1932

Abstract

As the volume of data associated with scientific research has exploded over recent years, the use of digital infrastructures to support this research and the data underpinning it has increased significantly. Physical chemists have been making use of eScience infrastructures since their conception, but in the last five years their usage has increased even more. While these infrastructures have not greatly affected the chemistry itself, they have in some cases had a significant impact on how the research is undertaken. The combination of the human effort of collaboration to create open source software tools and semantic resources, the increased availability of hardware for the laboratories, and the range of data management tools available has made the life of a physical chemist significantly easier. This review considers the different aspects of eScience infrastructures and explores how they have improved the way in which we can conduct physical chemistry research.

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2022-04-20
2024-04-27
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Literature Cited

  1. 1. 
    Atkins DE, Borgman CL, Bindhoff N, Ellisman M, Felman S et al. 2010. RCUK review of e-Science 2009: building a UK foundation for the transformative enhancement of research innovation Tech. Rep., Res. Counc. U. K. Swindon, UK:
  2. [Google Scholar]
  3. [Google Scholar]
  4. 4. 
    Wilbanks J 2009. I have seen the paradigm shift, and it is us. The Fourth Paradigm: Data-Intensive Scientific Discovery T Hey, S Tansley, K Tolle 209–14 Redmond, WA: Microsoft Res.
    [Google Scholar]
  5. 5. 
    Frey JG, Roure DD, Carr L. 2002. Publication at source: scientific communication from a publication web to a data grid. EuroWeb 2002—The Web and the GRID1–3 Oxford, UK: EuroWeb
    [Google Scholar]
  6. 6. 
    Hey T, Trefethen AE 2002. The UK e-Science core programme and the grid. Computational Science – ICCS 2002 PMA Sloot, AG Hoekstra, CJK Tan, JJ Dongarra 3–21 Lect. Notes Comput. Sci. Ser Berlin/Heidelberg: Springer
    [Google Scholar]
  7. 7. 
    Gervasi O, Dittamo C, Laganà A 2005. A grid molecular simulator for e-Science. Advances in Grid Computing—EGC 2005 PMA Sloot, AG Hoekstra, T Priol, A Reinefeld, M Bubak 16–22 Lect. Notes Comput. Sci. Ser Berlin/Heidelberg: Springer
    [Google Scholar]
  8. 8. 
    Robinson JM, Frey JG, Stanford-Clark AJ, Reynolds AD, Bedi BV 2005. Sensor networks and grid middleware for laboratory monitoring. First International Conference on e-Science and Grid Computing (e-Science'05)562–69 New York: IEEE
    [Google Scholar]
  9. 9. 
    Wang J, Korambath P, Kim S, Johnson S, Jin K et al. 2011. Facilitating e-Science discovery using scientific workflows on the grid. Guide to e-Science: Next Generation Scientific Research and Discovery X Yang, L Wang, W Jie 353–82 Comput. Commun. Netw. Ser London: Springer
    [Google Scholar]
  10. 10. 
    Khillar S. 2018. Difference between grid computing and cloud computing. DifferenceBetween.net. http://www.differencebetween.net/technology/difference-between-grid-computing-and-cloud-computing/
    [Google Scholar]
  11. 11. 
    Frey J, De Roure D, Taylor K, Essex J, Mills H, Zaluska E. 2006. CombeChem: a case study in provenance and annotation using the Semantic Web. Proceedings of the 2006 International Conference on Provenance and Annotation of Data270–77 Berlin/Heidelberg: Springer
    [Google Scholar]
  12. 12. 
    Taylor KR, Essex JW, Frey JG, Mills HR, Hughes G, Zaluska E. 2006. The semantic grid and chemistry: experiences with CombeChem. J. Web Semant. 4:284–101
    [Google Scholar]
  13. 13. 
    Hey T. 2020. AI for science: transforming scientific research. AI3SD Summer Seminar Series 2020 Online S Kanza, JG Frey, M Niranjan, V Hooper Video: https://www.youtube.com/watch?v=qptQG5o0HN0
    [Google Scholar]
  14. 14. 
    Stodden V, Seiler J, Ma Z. 2018. An empirical analysis of journal policy effectiveness for computational reproducibility. PNAS 115:112584–89
    [Google Scholar]
  15. 15. 
    Halford S, Pope C, Carr L. 2010. A manifesto for web science. Proceedings of the WebSci10: Extending the Frontiers of Society On-Line, Raleigh, United States, 25–26 Apr 2010 J Erickson, S Gradmann 1–6 Southampton, UK: Univ. Southampton
    [Google Scholar]
  16. 16. 
    Bird CL, Willoughby C, Coles SJ, Frey JG. 2013. Data curation issues in the chemical sciences. Inform. Stand. Q. 25:34–12
    [Google Scholar]
  17. 17. 
    journal 2021. Jupyter. Project Jupyter. https://www.jupyter.org
    [Google Scholar]
  18. 18. 
    journal 2021. Overleaf, online LaTeX editor. Overleaf. https://www.overleaf.com
    [Google Scholar]
  19. 19. 
    Berners-Lee T, Hendler J, Lassila O. 2001. The semantic web. Sci. Am. 284:534–43
    [Google Scholar]
  20. 20. 
    journal 2014. RDF 1.1 concepts and abstract syntax. W3C. https://www.w3.org/TR/rdf11-concepts/
    [Google Scholar]
  21. 21. 
    journal 2012. OWL 2 Web ontology language document overview (second edition). W3C. https://www.w3.org/TR/owl2-overview/
    [Google Scholar]
  22. 22. 
    journal 2013. SPARQL 1.1 Query language. W3C. https://www.w3.org/TR/sparql11-query/
    [Google Scholar]
  23. 23. 
    Hendler J, Berners-Lee T. 2010. From the Semantic Web to social machines: a research challenge for AI on the World Wide Web. Artif. Intel. 174:2156–61
    [Google Scholar]
  24. 24. 
    Gray J, Szalay A 2007. eScience–a transformed scientific method Paper presented to the Computer Science and Technology Board of the National Research Council Mountain View, CA: Jan. 11
  25. 25. 
    Hey T, Tansley S, Tolle K 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery Redmond, WA: Microsoft Res.
  26. 26. 
    Hunt JR, Baldocchi DD, van Ingen C. 2009. Redefining ecological science using data. Fourth Paradigm: Data-Intensive Scientific Discovery Hey T, Tansley S, Tolle K 21–26 Redmond, WA: Microsoft Res.
    [Google Scholar]
  27. 27. 
    Goble C, De Roure D. 2009. The impact of workflow tools on data-centric research. Fourth Paradigm: Data-Intensive Scientific Discovery Hey T, Tansley S, Tolle K 137–46 Redmond, WA: Microsoft Res.
    [Google Scholar]
  28. 28. 
    Lynch C. 2009. Jim Gray's fourth paradigm and the construction of the scientific record. Fourth Paradigm: Data-Intensive Scientific Discovery Hey T, Tansley S, Tolle K 177–84 Redmond, WA: Microsoft Res.
    [Google Scholar]
  29. 29. 
    Fitzgerald A, Fitzgerald B, Pappalardo K 2009. The future of data policy. Fourth Paradigm: Data-Intensive Scientific Discovery Hey T, Tansley S, Tolle K 201–8 Redmond, WA: Microsoft Res.
    [Google Scholar]
  30. 30. 
    Jirotka M, Lee CP, Olson GM. 2013. Supporting scientific collaboration: methods, tools and concepts. Comput. Support. Coop. Work 22:4667–715
    [Google Scholar]
  31. 31. 
    Bird CL, Frey JG. 2013. Chemical information matters: an e-research perspective on information and data sharing in the chemical sciences. Chem. Soc. Rev. 42:166754–76
    [Google Scholar]
  32. 32. 
    R. Soc., Chem. 2020. Welcoming a new era: a time for digital scientific discovery. Chemistry World. https://www.chemistryworld.com/rsc/welcoming-a-new-era-a-time-for-digital-scientific-discovery/4012131.article
    [Google Scholar]
  33. 33. 
    Bird C, Coles SJ, Frey JG. 2015. The evolution of digital chemistry at Southampton. Mol. Inform. 34:9585–97
    [Google Scholar]
  34. 34. 
    Badiola KA, Bird C, Brocklesby WS, Casson J, Chapman RT et al. 2015. Experiences with a researcher-centric ELN. Chem. Sci. 6:31614–29
    [Google Scholar]
  35. 35. 
    Badiola KA, Quan DH, Triccas JA, Todd MH. 2014. Efficient synthesis and anti-tubercular activity of a series of spirocycles: an exercise in open science. PLOS ONE 9:12e111782
    [Google Scholar]
  36. 36. 
    Lyon L, Coles S, Duke M, Koch T 2008. Scaling Up: Towards a Federation of Crystallography Data Repositories. Bath, UK: UKOLN
  37. 37. 
    Coles S, Frey J, DeRoure D, Hursthouse M 2004. A selection of presentations. The CrystalGrid Collaboratory Foundation Workshop, Southampton, 13–17 September, 2004. Southampton, UK: Univ. Southampton
    [Google Scholar]
  38. 38. 
    journal 2021. UK National Crystallography Service. NCS. http://www.ncs.ac.uk/
    [Google Scholar]
  39. 39. 
    Kanza S, Bird CL, Niranjan M, McNeill W, Frey JG. 2021. The AI for Scientific Discovery Network+. Patterns 2:1100162
    [Google Scholar]
  40. 40. 
    Frey JG, Bird C. 2018. Reducing uncertainty: the raison d'être of open science?. Beilstein Mag. 4:10 https://doi.org/10.3762/bmag.10
    [Crossref] [Google Scholar]
  41. 41. 
    Serra-Garcia M, Gneezy U. 2021. Nonreplicable publications are cited more than replicable ones. Sci. Adv. 7:21eabd1705
    [Google Scholar]
  42. 42. 
    Coveney PV, Highfield RR. 2021. When we can trust computers (and when we can't). Philos. Trans. R. Soc. A 379:219720200067
    [Google Scholar]
  43. 43. 
    Knight NJ, Kanza S, Cruickshank D, Brocklesby WS, Frey JG. 2020. Talk2Lab: the smart lab of the future. IEEE Internet Things J 7:98631–40
    [Google Scholar]
  44. 44. 
    journal 2020. MQTT: the standard for IoT messaging. MQTT. https://mqtt.org/
    [Google Scholar]
  45. 45. 
    Atmoko R, Riantini R, Hasin M. 2017. IoT real time data acquisition using MQTT protocol. J. Phys. Conf. Ser. 853:1012003
    [Google Scholar]
  46. 46. 
    Porr M, Schwarz S, Lange F, Niemeyer L, Hentrop T et al. 2020. Bringing IoT to the lab: Sila2 and open-source-powered gateway module for integrating legacy devices into the digital laboratory. HardwareX 8:e00118
    [Google Scholar]
  47. 47. 
    Perkel JM. 2017. The Internet of Things comes to the lab. Nature 542:7639125–26
    [Google Scholar]
  48. 48. 
    Echtler F, Häussler M, Klinker G. 2010. BioTISCH: the interactive molecular biology lab bench. CHI '10 Extended Abstracts on Human Factors in Computing Systems, April 20103439–44 New York: Assoc. Comput. Mach.
    [Google Scholar]
  49. 49. 
    Scholl PM, Van Laerhoven K. 2014. Wearable digitization of life science experiments. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, UbiComp '14 Adjunct1381–88 New York: Assoc. Comput. Mach.
    [Google Scholar]
  50. 50. 
    Tabard A, Hincapié-Ramos JD, Esbensen M, Bardram JE 2011. The eLabBench: an interactive tabletop system for the biology laboratory. Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces, ITS '11202–11 New York: Assoc. Comput. Mach.
    [Google Scholar]
  51. 51. 
    Arnstein L, Hung CY, Franza R, Zhou QH, Borriello G et al. 2002. Labscape: a smart environment for the cell biology laboratory. IEEE Pervasive Comput. 1:313–21
    [Google Scholar]
  52. 52. 
    Thorn A, Smith M, Matthews P, Chen S, O'Steen B, Brooks B. 2011. Ami - the chemist's amanuensis. J. Cheminform. 3:145
    [Google Scholar]
  53. 53. 
    Farazi F, Akroyd J, Mosbach S, Buerger P, Nurkowski D et al. 2020. OntoKin: an ontology for chemical kinetic reaction mechanisms. J. Chem. Inform. Model. 60:1108–20
    [Google Scholar]
  54. 54. 
    Niederer SA, Sacks MS, Girolami M, Willcox K 2021. Scaling digital twins from the artisanal to the industrial. Nat. Comput. Sci. 1:5313–20
    [Google Scholar]
  55. 55. 
    Armstrong MM. 2020. Cheat sheet: What is digital twin?. IBM Business Operations Blog Dec. 4. https://www.ibm.com/blogs/internet-of-things/iot-cheat-sheet-digital-twin/
    [Google Scholar]
  56. 56. 
    Girolami M. 2021. Digital twin technology a ‘powerful tool’ but requires significant investment, say experts. Science News May 24. https://www.sciencedaily.com/releases/2021/05/210524110204.htm
    [Google Scholar]
  57. 57. 
    Gibbon GA. 1996. A brief history of LIMS. Lab. Autom. Inform. Manag. 32:11–5
    [Google Scholar]
  58. 58. 
    Kanza S, Willoughby C, Gibbins N, Whitby R, Frey JG et al. 2017. Electronic lab notebooks: Can they replace paper?. J. Cheminform. 9:31
    [Google Scholar]
  59. 59. 
    Piccione PM. 2020. Systematizing scientific laboratory work by a workflow and template for electronic laboratory notebooks. Educ. Chem. Eng. 31:42–53
    [Google Scholar]
  60. 60. 
    Artrith N, Butler KT, Coudert FX, Han S, Isayev O et al. 2021. Best practices in machine learning for chemistry. Nat. Chem. 13:6505–8
    [Google Scholar]
  61. 61. 
    journal 2020. Open Source Malaria–looking for new medicines. Open Source Malaria. http://opensourcemalaria.org/
    [Google Scholar]
  62. 62. 
    journal 2021. openlabnotebooks.org. Open Lab Notebooks. http://openlabnotebooks.org
    [Google Scholar]
  63. 63. 
    Kanza S, Gibbins N, Frey JG. 2019. Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies. J. Cheminform. 11:23
    [Google Scholar]
  64. 64. 
    Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:1160018
    [Google Scholar]
  65. 65. 
    Frey J, Coles SJ, Bird C, Willoughby C 2015. Collection, curation, citation at source: Publication@Source 10 years on. Int. J. Digital Curation 10:2 https://doi.org/10.2218/ijdc.v10i2.377
    [Crossref] [Google Scholar]
  66. 66. 
    Bechhofer S, De Roure D, Gamble M, Goble C, Buchan I. 2010. Research objects: towards exchange and reuse of digital knowledge. Nat. Prec. https://doi.org/10.1038/npre.2010.4626.1
    [Crossref] [Google Scholar]
  67. 67. 
    journal 2021. Data management plans. Digital Curation Centre. https://www.dcc.ac.uk/resources/data-management-plans
    [Google Scholar]
  68. 68. 
    journal 2014. How to complete an outputs management plan. Wellcome. https://wellcome.org/grant-funding/guidance/how-complete-outputs-management-plan
    [Google Scholar]
  69. 70. 
    journal 2014. Example DMPs and guidance. Digital Curation Centre. https://dcc.ac.uk/resources/data-management-plans/guidance-examples
    [Google Scholar]
  70. 71. 
    journal 2014. Plan to make data work for you. Digital Curation Centre. https://dmponline.dcc.ac.uk/
    [Google Scholar]
  71. 72. 
    Fox P, Hendler J. 2011. Changing the equation on scientific data visualization. Science 331:6018705–8
    [Google Scholar]
  72. 73. 
    Davoudian A, Chen L, Liu M 2018. A survey on NoSQL stores. ACM Comput. Surv. 51:240
    [Google Scholar]
  73. 74. 
    Williams A, Tkachenko V 2014. The Royal Society of Chemistry and the delivery of chemistry data repositories for the community. J. Comput. Aided Mol. Des. 28:101023–30
    [Google Scholar]
  74. 75. 
    journal 2021. ChemSpider. Royal Society of Chemistry. http://www.chemspider.com/
    [Google Scholar]
  75. 76. 
    journal 2021. Biological magnetic resonance data bank. BMRB. https://bmrb.io/
    [Google Scholar]
  76. 77. 
    journal 2021. The Cambridge Structural Database (CSD). Cambridge Crystallographic Data Centre. https://www.ccdc.cam.ac.uk/solutions/csd-core/components/csd/
    [Google Scholar]
  77. 78. 
    journal 2021. caNanoLab. National Institutes of Health. https://cananolab.nci.nih.gov/caNanoLab/#/
    [Google Scholar]
  78. 79. 
    Williams AJ, Grulke CM, Edwards J, McEachran AD, Mansouri K et al. 2017. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J. Cheminform. 9:61
    [Google Scholar]
  79. 80. 
    journal 2021. Crystallography Open Database. COD. http://www.crystallography.net/cod/
    [Google Scholar]
  80. 81. 
    journal 2021. The Electron Microscopy Data Bank. EMDB. https://www.ebi.ac.uk/pdbe/emdb/
    [Google Scholar]
  81. 82. 
    journal 2021. PeptideAtlas. Institute for Systems Biology. http://www.peptideatlas.org/
    [Google Scholar]
  82. 83. 
    wwPDB Consort., Burley SK, Berman HM, Bhikadiya C, Bi C et al. 2019. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res 47:D1D520–28
    [Google Scholar]
  83. 84. 
    journal 2021. PubChem. National Institutes of Health. https://pubchem.ncbi.nlm.nih.gov/
    [Google Scholar]
  84. 85. 
    Thibault JC, Facelli JC, Cheatham TE. 2013. iBIOMES: managing and sharing biomolecular simulation data in a distributed environment. J. Chem. Inform. Model. 53:3726–36
    [Google Scholar]
  85. 86. 
    Johnson RD III 2020. Computational Chemistry Comparison and Benchmark Database, NIST Standard Reference Database 101. National Institute of Standards and Technology. https://cccbdb.nist.gov/
    [Google Scholar]
  86. 87. 
    journal 2021. BEGDB: Benchmark Energy and Geometry Database. Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences. http://www.begdb.org/
    [Google Scholar]
  87. 88. 
    Adams S, de Castro P, Echenique P, Estrada J, Hanwell MD et al. 2011. The Quixote project: Collaborative and Open Quantum Chemistry data management in the Internet age. J. Cheminform. 3:38
    [Google Scholar]
  88. 89. 
    ioChem-BD. 2021. ioChem-BD: the computational chemistry results repository. ioChem-BD. https://www.iochem-bd.org/
    [Google Scholar]
  89. 90. 
    Himanen L, Geurts A, Foster AS, Rinke P. 2019. Data-driven materials science: status, challenges, and perspectives. Adv. Sci. 6:211900808
    [Google Scholar]
  90. 91. 
    Jain A, Ong SP, Hautier G, Chen W, Richards WD et al. 2013. Commentary: the Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1:011002
    [Google Scholar]
  91. 92. 
    Kirklin S, Saal JE, Meredig B, Thompson A, Doak JW et al. 2015. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. NPJ Comput. Mater. 1:15010
    [Google Scholar]
  92. 93. 
    Draxl C, Scheffler M. 2018. NOMAD: the FAIR concept for big data-driven materials science. MRS Bull. 43:9676–82
    [Google Scholar]
  93. 94. 
    Curtarolo S, Setyawan W, Hart GLW, Jahnatek M, Chepulskii RV et al. 2012. AFLOW: an automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 58:218–26
    [Google Scholar]
  94. 95. 
    Álvarez Moreno M, de Graaf C, López N, Maseras F, Poblet JM, Bo C 2015. Managing the computational chemistry big data problem: the ioChem-BD platform. J. Chem. Inform. Model. 55:195–103
    [Google Scholar]
  95. 96. 
    Landis DD, Hummelshøj JS, Nestorov S, Greeley J, Dulak M et al. 2012. The Computational Materials Repository. Comput. Sci. Eng. 14:651–57
    [Google Scholar]
  96. 97. 
    Winther KT, Hoffmann MJ, Boes JR, Mamun O, Bajdich M, Bligaard T. 2019. Catalysis-Hub.org, an open electronic structure database for surface reactions. Sci. Data 6:75
    [Google Scholar]
  97. 98. 
    Buchholz PCF, Vogel C, Reusch W, Pohl M, Rother D et al. 2016. BioCatNet: a database system for the integration of enzyme sequences and biocatalytic experiments. ChemBioChem 17:212093–98
    [Google Scholar]
  98. 99. 
    Smith DGA, Altarawy D, Burns LA, Welborn M, Naden LN et al. 2021. The MolSSI QCArchive project: an open-source platform to compute, organize, and share quantum chemistry data. WIREs Comput. Mol. Sci. 11:2e1491
    [Google Scholar]
  99. 100. 
    Soedarmadji E, Stein HS, Suram SK, Guevarra D, Gregoire JM 2019. Tracking materials science data lineage to manage millions of materials experiments and analyses. NPJ Comput. Mater. 5:79
    [Google Scholar]
  100. 101. 
    Licari D, Fusé M, Salvadori A, Tasinato N, Mendolicchio M et al. 2018. Towards the SMART workflow system for computational spectroscopy. Phys. Chem. Chem. Phys. 20:4126034–52
    [Google Scholar]
  101. 102. 
    Somnath S, Smith CR, Laanait N, Vasudevan RK, Jesse S. 2019. USID and pycroscopy – open source frameworks for storing and analyzing imaging and spectroscopy data. Microsc. Microanal. 25:S2220–21
    [Google Scholar]
  102. 103. 
    Coudert FX. 2017. Reproducible research in computational chemistry of materials. Chem. Mater. 29:72615–17
    [Google Scholar]
  103. 104. 
    journal 2021. The IUPAC International Chemical Identifier (InChI). International Union of Pure and Applied Chemistry. https://iupac.org/who-we-are/divisions/division-details/inchi/
    [Google Scholar]
  104. 105. 
    McCusker JP, Keshan N, Rashid S, Deagen M, Brinson C, McGuinness DL 2020. NanoMine: a knowledge graph for nanocomposite materials science. The Semantic Web – ISWC 2020 JZ Pan, V Tamma, C d'Amato, K Janowicz, B Fu, et al. 144–59 Lect. Notes Comput. Sci. Cham Switz.: Springer Int.
    [Google Scholar]
  105. 106. 
    Phadungsukanan W, Kraft M, Townsend JA, Murray-Rust P. 2012. The semantics of Chemical Markup Language (CML) for computational chemistry: CompChem. J. Cheminform. 4:15
    [Google Scholar]
  106. 107. 
    journal 2017. Empty rhetoric over data sharing slows science. Nature 546:327
    [Google Scholar]
  107. 108. 
    Menon A, Krdzavac NB, Kraft M. 2019. From database to knowledge graph – using data in chemistry. Curr. Opin. Chem. Eng. 26:33–37
    [Google Scholar]
  108. 109. 
    Heath T, Bizer C. 2011. Linked data: evolving the web into a global data space. Synthesis Lectures on the Semantic Web: Theory and Technology1–36 http://linkeddatabook.com/editions/1.0/
    [Google Scholar]
  109. 110. 
    Borkum MI, Frey JG. 2014. Usage and applications of Semantic Web techniques and technologies to support chemistry research. J. Cheminform. 6:18
    [Google Scholar]
  110. 111. 
    Farazi F, Krdzavac NB, Akroyd J, Mosbach S, Menon A et al. 2020. Linking reaction mechanisms and quantum chemistry: an ontological approach. Comput. Chem. Eng. 137:106813
    [Google Scholar]
  111. 112. 
    Wang B, Dobosh PA, Chalk S, Ito K, Sopek M, Ostlund NS 2018. A portal for quantum chemistry data based on the Semantic Web. Concepts, Methods and Applications of Quantum Systems in Chemistry and Physics YA Wang, M Thachuk, R Krems, J Maruani 3–27 Prog. Theoret. Chem. Phys. Cham Switz: Springer Int.
    [Google Scholar]
  112. 113. 
    Krdzavac N, Mosbach S, Nurkowski D, Buerger P, Akroyd J et al. 2019. An ontology and Semantic Web service for quantum chemistry calculations. J. Chem. Inform. Model. 59:73154–65
    [Google Scholar]
  113. 114. 
    Wang B, Dobosh PA, Chalk S, Sopek M, Ostlund NS. 2017. Computational chemistry data management platform based on the Semantic Web. J. Phys. Chem. A 121:1298–307
    [Google Scholar]
  114. 115. 
    Kosinov AV, Erkimbaev AO, Zitserman VY, Kobzev GA. 2019. Ontology-based methods of thermophysical data integration. J. Phys. Conf. Ser. 1385:012033
    [Google Scholar]
  115. 116. 
    Horsch M, Chiacchiera S, Schembera B, Seaton M, Todorov I 2021. Semantic interoperability based on the European Materials and Modelling Ontology and its ontological paradigm: mereosemiotics. Proceedings of WCCM-ECCOMAS 2020 F Chinesta, R Abgrall, O Allix, M Kaliske. https://doi.org/10.5281/zenodo.4531422
    [Crossref] [Google Scholar]
  116. 117. 
    Picklum M, Beetz M. 2019. MatCALO: knowledge-enabled machine learning in materials science. Comput. Mater. Sci. 163:50–62
    [Google Scholar]
  117. 118. 
    Farazi F, Salamanca M, Mosbach S, Akroyd J, Eibeck A et al. 2020. Knowledge graph approach to combustion chemistry and interoperability. ACS Omega 5:2918342–48
    [Google Scholar]
  118. 119. 
    Lavrentiev N, Privezentsev A, Fazliev A 2008. Informational system for the solution of molecular spectroscopy problems. 4. Transitions in molecules of C2v and Cs symmetry. Atmos. Ocean. Opt. 21:836–41
    [Google Scholar]
  119. 120. 
    Lavrentiev NA, Rodimova OB, Fazliev AZ, Vigasin AA. 2017. Systematization of published research graphics characterizing weakly bound molecular complexes with carbon dioxide. Proc. SPIE 10466:104660E
    [Google Scholar]
  120. 121. 
    Lavrentiev NA, Privezentsev AI, Fazliev AZ 2019. Tabular and graphic resources in quantitative spectroscopy. Data Analytics and Management in Data Intensive Domains Y Manolopoulos, S Stupnikov 55–69 Commun. Comput. Inf. Sci. Cham Switz.: Springer Int.
    [Google Scholar]
  121. 122. 
    Schwab K. 2017. The Fourth Industrial Revolution London: Portf. Penguin
  122. 123. 
    Gressling T. 2020. Data Science in Chemistry: Artificial Intelligence, Big Data, Chemometrics, and Quantum Computing with Jupyter Berlin: De Gruyter
  123. 124. 
    journal 2022. Where the world builds software. GitHub https://github.com/
    [Google Scholar]
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