From Data Islands to Knowledge Networks in Tribology: Understanding the Modern Data Architecture
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Abstract
The scientific research community stands at a critical juncture in its approach to data management. While we have successfully transitioned from manual to computer-controlled experiments across diverse fields (from tribology to materials science, from chemistry to bioengineering), our data practices have not kept pace with the exponential growth in data generation and the emergence of artificial intelligence. This paper explores why investing time in understanding modern data architectures is essential, not just as another publication requirement, but as the foundation for accelerated collaborative discovery within our organizations and across the community. We examine the current landscape through recent survey data, explain the fundamental concepts behind FAIR (Findable, Accessible, Interoperable, and Reusable) data principles with the technical depth needed to assess true FAIR-ness, and present FAIR digital twins of scientific work as one concrete implementation that can transform isolated data into interconnected knowledge networks. Just as computer-controlled instruments revolutionized our experimental capabilities, modern data architectures promise to be the next enabling technology that amplifies our collective scientific output. While we use tribology as our primary field of interest, the principles apply equally to any experimental science dealing with multi-dimensional, context-dependent data.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · cited 1× · see citing works
“The most striking example is the archiving of experimental data that enabled AI (in a Nobel Prize-winning break-through) to predict three-dimensional protein structures with near-experimental accuracy [11], fundamentally transforming biology.”
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505 · see citing works
1,500 scientists lift the lid on reproducibility
Baker, M. (2016) · Nature · doi:10.1038/533452a · Cited by 4,209 · see citing works
Reproducible research in computational science
Peng, R. D. (2011) · Science · doi:10.1126/science.1213847 · Cited by 1,449 · see citing works
Enhancing reproducibility for computational methods
Stodden, V., Mcnutt, M. K., Bailey, D. H., Deelman, E., Gil, Y., Hanson, B., Heroux, M. A., Ioannidis, J. P. A., & Taufer, M. (2016) · Science · doi:10.1126/science.aah6168 · Cited by 414 · see citing works
Autonomous experimentation systems for materials development: A community perspective
Stach, E. A., DeCost, B., Kusne, A. G., Hattrick-Simpers, J. R., Brown, K. A., Reyes, K. G., Schrier, J., Billinge, S. J. L., Buonassisi, T., Foster, I. T., et al. (2021) · Matter · doi:10.1016/j.matt.2021.06.036 · Cited by 412 · see citing works
Synthesis of diamondlike carbon films with superlow friction and wear properties
Erdemir, A., Eryilmaz, O. L., & Fenske, G. R. (2000) · Journal of Vacuum Science & Technology A Vacuum Surfaces and Films · doi:10.1116/1.582459 · Cited by 372 · see citing works
Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot
Epps, R. W., Bowen, M., Volk, A. A., Abdel‐Latif, K., Han, S., Reyes, K. G., Amassian, A., & Abolhasani, M. (2020) · Advanced Materials · doi:10.1002/adma.202001626 · Cited by 326 · see citing works
The Materials Genome Initiative, the interplay of experiment, theory and computation
Pablo, J., Jones, B., Lind-Kovacs, C., Ozoliņš, V., & Ramirez, A. P. (2014) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2014.02.003 · Cited by 240 · see citing works
Volk, A. A., Epps, R. W., Yonemoto, D. T., Masters, B. S., Castellano, F. N., Reyes, K. G., & Abolhasani, M. (2023) · Nature Communications · doi:10.1038/s41467-023-37139-y · Cited by 218 · see citing works
Burgelman, J.-C., Pascu, C., Szkuta, K., Von Schomberg, R., Karalopoulos, A., Repanas, K., & Schouppe, M. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00043 · Cited by 215 · see citing works
Heyndrickx, W., Mervin, L., Morawietz, T., Sturm, N., Friedrich, L., Zalewski, A., Pentina, A., Humbeck, L., Oldenhof, M., Niwayama, R., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00799 · Cited by 180 · see citing works
PubChemRDF: towards the semantic annotation of PubChem compound and substance databases
Fu, G., Batchelor, C. R., Dumontier, M., Hastings, J., Willighagen, E., & Bolton, E. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0084-4 · Cited by 139 · see citing works
Advances of machine learning in materials science: Ideas and techniques
Chong, S. S., Ng, Y. S., Wang, H., & Zheng, J. (2023) · Frontiers of Physics · doi:10.1007/s11467-023-1325-z · Cited by 109 · see citing works
The NIH Big Data to Knowledge (BD2K) initiative
Bourne, P. E., Bonazzi, V. R., Dunn, M. C., Green, E. D., Guyer, M. S., Komatsoulis, G. A., Larkin, J. E., & Russell, B. M. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv136 · Cited by 78 · see citing works
PMD Core Ontology: Achieving semantic interoperability in materials science
Bayerlein, B., Schilling, M., Birkholz, H., Jung, M., Waitelonis, J., Mädler, L., & Sack, H. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.112603 · Cited by 76 · see citing works
Spikes, H. A. (2025) · Tribology Letters · doi:10.1007/s11249-025-01968-3 · Cited by 75 · see citing works
Facile, R., Muhlbradt, E., Gong, M., Li, Q., Popat, V., Pétavy, F., Ronald Cornet, A., Ruan, Y., Koide, D., Saito, T., et al. (2021) · JMIR Medical Informatics · doi:10.2196/30363 · Cited by 63 · see citing works
Pradhan, A., Müser, M. H., Miller, N., Abdelnabe, J. P., Afferrante, L., Albertini, D. F., Aldave, D. A., Algieri, L., Ali, N., Almqvist, A., et al. (2025) · Tribology Letters · doi:10.1007/s11249-025-02014-y · Cited by 42 · see citing works
Yang, K., Xiong, Y., Wu, G., Lin, H., Tang, J., Wu, C., Chen, H., & Wang, Y. (2024) · Advanced Materials Interfaces · doi:10.1002/admi.202400796 · Cited by 29 · see citing works
Generating FAIR research data in experimental tribology
Garabedian, N., Schreiber, P., Brandt, N., Zschumme, P., Blatter, I., Dollmann, A., Haug, C., Kümmel, D., Li, Y., Meyer, F. I., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01429-9 · Cited by 23 · see citing works
Self-citationUnderstanding the Tip–Sample Contact
Jacobs, T. D. B., Mate, C. M., Turner, K. T., & Carpick, R. W. (2013) · doi:10.1002/9781118723111.ch2 · Cited by 22 · see citing works
Thøgersen, K., Trømborg, J. K., Sveinsson, H. A., Malthe‐Sørenssen, A., & Scheibert, J. (2014) · Physical Review E · doi:10.1103/physreve.89.052401 · Cited by 19 · see citing works
The Australian Research Data Commons
Barker, M., Wilkinson, R. G., & Treloar, A. E. (2019) · Data Science Journal · doi:10.5334/dsj-2019-044 · Cited by 18 · see citing works
Zhang, J., Wang, Y., Chen, Q., Su, Y., Bai, S., Ootani, Y., Ozawa, N., Adachi, K., & Kubo, M. (2024) · Carbon · doi:10.1016/j.carbon.2024.119713 · Cited by 16 · see citing works
Library eArchiving with ZONTAL Space and the Allotrope Data Format
Corte, D. D., Colsman, W., Welker, B., & Rennick, B. (2020) · Digital Library Perspectives · doi:10.1108/dlp-09-2019-0036 · Cited by 12 · see citing works
Aggarwal, P., Dabo, A., Sun, C., Antonucci, V., Colsman, W., Fessenmayr, H., Wells, K. M., McComas, J., Nölken, G., & Nielsen, B. V. (2025) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2025.116907 · Cited by 6 · see citing works
Search and Harvesting across NFDI Consortia – Gaps and Challenges
Mathiak, B., Heyer, G., Widmann, H., Henzen, C., & Czerniak, A. (2024) · Datenbank-Spektrum · doi:10.1007/s13222-024-00480-7 · Cited by 1 · see citing works
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- Authors
- Garabedian, N., & Tysoe, W. T.
- Affiliations
- University of Wisconsin–Milwaukee (US)
- Corresponding author
- Tysoe, Wilfred T.
- ORCID
- Tysoe, Wilfred T.
- Venue
- Tribology Letters
- Published
- 2026-07-24
- Volume
- 74
- Issue
- 3
- ISSN
- 1023-8883 (ISSN-L)
- 1573-2711
- OpenAlex
- W7170251941
- Cited by
- 0
- References
- 30
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Tribology Letters (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Social Sciences › Sociology and Political Science › Data Analysis and Archiving
- Keywords
- data architecture, FAIR principles, data management, knowledge networks, digital twin, data interoperability, data reuse, data islands, tribology, artificial intelligence in research
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