{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:19:30Z","timestamp":1777508370459,"version":"3.51.4"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Innovative Medicines Initiative 2 Joint Undertaking","award":["777365"],"award-info":[{"award-number":["777365"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With the ongoing rapid growth of publicly available ligand\u2013protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers\u2019 time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data\u00a0points. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some examples of quantitative structure\u2013activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1186\/s13321-022-00672-x","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T12:02:57Z","timestamp":1673006577000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Papyrus: a large-scale curated dataset aimed at bioactivity predictions"],"prefix":"10.1186","volume":"15","author":[{"given":"O. J. M.","family":"B\u00e9quignon","sequence":"first","affiliation":[]},{"given":"B. J.","family":"Bongers","sequence":"additional","affiliation":[]},{"given":"W.","family":"Jespers","sequence":"additional","affiliation":[]},{"given":"A. P.","family":"IJzerman","sequence":"additional","affiliation":[]},{"given":"B.","family":"van der Water","sequence":"additional","affiliation":[]},{"given":"G. J. P.","family":"van Westen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"672_CR1","doi-asserted-by":"publisher","first-page":"2550","DOI":"10.1021\/ci3003304","volume":"52","author":"Y Hu","year":"2012","unstructured":"Hu Y, Bajorath J (2012) Growth of ligand-target interaction data in ChEMBL is associated with increasing and activity measurement-dependent compound promiscuity. J Chem Inf Model 52:2550\u20132558","journal-title":"J Chem Inf Model"},{"key":"672_CR2","doi-asserted-by":"publisher","first-page":"D20","DOI":"10.1093\/nar\/gkv1352","volume":"44","author":"CE Cook","year":"2016","unstructured":"Cook CE et al (2016) The European Bioinformatics Institute in 2016: Data growth and integration. Nucleic Acids Res 44:D20\u2013D26","journal-title":"Nucleic Acids Res"},{"key":"672_CR3","doi-asserted-by":"publisher","first-page":"D1083","DOI":"10.1093\/nar\/gkt1031","volume":"42","author":"AP Bento","year":"2014","unstructured":"Bento AP et al (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:D1083\u2013D1090","journal-title":"Nucleic Acids Res"},{"key":"672_CR4","doi-asserted-by":"publisher","first-page":"D400","DOI":"10.1093\/nar\/gkr1132","volume":"40","author":"Y Wang","year":"2012","unstructured":"Wang Y et al (2012) PubChem\u2019s BioAssay database. Nucleic Acids Res 40:D400\u2013D412","journal-title":"Nucleic Acids Res"},{"key":"672_CR5","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1021\/jm030580l","volume":"47","author":"R Wang","year":"2004","unstructured":"Wang R, Fang X, Lu Y, Wang S (2004) The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J Med Chem 47:2977\u20132980","journal-title":"J Med Chem"},{"key":"672_CR6","doi-asserted-by":"publisher","first-page":"4111","DOI":"10.1021\/jm048957q","volume":"48","author":"R Wang","year":"2005","unstructured":"Wang R, Fang X, Lu Y, Yang CY, Wang S (2005) The PDBbind database: methodologies and updates. J Med Chem 48:4111\u20134119","journal-title":"J Med Chem"},{"key":"672_CR7","doi-asserted-by":"publisher","first-page":"D1045","DOI":"10.1093\/nar\/gkv1072","volume":"44","author":"MK Gilson","year":"2016","unstructured":"Gilson MK et al (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D1045\u2013D1053","journal-title":"Nucleic Acids Res"},{"key":"672_CR8","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1021\/acs.chemrestox.6b00135","volume":"29","author":"AM Richard","year":"2016","unstructured":"Richard AM et al (2016) ToxCast chemical landscape: paving the road to 21st century toxicology. Chem Res Toxicol 29:1225\u20131251","journal-title":"Chem Res Toxicol"},{"key":"672_CR9","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1093\/toxsci\/kfl103","volume":"95","author":"DJ Dix","year":"2007","unstructured":"Dix DJ et al (2007) The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicol Sci 95:5\u201312","journal-title":"Toxicol Sci"},{"key":"672_CR10","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1289\/ehp.0901392","volume":"118","author":"RS Judson","year":"2010","unstructured":"Judson RS et al (2010) In vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project. Environ Health Perspect 118:485\u2013492","journal-title":"Environ Health Perspect"},{"key":"672_CR11","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1007\/s10822-015-9860-5","volume":"29","author":"G Papadatos","year":"2015","unstructured":"Papadatos G, Gaulton A, Hersey A, Overington JP (2015) Activity, assay and target data curation and quality in the ChEMBL database. J Comput Aided Mol Design 29:885\u2013896","journal-title":"J Comput Aided Mol Design"},{"key":"672_CR12","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1021\/ci400709d","volume":"54","author":"J Tang","year":"2014","unstructured":"Tang J et al (2014) Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model 54:735\u2013743","journal-title":"J Chem Inf Model"},{"key":"672_CR13","doi-asserted-by":"publisher","first-page":"eaan4368","DOI":"10.1126\/science.aan4368","volume":"358","author":"S Klaeger","year":"2017","unstructured":"Klaeger S et al (2017) The target landscape of clinical kinase drugs. Science 358:eaan4368","journal-title":"Science"},{"key":"672_CR14","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1021\/ci8002649","volume":"49","author":"SG Rohrer","year":"2009","unstructured":"Rohrer SG, Baumann K (2009) Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data. J Chem Inf Model 49:169\u2013184","journal-title":"J Chem Inf Model"},{"key":"672_CR15","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1021\/acs.jcim.7b00403","volume":"58","author":"I Wallach","year":"2017","unstructured":"Wallach I, Heifets A (2017) Most ligand-based classification benchmarks reward memorization rather than generalization. J Chem Inf Model 58:916\u2013932","journal-title":"J Chem Inf Model"},{"key":"672_CR16","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1021\/acs.jcim.0c00155","volume":"60","author":"V-K Tran-Nguyen","year":"2020","unstructured":"Tran-Nguyen V-K, Jacquemard C, Rognan D (2020) LIT-PCBA: an unbiased data set for machine learning and virtual screening. J Chem Inf Model 60:4263\u20134273","journal-title":"J Chem Inf Model"},{"key":"672_CR17","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13321-022-00590-y","volume":"14","author":"A Keshavarzi Arshadi","year":"2022","unstructured":"Keshavarzi Arshadi A, Salem M, Firouzbakht A, Yuan JS (2022) MolData, a molecular benchmark for disease and target based machine learning. J Cheminform 14:10","journal-title":"J Cheminform"},{"key":"672_CR18","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s13321-017-0232-0","volume":"9","author":"EB Lenselink","year":"2017","unstructured":"Lenselink EB et al (2017) Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. J Cheminform 9:45","journal-title":"J Cheminform"},{"key":"672_CR19","doi-asserted-by":"publisher","DOI":"10.4121\/uuid:b64986dd-3203-445e-9b93-13a5ac7ef999","author":"EB Lenselink","year":"2019","unstructured":"Lenselink EB et al (2019) Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. J Cheminform. https:\/\/doi.org\/10.4121\/uuid:b64986dd-3203-445e-9b93-13a5ac7ef999"},{"key":"672_CR20","doi-asserted-by":"publisher","unstructured":"B\u00e9quignon O et al (2021) Papyrus\u2014a large scale curated dataset aimed at bioactivity predictions. https:\/\/doi.org\/10.4121\/16896406.v1","DOI":"10.4121\/16896406.v1"},{"key":"672_CR21","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1021\/ed003p1149","volume":"3","author":"ER Caley","year":"1926","unstructured":"Caley ER (1926) The Leyden Papyrus X. An English translation with brief notes. J Chem Educ 3:1149","journal-title":"J Chem Educ"},{"key":"672_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-016-0187-6","volume":"9","author":"J Sun","year":"2017","unstructured":"Sun J et al (2017) ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. J Cheminform 9:1\u20139","journal-title":"J Cheminform"},{"key":"672_CR23","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.8950.3","author":"R Sharma","year":"2016","unstructured":"Sharma R, Sch\u00fcrer SC, Muskal SM (2016) High quality, small molecule-activity datasets for kinase research. F1000Res. https:\/\/doi.org\/10.12688\/f1000research.8950.3","journal-title":"F1000Res"},{"key":"672_CR24","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1021\/acs.jcim.6b00122","volume":"56","author":"S Christmann-Franck","year":"2016","unstructured":"Christmann-Franck S et al (2016) Unprecedently large-scale kinase inhibitor set enabling the accurate prediction of compound-kinase activities: a way toward selective promiscuity by design? J Chem Inf Model 56:1654\u20131675","journal-title":"J Chem Inf Model"},{"key":"672_CR25","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1021\/acs.jmedchem.6b01611","volume":"60","author":"B Merget","year":"2017","unstructured":"Merget B, Turk S, Eid S, Rippmann F, Fulle S (2017) Profiling prediction of kinase inhibitors: toward the virtual assay. J Med Chem 60:474\u2013485","journal-title":"J Med Chem"},{"key":"672_CR26","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/nar\/28.1.235","volume":"28","author":"HM Berman","year":"2000","unstructured":"Berman HM et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235\u2013242","journal-title":"Nucleic Acids Res"},{"key":"672_CR27","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento AP et al (2020) An open source chemical structure curation pipeline using RDKit. J Cheminform 12:51","journal-title":"J Cheminform"},{"key":"672_CR28","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/1758-2946-3-33","volume":"3","author":"NM O\u2019Boyle","year":"2011","unstructured":"O\u2019Boyle NM et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33","journal-title":"J Cheminform"},{"key":"672_CR29","unstructured":"The Open Babel Package, version 3.0.1."},{"key":"672_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-019-0336-9","volume":"11","author":"PJ Ropp","year":"2019","unstructured":"Ropp PJ, Kaminsky JC, Yablonski S, Durrant JD (2019) Dimorphite-DL: An open-source program for enumerating the ionization states of drug-like small molecules. J Cheminform 11:1\u20138","journal-title":"J Cheminform"},{"key":"672_CR31","doi-asserted-by":"publisher","first-page":"D158","DOI":"10.1093\/nar\/gkw1099","volume":"45","author":"The UniProt Consortium","year":"2017","unstructured":"The UniProt Consortium (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45:D158\u2013D169","journal-title":"Nucleic Acids Res"},{"key":"672_CR32","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13321-015-0068-4","volume":"7","author":"SR Heller","year":"2015","unstructured":"Heller SR, McNaught A, Pletnev I, Stein S, Tchekhovskoi D (2015) InChI, the IUPAC International Chemical Identifier. J Cheminform 7:23","journal-title":"J Cheminform"},{"key":"672_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-018-0321-8","volume":"10","author":"D Probst","year":"2018","unstructured":"Probst D, Reymond J-L (2018) A probabilistic molecular fingerprint for big data settings. J Cheminform 10:1\u201312","journal-title":"J Cheminform"},{"key":"672_CR34","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13321-020-0416-x","volume":"12","author":"D Probst","year":"2020","unstructured":"Probst D, Reymond J-L (2020) Visualization of very large high-dimensional data sets as minimum spanning trees. J Cheminform 12:12","journal-title":"J Cheminform"},{"key":"672_CR35","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13321-021-00516-0","volume":"13","author":"M Thomas","year":"2021","unstructured":"Thomas M, Smith RT, O\u2019Boyle NM, de Graaf C, Bender A (2021) Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform 13:39","journal-title":"J Cheminform"},{"key":"672_CR36","doi-asserted-by":"publisher","unstructured":"RDKit: Open-source cheminformatics (version 2021.03.5). Preprint at https:\/\/doi.org\/10.5281\/zenodo.5242603.","DOI":"10.5281\/zenodo.5242603"},{"key":"672_CR37","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1021\/ci025554v","volume":"43","author":"A Gobbi","year":"2003","unstructured":"Gobbi A, Lee ML (2003) DISE: Directed sphere exclusion. J Chem Inf Comput Sci 43:317\u2013323","journal-title":"J Chem Inf Comput Sci"},{"key":"672_CR38","unstructured":"Sayle, R. A. 2D similarity, diversity and clustering in RDKit. in RDKit: UGM (2019)."},{"issue":"1","key":"672_CR39","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1021\/ci300535x","volume":"53","author":"L Ruddigkeit","year":"2013","unstructured":"Ruddigkeit L, Blum LC, Reymond JL (2013) Visualization and virtual screening of the chemical universe database GDB-17. J Chem Inf Model 53(1):56\u201365","journal-title":"J Chem Inf Model"},{"key":"672_CR40","doi-asserted-by":"publisher","first-page":"8732","DOI":"10.1021\/ja902302h","volume":"131","author":"LC Blum","year":"2009","unstructured":"Blum LC, Reymond JL (2009) 970 Million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc 131:8732\u20138733","journal-title":"J Am Chem Soc"},{"key":"672_CR41","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1021\/ci800038f","volume":"48","author":"H Hong","year":"2008","unstructured":"Hong H et al (2008) Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model 48:1337\u20131344","journal-title":"J Chem Inf Model"},{"key":"672_CR42","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1039\/C8SC04175J","volume":"10","author":"R Winter","year":"2019","unstructured":"Winter R, Montanari F, No\u00e9 F, Clevert DA (2019) Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations. Chem Sci 10:1692\u20131701","journal-title":"Chem Sci"},{"key":"672_CR43","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13321-018-0258-y","volume":"10","author":"H Moriwaki","year":"2018","unstructured":"Moriwaki H, Tian Y-S, Kawashita N, Takagi T (2018) Mordred: a molecular descriptor calculator. J Cheminform 10:4","journal-title":"J Cheminform"},{"key":"672_CR44","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"672_CR45","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","volume":"16","author":"EC Alley","year":"2019","unstructured":"Alley EC, Khimulya G, Biswas S, AlQuraishi M, Church GM (2019) Unified rational protein engineering with sequence-based deep representation learning. Nat Methods 16:1315\u20131322","journal-title":"Nat Methods"},{"key":"672_CR46","unstructured":"Paszke A. et al. PyTorch: an imperative style, high-performance deep learning library. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019)"},{"key":"672_CR47","unstructured":"Kingma DP, Lei Ba J. Adam: a method for stochastic optimization."},{"key":"672_CR48","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1021\/ci400084k","volume":"53","author":"RP Sheridan","year":"2013","unstructured":"Sheridan RP (2013) Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model 53:783\u2013790","journal-title":"J Chem Inf Model"},{"key":"672_CR49","doi-asserted-by":"publisher","first-page":"12243","DOI":"10.1021\/acs.jmedchem.0c00445","volume":"63","author":"T James","year":"2020","unstructured":"James T, Sardar A, Anighoro A (2020) Enhancing chemogenomics with predictive pharmacology. J Med Chem 63:12243\u201312255. https:\/\/doi.org\/10.1021\/acs.jmedchem.0c00445","journal-title":"J Med Chem"},{"key":"672_CR50","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.drudis.2014.12.004","volume":"20","author":"D Reker","year":"2015","unstructured":"Reker D, Schneider G (2015) Active-learning strategies in computer-assisted drug discovery. Drug Discov Today 20:458\u2013465. https:\/\/doi.org\/10.1016\/j.drudis.2014.12.004","journal-title":"Drug Discov Today"},{"key":"672_CR51","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1007\/s10822-020-00315-z","volume":"34","author":"D Stumpfe","year":"2020","unstructured":"Stumpfe D, Hu H, Bajorath J (2020) Advances in exploring activity cliffs. J Comput Aided Mol Des 34:929\u2013942","journal-title":"J Comput Aided Mol Des"},{"key":"672_CR52","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/s41573-020-00135-8","volume":"20","author":"M Muttenthaler","year":"2021","unstructured":"Muttenthaler M, King GF, Adams DJ, Alewood PF (2021) Trends in peptide drug discovery. Nat Rev Drug Discov 20:309\u2013325. https:\/\/doi.org\/10.1038\/s41573-020-00135-8","journal-title":"Nat Rev Drug Discov"},{"key":"672_CR53","doi-asserted-by":"publisher","first-page":"4538","DOI":"10.1038\/s41467-019-12364-6","volume":"10","author":"R Spohn","year":"2019","unstructured":"Spohn R et al (2019) Integrated evolutionary analysis reveals antimicrobial peptides with limited resistance. Nat Commun 10:4538","journal-title":"Nat Commun"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-022-00672-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-022-00672-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-022-00672-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T12:08:41Z","timestamp":1673093321000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-022-00672-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,6]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["672"],"URL":"https:\/\/doi.org\/10.1186\/s13321-022-00672-x","relation":{"references":[{"id-type":"uri","id":"","asserted-by":"subject"}],"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2021-1rxhk","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,6]]},"assertion":[{"value":"10 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3"}}