Skip to main content
SpringerLink
Log in

Interoperability and Architecture Requirements Analysis and Metadata Standardization for a Research Data Infrastructure in Catalysis

  • Conference paper
  • First Online:
Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1620))

Abstract

The National Research Data Infrastructure for Catalysis-Related Sciences (NFDI4Cat) is one of the disciplinary consortia formed within the German national research data infrastructure (NFDI), an effort undertaken by the German federal and state governments to advance the digitalization of all scientific research data within the German academic system in accordance with the FAIR principles. This work reports on initial outcomes from the NFDI4Cat project. The data value chain in catalysis research is analysed, and architecture and interoperability requirements are identified by conducting user interviews, collecting competency questions, and exploring the landscape of semantic artefacts. Methods from agile software development are employed to collect, organize, and present the collected requirements; workflows are annotated on the basis of metadata standards for research data provenance, by which requirements for domain ontologies in catalysis are deduced.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    VIMMP ontologies [31]: https://gitlab.com/vimmp-semantics/vimmp-ontologies/.

  2. 2.

    Ontology accessible at http://www.molmod.info/semantics/pims-ii.ttl.

  3. 3.

    The collection of relevant semantic assets can be found on https://nfdi4cat.org/en/services/ontology-collection/.

  4. 4.

    Documentation and ontology accessible at https://nfdi4ing.pages.rwth-aachen.de/metadata4ing/metadata4ing/.

  5. 5.

    XML schema definition accessible at https://dx.doi.org/10.18419/darus-500.

References

  1. Allweyer, T.: BPMN 2.0: Introduction to the Standard for Business Process Modeling, 2nd edn. BoD, Norderstedt (2016). ISBN 978-3-8370-9331-5

    Google Scholar 

  2. Annane, A., Aussenac-Gilles, N., Kamel, M.: Une ontologie des processus métier (BBO) pour guider un agent virtuel. In: Hernandez, N. (ed.) Proceedings of IC 2019, pp. 183–198. AFIA (2019). https://hal.archives-ouvertes.fr/hal-02284535

  3. Appl, C., Baganz, F., Hass, V.C.: Development of a digital twin for enzymatic hydrolysis processes. Processes 9(10), 1734 (2021). https://doi.org/10.3390/pr9101734

    Article  Google Scholar 

  4. Asprion, N., et al.: INES: an interface between experiments and simulation to support the development of robust process designs. Chem. Ing. Tech. 87(12), 1810–1825 (2015). https://doi.org/10.1002/cite.201500020

    Article  Google Scholar 

  5. Asprion, N., et al.: INES: interface between experiments and simulation. Comput. Aided Chem. Eng. 33, 1159–1164 (2014). https://doi.org/10.1016/B978-0-444-63455-9.50028-3

    Article  Google Scholar 

  6. Bai, J., Cao, L., Mosbach, S., Akroyd, J., Lapkin, A.A., Kraft, M.: From platform to knowledge graph: evolution of laboratory automation. JACS Au (2022). https://doi.org/10.1021/jacsau.1c00438

  7. Barbosa Fernandes, P.C., Guizzardi, R.S.S., Guizzardi, G.: Using goal modeling to capture competency questions in ontology-based systems. J. Inf. Data Manag. 2(3), 527–540 (2011)

    Google Scholar 

  8. Batres, R.: Ontologies in process systems engineering. Chem. Ing. Tech. 89(11), 1421–1431 (2017). https://doi.org/10.1002/cite.201700037

    Article  Google Scholar 

  9. Batres, R., et al.: An upper ontology based on ISO 15926. Comput. Chem. Eng. 31(5–6), 519–534 (2007). https://doi.org/10.1016/j.compchemeng.2006.07.004

    Article  Google Scholar 

  10. Bronger, T., Demandt, É., Heine, I., Kraft, A., Preuß, N., Schwarz, A.: Die Nationale Forschungsdateninfrastruktur für die Ingenieurwissenschaften (NFDI4Ing). Bausteine Forschungsdatenmanagement 2021(2), 110–123 (2021). https://doi.org/10.17192/bfdm.2021.2.8329

  11. Buglione, L., Abran, A.: Improving the user story agile technique using the INVEST criteria. In: Demirors, O., Can, A.B., Eşmelioğlu, S. (eds.) Proceedings of ISWM-MENSURA 2013, pp. 49–53. IEEE (2014). ISBN 978-0-7695-5078-7

    Google Scholar 

  12. Clark, S., et al.: Toward a unified description of battery data. Adv. Energy Mater. (2022). https://doi.org/10.1002/aenm.202102702

  13. Cohn, M.: User Stories Applied for Agile Software Development. Pearson Education, Boston (2004). ISBN 978-0-321-20568-1

    Google Scholar 

  14. Deagen, M.E., Brinson, L.C., Vaia, R.A., Schadler, L.S.: The materials tetrahedron has a “digital twin”. MRS Bull. (2022). https://doi.org/10.1557/s43577-021-00214-0

  15. Eifert, T., Eisen, K., Maiwald, M., Herwig, C.: Current and future requirements to industrial analytical infrastructure—part 2: smart sensors. Anal. Bioanal. Chem. 412(9), 2037–2045 (2020). https://doi.org/10.1007/s00216-020-02421-1

    Article  Google Scholar 

  16. Espinoza, S., et al.: NFDI for catalysis-related sciences. Bausteine Forschungsdatenmanagement 2021(2), 57–71 (2021). https://doi.org/10.17192/bfdm.2021.2.8333

  17. European Committee for Standardization: Materials Modelling: Terminology, Classification and Metadata. CEN Workshop Agreement (expired) 17284:2018 (E), CEN (2018). https://www.cencenelec.eu/media/CEN-CENELEC/CWAs/RI/cwa17284_2018.pdf, validity expired (the validity of this agreement extended until 17th April 2021, three years after its publication)

  18. Farazi, F., et al.: Linking reaction mechanisms and quantum chemistry: an ontological approach. Comput. Chem. Eng. 137, 106813 (2020). https://doi.org/10.1016/j.compchemeng.2020.106813

  19. Farazi, F., et al.: OntoKin: an ontology for chemical kinetic reaction mechanisms. J. Chem. Inf. Model. 60(1), 108–120 (2020). https://doi.org/10.1021/acs.jcim.9b00960

    Article  Google Scholar 

  20. Fernández-Izquierdo, A., García-Castro, R.: Requirements behaviour analysis for ontology testing. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds.) EKAW 2018. LNCS (LNAI), vol. 11313, pp. 114–130. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03667-6_8. ISBN 978-3-030-03666-9

  21. Francisco Morgado, J., et al.: Mechanical testing ontology for digital-twins: a roadmap based on EMMO. In: García Castro, R., Davies, J., Antoniou, G., Fortuna, C. (eds.) Proceedings of SeDiT 2020, p. 3. CEUR-WS (2020). http://ceur-ws.org/Vol-2615/

  22. Glatt, M., Sinnwell, C., Yi, L., Donohoe, S., Ravani, B., Aurich, J.C.: Modeling and implementation of a digital twin of material flows based on physics simulation. J. Manuf. Syst. 58B, 231–245 (2021). https://doi.org/10.1016/j.jmsy.2020.04.015

    Article  Google Scholar 

  23. Görgen, C., Sinn, R.: Mathematik in der Nationalen Forschungsdateninfrastruktur. Mitteilungen der Deutschen Mathematiker-Vereinigung 29(3), 122–123 (2021). https://doi.org/10.1515/dmvm-2021-0049

    Article  Google Scholar 

  24. Gressling, T.: Data Science in Chemistry. De Gruyter, Berlin (2021). ISBN 978-3-11-062939-2

    Google Scholar 

  25. Grüninger, M., Fox, M.S.: The role of competency questions in enterprise engineering. In: Rolstadås, A. (ed.) Benchmarking: Theory and Practice. IAICT, pp. 22–31. Springer, Boston (1995). https://doi.org/10.1007/978-0-387-34847-6_3. ISBN 978-0-412-62680-7

  26. Hartl, N., Wössner, E., Sure-Vetter, Y.: Nationale Forschungsdateninfrastruktur (NFDI). Informatik Spektrum 44(5), 370–373 (2021). https://doi.org/10.1007/s00287-021-01392-6

    Article  Google Scholar 

  27. Herres-Pawlis, S.: NFDI4Chem: Fachkonsortium für die Chemie. Bausteine Forschungsdatenmanagement 2021(2), 34–45 (2021). https://doi.org/10.17192/bfdm.2021.2.8340

  28. Herres-Pawlis, S., Koepler, O., Steinbeck, C.: NFDI4Chem: shaping a digital and cultural change in chemistry. Angew. Chem. Int. Ed. 58(32), 10766–10768 (2019). https://doi.org/10.1002/anie.201907260

    Article  Google Scholar 

  29. Herres-Pawlis, S., Liermann, J.C., Koepler, O.: Research data in chemistry: results of the first NFDI4Chem community survey. Z. Anorg. Allg. Chem. 646(21), 1748–1757 (2020). https://doi.org/10.1002/zaac.202000339

    Article  Google Scholar 

  30. Höche, D., Konchakova, N., Zheludkevich, M., Hagelien, T., Friis, J.: Ontology assisted modelling of galvanic corrosion of magnesium. In: Chinesta, F., Abgrall, R., Allix, O., Kaliske, M. (eds.) Proceedings of WCCM-ECCOMAS 2020. Scipedia (2021). https://doi.org/10.23967/wccm-eccomas.2020.263

  31. Horsch, M.T., et al.: Ontologies for the virtual materials marketplace. KI - Künstliche Intelligenz 34(3), 423–428 (2020). https://doi.org/10.1007/s13218-020-00648-9

    Article  Google Scholar 

  32. Horsch, M.T.: Mereosemiotics: parts and signs. In: Sanfilippo, E.M., et al. (eds.) Proceedings of JOWO 2021, p. 3. CEUR-WS (2021). http://ceur-ws.org/Vol-2969/

  33. Horsch, M.T., Chiacchiera, S., Schembera, B., Seaton, M.A., Todorov, I.T.: Semantic interoperability based on the European materials and modelling ontology and its ontological paradigm: mereosemiotics. In: Chinesta, F., Abgrall, R., Allix, O., Kaliske, M. (eds.) Proceedings of WCCM-ECCOMAS 2020. Scipedia (2021). https://doi.org/10.23967/wccm-eccomas.2020.297

  34. Horsch, M.T., et al.: Semantic interoperability and characterization of data provenance in computational molecular engineering. J. Chem. Eng. Data 65(3), 1313–1329 (2020). https://doi.org/10.1021/acs.jced.9b00739

    Article  Google Scholar 

  35. Horsch, M.T., Toti, D., Chiacchiera, S., Seaton, M.A., Goldbeck, G., Todorov, I.T.: OSMO: ontology for simulation, modelling, and optimization. In: Sanfilippo, E.M., et al. (eds.) Proceedings of JOWO 2021, p. 47. CEUR-WS (2021). http://ceur-ws.org/Vol-2969/

  36. Jagusch, G.W., Preuß, N.: Umfragedaten zu “NFDI4Ing - Rückmeldung aus den Forschungscommunities”. Data collection, NFDI4Ing (2019). https://doi.org/10.25534/tudatalib-104

  37. Jupp, S., et al.: A new ontology lookup service at EMBL-EBI. In: Malone, J., Stevens, R., Forsberg, K., Splendiani, A. (eds.) Proceedings of SWAT4LS, pp. 118–119. CEUR-WS (2015). http://ceur-ws.org/Vol-1546/

  38. Kchaou, M., Khlif, W., Gargouri, F., Mahfoudh, M.: Transformation of BPMN model into an OWL2 ontology. In: Ali, R., Kaindl, H., Maciaszek, L.A. (eds.) Proceedings of ENASE 2021, pp. 380–388. SciTePress (2021). ISBN 978-989-758-508-1

    Google Scholar 

  39. Klein, P., Preisig, H.A., Horsch, M.T., Konchakova, N.: Application of an ontology based process model construction tool for active protective coatings: corrosion inhibitor release. In: Sanfilippo, E.M., et al. (eds.) Proceedings of JOWO 2021, p. 26. CEUR-WS (2021). http://ceur-ws.org/Vol-2969/

  40. Kraft, S., et al.: Nationale Forschungsdateninfrastruktur (NFDI) e.V.: Aufbau und Ziele. Bausteine Forschungsdatenmanagement 2021(2), 1–9 (2021). https://doi.org/10.17192/bfdm.2021.2.8332

  41. Liskin, O., Pham, R., Kiesling, S., Schneider, K.: Why we need a granularity concept for user stories. In: Cantone, G., Marchesi, M. (eds.) XP 2014. LNBIP, vol. 179, pp. 110–125. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06862-6_8. ISBN 978-3-319-06861-9

  42. Metadata4Ing Working Group: Metadata4Ing: An ontology for describing the generation of research data within a scientific activity. version 1.0.0, NFDI4Ing (2022). https://nfdi4ing.pages.rwth-aachen.de/metadata4ing/metadata4ing/

  43. Mittal, V.K., Bailin, S.C., Gonzalez, M.A., Meyer, D.E., Barrett, W.M., Smith, R.L.: Toward automated inventory modeling in life cycle assessment: the utility of semantic data modeling to predict real-world chemical production. ACS Sustain. Chem. Eng. 6(2), 1961–1976 (2018). https://doi.org/10.1021/acssuschemeng.7b03379

    Article  Google Scholar 

  44. NFDI-MatWerk: Die große Digitalisierungsumfrage. Data, NFDI-MatWerk (2020)

    Google Scholar 

  45. Ngandjong, A.C., et al.: Investigating electrode calendering and its impact on electrochemical performance by means of a new discrete element method model: towards a digital twin of Li-ion battery manufacturing. J. Power Sources 495, 229320 (2021). https://doi.org/10.1016/j.jpowsour.2020.229320

  46. Range, J., et al.: EnzymeML: a data exchange format for biocatalysis and enzymology. FEMS J. (2022). https://doi.org/10.1111/febs.16318

  47. Riehl Figueiredo, L., Carvalho de Oliveira, H.: Automatic generation of ontologies from business process models. In: Hammoudi, S., Smialek, M., Camp, O., Filipe, J. (eds.) Proceedings of ICEIS 2018, pp. 81–91. SciTePress (2018). ISBN 978-989-758-298-1

    Google Scholar 

  48. Romanos, N., Kalogerini, M., Koumoulos, E.P., Morozinis, K., Sebastiani, M., Charitidis, C.: Innovative data management in advanced characterization: implications for materials design. Mater. Today Commun. 20, 100541 (2019). https://doi.org/10.1016/j.mtcomm.2019.100541

  49. Rospocher, M., Ghidini, C., Serafini, L.: An ontology for the business process modelling notation. In: Garbacz, P., Kutz, O. (eds.) Proceedings of FOIS 2014, pp. 133–146. IOS (2014). ISBN 978-1-61499-437-4

    Google Scholar 

  50. Rößler, M., Huth, P.U., Liauw, M.A.: Process analytical technology (PAT) as a versatile tool for real-time monitoring and kinetic evaluation of photocatalytic reactions. React. Chem. Eng. 5(10), 1992–2002 (2020). https://doi.org/10.1039/d0re00256a

    Article  Google Scholar 

  51. Schembera, B., Durán, J.M.: Dark data as the new challenge for big data science and the introduction of the scientific data officer. Philos. Technol. 33(1), 93–115 (2019). https://doi.org/10.1007/s13347-019-00346-x

    Article  Google Scholar 

  52. Schembera, B., Iglezakis, D.: EngMeta: metadata for computational engineering. Int. J. Metadata Semant. Ontol. 14(1), 26–38 (2020). https://doi.org/10.1504/IJMSO.2020.107792

    Article  Google Scholar 

  53. Schimmler, S., et al.: NFDI4Cat: local and overarching data infrastructures. In: Proceedings of e-Science Days 2021, heiBOOKS (2022, to appear)

    Google Scholar 

  54. Shao, Q., et al.: Material twin for composite material microstructure generation and reconstruction based on statistical continuum theory. Composites C 7, 100216 (2022). https://doi.org/10.1016/j.jcomc.2021.100216

  55. Strecker, D., Bossert, L.C., Demandt, É.: Das Versprechen der Vernetzung der NFDI. Bausteine Forschungsdatenmanagement 2021(3), 39–55 (2021). https://doi.org/10.17192/bfdm.2021.3.8336

  56. Theißen, M., Wiedau, M.: DEXPI P &ID specification. Version 1.3, ProcessNet, DEXPI Initiative (2021). https://dexpi.org/specifications/

  57. Whetzel, P.L., et al.: BioPortal: enhanced functionality via new web services from the national center for Biomedical ontology to access and use ontologies in software applications. Nucleic Acids Res. 39(S2), W541–W545 (2011). https://doi.org/10.1093/nar/gkr469

    Article  Google Scholar 

  58. Wiesner, A., Morbach, J., Marquardt, W.: Information integration in chemical process engineering based on semantic technologies. Comput. Chem. Eng. 35(4), 692–708 (2011). https://doi.org/10.1016/j.compchemeng.2010.12.003

    Article  Google Scholar 

  59. Wiśniewski, D., Potoniec, J., Ławrynowicz, A., Keet, C.M.: Analysis of ontology competency questions and their formalizations in SPARQL-OWL. J. Web Semant. 59, 100534 (2019). https://doi.org/10.1016/j.websem.2019.100534

  60. Wulf, C., et al.: A unified research data infrastructure for catalysis research: challenges and concepts. ChemCatChem 13(14), 3223–3236 (2021). https://doi.org/10.1002/cctc.202001974

    Article  Google Scholar 

Download references

Acknowledgment

This work was funded by DFG through NFDI4Cat, DFG project no. 441926934, within the NFDI programme of the Joint Science Conference (GWK).

Author information

Authors and Affiliations

  1. High Performance Computing Center Stuttgart (HLRS), Nobelstr. 19, 70569, Stuttgart, Germany

    Martin Horsch, Taras Petrenko, Volodymyr Kushnarenko, Bjoern Schembera & Thomas Bönisch

  2. School of Psychology and Computer Science, University of Central Lancashire, 45 Fylde Road, Preston, Lancashire, PR1 1JN, UK

    Martin Horsch

  3. Institute of Applied Analysis and Numerical Simulation, University of Stuttgart, Allmandring 5b, 70569, Stuttgart, Germany

    Bjoern Schembera

  4. Fraunhofer Institute for Open Communication Systems (FOKUS), Kaiserin-Augusta-Allee 31, 10589, Berlin, Germany

    Bianca Wentzel & Sonja Schimmler

  5. Department of Biochemical and Chemical Engineering, Laboratory of Equipment Design, TU Dortmund University, Emil-Figge-Str. 68, 44227, Dortmund, Germany

    Alexander Behr & Norbert Kockmann

Authors
  1. Martin Horsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Taras Petrenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Volodymyr Kushnarenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bjoern Schembera
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bianca Wentzel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alexander Behr
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Norbert Kockmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sonja Schimmler
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Thomas Bönisch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Horsch .

Editor information

Editors and Affiliations

  1. Space Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Alexei Pozanenko

  2. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Sergey Stupnikov

  3. Christian-Albrecht University of Kiel, Kiel, Germany

    Bernhard Thalheim

  4. Universidad Carlos III de Madrid, Getafe, Spain

    Eva Mendez

  5. A. A. Baikov Institute of Metallurgy and Materials Science of RAS (IMET RAS), Moscow, Russia

    Nadezhda Kiselyova

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Horsch, M. et al. (2022). Interoperability and Architecture Requirements Analysis and Metadata Standardization for a Research Data Infrastructure in Catalysis. In: Pozanenko, A., Stupnikov, S., Thalheim, B., Mendez, E., Kiselyova, N. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2021. Communications in Computer and Information Science, vol 1620. Springer, Cham. https://doi.org/10.1007/978-3-031-12285-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-12285-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12284-2

  • Online ISBN: 978-3-031-12285-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation