Data • Publications will be listed in the major indexes and will be citeable • Focused on Data Reuse • All the information others need to reuse the data; no interpretative analysis or hypothesis testing • Open-access • Authors select from three Creative Commons licences for the main • Data Descriptor. Each publication supported by curated CC0 metadata • Peer-reviewed • Rigorous peer-review managed by our Editorial Board of academic researchers ensures data quality and standards • Promoting Community Data Repositories • Data stored in community data repositories
Supported by:! Advisory Panel including senior researchers, funders, librarians and curators Michael Huerta ● National Institutes of Health, USA ● Mark Thorley ● Natural Environment Research Council, UK ● Patricia Cruse ● University of California, USA ● Susan Gregurick ● Office of Biological and Environmental Research, Department of Energy, USA ● Ioannis Xenarios ● Swiss Institute of Bioinformatics, Switzerland ● Chris Bowler ● IBENS, France ● Mark Forster ● Syngenta, UK ● Anthony Rowe ● Johnson & Johnson, USA ● Stephen Chanock ● National Cancer Institute, USA ● Weida Tong ● National Center for Toxicological Research, FDA, USA ● Albert J. R. Heck ● Utrecht University, The Netherlands ● Johanna McEntyre ● EMBL-EBI, European Bioinformatics Institute, UK ● Simon Hodson ● CODATA, France ● Joseph R. Ecker ● Howard Hughes Medical Institute & Salk Institute, USA ● Stephen Friend ● Sage Bionetworks, USA ● Jessica Tenenbaum ● Duke Translational Medicine Institute, USA ● Anne-Claude Gavin ● EMBL, Germany ● David Carr ● Wellcome Trust, UK ● Wolfram Horstmann ● University of Oxford, UK ● Piero Carninci ● RIKEN Omics Science Center, Japan ● Pascale Gaudet ● Swiss Institute of Bioinformatics, Switzerland ● Judith A. Blake ● The Jackson Laboratory, USA ● Richard H. Scheuermann ● J. Craig Venter Institute, USA ● Caroline Shamu ● Harvard Medical School, USA Susanna-Assunta Sansone Honorary Academic Editor (University of Oxford, UK) Andrew L Hufton Managing Editor Varsha Khodiyar Editorial Curator Iain Hrynaszkiewicz Publisher An open access, peer-reviewed publication for descriptions of scientifically valuable datasets! Launched May 2014
Introducing a new content type: the Data Descriptor • The data descriptor is only concerned with the facts behind the methodology of data generation/collection and processing! • A data descriptor complements a journal Synthesis Analysis Data Descriptor Conclusions Interpretation What is the sample? What did I do to generate the data? How was the data processed? Where is the data? Who did what when? Summary of Data Descriptor Facts Data Descriptor Journal article NARRATIVE article!
Data Descriptor: narrative and structure! Article or ! narrative component! (PDF and HTML) ! ! ! ! Experimental metadata or ! structured component! (in-house curated, machine-readable formats)!
Data Descriptor: narrative and structure! Article or ! narrative component! (PDF and HTML) ! ! ! ! Experimental metadata or ! structured component! (in-house curated, machine-readable formats)!
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! In traditional publications this information is not provided in a sufficiently detailed manner However this information is essential for understanding, reusing, and reproducing datasets Sections:! • Title! • Abstract! • Background & Summary! • Methods! • Technical Validation! • Data Records! • Usage Notes ! • Figures & Tables ! • References! • Data Citations! !
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! !
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
Data Descriptor: structure - content ! Includes fields describing: • each study, linking to relevant sections of the Data Descriptor article • authors’ details, including ORCID • publications • funding sources and funders’ name, via FundRef • experimental factors • study design • assays • protocols Data file or ! record in a database! analysis ! method! script!
Data Descriptor: structure - content ! 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 will release a technical specification:! Data file or ! record in a database! analysis ! method! script!
Relation with traditional articles - 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!
Relation with traditional articles - 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
Citations of and links to data files - databases!
Value added component integrated in a growing ecosystem! We currently recognize over 60 public data repositories! ! Research papers Descriptors Data Data records
Over 500 Over 600 A web-based, curated and searchable portal works to ensure the standards and databases are registered, informative and discoverable and accessible, monitoring the development and evolution of standards, their use in databases and the adoption of both in data policies.
Over 500 Over 600 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
Over 500 Over 600 Mapping the landscape of community –developed standards, databases and data policies in the life sciences, broadly covering biological, natural an biomedical sciences
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
Advisory Board and Working Group - core members and adopters Operational Team
Helping authors find the right place for the data! • We currently recognize over 60 public data repositories, and provide advice on the best place for authors to archive their data! • We have integrated systems with both:! ! ! 2 4 3 10 4 1 4 3 4 “Omics” is emphasized among basic life-sciences repositories 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
4 Big data | CSE 2014 6 Repositories criteria! 1. Broad support and recognition within their scientific community ! 2. Ensure long-term persistence and preservation of datasets! 3. Provide expert curation ! 4. Implement relevant, community-endorsed reporting requirements ! Progressively monitor this via ! 5. Provide for confidential review of submitted datasets ! 6. Provide stable identifiers for submitted datasets ! 7. Allow public access to data without unnecessary restrictions !
Open Access – APC supported! Data: the primary datasets resides 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 is CC0! Data Descriptor - narrative component: describing the methodology of data generation/collection and processing is licensed under either of the following, by author choice: OA Article processing charges: $1,000 USD / £650 GBP / €750 for each accepted article
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 data repository!
Current content is diverse - bimonthly releases ! • Neuroscience, ecology, epidemiology, environmental science, functional genomics, metabolomics, toxicology etc.! • New previously published individual datasets, curated aggregation and citizen science:! 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, Dryad and domain specific databases! • Code deposited in figshare and GitHub! • First collection:! 49
Hanke: Neuroscience ! New Dataset Data in OpenfMRI Source code in GitHub Big Data ! ! ! ! ! ! ! ! ! Code in GitHub
Stefano: Stem Cells! Associated Nature Article Data - figshare - NCBI GEO Integrated figshare data viewer
Hao: Environmental! New Dataset Data in figshare Code in figshare Integrated figshare data viewer Cited in Science
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
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/
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/
Acknowledgements! Advisory Boards and Collaborators Philippe Rocca-Serra, PhD Alejandra Gonzalez-Beltran, PhD Milo Thurston, PhD 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 Eamonn Maguire, DPhil candidate And we are hiring a software developer!

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Open Access Week - Oxford, 20-24 Oct 2014

  • 1.
    Consultant, Honorary AcademicEditor Associate Director, Principal Investigator ! Open access and open data at ! Nature Publishing Group: ! better data = better science! ! Susanna-Assunta Sansone, PhD! ! ! @biosharing! @isatools! @scientificdata! ! Open Access Week at Oxford, 20-24 October, 2014 http://www.slideshare.net/SusannaSansone
  • 2.
  • 3.
    A community mobilization http://discovery.urlibraries.org/ image by Greg Emmerich http://www.theguardian.com/higher-education-network/blog/2014/jun/26 https://okfn.org
  • 4.
    Open access isnot enough on its own http://www.theguardian.com/higher-education-network/blog/2014/jun/26 If your research has been funded by the taxpayer, there's a good chance you'll be encouraged to publish your results on an open access basis….. This final article makes publicly available the hypotheses, interpretations and conclusions of your research. But what about the data that led you to those results and conclusions?
  • 5.
    Also open datais not always enough http://www.theguardian.com/higher-education-network/blog/2014/jun/26 So data that is in theory open and free to access! • may still be hard to get hold of! • it may not have been stored or cited in the appropriate manner! • it may not be interoperable with related data because it is not formatted appropriately; or! • it may not be reusable because it may not contain enough information for others to understand it!
  • 6.
    Benefits and barriersto data sharing Credit to: Iain Hrynaszkiewicz Benefits! Barriers! • Reduction of error and fraud! • Increased return on investment in research! • Compliance with funder and journal mandates! • Reduce duplication and bias! • Reproduction/validation of research! • Testing additional hypotheses! • Use for teaching! • Integration with other data sets! • Increased citations ! • Concerns over inappropriate reuse! • Limited time/resources! • Costs associated with data sharing! • Human privacy concerns! • Unclear ownership of data/ authority to release data! • Lack of academic incentives/ recognition! • Lack of repositories or lack of awareness of repositories! • Protecting commercially sensitive information !
  • 7.
    Movement for FAIRdata in life and medical sciences http://bd2k.nih.gov/workshops.html#ADDS
  • 8.
    The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 8 • make annotation explicit and discoverable • structure the descriptions for consistency • ensure/regulate access • deposit and publish • etc…. § To make any dataset ‘FAIR’, one must have standards, tools and best practices to: • report sufficient details • capture all salient features of the experimental workflow
  • 9.
    The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 9 …breadth and depth ! of the experimental context! …is pivotal!
  • 10.
    sample characteristic(s)! TheInternational Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 1 0 experimental design! experimental variable(s)! technology(s)! measurement(s)! protocols(s)! data file(s)! ......!
  • 11.
    Doing my fairshare of work Working with and for: Increase the level of annotation at the source, tracking provenance and using community standards Notes and narrative! Spreadsheets and tables! Linked data and nanopublications! Notes in Lab Books (information for humans) Spreadsheets and Tables ( the compromise) Facts as RDF statements (information for machines)
  • 12.
    Because, in allfairness, no much data is FAIR! 12
  • 13.
    Because, in allfairness, no much data is FAIR!
  • 14.
    Role of publishersas “agents of change” • 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! • Publishers occupy a leverage point in this process!
  • 15.
    Publishers and data/reproducibility • Policies on access (to data, code, reagents etc.)! o Supporting funder & community needs! • Format and amount of content! o Methodological details, supplementary info, data integration and links to repositories! • Licensing for reuse! • Incentives to share! o Data citations! o Data journals and articles! • Quality assurance through peer review! Credit to: Iain Hrynaszkiewicz
  • 16.
    Data/reproducibility at NPG Some important events! ! • 1996: Bermuda Principles! o prepublication of DNA sequence data! • 1998: Structural data! o accession codes required by Nature & Science! • 2002: MIAME community standards! • microarray data deposition public repositories required! • 2007: Methods sections! o Limitations for the online version removed! • 2009: Ioannidis et al. Nat Gen 41, 2, 149 ! Credit to: Veronique Kiermer
  • 17.
    2013 Credit to: Iain Hrynaszkiewicz
  • 18.
    Data/reproducibility at NPG Some important recent events 2013-2014 • Figure source data o putting data behind figures/graphs o rolled out at Nature and progressively across all other Nature branded titles Wang et al, Nature, 2013 doi:10.1038/nature12730
  • 19.
    Data/reproducibility at NPG Some important recent events 2013-2014 • Figure source data o putting data behind figures/graphs o rolled out at Nature and progressively across all other Nature branded titles • Extended data o expandable text and extra figures; rolled out at Nature
  • 20.
    Data/reproducibility at NPG Some important recent events 2013-2014 • Figure source data o putting data behind figures/graphs o rolled out at Nature and progressively across all other Nature branded titles • Extended data o expandable text and extra figures; rolled out at Nature • Data citation o tackling both styling and format; monitoring community developments, such the Data Citation Synthesis Group o to be rolled out across all Nature branded titles and Scientific Data • Code reproducibility o peer review, availability and reuse • NPG’s Linked Data release – CC0 • A new data publication platform:
  • 21.
    Nature Publishing Group:the changing landscape Human Genome 2001 62 Pages, 150 Authors, 49 Figure, 27 tables Encode Project 2012 30 papers, 3 Journals
  • 22.
    The role ofdata journals/articles • Credit! • Unpublished data! • Peer review focus! • Value of data vs. analysis! • Discoverability! • Reusability! • Narrative/context! • “Intelligently open data”! Credit to: Iain Hrynaszkiewicz
  • 23.
    Data journals everywhere? Credit to: Iain Hrynaszkiewicz
  • 24.
    market research (2011) • Scope of survey! o How much data researchers produce, in what format and what they do with it! o Perceived availability of public repositories! o Perceptions of the Scientific Data concept! o Level/nature of data journal peer review! • Respondent characteristics! o 387 respondents (329 active researchers)! o Physics (24%), Earth and environmental science (21%), Biology (20%) Chemistry (19%) Others (16%)! Credit to: Iain Hrynaszkiewicz
  • 25.
    market research (2011) • Key survey data o 60% share their data with their colleagues o 50% look at other researchers’ datasets at least once a month o very few respondents produce more than 1TB of data per year; the majority produce less than 1G o 45% unaware of a repository for some of their data o 90% reacted positively to the concept of Scientific Data o 80% believed Scientific Data would increase data deposition o what do researchers want from a data publication? 96% - increased visibility and discovery 95% - increased usability of their research data 93% - credit mechanism for deposit of data 80% - peer review of content/datasets Credit to: Iain Hrynaszkiewicz
  • 26.
    • Get Creditfor Sharing Your Data • Publications will be listed in the major indexes and will be citeable • Focused on Data Reuse • All the information others need to reuse the data; no interpretative analysis or hypothesis testing • Open-access • Authors select from three Creative Commons licences for the main • Data Descriptor. Each publication supported by curated CC0 metadata • Peer-reviewed • Rigorous peer-review managed by our Editorial Board of academic researchers ensures data quality and standards • Promoting Community Data Repositories • Data stored in community data repositories
  • 27.
    Supported by:! AdvisoryPanel including senior researchers, funders, librarians and curators Michael Huerta ● National Institutes of Health, USA ● Mark Thorley ● Natural Environment Research Council, UK ● Patricia Cruse ● University of California, USA ● Susan Gregurick ● Office of Biological and Environmental Research, Department of Energy, USA ● Ioannis Xenarios ● Swiss Institute of Bioinformatics, Switzerland ● Chris Bowler ● IBENS, France ● Mark Forster ● Syngenta, UK ● Anthony Rowe ● Johnson & Johnson, USA ● Stephen Chanock ● National Cancer Institute, USA ● Weida Tong ● National Center for Toxicological Research, FDA, USA ● Albert J. R. Heck ● Utrecht University, The Netherlands ● Johanna McEntyre ● EMBL-EBI, European Bioinformatics Institute, UK ● Simon Hodson ● CODATA, France ● Joseph R. Ecker ● Howard Hughes Medical Institute & Salk Institute, USA ● Stephen Friend ● Sage Bionetworks, USA ● Jessica Tenenbaum ● Duke Translational Medicine Institute, USA ● Anne-Claude Gavin ● EMBL, Germany ● David Carr ● Wellcome Trust, UK ● Wolfram Horstmann ● University of Oxford, UK ● Piero Carninci ● RIKEN Omics Science Center, Japan ● Pascale Gaudet ● Swiss Institute of Bioinformatics, Switzerland ● Judith A. Blake ● The Jackson Laboratory, USA ● Richard H. Scheuermann ● J. Craig Venter Institute, USA ● Caroline Shamu ● Harvard Medical School, USA Susanna-Assunta Sansone Honorary Academic Editor (University of Oxford, UK) Andrew L Hufton Managing Editor Varsha Khodiyar Editorial Curator Iain Hrynaszkiewicz Publisher An open access, peer-reviewed publication for descriptions of scientifically valuable datasets! Launched May 2014
  • 28.
    Introducing a newcontent type: the Data Descriptor • The data descriptor is only concerned with the facts behind the methodology of data generation/collection and processing! • A data descriptor complements a journal Synthesis Analysis Data Descriptor Conclusions Interpretation What is the sample? What did I do to generate the data? How was the data processed? Where is the data? Who did what when? Summary of Data Descriptor Facts Data Descriptor Journal article NARRATIVE article!
  • 29.
    Data Descriptor: narrativeand structure! Article or ! narrative component! (PDF and HTML) ! ! ! ! Experimental metadata or ! structured component! (in-house curated, machine-readable formats)!
  • 30.
    Data Descriptor: narrativeand structure! Article or ! narrative component! (PDF and HTML) ! ! ! ! Experimental metadata or ! structured component! (in-house curated, machine-readable formats)!
  • 31.
    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! In traditional publications this information is not provided in a sufficiently detailed manner However this information is essential for understanding, reusing, and reproducing datasets Sections:! • Title! • Abstract! • Background & Summary! • Methods! • Technical Validation! • Data Records! • Usage Notes ! • Figures & Tables ! • References! • Data Citations! !
  • 32.
    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! !
  • 33.
    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
  • 34.
    Data Descriptor: structure- content ! Includes fields describing: • each study, linking to relevant sections of the Data Descriptor article • authors’ details, including ORCID • publications • funding sources and funders’ name, via FundRef • experimental factors • study design • assays • protocols Data file or ! record in a database! analysis ! method! script!
  • 35.
    Data Descriptor: structure- content ! 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 will release a technical specification:! Data file or ! record in a database! analysis ! method! script!
  • 36.
    Relation with traditionalarticles - 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!
  • 37.
    Relation with traditionalarticles - 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
  • 38.
    Citations of andlinks to data files - databases!
  • 39.
    Value added componentintegrated in a growing ecosystem! We currently recognize over 60 public data repositories! ! Research papers Descriptors Data Data records
  • 40.
    Over 500 Over600 A web-based, curated and searchable portal works to ensure the standards and databases are registered, informative and discoverable and accessible, monitoring the development and evolution of standards, their use in databases and the adoption of both in data policies.
  • 41.
    Over 500 Over600 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
  • 42.
    Over 500 Over600 Mapping the landscape of community –developed standards, databases and data policies in the life sciences, broadly covering biological, natural an biomedical sciences
  • 43.
    Researchers, developers andcurators 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
  • 44.
    Advisory Board andWorking Group - core members and adopters Operational Team
  • 45.
    Helping authors findthe right place for the data! • We currently recognize over 60 public data repositories, and provide advice on the best place for authors to archive their data! • We have integrated systems with both:! ! ! 2 4 3 10 4 1 4 3 4 “Omics” is emphasized among basic life-sciences repositories 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
  • 46.
    4 Big data | CSE 2014 6 Repositories criteria! 1. Broad support and recognition within their scientific community ! 2. Ensure long-term persistence and preservation of datasets! 3. Provide expert curation ! 4. Implement relevant, community-endorsed reporting requirements ! Progressively monitor this via ! 5. Provide for confidential review of submitted datasets ! 6. Provide stable identifiers for submitted datasets ! 7. Allow public access to data without unnecessary restrictions !
  • 47.
    Open Access –APC supported! Data: the primary datasets resides 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 is CC0! Data Descriptor - narrative component: describing the methodology of data generation/collection and processing is licensed under either of the following, by author choice: OA Article processing charges: $1,000 USD / £650 GBP / €750 for each accepted article
  • 48.
    Peer review processfocused 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 data repository!
  • 49.
    Current content isdiverse - bimonthly releases ! • Neuroscience, ecology, epidemiology, environmental science, functional genomics, metabolomics, toxicology etc.! • New previously published individual datasets, curated aggregation and citizen science:! 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, Dryad and domain specific databases! • Code deposited in figshare and GitHub! • First collection:! 49
  • 50.
    Hanke: Neuroscience ! New Dataset Data in OpenfMRI Source code in GitHub Big Data ! ! ! ! ! ! ! ! ! Code in GitHub
  • 51.
    Stefano: Stem Cells! Associated Nature Article Data - figshare - NCBI GEO Integrated figshare data viewer
  • 52.
    Hao: Environmental! NewDataset Data in figshare Code in figshare Integrated figshare data viewer Cited in Science
  • 54.
    http://www.flickr.com/photos/12308429@N03/4957994485/ u Makesure 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
  • 55.
    u Uniquely identifyyourself 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/
  • 56.
    u Learn aboutopen 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/
  • 57.
    Acknowledgements! Advisory Boardsand Collaborators Philippe Rocca-Serra, PhD Alejandra Gonzalez-Beltran, PhD Milo Thurston, PhD 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 Eamonn Maguire, DPhil candidate And we are hiring a software developer!