Key Points
Introduction
- 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.
Case 1: Moving Patient Data from Excel to a Database
- 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.
Case 2: A Team Overwriting Each Other's Scripts
- 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.
Case 3: Enriching a List of Journals with Metadata
- 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.
Decision Guide: Which Tool for Which Question?
- 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.