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research data and code management - an introduction
Research is increasingly computational, data-driven, and collaborative. The increasing size of digital research data that are generated or collected in almost every research project requires us to be more responsible, proactive data managers. We are faced with the challenge of not only managing an...
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| Main Authors: | , |
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| Format: | Presentation |
| Language: | English |
| Published: |
Philipps-Universität Marburg
2023
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| Subjects: | |
| Online Access: | PDF Full Text |
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| Summary: | Research is increasingly computational, data-driven, and collaborative.
The increasing size of digital research data that are generated or collected in almost every research project requires us to be more responsible, proactive data managers.
We are faced with the challenge of not only managing and documenting these data, but also preserving them and making them available for reuse.
In addition, more and more automated (analysis) pipelines are playing a role in modern research. Therefore, code management is becoming more and more essential in research to ensure the reproducibility of results, as it allows for the systematic organization, version control, and sharing of the software and algorithms.
While data and code management can sound like a lot of work for little payoff, managing our research data well actually provides a lot of personal and practical benefits. Well managed and well described data is easier to sort through, access, and understand, making our research project more efficient. Data Management protects against data loss and - increasingly important - publication retractions, possibly sparing you a frustrating experience.
This workshop will focused on best practices for managing digital research data. |
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| DOI: | 10.5281/zenodo.8375595 |