Everybody talks about research data management — But where is the science?

Newton, when seeing further, stood on the shoulder of giants,[1] not on the piles of his predecessors‘crufty research data. Nearly 300 years later, Richard Feynman coined the term “cargo cult science” forresearch that appears scientific but has no scholarly contribution nor impact.[2] With all the currenthype about research data management, we tend to forget that science is about gaining knowledge, notaccumulating research data to train large-language models and other AI tools. Data is not insight,but its prerequisite at best. And sharing data is a highly non-trivial concept resting on many implicitassumptions often not fulfilled.[3] We present a number of strategies and tools that, if used competently,will help us to improve the quality of our research and ultimately contribute to science and scholarship.First come tools for capturing all relevant information during data acquisition,[4] and electronic labnotebooks.[5] A framework for scientific data analysis that provides a gap-less and complete protocol ofeach step and relieves the user of actual programming [6] is a huge step forward. This is complementedby a larger (local) infrastructure consisting of persistent and unique IDs (PID, UID), a repository for“warm” research data, lab management, and knowledge base.[7] All these strategies and tools focus onthe individual scientists, as only they can potentially ensure the urgently required quality of data andresults that underpin scientific insight. Eventually, we need to teach [8, 9] young researchers early on whatscience is all about and why properly handling research data is a prerequisite for scholarly contribution.“At stake is the future of scholarship.”[3] References[1] I. Newton, letter to Robert Hooke, February 5th, 1676[2] R. P. Feynman, Cargo cult science, Eng. Sci. 1974, 37(7), 10.[3] C. L. Borgman, Big Data, Little Data, No Data. MIT Press, Cambridge MA 2015.[4] B. Paulus, T. Biskup, Towards more reproducible and FAIRer research data: documenting provenanceduring data acquisition using the Infofile format, Digit. Discov. 2023, 2, 234.[5] M. Schröder, T. Biskup, LabInform ELN: A lightweight and flexible electronic laboratory notebook foracademic research based on the open-source software DokuWiki, ChemRxiv 2023,doi:10.26434/chemrxiv-2023-2tvct.[6] J. Popp, T. Biskup, ASpecD: A modular framework for the analysis of spectroscopic data focussing onreproducibility and good scientific practice, Chem. Meth. 2022, 2, e202100097[7] T. Biskup, LabInform: A modular laboratory information system built from open source components,ChemRxiv 2022, doi:10.26434/chemrxiv-2022-vz360[8] https://www.till-biskup.de/de/lehre/programmierkonzepte/.[9] https://www.till-biskup.de/de/lehre/forschungsdatenmanagement/.