Alvin Weinberg in 1961 asked whether Big Science is ruining science, and he gave an answer that is similarly valid for research data management: “[...] one sees evidence of scientists’ spending money instead of thought. This is one of the most insidious effects of large-scale support of science. In the past the two commodities, thought and money, have both been hard to come by. Now that money is relatively plentiful but thought is still scarce, there is a natural rush to spend dollars rather than thought.” [1]The result is what Richard Feynman termed “Cargo Cult Science”: research that appears scientific but has neither scholarly contribution nor impact. [2] Science is about understanding, and data are not insight, but play the role of critical arguments at best. [3] Hence we need to remember what science and scholarship is all about, and learn to apply the scientific method to our research. Only one part in this is managing our research data, and rather than reinventing the wheel over and over again, we better learn from the expertise of other disciplines, such as engineering, software engineering, or library sciences – and other attempts similar to the NFDI. [4] If we were really to advance science, we better understand that research can be without scholarly contribution, and that data, let alone sharing data, is a highly non-trivial concept resting on many implicit assumptions often not fulfilled. [5] Here, I present a series of (digital) tools developed for “little science” in a spectroscopy laboratory predating the NFDI by several years that have become solutions for my daily work, span the entire data life cycle, and help with enhancing the overall quality of the research: Data provenance during data acquisition [6], an electronic lab notebook (ELN) [7], a framework for fully reproducible data analysis providing a gap-less and complete protocol of each step and relieving the user from actually programming [8], [9] and packages based on it [10]–[15], a repository for “warm” research data [16], lab management and a knowlede base [17]. Furthermore, two lecture series on scientific software development [18] and research data management [19] have emerged from 15+ years of reflecting on the essence of science. We need to teach the students early on what science is all about and why properly handling research data is a prerequisite for scholarly contribution, rather than assuming that they’ve “caught on by osmosis”. [2] “At stake is the future of scholarship.” [5] [1] A. Weinberg, “Impact of large-scale science on the United States,” Science, vol. 134, pp. 161–164, 1961. DOI: 10.1126/science.134.3473.161.[2] R. P. Feynman, “Cargo cult science,” Engineering and Science, vol. 37, no. 7, pp. 10–13, 1974.[3] K. R. Popper, Vermutungen und Widerlegungen. Das Wachstum der wissenschaftlichen Erkenntnis, 2nd ed. Tübingen: Mohr Siebeck, 2009.[4] D. E. Atkins, C. L. Borgman, N. Bindhoff, M. Ellisman, S. Felman, I. Foster, A. Heck, D. Heerman, J. Lane, L. Milanesi, J. Paraki, W. v. Ruden, A. Szalay, P. Tackley, H. Wensink, and A. Ynnerman, “RCUK review of e-science 2009: Building a UK foundation for the transformative enhancement of research innovation,” Research Councils UK, UCLA: Center for Knowledge Infrastructures, Tech. Rep. Available: https://escholarship.org/uc/item/891056g2[5] C. L. Borgman, Big Data, Little Data, No Data: Scholarship in the Networked World. Cambridge, MA: MIT Press, 2015.[6] B. Paulus and T. Biskup, “Towards more reproducible and FAIRer research data: Documenting provenance during data acquisition using the Infofile format,” Digital Discovery, vol. 2, pp. 234–244, 2023. DOI: 10.1039/D2DD00131D.[7] M. Schröder and T. Biskup, “LabInform ELN: A lightweight and flexible electronic laboratory notebook for academic research based on the open-source software DokuWiki,” ChemRxiv, 2023. DOI: 10.26434/chemrxiv-2023-2tvct.[8] J. Popp and T. Biskup, “ASpecD: A modular framework for the analysis of spectroscopic data focussing on reproducibility and good scientific practice,” Chemistry–Methods, vol. 2, e202100097, 2022. DOI: 10.1002/cmtd.202100097.[9] T. Biskup, ASpecD Python package, 2022. DOI: 10.5281/zenodo.4717937. Available: https://docs.aspecd.de/.[10] M. Schröder and T. Biskup, “cwepr - a Python package for analysing cw-EPR data focussing on reproducibility and simple usage,” Journal of Magnetic Resonance, vol. 335, p. 107 140, 2022. DOI: 10.1016/j.jmr.2021.107140.[11] M. Schröder and T. Biskup, cwepr Python package, 2021. DOI: 10.5281/zenodo.4896687. Available: https://docs.cwepr.de/.[12] J. Popp, M. Schröder, and T. Biskup, trEPR Python package, 2021. DOI: 10.5281/zenodo.4897112. Available: https://docs.trepr.de/.[13] M. Schröder, NMRAspecds Python package, 2024. DOI: 10.5281/zenodo.13293054. Available: https://docs.nmraspecds.de/.[14] T. Biskup, UVVisPy Python package, 2021. DOI : 10.5281/zenodo.5106817. Available: https://docs.uvvispy.de/.[15] T. Biskup, FitPy Python package, 2022. DOI: 10.5281/zenodo.5920380. Available: https://docs.fitpy.de/.[16] M. Schröder and T. Biskup, LabInform datasafe, 2023. DOI: 10.5281/zenodo.13763299. Available: https://datasafe.docs.labinform.de/.[17] T. Biskup, “LabInform: A modular laboratory information system built from open source components,” ChemRxiv, 2022. DOI: 10.26434/chemrxiv-2022-vz360.[18] T. Biskup, “Vorlesung Wissenschaftliche Softwareentwicklung.” (2025), Available: https://www.till-biskup.de/de/lehre/softwareentwicklung/ (visited on 04/17/2025).[19] T. Biskup, “Vorlesung Forschungsdatenmanagement.” (2025), Available: https://www.till-biskup.de/de/lehre/forschungsdatenmanagement/ (visited on 04/17/2025).