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Content from Introduction

Last updated on 2026-10-23 | Edit this page

Estimated time: 15 minutes

Overview

Questions

  • What kinds of problems do researchers bring to RDM consulting?
  • How can we compare tools without becoming experts in all of them?

Objectives

  • Describe typical data problems researchers bring to RDM consulting.
  • Name criteria for comparing tools from a consulting perspective.

Consulting cases instead of tool tutorials

What matters when we recommend a tool

Discussion

Your consulting questions

Which data problems do researchers bring to you most often? Write one example into the shared document.

Key Points
  • Good advice depends on the researcher’s problem, skills and environment, not only on the tool.
  • A few criteria help to compare tools without knowing them in depth.

Content from Case 1: Moving Patient Data from Excel to a Database

Last updated on 2026-10-23 | Edit this page

Estimated time: 45 minutes

Overview

Questions

  • Which tools can help to clean spreadsheet data and move it into a database?
  • How can a data steward work with data they do not own?

Objectives

  • Compare spreadsheet programs, OpenRefine and Python with pandas for cleaning tabular data.
  • Explain why a database system such as REDCap can be better than spreadsheets for clinical data.
  • Recommend a tool that fits the project’s skills and the size of the problem.
Callout

Scarytale: Gene dating

Meta-analyses suggest that the 2nd of September plays a crucial role in the function of cells. What happened?

Play it as a yes/no guessing game, then read the solution: Gen-Datierung (spreadsheet software turns the gene name SEPT2 into a date). Alternative: Die Tabelle ist voll (UK COVID-19 cases lost at the row limit of an old Excel format).

Source: Research Data Scarytales, Kompetenznetzwerk Forschungsdatenmanagement Thüringen, CC0.

The case

A clinical research project has collected patient data in Excel spreadsheets. Now the project introduces the clinical database system REDCap, and the existing data has to be moved there. The data steward who does the migration is not the owner of the data: whenever values are missing or do not fit the new structure, he has to ask the other project members. The target structure in REDCap has to accommodate all existing data, and the existing data has to be adapted to the import template.

This case is based on a real migration, documented by Michael Rabenstein as a project of the certificate course Research Data Management: Migrating Patient Data from an Excel Spreadsheet to REDCap – Workflow from the perspective of a data steward without data ownership (CC BY 4.0).

Challenge

What would you recommend?

In small groups, discuss for 15 minutes:

  1. What exactly is the problem? What does the project need in the end?
  2. Which tools could help with cleaning and transforming the data? What would the data steward have to learn for each?
  3. How would you document the migration and the questions to the data owners?

Tools in this case

Tool Good for What the researcher needs to learn Installation
Spreadsheet program with tidy data rules transforming data, familiar to everyone tidy data principles, careful import (formats) none
OpenRefine cleaning and harmonising values, no programming, records every step point-and-click, facets, clustering local installation
Python with pandas automating repeated steps, documented workflow basic programming local installation or JupyterLite
REDCap capturing and managing clinical data with validation data dictionary, import template provided by the institution

A small first step

Where to learn more

Key Points
  • Spreadsheets are a common starting point, but clinical data belongs in a system with validation.
  • OpenRefine helps without programming, Python pays off for repeated work.
  • Documenting every change and every open question is part of a migration.

Content from Case 2: A Team Overwriting Each Other's Scripts

Last updated on 2026-10-23 | Edit this page

Estimated time: 45 minutes

Overview

Questions

  • How can a team work on the same scripts and documents without losing changes?
  • What does it take to introduce version control in a research group?

Objectives

  • Explain the difference between file sharing, file naming conventions and version control.
  • Recommend a setup that fits the team’s skills and the institution’s infrastructure.
Callout

Scarytale: The good old script

Who was Svenja?

Play it as a yes/no guessing game, then read the solution: Das gute alte Skript (a group wants to publish collate_samples_SvenjaMax.py, but nobody knows who wrote which part, and the script has no licence).

Source: Research Data Scarytales, Kompetenznetzwerk Forschungsdatenmanagement Thüringen, CC0.

The case

A research group of five people analyses its data with R and Python scripts. The scripts are stored in a shared cloud folder. Several times, someone has overwritten a colleague’s changes, and nobody knows which version produced the figures in the last paper. The group leader asks what they should do.

Challenge

What would you recommend?

In small groups, discuss for 15 minutes:

  1. What exactly is the problem? What does the team need?
  2. Which tools or practices could help? What would the team have to learn?
  3. What would you recommend first, and where would you refer them?

Tools in this case

Tool or practice Good for What the team needs to learn Infrastructure
File naming and folder conventions small teams, first step agreeing on rules none
Git tracking changes, reproducibility Git basics local installation
GitLab, Codeberg, GitHub collaboration, review, issues web interface, merge requests institutional GitLab or external platform

A small first step

Before introducing version control, the team can agree on file naming rules and a folder structure. A good starting point is the poster File Naming Best Practices for Researchers: “No more final_final_really” by Avinash Gupta (CC BY 4.0), created as a project of the certificate course Research Data Management.

Where to learn more

Key Points
  • Shared folders do not keep track of who changed what and why.
  • Version control needs a small learning investment but makes results traceable.
  • Check which Git platform the institution offers before recommending one.

Content from Case 3: Enriching a List of Journals with Metadata

Last updated on 2026-10-23 | Edit this page

Estimated time: 45 minutes

Overview

Questions

  • How can metadata be retrieved automatically instead of being collected by hand?
  • What is an API, and when is it worth using one?
  • What can AI tools contribute, and what do they not replace?

Objectives

  • Explain in simple terms what an API is and how identifiers such as the ISSN are used to query it.
  • Compare ways to enrich a list with metadata with and without programming.
  • Assess the use of AI tools for collecting metadata, including the need for verification.
Callout

Scarytale: Babylonian confusion

Do you have a Japanese name? Fear for your reputation.

Play it as a yes/no guessing game, then read the solution: Babylonische Verwirrung (a change in the order of Japanese names leads to missed citations; persistent identifiers such as ORCID avoid the problem).

Source: Research Data Scarytales, Kompetenznetzwerk Forschungsdatenmanagement Thüringen, CC0.

The case

Researchers who want to publish a data paper ask which data journals exist in their discipline. A community-curated list of data journals by Maxi Kindling and Dorothea Strecker (2022, CC0) gives a good starting point: it contains the journals with their ISSNs. But to choose a journal, researchers need more: Is it open access? Does it charge publication fees? Under which licence are articles published? Which subjects does it cover? Is it still publishing?

Collecting this information by hand from every journal’s website would take days, and it would be outdated soon.

This case is based on the Data Journals Dashboard by Thomas Schmidt (UB Mannheim), presented as a project of the certificate course Research Data Management: Data Journals Dashboard: A web app for community-curated data journal metadata (CC BY 4.0).

Challenge

What would you recommend?

In small groups, discuss for 15 minutes:

  1. Which sources could provide the missing information for each journal?
  2. Which tools could retrieve it? What would one have to learn for each?
  3. What would you do for journals that are not covered by these sources?

What was done in the original case

  • Each ISSN of the list was queried against the API of the Directory of Open Access Journals (DOAJ). If the journal was found, its metadata was merged into the journal record.
  • For ISSNs not in the DOAJ, the metadata was collected with an AI coding assistant with web search, using the journals’ websites, the ISSN Portal and Wikidata. The results were checked and cleaned by hand, and all journals were checked manually whether they are still publishing.
  • For each journal, the source of the added metadata is recorded.
  • The result is a searchable dashboard and metadata exports (CSV, YAML, JSON), all under CC0. The code is on GitHub.

Tools in this case

Tool Good for What one needs to learn Installation
Web search of the DOAJ, ISSN Portal a few journals, checking nothing none
API in the browser understanding what an API returns reading JSON none
OpenRefine (add column by fetching URLs) lists of identifiers, no programming OpenRefine basics local installation
Python with requests many records, repeatable and documented workflow basic programming, JSON local installation or JupyterLite
AI assistant with web search filling gaps where no structured source exists prompting, verifying results depends on the tool
  1. Open the DOAJ API for one ISSN in the browser, e.g. Scientific Data: https://doaj.org/api/search/journals/issn%3A2052-4463. Show title, publisher, licence, publication fee (apc) and subjects in the JSON. Most browsers display JSON in a readable form.
  2. Show the same journal in the Data Journals Dashboard.
  3. Optional: show the field enrichment_source in the metadata collection to discuss documenting sources and AI use.

A small first step

Work with identifiers (ISSN, DOI, ORCID) instead of names and titles, and look up one identifier in an API in the browser. Seeing the result once makes it much easier to judge what can be automated.

Where to learn more

Key Points
  • Many metadata sources offer APIs, so manual collection is rarely necessary.
  • Identifiers such as the ISSN are the key to querying them reliably.
  • AI tools can help to fill gaps, but the results must be verified and their sources documented.

Content from Decision Guide: Which Tool for Which Question?

Last updated on 2026-10-23 | Edit this page

Estimated time: 15 minutes

Overview

Questions

  • How can we summarise what we learned for our own consulting practice?

Objectives

  • Use a decision guide to choose tools for typical consulting questions.
  • Identify training offers to refer researchers to.

Decision guide

Question of the researcher First recommendation Next step Where to learn
“Our data is in spreadsheets and has to move to a database.”
“We keep overwriting each other’s work.”
“I collect metadata by hand.”
Discussion

Your own cases

Look at the consulting questions you collected in the introduction. Which of them can you now answer better? Which remain open?

An overview for your own institution

Researchers often do not know which tools their own institution offers. The templates Research Tools at a Glance by Alexandra Mielke (CC BY 4.0), created as a project of the certificate course Research Data Management, give an overview of digital collaboration tools and storage options. They are available as pptx and xlsx and can be adapted to any institution.

Challenge

Your first step

Open the templates and note which tools from today’s cases your institution offers. Where are the gaps? Whom would you ask?

Where to go from here

Key Points
  • Start from the researcher’s question, not from the tool.
  • Know where to refer researchers for in-depth training.
  • An overview of the tools your institution offers helps researchers and consultants alike.