Generating FAIR research data in experimental tribology

Abstract

Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.

The Intersection Between Semantic Web and Materials Science

“…In particular, agile processes concerning data‐driven materials research and development were pointed out in the context of the Mat‐o‐Lab framework. The work of Garabedian and Schreiber et al [ 38 ] describes another example of collaborative work. Here, the domain experts derived a controlled vocabulary describing tribological processes and objects with basic semantics in a MediaWiki‐based database.…”

“…Regarding FAIR (research) data management, TriboDataFAIR Ontology was designed and deployed by Garabedian and Schreiber et al [ 38 ] with a specific focus on data interoperability and reusability. In this work, a knowledge graph based on collected experimental tribological data and metadata is created in a scalable environment, enabling the targeted retrieval of information.…”

“…Li et al [ 40 ] developed the materials design ontology (MDO), where the provenance was given using a design pattern that provides information in the repository of ontologies, used together with entities from PROV‐O. [ 43 ] MSE‐related data provenance was approached by Bayerlein et al, [ 37 ] Garabedian and Schreiber et al, [ 38 ] and Nikooie et al, [ 34 ] using the concept of FAIR data, which implies the ability to provide the provenance of the data. Vardeman et al [ 32 ] introduced a work where an ontology design pattern was created for modeling material transformation.…”

“…In 16 of the selected papers, ontologies are created (Figure 3a) for specific MSE tasks. [8,10,[28][29][30][31][32][33][34][35][36][37][38][39][40][41] Ontologies are formulated in OWL to express knowledge in a structured, flexible, and extensible way.…”

Surface topography as a material parameter

“…To achieve significant advances, researchers must more commonly publish their raw topography data, and do so in accordance with the principles of FAIR data, 31 which are just starting to be embraced in the tribology and surface-roughness communities. 32 , 33 One way to publish topography data in an accessible format with a Digital Object Identifier (DOI) is to use the contact.engineering platform developed by the present authors. 33 Regardless of platform, the publication of topography data will reduce the gulf that currently exists between modelers, who have accurate theory/simulations of topography-dependent performance but inadequate access to the topography of real-world surfaces, and experimentalists, who have access to real-world surfaces but typically lack the specialized knowledge and computing resources to implement complex theories or models.…”