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📕 Research Data Spotlight — Edition 4

Julia Beets

Spotlight on data by

Julia Beets · Ecology & Evolution

Interviewed on 2025-05-06

"If you incorporate the goal of accessible data in your workflow early on, the process won't take much extra time near publication."

💬 The interview

Can you tell us a bit about yourself and what your research is about?

With a master's degree in Biomolecular sciences, I am currently working as a PhD student in the Ecology & Evolution section of A-LIFE. My research focuses on the impact of genetic variation on life-history traits of the Drosophila melanogaster, the fruit fly. More specifically, I am interested in the impact of single nucleotide variations and to study this, I work both on bioinformatics and data, as well as functional genomics in the lab.

How do you keep track of your data? Do you collaborate with others?

My main strategy is to start early with documentation and data storage structures. I keep a descriptive file throughout the research process, which can easily be translated into a documentation file for data sharing purposes. For code, it is very important to properly annotate the scripts.

In collaborations, I like to work in the same document with track changes or comments, so all contributors have equal opportunities to read and edit, and regular backups of this document for version control. My data is currently in SciStor, with access provided to most collaborators via the BAZIS HPC Cluster.

Is your data accessible? If yes, where can we find it? What was the most challenging part of sharing your data?

Yes, the data is accessible. For my most recent project, I developed a tool, FlyCADD, to score the impact of any single nucleotide variant in the fruit fly genome based on the CADD framework applied to the human genome. The data (both the data required for development, as well as the data generated by the tool) is available on Zenodo, and is linked with the code on GitHub.

Deciding where to share the data and code was challenging, as there are many options for repositories. These repositories differed in regard to data requirements, storage space, integration with other repositories, preprint requirements and licencing, for example. Our data consisted of different data types, and code for the prediction tool, and additionally the manuscript was preprinted. We picked Zenodo as data repository as it has the possibility of linking a GitHub repository with the tool, applying version control and an intuitive upload interface.

If a colleague asked you for some advice about managing their data, what would you suggest?

Keep your files organized and keep track of any updates, changes or important parameters. Being able to quickly retrieve any information, whether it is data or metadata, will make your workflow easier to execute, and will be a preparation for a smooth conversion of all (meta)data into a format suitable for publishing, as all information will already be written up.

Journals more often ask for data to be accessible. Does that just feel like extra work, or do you think it makes sense nowadays and you would share it anyways?

It is some extra work to make data accessible when the journal requires this. However, it does make your article more accessible, reproducible and interesting for readers. Sharing all data that was used in the article helps other researchers to build further on similar topics. If you incorporate the goal of accessible data in your workflow early on, the process won't take much extra time near publication.

⭐ Thank you, Julia!

Thank you, Julia, for sharing your fascinating research and also for sharing thoughtful tips on data management and collaboration — a great inspiration for anyone working with data!