Upcoming Webinars

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The digital change and the huge increase of volume and complexity of data will revolutionize science and industry. 

To make sense of this evolving and complex landscape, and to make sure the work done has a high impact, RDA will continue to provide training opportunities.

RDA offers a series of training webinars, face-to-face workshops, hackathons/datathons partly organized as “summer schools” and special meetings on request. The topics will be primarily related with RDA recommendations and outputs, but it will also address general topics facilitating data sharing and re-use, interviews with notable people and information sessions such as reports from RDA plenaries. 

In case that you are unable to attend a webinar: All webinars will be recorded, and the videos will be published shortly after the event. They are available in the Past Webinars section of this website.

 

Important: all times indicated in this list are in UTC time zone!

See What time is it ... in RDA? for reference

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27 May 2021 UTC

Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles

Date: 
27 May 2021 - 23:00 UTC

Register for the Webinar Here

 

 

This 1-hour webinar will address the issue of a lack of a consistent framework and agreed definitions of best practices across data communities in guiding the management of data versioning,  including the citation and identification of different versions of a data set. The talk will introduce six foundational principles for versioning of datasets: Revision, Release, Granularity, Manifestation, Provenance and Citation which were derived from the collection and analysis of 39 use cases and current practices of data versioning across 33 organisations, and used the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework. The six principles provide a high-level framework for researchers in guiding a consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures, as well as systematic identification of their data products. 

 

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