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Nature Research Academies – Research Data Principles and Practices
Nature Research Academies – Research Data Principles and Practices

Nature Research Academies – Research Data Principles and Practices

Available from Nature Research Academies: workshops in Research Data Principles and Practices. Nature Research Academies is a series of training workshops for researchers, developed by Nature Research.

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Academy in Research Data Principles and Practices

While stakeholders including funding agencies and publishers increasingly require researchers to share their data openly, researchers report that they are unsure about how to manage and share their data properly.*

To meet the growing demand for research data management support, Nature Research Academies has developed a training workshop to help participants understand the value of research data and how data can be shared effectively

  • 1-day and 2-day workshops
  • Available to institutions globally, to host on-site
  • For up to 30 researchers – appropriate for those new to data sharing, at any career stage

Interactive

The academy prominently features active discussion and activities throughout each module to ensure participants understand how to implement the presented content.

Tailored

You can choose from a 1-day workshop covering 4 modules, or a 2-day workshop with 8 modules.

We will assist you in selecting modules to create a relevant and engaging agenda for your researchers, and content can be adapted according to the discipline of the participants.

Academy modules

1. The context for data sharing

This module provides the context for Open Science and data sharing, and why it is important for researchers to share their data. The module covers the common drivers for data sharing, including institutional, funder and journal policies, as well as the ways that data sharing requirements impact directly on researchers. Participants also learn how they can ensure compliance with data policies. The benefits to both researchers and the wider researcher community are also discussed.

2. Allowing reuse and gaining credit for your research

In this module, external data sharing is considered. Copyright for data and data licensing options are reviewed and their value is described. The importance of data citation is discussed, and participants will prepare data citations based on best practice examples.

3. Standards for data sharing

This module introduces concepts in planning for data management, including elements of data management plans which allow researchers to plan their approach for data management and sharing. The FAIR Principles which outline the ways which data can be made Findable, Accessible, Interoperable and Reusable are reviewed, as well as the practical ways that these can be applied.

4. Data Publishing

In this module, we explain the breadth of options for data publication. Participants learn about publishing options for data papers and data journals, as well as the way metrics can be used to track use and citation of their data. Data indexing is also discussed.

5. From active data to archived data

This module introduces the practical aspects of data sharing, which researchers will need to understand before making their data openly available. We discuss the importance of metadata and practical ways to capture it, as well as the necessity of storage and back-up while research is being conducted. Participants also learn how to identify appropriate repositories for data sharing.

6. Preparing data files for sharing

In this module, we discuss practical skills for data sharing. We consider the impacts of poor file naming and badly organized data, as well as techniques for making data more accessible both for the researcher and for others who may wish to reuse their data. Participants also learn how to create or edit spreadsheets to ensure that their data are reusable in the future.

7. Practical Application of the FAIR Data Principles

This module gives an in-depth introduction to the practical applications of the FAIR Data Principles; a standard for data sharing all researchers should be familiar with. Using contextual examples, we discuss the ways that researchers can apply the principles to their own data, as well as the relevance of these standards to all aspects of data infrastructure.

8. Sharing sensitive research data

This module addresses the challenges and techniques in sensitive data sharing. Participants learn how to address the collection and dissemination of sensitive data, including preparation before their research begins. Techniques for anonymizing or de-identifying data are also discussed, with reference to both qualitative and quantitative sensitive data.

 

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*Stuart, D., Baynes, G., Hrynaszkiewicz, I., Allin, K., Penny, D., Lucraft, M., & Astell, M. (2018). Whitepaper: Practical challenges for researchers in data sharing. https://figshare.com/articles/Whitepaper_Practical_challenges_for_researchers_in_data_sharing/5975011

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