This document discusses data publishing models and workflows, emphasizing the importance of making research data available and discoverable online through dedicated repositories and journals. It outlines various components involved in data publishing, such as quality assurance, peer review, and persistent identification of data, and highlights the need for standardized practices to enhance the usability and interoperability of published data. The concluding sections focus on current challenges in data publishing, including discoverability, versioning, and the necessity for robust documentation and quality assurance processes.

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Data Publishing Models by Sünje Dallmeier-Tiessen

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  Data Publishing ModelsSünjeDallmeier-Tiessen, PhDCERN, Harvard UniversityFor the RDA-WDS Data Publishing Workflow GroupJune 9th, 2015
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  Topics• What isdata publishing• Why do we care about it (today)• Models in data publishing• Building blocks• Information gathered through trusted data publishing• Relevance and conclusions for today’s workshopThis is work conducted by the RDA-WDS group on datapublishing workflows, chaired in collaboration with FionaMurphy and Theo Bloom.
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  Data Publishing… describesthe process of making research data andother research objects available on the web so that theycan be discovered and referred to in a unique andpersistent way.At its best, data publishing takes place through dedicateddata repositories and data journals and ensures that thepublished research objects are well documented, curated,archived for the long term, interoperable, citable andquality assured.Thus, they are reusable and discoverable on the longterm.
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  Examples
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  Analysis elements• Discipline,responsible units (i.e. their roles)• Function of workflow• PID assignment: DOI, ARK, etc.• Peer review of data (e.g. by researcher & editorial review)• Curatorial review of metadata (e.g. by institutional or subject repository?)• Technical review & checks (e.g. for data integrity at repository uponingestion)• Formats covered• Persons/Roles involved, e.g. editor, publisher, data repository manager,etc.• Links to additional data products (data paper; review documents; otherjournal articles) or “stand-alone” product• Links to grants, usage of author PIDs• Discoverability: Indexing of the data -- if yes, where?• Data citation facilitated• Data life cycle reference• Standards compliance
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  Repository’s perspective
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  DataDepositIngestQualityAssuranceDataManagementLT ArchivingDisseminationAccessProducer Consumer/ReuseSimplifiedgeneric repositoryworkflowResearcher with a central role during submission/depositionReview/QAmainlyinternalthroughdedicatedcurationpersonnel
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  DataDepositIngestQualityAssuranceLightDataManagementLT ArchivingDisseminationAccessProducerConsumer(disciplinary)IngestQualityAssuranceDetailedProject Repositories:•Data are published in a federateddata infrastructure• Data are added and corrected• Poor documentation• Usually no data backup• Light-weight quality assuranceagainst intl. and project standards• Tendency that the project datanever become stable• Currently no PIDs assigned orreserved but Handles plannedLong-term Archive:• Data are archived for the long term at asingle location• Data are stable and curated• Detailed documentation• Data backup/redundancy• Quality assurance process is moredetailed and includes a review• Data is a “snapshot” of the projectdata at a certain time• DOIs assigned to data collectionsConsumer(interdisciplinary)DisseminationAccessContent provided byM. StockhauseDisciplinaryrepositoryexample
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  Lessons learnt andquestions• Very diverse landscape• Discipline-specific and cross-discipline actions• Quality assurance a big topic in discipline-specificrepositories• Widespread persistent identification• Data citation awareness• Challenge: Versioning
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  Publisher’s perspective
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  ArticlepreparationDataSubmissionArticlesubmissionPeer ReviewProcess EditingProducerConsumer/ReuseSimplified generic publisher workflowResearcher takes over several roles: submitter, reviewer,editor potentially?- Article/datacontainer- Separatearticle anddatasetsPublishingDatarepositories
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  Example Workflows inDataverse:Connect Data to JournalsA. Journals include Dataverse as a Recommended RepositoryB. Authors Contribute Directly to a Journal’s DataverseC. Automated Integration of Journal + Dataverse (e.g., OJS)Slide by Eleni Castro
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  Example: Dryad repositoryintegratedwith journalsSlide by T. Bloom
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  Data publishing buildingblocksPrimary dataentry with PIDRepositoryentryMetadataCurationParallel datadescriptionData Paper orlink to itLink to resultspaperLinked andpublished qualityassuranceCuration,EditingprocessPeer reviewAny kind ofQA processAdditionalvisibilityPush toORCID, authorpages,impact/reputation buildingtoolsEnable index(Data citationindex, crawledby Google)Basic publishedproductAdd-ons: workflows for more documentation, QA, visibility
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  Trusted data publishingcontains:• Standardized information about the data– Disciplinary standards– Basic common metadata sets• Distinct Roles, Workflows and Responsibilities– Authorship, Submission– Curation– Quality Assurance– Peer review• Persistent Identification– Permanent reference– Data citation
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  Challenges• Interoperability challenges–Different metadata schemas– Rich vs. limited metadata• Discoverability challenges– E.g. no bi-directional linking– Usability challenges in aggregators• Metrics and accreditation• What information is needed for futurereuse/remix/reproducibility• How can this information be exposed – humanand machine readable
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  Thank you!
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  Data Publishing WorkflowsActivitiesand processes in a digital environmentthat lead to the publication of research data andother research objects on the Web. Theseactivities may be performed by humans or in anautomated fashion.In contrast to the interim or final publishedproducts, workflows are the means to curate,document, peer review and thus ensure andenhance the value of the published product.