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Published March 19, 2026 | Version v4
Presentation Open

Research Data and Code Management - an Introduction

Authors/Creators

  • 1. Philipps-Universität Marburg

Description

This is a workshop in two parts.

 

Part 1: 

Science is becoming increasingly digital, data-driven, and collaborative. This workshop will provide an overview of methods, platforms, and tools to help you manage your digital data and collaborate more efficiently.
Digital research data is on the rise: Most research projects generate and/or collect digital data, thus requiring researchers to learn how to handle data responsibly and proactively. Not only do we need to manage and annotate our data; we should also preserve our data and make them available for re-use by publishing them in accordance with FAIR (findability, accessibility, interoperability, and reusability) open data principles.
While data management seems to imply plenty of work and little benefit initially, it does come with considerable personal and practical advantages in the longer term:

  • Well-managed and annotated data are easier to sort, retrieve, and understand, thus boosting research efficiency.
  • High-quality data management can shield you from accidental data loss.
  • Well-managed data may also be an advantage when submitting manuscripts, as data quality just might tip the scales in your favor when acceptance is a close call.

What is more, good research data management is a vital ingredient in fostering open science because sharing your data responsibly makes it available to the scientific community.

Part 1 focusses on the basics of research data management and FAIR data publication.

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Part 2:

Research data management begins even before the data is collected: A detailed project plan helps to manage research data and make workflows transparent throughout the entire research cycle. Funding organizations often require a detailed data management plan before the start of a project. A reference to published project plans can also be essential for the subsequent publication of your research results. 
For this reason, this workshop will focus on data management plans and pre-registrations for efficient and transparent project management.
Research data management does not end after data collection but also includes the crucial phase of data analysis. We will therefore also focus on the management of data processing and analysis.
By engaging in good project management and describing your entire research workflow transparently and comprehensibly for the scientific community, you not only ensure a high quality of your research but also contribute to open science.

Part 2 focusses on code management and planning your research in order to make your research reproducible.

Table of contents (En)

Program Part 1

Time Topic Content Learning Objective Methods / Materials
9:00-9:30 Welcome & Introduction to RDM Basics introducing attendees, ice breaker Knowing each other, icebreaking, getting an idea of the aspects of RDM ice breaker game, data scary tales, brainstorming
9:30-10:00 Collect & Process RDM basics: naming, structure, versioning overview over the RDM basics including “checklist” of the most important aspects to consider trainer input incl short questions/discussions with the attendees (10 min), exercises on naming (10 min) + structure (10 min) with individual datasets, discussions with partner
10:00-10:35 Describe data, metadata, standards, README etc. overview over methods how to describe data including “checklist” of the most important aspects to consider, knowledge about metadata and standards trainer input (10 min) + short exercise writing an individual data dictionary (10 min) + analyzing an example README (10 min + 5 min discussion) OR exercise with individual README (15 min)
10:35-10:45 BREAK      
10:45-11:00 Share & Reuse FAIR principles & repositories overview over the FAIR principles for data including “checklist” of the most important aspects to consider, knowledge about the concept of repositories trainer input (15 min) + if time: short exercise on finding a suitable repo
11:00-12:00 open_UMR live demo open_UMR knowledge how to prepare & publish a dataset live demo + exercise in open_UMR
12:00-12:30 BREAK      
12:30-13:00 Wrapup Open Science & Feedback getting an idea why RDM is important in the context of Open Science & getting feedback from attendees trainer input incl short video (10 min) + summarizing (3-2-1), evaluation link

Program Part 2

Time Topic Content Learning Objective Methods / Materials
9:00-09:15 Welcome & Recap RDM Basics RDM basics: naming, structure, documentation getting an overview of the basics of RDM data reuse youtube video, brainstorming in the group
9:15-10:00 Reproducibility game Complexity of workflow documentation & reuse understanding of importance of workflow documentation and automatization Lego metadata for reproducibility game
10:00-10:30 FAIR data & code and data/code publishing FAIR principles & repositories overview over the FAIR principles for data and code including “checklist” of the most important aspects to consider, knowledge about the concept of repositories trainer input (15 min) + exercise on evaluating a dataset after FAIR criteria + exercise on finding a suitable repo
10:30-10:45 BREAK      
10:45-11:45 Versioning & automation Learning versioning tools in text software (libre office) and git incl. GitLab, getting an overview of other workflow tools for integrating text, data, code getting an impression on versioning and how to use it in different tools Live demo + trainer input (10 min)
11:45-12:00 BREAK      
12:00-12:30 DMP & Preregistration creating a DMP & learning about preregistrations getting an impression on the complexity of DMPs and knowing preregistrations trainer input (5 min) & exercise on creating a living DMP
12:30-13:00 Wrapup Open Science & Feedback getting an idea why Reproducibility is important in the context of Open Science & getting feedback from attendees trainer input incl short video (10 min) + summarizing (3-2-1), evaluation link

Files

RDM.pdf

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