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Dataverse SSHOC enrichment of DDI support at EDDI'19 2

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The presentation for European DDI User Conference EDDI 2019 about DDI convertor tool and maturity of Dataverse based services.

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Dataverse SSHOC enrichment of DDI support at EDDI'19 2

  1. 1. Enrichment of DDI support in the Dataverse data repository Slava Tykhonov, Marion Wittenberg (DANS-KNAW) EDDI 2019, Tampere, Finland, December 3, 2019 Creative Commons Attribution 4.0 International (CC BY 4.0)
  2. 2. SSHOC objective and deliverables Objective Development of a research data repository service on EOSC, for SSH institutions currently without such a facility for their designated communities Deliverables After 38 months: Data repository service running on EOSC After 40 months: Report on principles of governance and sustainability of the data repository service
  3. 3. Development process DataverseSSHOC project has two parallel tracks of the development: ● Core development team is working on the modification and extension of the Dataverse core functionality. ● The application development team will create new or will integrate existent tools that will be published on Dataverse App Store website. Our goal is to build the distributed and mature data infrastructure based on sustainable microservices.
  4. 4. Services in European Open Science Cloud (EOSC) ● EOSC requires the level 8 of maturity (at least) ● we need the highest quality of software to be accepted as a service ● clear and transparent evaluation of services is essential ● the evidence of technical maturity is the key to success ● the limited warranty will allow to stop out-of-warranty services
  5. 5. Applications maturity level Every software package should follow the same CESSDA Maturity Model to be accepted as a service. https://zenodo.org/record/2591055#.XKR6ny2B2u5 Must have: k8s infrastructure with upstream Docker images, warranty statement, documentation, unit tests, Selenium tests, jenkins pipeline. Dataverse external applications with enough maturity that are deployed as a Cloud services can be connected to any Dataverse repository by using API Token.
  6. 6. Dataverse App Store We’re building a different services out of tools! Data preview: DDI Explorer, Spreadsheet/CSV, PDF, Text files, HTML, Images, video render, audio, JSON, GeoJSON/Shapefiles/Map, XML Interoperability: external controlled vocabularies (CESSDA CV Manager) Data processing: NESSTAR DDI migration tool Linked Data: RDF compliance including SPARQL endpoint Federated login: eduGAIN, PIONIER ID
  7. 7. DDI Converter tool It usually takes a lot of efforts and time to migrate metadata and data to any data repository like NESSTAR or DSpace to another repository. The main idea of the DDI Converter is to separate mappings from the conversion process and let metadata specialist to do it separately from the DDI migration pipeline. DDI Converter has a Docker infrastructure that allows to deploy it as image on Kubernetes or other Cloud platforms. You don’t need any development capacity to use it, just create mappings and the tool will do the rest!
  8. 8. Dataverse Metadata Crosswalk Source: https://docs.google.com/spreadsheets/d/10Luzti7svVTVKTA-px27oq3RxCUM-QbiTkm8iMd5C54/edit#gid=0
  9. 9. Why XSLT mappings? ● XSLT (1998) is a language designed primarily for transforming human readable documents into other self describing documents. ● DDI community is already using XSLT to map metadata from one format to another and collected a lot of mappings that can be reused. ● XSLT mappings for different DDI standards can be managed in the same github repository ● At the moment the knowledge of XSLT is a common job requirement for metadata specialists.
  10. 10. DDI Converter in a nutshell ● Developed in Python3 as Flask application with pyDataverse module (AUSSDA) ● DDI Converter uses XSLT mappings stored in github ● all CESSDA DDI transformations are also supported https://github.com/MetadataTransform/ddi-xslt ● Swagger framework allows to use the tool as a manual deposit form and in the same time as a microservice builtin in the migration pipeline ● Docker image deployed locally or on Cloud can connect DDI Converter to any Dataverse instance by API ● You can migrate your data even if Dataverse instance is maintained by someone else. Just copy API Token from your Dataverse account and put in DDI Converter, and it will do the job for you!
  11. 11. Using Swagger as dataset deposit form Import steps: 1. Open Swagger page 2. Upload DDI file 3. Select XSLT mapping from github 4. Copy API Token from user page in Dataverse 5. Choose a subdataverse where dataset shoud go 6. Start migration process in one click 7. Check result in Dataverse Interested? https://github.com/IQSS/dataverse- ddi-converter-tool
  12. 12. What’s next? DDI explorer as a service DDI Explorer is a Dataverse application developed by Scholars Portal dataverse.scholarsportal.info Dataverse SSHOC project got it integrated in Docker image and incorporated in the Kubernetes infrastructure Dataverse-docker module DDI explorer will be delivered as a Cloud service that can be connected to any Dataverse instance!
  13. 13. Spreadsheet previewer This tool was contributed by Dataverse SSHOC project and integrated by Harvard IQSS in Dataverse 4.18 It allows to browse through web interface for viewing data directly without download. Spreadsheet viewer can increase chances to find a proper data and to get a citation - more FAIRness!
  15. 15. Join our community https://www.sshopencloud.eu info@sshopencloud.eu @SSHOpenClou d/in/sshopencloud