Peatland monitoring and research generates data that is used for management, conservation and policy related to these ecosystems. To ensure that your project data is collected systematically and can be analysed, shared, and re-used it is important to have a data management plan.

This tutorial is a short introduction to writing a Data Management Plan (DMP) with examples for peatland monitoring projects and initiatives.

You can find more information on DMPs in the Research Data Management Toolkit for Life Sciences

Agenda

In this tutorial we will cover:

1. What is a Data Management Plan?
2. Key components of a DMP
   1. Hands-on: Create a DMP for peatland research
3. FAIR data management solutions
4. Conclusion
   1. Suggested reading
   2. Resources

What is a Data Management Plan?

A Data Management Plan (DMP) is a live document that provides a framework for managing data material during and after the research project.

A data management plan will facilitate the process of collecting and managing your peatland data and will allow you to discuss with your team how to share data, considering the requirements of your organisation and funders.

Key components of a DMP

DMP templates vary by institution and funding agencies.

Hands-on: Create a DMP for peatland research

• Download the DMP template for Peatland Research and Monitoring.
• Fill in the DMP template using the prompt questions.
• Review your DMP with your team and collaborators.

FAIR data management solutions

After you have filled in the DMP template, review how you can make your DMP FAIR.

There are several ways to set up FAIR (Findable, Accessible, Interoperable, Reusable) data management plans (DMPs)

• Findable (F): Data description and collection or reuse of existing data. Using metadata and community recognised standards for curation that enable someone to find the data.
• Accessible (A): Standardised authentication or authorisation (e.g. HTTP, HTTPS) for open and restricted data access.
• Interoperable (I): Documentation and data quality, and community standards for data exchange (e.g. tab-delimited text).
• Reusable (R): Storage and backup supported by legal and ethical requirements, as well as data release policies.

More information is available in the FAIR data management tutorial

Conclusion

Creating a DMP will allow you and your institution to make the most of your peatland data. This introduction to DMP aims to highlight the usefulness of data management for peatland research and monitoring.

Suggested reading

• FAIR in a nutshell
• [The published FAIR Guiding Principles: Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016)](Wilkinson et al. 2016)
• Recipes for data FAIRification, written by domain experts giving real-world examples: FAIR Cookbook
• Documentation and frameworks for data FAIRification. Each of the 15 FAIR principles is put into context with real data examples: GO FAIR
• FAIR walkthrough using examples from across all academic disciplines: How to FAIR

Resources

• How to write a data-sharing agreement between collaborators
• DMP online
• DSWizard
• WaterLands
• PeatDataHub

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Key points

• A Data Management Plan (DMP) is a live document that provides a framework for managing data material during and after the research project.

• FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability (FAIR).

Frequently Asked Questions

References

1. Wilkinson, M. D., M. Dumontier, I. J. J. Aalbersberg, G. Appleton, M. Axton et al., 2016 The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3: 10.1038/sdata.2016.18

Glossary

DMP

Data Management Plan

FAIR

Findable, Accessible, Interoperable, Reusable

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Citing this Tutorial

1. Gabriela Lopez-Gonzalez, Katarzyna Kamieniecka, Robert Andrews, Emily Angiolini, Krzysztof Poterlowicz, Introduction to Data Management Plan (DMP) for Peatland Research and PeatDataHub (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/fair/tutorials/data-management-peatlands/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{fair-data-management-peatlands,
author = "Gabriela Lopez-Gonzalez and Katarzyna Kamieniecka and Robert Andrews and Emily Angiolini and Krzysztof Poterlowicz",
	title = "Introduction to Data Management Plan (DMP) for Peatland Research and PeatDataHub (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/fair/tutorials/data-management-peatlands/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Funding

These individuals or organisations provided funding support for the development of this resource

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/fair/tutorials/data-management-peatlands/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools: []

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