Download as PDF, PPTX
Uploaded bySusanna-Assunta Sansone
SciDataCon 2014 Data Papers and their applications workshop - NPG Scientific Data
The document discusses the evolution of data papers and their significance in scientific publishing, emphasizing the role of publishers in facilitating data sharing and reproducibility. It highlights the challenges of data unavailability, legal barriers to sharing, and the need for better-supported open data practices. The introduction of data descriptors aims to improve the discoverability and reusability of datasets, ensuring that critical methodological details are accessible for future research applications.
More Related Content
Similar to SciDataCon 2014 Data Papers and their applications workshop - NPG Scientific Data
SciDataCon 2014 Data Papers and their applications workshop - NPG Scientific Data
- 1. SciDataCon2014, 2-5 November,2014 Data Papers and their applications:! examples from ! Nature Publishing Group and Ubiquity Press! 1. Introduction! 2. Anatomy of a data paper - cases studies from specific journals! • Nature Publishing Group - Scientific Data, Susanna-Assunta Sansone! • Ubiquity Press - Open Health Data, Brian Hole! 3. Feedback and discussion!
- 2. Consultant, Honorary AcademicEditor Associate Director, Principal Investigator ! Introduction! The role of publishers and data papers ! ! Susanna-Assunta Sansone, PhD! ! ! @biosharing! @isatools! @scientificdata! ! SciDataCon2014, 2-5 November, 2014
- 3.
- 4. A community mobilizationfor “openness” http://discovery.urlibraries.org/ https://okfn.org image by Greg Emmerich • Open data is a means to do better science more efficiently! • Licenses, copyright and IP are legal barriers to data sharing and reuse! o Licenses are for asserting rights; waivers are for giving them up, maximising potential for data reuse, integration and discovery of new knowledge! • Creative Commons CC0! o interoperability: CC0 is human and machine-readable! o universality: CC0 is global and universal and widely recognized! o simplicity: no need for humans to make, and respond to, individual data requests! http://opendefinition.org/licenses/ http://pantonprinciples.org https://www.copyrightsworld.com https://creativecommons.org
- 5. 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?
- 6. 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!
- 7. Movement for FAIRdata in life and medical sciences http://bd2k.nih.gov/workshops.html#ADDS
- 8. Because, in allfairness, not much data is FAIR! 8
- 9. Data unavailability andincomplete annotations
- 10. 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 !
- 11. Responsibilities lie acrossseveral stakeholder groups
- 12. Role of publishersas “agents of change” • Data has to become an integral part of scholarly communications! ! • Publishers occupy a leverage point in this process!
- 13. 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
- 14. 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
- 15. Nature Publishing Group:the changing landscape Human Genome 2001 62 Pages, 150 Authors, 49 Figure, 27 tables Encode Project 2012 30 papers, 3 Journals
- 16. 2013 Credit to: Iain Hrynaszkiewicz
- 17. 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
- 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 • Extended data o expandable text and extra figures; rolled out at Nature
- 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 • 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:
- 20. From made reproducibleto 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”
- 21. Data journals everywhere? Credit to: Iain Hrynaszkiewicz
- 22. Consultant, Honorary AcademicEditor Associate Director, Principal Investigator ! ! ! ! ! ! ! ! ! ! @scientificdata! Susanna-Assunta Sansone, PhD! @biosharing! @isatools! ! ! SciDataCon2014, 2-5 November, 2014 A new open-access, online-only publication for descriptions of scientifically valuable datasets !
- 23. • 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
- 24. Introducing a newcontent type: the Data Descriptor • Designed to make data more discoverable, interpretable and reusable! • Concerned with the facts behind the methodology of data generation/collection and processing! • Complements a journal article! 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
- 25. Data Descriptor: narrativeand structure! ! ! ! Experimental metadata or ! structured component! (in-house curated, machine-readable formats)! Article or ! narrative component! (PDF and HTML) !
- 26. 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! !
- 27. 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! !
- 28. 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
- 29. Data Descriptor: structure- content ! General-purpose, machine-readable format, designed to support: • description of the experimental workflow • explicit and discoverable annotations • provenance tracking • use community-defined minimal reporting guidelines and terminologies Data file or ! record in a database! analysis ! method! script!
- 30. 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!
- 31. Data Descriptor: structure- content ! It allows to relate samples, and their descriptions to the data files
- 32. 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 have released a technical specification:! Data file or ! record in a database! analysis ! method! script!
- 33. Workflow overview! Green:author; Purple: repository; Blue: SciData; Red: production
- 34. Collect Data! Publishyour data early! Follow-up experiments! Publish Findings! Publish Data! Scientific Data’s prior publication policy with other NPG journals protects your ability to publish the screen data and the hits later Credit to: Andrew Hufton
- 35. Hao et al.:Environmental! 8 citations Data sets from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS), which provides drought information based on multiple drought indicators
- 36. Hao et al.:Environmental! 8 citations New Dataset • Data in figshare • Code in figshare
- 37. Hao et al.:Environmental! 8 citations New Dataset • Data in figshare • Code in figshare • Cited in Science
- 38. Or your dataand findings simultaneously/after! Collect Data! Follow-up experiments! Publish Findings! Submit Data! Hold publication! Scientific Data will hold a Data Descriptor publication that has been accepted for publication, while your other related research publications clear peer review Credit to: Andrew Hufton
- 39. Messina et al.:Epidemiology! 4 citations The most comprehensive geographic collection of human dengue virus occurrence data (1960 -2012), linked to point or polygon locations, derived from peer-reviewed literature and case reports as well as informal online sources
- 40. ! ! ! ! ! ! ! ! Scientific hypotheses:! Synthesis! Analysis! Conclusions! Messina et al.: Epidemiology! 4 citations Associated Nature Article • Data in figshare 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!
- 41. Value added componentintegrated in a growing ecosystem! Research papers Descriptors Data Data records
- 42. Progressively refine theguidance to authors ! 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.
- 43. 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
- 44. 4 Big data | CSE 2014 4 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 !
- 45. Citations of andlinks to data files - databases!
- 46. 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!
- 47. 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:! 47
- 49. 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
- 50. 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 ● Göttingen State and University Library, Germany ● 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
- 51. SciDataCon2014, 2-5 November,2014 Data Papers and their applications:! examples from ! Nature Publishing Group and Ubiquity Press! Feedback and discussion! • Based on what you have heard today, how well do these journals fit with your/researchers at your instituteʼs publication and data management workflow? ! • What are the benefits to data publication? ! • What are the risks/barriers?! • What can publishers/journal do to incentivise data publication?!