FAIRification of Legacy Devices and Measurement Applications with Single Board Computers
Source abstract
Abstract
Abstract Digitalization in science demands the provision of all data required to understand the underlying scientific process and its objects. In engineering science, these objects are mainly characterized by quantitative data from processes applied for their generation as well as from measurement devices. However, the information from many processes and devices cannot be captured because they lack any electronic interfaces, in particular in case of legacy devices. Therefore, to make these information accessible as data according to the FAIR principles, a general concept is presented. The application of simple computers, usually single board computers (SBCs), is described based on a specific SBC, the Raspberry Pi, as a general purpose electronic device. It is shown how this SBC can be equipped with additional hardware like sensors or interface shields to yield a measurement device or just a gateway to connect outdated operating systems to scientific infrastructure like electronic laboratory notebooks, a procedure which might be called FAIRification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced X-ray and CT Imaging · Air Quality Monitoring and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (11)
Show source-provided references
1. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, Lei J, Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data, 3:160018–1910.1038/sdata.2016.18 ↗
2. Kritzinger W, Karner M, Traar G, Henjes J, Sihn W (2018) Digital twin in manufacturing: a categorical literature review and classification. IFAC PapersOnLine 51:1016–102210.1016/j.ifacol.2018.08.474 ↗
3. Bayerlein B, Schilling M, Birkholz H, Jung M, Waitelonis J, Mädler L, Sack H (2024) PMD core ontology: achieving semantic interoperability in materials science. Mater Design 237:11260310.1016/j.matdes.2023.112603 ↗
4. Diviacco P, Iurcev M, Carbajales RJ, Viola A, Potleca N (2023) Design and implementation of a crowdsensing-based air quality monitoring open and fair data infrastructure. Processes 11:188110.3390/pr11071881 ↗
5. Celicourt P, Sam RD, Piasecki M (2023) Rapid prototyping of an automated sensor-to-server environmental data acquisition system adopting a fair-oriented approach. J Environ Inf 41:1–15
6. Bodenbenner M, Pennekamp J, Montavon B, Wehrle K, Schmitt RH (2023) FAIR sensor ecosystem: long-term (re-)usability of fair sensor data through contextualization. IEEE international conference industrial, information10.1109/INDIN51400.2023.10218149 ↗
7. Bell C (2022) Beginning MicroPython with the raspberry pi pico: build electronics and IoT projects. Apress10.1007/978-1-4842-8135-2 ↗
8. Hattersley L (2022) The official raspberry pi handbook 2022. MagPi Magazine
9. Cox T, Fernandes SL (2018) Raspberry Pi 3 cookbook for python programmers. Packt
10. Kofler M, Kühnast C, Scherbeck C (2015) Raspberry Pi. Das Umfassende Handbuch, Rheinwerk
11. Lyu X, Luo Z, Shao L, Awbi H, Piano SL (2023) Safe CO2 threshold limits for indoor long-range airborne transmission control of covid-19. Build Envrion 234:10996710.1016/j.buildenv.2022.109967 ↗