10
1 Introduction
References
1. E. Forte, F. Jirasek, M. Bortz, J. Burger, J. Vrabec, H. Hasse, Digitalization in thermodynamics.
Chem. Ing. Tech. 91(3), 201–214 (2019)
2. N. Tchipev, S. Seckler, M. Heinen, J. Vrabec, F. Gratl, M. Horsch, M. Bernreuther, C.W.
Glass, C. Niethammer, N. Hammer, B. Krischok, M. Resch, D. Kranzlmüller, H. Hasse, H.J.
Bungartz, P. Neumann, TweTriS: twenty trillion-atom simulation. Int. J. HPC Appl. 33(5),
838–854 (2019)
3. Å. Ervik, A. Mejía, E.A. Müller, Bottled SAFT: a web app providing SAFT-γ Mie force field
parameters for thousands of molecular fluids. J. Chem. Inf. Model. 56(9), 1609–1614 (2016)
4. S. Stephan, M.T. Horsch, J. Vrabec, H. Hasse, MolMod - an open access database of force
fields for molecular simulations of fluids. Mol. Sim. 45(10), 806–814 (2019)
5. A. Merkys, N. Mounet, A. Cepellotti, N. Marzari, S. Gražulis, G. Pizzi, A posteriori metadata
from automated provenance tracking: integration of AiiDA and TCOD. J. Cheminform. 9, 56
(2017)
6. M.T. Horsch, C. Niethammer, G. Boccardo, P. Carbone, S. Chiacchiera, M. Chiricotto, J.D.
Elliott, V. Lobaskin, P. Neumann, P. Schiffels, M.A. Seaton, I.T. Todorov, J. Vrabec, W.L.
Cavalcanti, Semantic interoperability and characterization of data provenance in computational
molecular engineering. J. Chem. Eng. Data 65(3), 1313–1329 (2020)
7. X. Xu, J. Range, G. Gygli, J. Pleiss, Analysis of thermophysical properties of deep eutectic
solvents by data integration. J. Chem. Eng. Data 65(3), 1172–1179 (2019)
8. G.J. Schmitz, Microstructure modeling in integrated computational materials engineering
(ICME) settings: can HDF5 provide the basis for an emerging standard for describing
microstructures? JOM 68(1), 77–83 (2016)
9. G.J. Schmitz, B. Böttger, M. Apel, J. Eiken, G. Laschet, R. Altenfeld, R. Berger, G. Boussinot,
A. Viardin, Towards a metadata scheme for the description of materials: the description of
microstructures. Sci. Technol. Adv. Mater. 17(1), 410–430 (2016)
10. J. Morbach, A. Wiesner, W. Marquardt, OntoCAPE 2.0: a (re−)usable ontology for computeraided process engineering. Comput. Aid. Chem. Eng. 25, 991–996 (2008)
11. J.P. Belaud, M. Pons, CAPE-OPEN: interoperability in industrial flowsheet simulation software. Chem. Ing. Tech. 86(7), 1052–1064 (2014)
12. L. Koo, N. Trokanas, F. Cecelja, A semantic framework for enabling model integration for
biorefining. Comput. Chem. Eng. 100, 219–231 (2017)
13. P.B. Heidorn, Shedding light on the dark data in the long tail of science. Libr. Trends 57(2),
280–299 (2008)
14. B. Schembera, J.M. Durán, Dark data as the new challenge for big data science and the introduction of the scientific data officer. Philos. Technol. 33, 93–115 (2020)
15. J. Bicarregui, Building and sustaining data infrastructures: putting policy into practice. Policy
document (2016). https://doi.org/10.6084/m9.figshare.4055538.v2
16. B. Mons, Data Stewardship for Open Science (CRC Press, Boca Raton, 2018)
17. G. Guizzardi, Ontology, ontologies and the “I” of FAIR. Data Intell. 2(1–2), 181–191 (2020)
18. B. Bouchou, C. Niang, Semantic mediator querying, in Proceedings of IDEAS ’14, ed. by A.M.
Almeida, J. Bernardino, E. Ferreira Gomes (ACM, New York, USA, 2014), pp. 29–38
19. G. Fusco, L. Aversano, An approach for semantic integration of heterogeneous data sources.
Peer J. Comput. Sci. 6, e254 (2020). https://doi.org/10.7717/peerj-cs.254
20. D. Lembo, R. Rosati, V. Santarelli, D.F. Savo, E. Thorstensen, Mapping repair in ontologybased data access evolving systems, in Proceedings of IJCAI, IJCAI, ed. by C. Sierra (San José,
California, USA, 2017), pp. 1160–1166
21. G. Xiao, D. Calvanese, R. Kontchakov, D. Lembo, A. Poggi, R. Rosati, M. Zakharyaschev,
Ontology-based data access: a survey, in Proceedings of IJCAI, IJCAI, ed. by J. Lang (San
José, California, USA, 2018), pp. 5511–5519
22. A. Ribes, C. Caremoli, Salomé platform component model for numerical simulation, in Proceedings of COMPSAC 2007, IEEE Computer Society, vol. 2, ed. by C.K. Chang (Los Alamitos,
California, USA, 2007), pp. 553–564
1 Introduction
References
1. E. Forte, F. Jirasek, M. Bortz, J. Burger, J. Vrabec, H. Hasse, Digitalization in thermodynamics.
Chem. Ing. Tech. 91(3), 201–214 (2019)
2. N. Tchipev, S. Seckler, M. Heinen, J. Vrabec, F. Gratl, M. Horsch, M. Bernreuther, C.W.
Glass, C. Niethammer, N. Hammer, B. Krischok, M. Resch, D. Kranzlmüller, H. Hasse, H.J.
Bungartz, P. Neumann, TweTriS: twenty trillion-atom simulation. Int. J. HPC Appl. 33(5),
838–854 (2019)
3. Å. Ervik, A. Mejía, E.A. Müller, Bottled SAFT: a web app providing SAFT-γ Mie force field
parameters for thousands of molecular fluids. J. Chem. Inf. Model. 56(9), 1609–1614 (2016)
4. S. Stephan, M.T. Horsch, J. Vrabec, H. Hasse, MolMod - an open access database of force
fields for molecular simulations of fluids. Mol. Sim. 45(10), 806–814 (2019)
5. A. Merkys, N. Mounet, A. Cepellotti, N. Marzari, S. Gražulis, G. Pizzi, A posteriori metadata
from automated provenance tracking: integration of AiiDA and TCOD. J. Cheminform. 9, 56
(2017)
6. M.T. Horsch, C. Niethammer, G. Boccardo, P. Carbone, S. Chiacchiera, M. Chiricotto, J.D.
Elliott, V. Lobaskin, P. Neumann, P. Schiffels, M.A. Seaton, I.T. Todorov, J. Vrabec, W.L.
Cavalcanti, Semantic interoperability and characterization of data provenance in computational
molecular engineering. J. Chem. Eng. Data 65(3), 1313–1329 (2020)
7. X. Xu, J. Range, G. Gygli, J. Pleiss, Analysis of thermophysical properties of deep eutectic
solvents by data integration. J. Chem. Eng. Data 65(3), 1172–1179 (2019)
8. G.J. Schmitz, Microstructure modeling in integrated computational materials engineering
(ICME) settings: can HDF5 provide the basis for an emerging standard for describing
microstructures? JOM 68(1), 77–83 (2016)
9. G.J. Schmitz, B. Böttger, M. Apel, J. Eiken, G. Laschet, R. Altenfeld, R. Berger, G. Boussinot,
A. Viardin, Towards a metadata scheme for the description of materials: the description of
microstructures. Sci. Technol. Adv. Mater. 17(1), 410–430 (2016)
10. J. Morbach, A. Wiesner, W. Marquardt, OntoCAPE 2.0: a (re−)usable ontology for computeraided process engineering. Comput. Aid. Chem. Eng. 25, 991–996 (2008)
11. J.P. Belaud, M. Pons, CAPE-OPEN: interoperability in industrial flowsheet simulation software. Chem. Ing. Tech. 86(7), 1052–1064 (2014)
12. L. Koo, N. Trokanas, F. Cecelja, A semantic framework for enabling model integration for
biorefining. Comput. Chem. Eng. 100, 219–231 (2017)
13. P.B. Heidorn, Shedding light on the dark data in the long tail of science. Libr. Trends 57(2),
280–299 (2008)
14. B. Schembera, J.M. Durán, Dark data as the new challenge for big data science and the introduction of the scientific data officer. Philos. Technol. 33, 93–115 (2020)
15. J. Bicarregui, Building and sustaining data infrastructures: putting policy into practice. Policy
document (2016). https://doi.org/10.6084/m9.figshare.4055538.v2
16. B. Mons, Data Stewardship for Open Science (CRC Press, Boca Raton, 2018)
17. G. Guizzardi, Ontology, ontologies and the “I” of FAIR. Data Intell. 2(1–2), 181–191 (2020)
18. B. Bouchou, C. Niang, Semantic mediator querying, in Proceedings of IDEAS ’14, ed. by A.M.
Almeida, J. Bernardino, E. Ferreira Gomes (ACM, New York, USA, 2014), pp. 29–38
19. G. Fusco, L. Aversano, An approach for semantic integration of heterogeneous data sources.
Peer J. Comput. Sci. 6, e254 (2020). https://doi.org/10.7717/peerj-cs.254
20. D. Lembo, R. Rosati, V. Santarelli, D.F. Savo, E. Thorstensen, Mapping repair in ontologybased data access evolving systems, in Proceedings of IJCAI, IJCAI, ed. by C. Sierra (San José,
California, USA, 2017), pp. 1160–1166
21. G. Xiao, D. Calvanese, R. Kontchakov, D. Lembo, A. Poggi, R. Rosati, M. Zakharyaschev,
Ontology-based data access: a survey, in Proceedings of IJCAI, IJCAI, ed. by J. Lang (San
José, California, USA, 2018), pp. 5511–5519
22. A. Ribes, C. Caremoli, Salomé platform component model for numerical simulation, in Proceedings of COMPSAC 2007, IEEE Computer Society, vol. 2, ed. by C.K. Chang (Los Alamitos,
California, USA, 2007), pp. 553–564