23 (research data) Things

What is 23 (research data) Things? 23 Things is a recognised training concept with several organisations already using the idea to help Librarians, data managers and others to build their understanding of research data and its potential.
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Thing 3. DCC Curation Lifecycle Model Our Curation Lifecycle Model provides a graphical, high-level overview of the stages required for successful curation and preservation of data from initial conceptualisation or receipt through the iterative curation cycle.   You can use our model to plan activities within your organisation or consortium to ensure that all of the necessary steps in the curation lifecycle are covered.  It is important to note that the model is an ideal. In reality, users…

How to use the Curation Lifecycle Model Our Curation Lifecycle Model provides a graphical, high-level overview of the stages required for successful curation and preservation of data from initial conceptualisation or receipt.

Thing 2: Citing Data - Research Data Management - Library Guides at University of the Sunshine Coast

Citing Data - Research Data Management - Library Guides at University of the Sunshine Coast

Thing 2: Issues in research data management  Research data is critical to solving the big questions of our time.  So what are some of the issues we face in managing research data?

Thing Data in the research lifecycle Data and its management change over time. Here we look at data and research lifecycles and make connections between them.

Thing 3: Data in the research lifecycle  Data and its management change over time.  Here we look at data and research lifecycles and make connections between them.

Thing Data in the research lifecycle Data and its management change over time. Here we look at data and research lifecycles and make connections between them.

Thing 2: Data Sharing and Management Snafu in 3 Short Acts (Higher Quality) NYU Health Sciences Library   A higher resolution version of the video. A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci. This is what shouldn't happen when a researcher makes a data sharing request! Topics include storage, documentation, and file formats.

A higher resolution version of the video. A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci. This is what shouldn't happen whe.

Thing 4: Data discovery Repositories and portals play an important role in making research data discoverable and accessible.

Thing Data in the research lifecycle Data and its management change over time. Here we look at data and research lifecycles and make connections between them.

Thing 14: Identifiers and linked data  What is ORCID and why is the Australian academic world buzzing about it?  Get hands on with linked data and the semantic web.

Thing Vocabularies for data description Data descriptor, keyword, subject … these are all terms commonly used when discussing metadata. Learn about the use of controlled vocabularies to enhance data discovery.

Thing 13: Walk the crosswalk  There are times when metadata created using one standard will need to be transformed or crosswalked to another standard so that metadata can been shared between systems.

Thing Vocabularies for data description Data descriptor, keyword, subject … these are all terms commonly used when discussing metadata. Learn about the use of controlled vocabularies to enhance data discovery.

Thing 12: Vocabularies for data description  Data descriptor, keyword, subject … these are all terms commonly used when discussing metadata.  Learn about the use of controlled vocabularies to enhance data discovery.

Thing Vocabularies for data description Data descriptor, keyword, subject … these are all terms commonly used when discussing metadata. Learn about the use of controlled vocabularies to enhance data discovery.

Thing 11: What's my schema?  Metadata elements are the lifeblood for finding and reusing research data. Data is only as valuable as the metadata which describes and connects it.

Thing Vocabularies for data description Data descriptor, keyword, subject … these are all terms commonly used when discussing metadata. Learn about the use of controlled vocabularies to enhance data discovery.

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