Research Software, Digital Tools & Computational Workflows
Choose, use, document, verify, and preserve research software, digital tools, and computational workflows without letting technology replace scientific judgment.
A research tool is useful only when you can trust the workflow around it.
Researchers now depend on software for literature searching, data collection, analysis, visualization, writing, collaboration, storage, and many other tasks. The convenience is real, but a tool can also introduce errors, hidden assumptions, compatibility problems, security risks, and dependencies that become visible only when something goes wrong.
Choosing software is therefore a research decision rather than merely a technical one. Researchers need to consider whether a tool is appropriate for the task, whether its outputs can be verified, how files and versions will be managed, and what happens when the software changes or disappears.
A reliable computational workflow keeps technology in its proper role. Software can perform operations quickly and consistently, but the researcher remains responsible for understanding what the tool did, checking whether the result makes sense, and preserving enough information for the work to remain understandable later.
Start with the first guide in this supplement.
If you're exploring this supplement from the beginning, this is the first guide in the collection.
How to Use Zotero, Mendeley, EndNote, and Reference Managers Effectively
Reference managers can do much more than generate bibliographies. Learn how to use Zotero, Mendeley, EndNote, and similar tools to capture accurate references, organize literature, manage PDFs, take useful notes, cite while writing, and protect your research library.
From digital tools to reliable research workflows
Choose, evaluate, document, verify, and preserve the digital tools and computational workflows that support your research.
A useful tool should leave your research more reliable, not more mysterious.
Once the digital workflow is documented and tested, the next concern is maintaining it over time as software, files, devices, platforms, and research requirements change.
Artificial Intelligence in Research
Use artificial intelligence in research responsibly, understand what AI can and cannot do, verify its outputs, protect research data, and document AI-assisted work transparently.
Explore Supplement D →