August 16, 2020 Presentation Open Access

Natasha Simons; Chris Erdmann; Daniel Bangert; Fiona Murphy

DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3987052</identifier>
  <creators>
    <creator>
      <creatorName>Natasha Simons</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0635-1998</nameIdentifier>
      <affiliation>Australian Research Data Commons</affiliation>
    </creator>
    <creator>
      <creatorName>Chris Erdmann</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2554-180X</nameIdentifier>
      <affiliation>RENCI, United States</affiliation>
    </creator>
    <creator>
      <creatorName>Daniel Bangert</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4981-2870</nameIdentifier>
      <affiliation>University of Gottingen and RDA, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Fiona Murphy</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1693-1240</nameIdentifier>
      <affiliation>More Brains</affiliation>
    </creator>
  </creators>
  <titles>
    <title>FAIR Data in the Scholarly Communications Lifecycle</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>FAIR Data</subject>
    <subject>FSCI</subject>
    <subject>FORCE11</subject>
    <subject>Scholarly Communications</subject>
    <subject>Research Data</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-08-16</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3987052</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3987051</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;Course W21 FAIR Data in the Scholarly Communications Lifecycle delivered completely online at the FORCE11 Scholarly Communications Institute&amp;nbsp;(FSCI) 2020.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;From the FSCI 2020 website description:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This course was will focus on FAIR research data management and stewardship practices. It will provide an understanding of FAIR (findable, accessible, interoperable, and reusable) data and how it fits into scholarly communication workflows. Participants will learn about the FAIR Data Principles and how they can be applied in practice.&lt;/p&gt;

&lt;p&gt;Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. In research libraries, the principles can be used as a framework for fostering and extending research data services.&lt;/p&gt;

&lt;p&gt;This course will provide an overview of the FAIR Data Principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Along the way, we will get hands-on with data and tools through self-paced exercises. There will be opportunities for participants to learn from each other and to develop skills in data management and expertise in making data FAIR.&lt;/p&gt;

&lt;p&gt;Level:&amp;nbsp;Beginner to intermediate&lt;/p&gt;

&lt;p&gt;Intended Audience:&amp;nbsp;The course is aimed at individuals working with or expecting to work with data as researchers, publishers, librarians, or in research support, especially those seeking to develop their skills in managing FAIR data in practice and to understand the tools that can support them in doing this.&lt;/p&gt;</description>
  </descriptions>
</resource>