Data sharing as part of the research workflow

Data sharing as part of the research workflow

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  Data sharing aspart of the research workflow Varsha Khodiyar, PhD Data Curation Editor, Scientific Data Nature Publishing Group @varsha_khodiyar @scientificdata Perspective from Scientific Data Data Perspective beyond Alliances, 3rd March 2016
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  Why the pushto share data? Research conduct Publication bias – what is submitted Experimental design Statistics Lab supervision and training Research reporting and sharing Gels, microscopy images Statistical reporting Methods description Data deposition and availability 2
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  Generating research datais expensive Just 18.1% NIH grant applications funded in 2014* • Hours spent writing grants? • Hours spent reviewing grants? Resources are finite/expensive • Modified animals • Specialized reagents Time and effort taken in the laboratory to generate good, valid data * report.nih.gov/success_rates/Success_ByIC.cfm
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  Data needs tobe… Discoverable Need to know it’s there Accessible Must be able to get to the data Usable Require sufficient information about how the data was generated Persistent Historical data access as part of the scientific record, as well as for new research Reliable Data provenance informs data reuse decisions Joint Declaration of Data Citation Principles www.force11.org/group/joint-declaration- data-citation-principles-final Achieving human and machine accessibility of cited data in scholarly publications Starr et al. PeerJ Computer Science (2015). doi:10.7717/peerj-cs.1 Making data count Kratz & Strasser. Sci. Data (2015). doi:10.1038/sdata.2015.39 The FAIR guiding principles for scientific data management and stewardship Williams et al. Sci. Data (in press)
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  Researchers already sharedata • Most researchers are sharing data, and using the data of others • Direct contact between researchers (on request) is a common way of sharing data • Repositories are second most common method of sharing Kratz and Strasser (2015) doi: 10.1371/journal.pone.0117619 9
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  But… Sharing of dataupon request from published articles • relies heavily on trust • when stored informally, disappears at a rate of ~17% per year (Vines et al. 2014; doi: 10.1016/j.cub.2013.11.014) Data shared in a repository • often not reusable due to insufficient context • may not be possible to determine reliability (peer review?) • may not be easily findable, if not referenced in a scholarly article • no scholarly credit for data producers
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  Data papers andjournals • Ensure formal storage in repository • Allow space for authors to include sufficient context for reuse • Peer reviewers often specifically requested to comment on data archive reusability • Data paper are formal works, giving scholarly credit to data producers • Formal data citations enabling data discovery via bibliographic indexes that researchers are used to using
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  Data journals andmultidisciplinary research Cross-domain data sharing vital for solving the most pressing world issues: • Public health (social science, epidemiology & molecular biology) • Resource management & sustainability (energy research, policy, ecology & climate science) Differences between researchers of vocabulary and expressions of reliability, mean clear descriptions of data become even more essential for cross-domain data sharing. Multidisciplinary data journals (e.g. Data Science Journal, Scientific Data): • provide a data sharing outlet to researchers in all domains • help datasets cross domain boundaries, data is more visible and searchable i.e. less siloing 8
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  Data reuse bythe research community 9 “The Data Descriptor made it easier to use the data, for me it was critical that everything was there…all the technical details like voxel size.” Professor Daniele Marinazzo
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  Data reuse bythe non-research community 10 http://www.nytimes.com/interactive/2014/12/30/science/history-of-ebola-in-24-outbreaks.html
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  Increasing the discoverabilityof data • Is data truly discoverable by researchers outside the original authors domain? • Too many papers to read in each person’s own field. • Could increasing the machine accessibility of data, result in increased data reuse?
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  Data Descriptors havehuman and machine readable components 12 Human readable representation of study i.e. article (HTML & PDF) Human readable representation of study i.e. article (HTML & PDF) Machine readable representation of study i.e. metadata
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  • We capturemetadata about the data being described in each Data Descriptor • The manuscript captures human readable metadata needed for data reuse • The curated metadata records capture machine readable metadata needed for machine based data discovery Metadata at Scientific Data
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  ISA format formachine readable metadata 14 • Study workflow • Key sample characteristics needed for data discovery • Relates samples to data files • Shows location of dataset • Uses controlled vocabularies and ontologies (where possible)
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  Metadata for datadiscovery Search by: • Data Repositories • Experiment design • Measurements made • Technologies used • Factor types • Sample Characteristics • Organism • Environment types • Geographic locations scientificdata.isa-explorer.org
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  16 After data analysis has been published Beforeanalysis has been published Authors not intending to analyse data Data Descriptors can be submitted and published at any point in the research workflow After data analysis has been published Before the analysis has been published Publication alongside analysis article Data as part of the publication workflow
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  Data as partof the research workflow? Papers usually written after analyses, key details can be forgotten • Ideally metadata would be captured during data generation process • Takes time and effort to capture adequate metadata of sufficient quality for data reuse Machine readable metadata • Metadata format needs to be decided prospectively • Researchers require professional expertise and guidance to use ontologies (essential for machine readability and discovery) How to ensure data generators are able to capture metadata easily and in sufficient detail for reuse? 17
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  Discoverable Machine based data discovery Implement data citations Use community ontologies Accessible& Persistent Encourage use of repositories Use persistent identifiers for data Usable Metadata capture during data generation process Encourage use of minimal reporting standards Reliable Encourage peer reviewers to evaluate data archive (structure, format) alongside the article Researcher incentives Recognise data as a first class scholarly work Provide tools for data visualization and discovery Building infrastructure to promote data sharing as part of the research workflow
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  Scientific Data atRDA Working groups Publishing Data Workflows (co-chair) BioSharing Registry (Susanna Sansone is co-chair) Interest groups Publishing Data Data Fabric Data in Context Metadata Certification of Digital Repositories 19
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  Visit nature.com/sdata Email [email protected] Tweet@ScientificData Honorary Academic Editor Susanna-Assunta Sansone Managing Editor Andrew L. Hufton Data Curation Editor Varsha K. Khodiyar Advisory Panel and Editorial Board including senior researchers, funders, librarians and curators Supported by