DKZ.2R@ IAS Junior Retreat

Comments on Collaboration - My Experience with the DKZ.2R Rent-an-Expert Program

• Y. Zhou
• Data , Testimonial

Comments on Collaboration - My Experience with the DKZ.2R Rent-an-Expert Program

At the beginning of this year (2025), I received an email regarding the DKZ.2R “Rent an expert” program. I was very interested in this initiative and therefore applied for support from the scientific consulting team at the Rhine-Ruhr Center for Scientific Data Literacy (DKZ.2R) for assistance with my data analysis.

I obtained my master’s degree in Plant Nutrition from the China Agricultural University and pursued my PhD study at the University of Hohenheim. I am currently a postdoctoral researcher in the Institute of Crop Science and Resource Conservation, Crop Functional Genomics, at the University of Bonn.
My research expertise includes plant culturing, molecule cloning, biochemical analysis and limited data analysis experience on large-scale NGS datasets.
Since the beginning of April 2025, two DKZ.2R consultants were assigned to me: Tarek Iraki, who is proficient in programming languages such as Python, and Lennard Maßmann, who specializes in working with R. Together, we collaboratively worked on my Postdoctoral project, which focuses on the molecular and genomic dissection of lateral root development in maize.

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Do's and Don'ts in Research Data Management

• C. Hillen
• Data , RDM

Research Data Management Do’s and Don’ts - Step up your RDM skills!

1. Structuring and naming your folders There is an easy way to make your data findable for you and your team: establish a folder structure which makes sense for you and your working group as well as naming conventions for your folders.

Don’t:

Paul and Suzie
»Guideline
>application
»version2_final
»v.3
»review
»3rd.version
>JD
»qn
»0-1

Instead do:

000_int_orga
»01_application
»02_review 120_questionaires
»01_qualitative »02_quantitative 130_data
»01_qualitative »02_quantitative

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Documentation From User Experience

• Marvin Lorber
• Documentation , Software , RDM

This post is a condensed version of a talk at our Data Compentcy College

If you regularly use scientific software written by others, or tried to replicate interesting research that relies on software, you have probably also invested weeks of work to solve a software problem or even given up on a software because of missing documentation. Finding a project that might be the solution to your problem and then failing to run the code is frustrating. Being unable to run a project you have built yourself years ago is even worse. Having experienced all those setbacks myself in the past I want to use this post to channel that frustration to fuel solutions for better documentation for our current and future projects.

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