{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T19:10:26Z","timestamp":1768417826692,"version":"3.49.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s41060-022-00366-5","type":"journal-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T06:02:31Z","timestamp":1666245751000},"page":"101-117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Functional anomaly detection: a benchmark study"],"prefix":"10.1007","volume":"16","author":[{"given":"Guillaume","family":"Staerman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Adjakossa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pavlo","family":"Mozharovskyi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vera","family":"Hofer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayant","family":"Sen Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephan","family":"Cl\u00e9men\u00e7on","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"366_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-3994-4","volume-title":"Identification of Outliers. Monographs on Applied Probability and Statistics","author":"DM Hawkins","year":"1980","unstructured":"Hawkins, D.M.: Identification of Outliers. Monographs on Applied Probability and Statistics. Chapman and Hall, London (1980)"},{"issue":"2","key":"366_CR2","doi-asserted-by":"publisher","first-page":"e1236","DOI":"10.1002\/widm.1236","volume":"8","author":"PJ Rousseeuw","year":"2018","unstructured":"Rousseeuw, P.J., Hubert, M.: Anomaly detection by robust statistics. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(2), e1236 (2018)","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"366_CR3","unstructured":"Staerman, G., Mozharovskyi, P., Cl\u00e9men\u00e7on, S., d\u2019Alch\u00e9 Buc, F.: Functional isolation forest. In: Proceedings of The 11th Asian Conference on Machine Learning, pp. 332\u2013347 (2019)"},{"key":"366_CR4","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1146\/annurev-statistics-041715-033624","volume":"3","author":"J-L Wang","year":"2016","unstructured":"Wang, J.-L., Chiou, J.-M., M\u00fcller, H.-G.: Functional data analysis. Annu. Rev. Stat. Appl. 3, 257\u2013295 (2016)","journal-title":"Annu. Rev. Stat. Appl."},{"key":"366_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/b98888","volume-title":"Functional Data Analysis","author":"JO Ramsay","year":"2005","unstructured":"Ramsay, J.O., Silverman, B.W.: Functional Data Analysis. Springer, New York (2005)"},{"key":"366_CR6","volume-title":"Nonparametric Functional Data Analysis: Theory and Practice","author":"F Ferraty","year":"2006","unstructured":"Ferraty, F., Vieu, P.: Nonparametric Functional Data Analysis: Theory and Practice. Springer, Berlin (2006)"},{"key":"366_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/b98886","volume-title":"Applied Functional Data Analysis: Methods and Case Studies","author":"JO Ramsay","year":"2002","unstructured":"Ramsay, J.O., Silverman, B.W.: Applied Functional Data Analysis: Methods and Case Studies. Springer, Berlin (2002)"},{"issue":"2","key":"366_CR8","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s10260-015-0297-8","volume":"24","author":"M Hubert","year":"2015","unstructured":"Hubert, M., Rousseeuw, P.J., Segaert, P.: Multivariate functional outlier detection. Stat. Methods Appl. 24(2), 177\u2013202 (2015)","journal-title":"Stat. Methods Appl."},{"issue":"3","key":"366_CR9","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/s00180-007-0053-0","volume":"22","author":"A Cuevas","year":"2007","unstructured":"Cuevas, A., Febrero, M., Fraiman, R.: Robust estimation and classification for functional data via projection-based depth notions. Comput. Stat. 22(3), 481\u2013496 (2007)","journal-title":"Comput. Stat."},{"key":"366_CR10","unstructured":"Staerman, G., Mozharovskyi, P., Cl\u00e9men\u00e7on, S.: The area of the convex hull of sampled curves: a robust functional statistical depth measure. In: Proceedings of the 23nd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), vol. 108, pp. 570\u2013579 (2020)"},{"key":"366_CR11","unstructured":"Tukey, J.W.: Mathematics and the picturing of data. In: Proceedings of the International Congress of Mathematicians. Vancouver, 1975, vol. 2, pp. 523\u2013531 (1975)"},{"issue":"4","key":"366_CR12","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1214\/aos\/1176348890","volume":"20","author":"DL Donoho","year":"1992","unstructured":"Donoho, D.L., Gasko, M., et al.: Breakdown properties of location estimates based on halfspace depth and projected outlyingness. Ann. Stat. 20(4), 1803\u20131827 (1992)","journal-title":"Ann. Stat."},{"key":"366_CR13","volume-title":"Festschrift in Honour of Ursula Gather","author":"C Becker","year":"2014","unstructured":"Becker, C., Fried, R., Kuhnt, S.: Festschrift in Honour of Ursula Gather. Springer, Berlin (2014)"},{"issue":"4","key":"366_CR14","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1080\/10618600.2017.1336445","volume":"26","author":"S Nagy","year":"2017","unstructured":"Nagy, S., Gijbels, I., Hlubinka, D.: Depth-based recognition of shape outlying functions. J. Comput. Graph. Stat. 26(4), 883\u2013893 (2017)","journal-title":"J. Comput. Graph. Stat."},{"key":"366_CR15","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1080\/00401706.1999.10485670","volume":"41","author":"PJ Rousseeuw","year":"1999","unstructured":"Rousseeuw, P.J., Van Driessen, K.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212\u2013223 (1999)","journal-title":"Technometrics"},{"issue":"1","key":"366_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0304-4149(97)00028-8","volume":"69","author":"W Polonik","year":"1997","unstructured":"Polonik, W.: Minimum volume sets and generalized quantile processes. Stoch. Process. Appl. 69(1), 1\u201324 (1997)","journal-title":"Stoch. Process. Appl."},{"key":"366_CR17","first-page":"665","volume":"7","author":"C Scott","year":"2006","unstructured":"Scott, C., Nowak, R.: Learning minimum volume sets. J. Mach. Learn. Res. 7, 665\u2013704 (2006)","journal-title":"J. Mach. Learn. Res."},{"issue":"7","key":"366_CR18","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001)","journal-title":"Neural Comput."},{"key":"366_CR19","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, vol. 29, pp. 93\u2013104. ACM (2000)","DOI":"10.1145\/335191.335388"},{"key":"366_CR20","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.: Isolation forest. In: Proceedings of the Eighth IEEE International Conference on Data Mining, pp. 413\u2013422 (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"366_CR21","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1109\/TKDE.2019.2947676","volume":"33","author":"S Hariri","year":"2019","unstructured":"Hariri, S., Kind, M.C., Brunner, R.J.: Extended isolation forest. IEEE Trans. Knowl. Data Eng. 33, 1479\u20131489 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"366_CR22","first-page":"461","volume":"28","author":"Y Zuo","year":"2000","unstructured":"Zuo, Y., Serfling, R.: General notions of statistical depth function. Ann. Stat. 28(2), 461\u2013482 (2000). (04)","journal-title":"Ann. Stat."},{"key":"366_CR23","unstructured":"Staerman, G.: Functional anomaly detection and robust estimation. PhD thesis, Institut polytechnique de Paris (2022)"},{"key":"366_CR24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-642-35494-6_2","volume-title":"Robustness and Complex Data Structures: Festschrift in Honour of Ursula Gather","author":"K Mosler","year":"2013","unstructured":"Mosler, K.: Depth statistics. In: Becker, C., Fried, R., Kuhnt, S. (eds.) Robustness and Complex Data Structures: Festschrift in Honour of Ursula Gather, pp. 17\u201334. Springer, Berlin (2013)"},{"key":"366_CR25","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.jmva.2015.07.012","volume":"142","author":"J Kuelbs","year":"2015","unstructured":"Kuelbs, J., Zinn, J.: Half-region depth for stochastic processes. J. Multivar. Anal. 142, 86\u2013105 (2015)","journal-title":"J. Multivar. Anal."},{"key":"366_CR26","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1214\/15-STS532","volume":"31","author":"A Nieto-Reyes","year":"2016","unstructured":"Nieto-Reyes, A., Battey, H.: A topologically valid definition of depth for functional data. Stat Sci 31, 61\u201379 (2016)","journal-title":"Stat Sci"},{"issue":"4","key":"366_CR27","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1214\/17-STS625","volume":"32","author":"I Gijbels","year":"2017","unstructured":"Gijbels, I., Nagy, S., et al.: On a general definition of depth for functional data. Stat. Sci. 32(4), 630\u2013639 (2017)","journal-title":"Stat. Sci."},{"key":"366_CR28","unstructured":"Mosler, K., Polyakova, Y.: General notions of depth for functional data (2018). arXiv:1208.1981"},{"issue":"505","key":"366_CR29","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1080\/01621459.2013.856795","volume":"109","author":"G Claeskens","year":"2014","unstructured":"Claeskens, G., Hubert, M., Slaets, L., Vakili, K.: Multivariate functional halfspace depth. J. Am. Stat. Assoc. 109(505), 411\u2013423 (2014)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"366_CR30","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/BF02595706","volume":"10","author":"R Fraiman","year":"2001","unstructured":"Fraiman, R., Muniz, G.: Trimmed means for functional data. TEST 10(2), 419\u2013440 (2001)","journal-title":"TEST"},{"key":"366_CR31","unstructured":"Staerman, G., Mozharovskyi, P., Cl\u00e9men\u00e7on, S., d\u2019Alch\u00e9 Buc, F.: A pseudo-metric between probability distributions based on depth-trimmed regions (2021). arXiv:2103.12711"},{"key":"366_CR32","unstructured":"Staerman, G., Mozharovskyi, P., Cl\u00e9men\u00e7on, S.: Affine-invariant integrated rank-weighted depth: definition, properties and finite sample analysis (2021). arXiv:2106.11068"},{"issue":"4","key":"366_CR33","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1198\/106186004X12632","volume":"13","author":"G Brys","year":"2004","unstructured":"Brys, G., Hubert, M., Struyf, A.: A robust measure of skewness. J. Comput. Graph. Stat. 13(4), 996\u20131017 (2004)","journal-title":"J. Comput. Graph. Stat."},{"key":"366_CR34","doi-asserted-by":"crossref","unstructured":"Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 90\u201398. SIAM (2017)","DOI":"10.1137\/1.9781611974973.11"},{"key":"366_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 665\u2013674 (2017)","DOI":"10.1145\/3097983.3098052"},{"key":"366_CR36","doi-asserted-by":"crossref","unstructured":"Ngo, P.C., Winarto, A.A., Kou, C.K.L., Park, S., Akram, F., Lee, H.K.: Fence gan: towards better anomaly detection. In: 2019 IEEE 31St International Conference on Tools with Artificial Intelligence (ICTAI), pp. 141\u2013148. IEEE (2019)","DOI":"10.1109\/ICTAI.2019.00028"},{"key":"366_CR37","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019)","journal-title":"Med. Image Anal."},{"issue":"2","key":"366_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang, G., Shen, C., Cao, L., Van Den Hengel, A.: Deep learning for anomaly detection: a review. ACM Comput. Surv.: CSUR 54(2), 1\u201338 (2021)","journal-title":"ACM Comput. Surv.: CSUR"},{"key":"366_CR39","doi-asserted-by":"crossref","unstructured":"Pang, G., Cao, L., Chen, L., Liu, H.: Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2041\u20132050 (2018)","DOI":"10.1145\/3219819.3220042"},{"key":"366_CR40","unstructured":"Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., Chen, H.: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)"},{"key":"366_CR41","doi-asserted-by":"crossref","unstructured":"Wang, H., Pang, G., Shen, C., Ma, C. Unsupervised representation learning by predicting random distances (2019). arXiv:1912.12186","DOI":"10.24963\/ijcai.2020\/408"},{"key":"366_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., Chawla, N.V.: A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 1409\u20131416 (2019)","DOI":"10.1609\/aaai.v33i01.33011409"},{"key":"366_CR43","doi-asserted-by":"crossref","unstructured":"Ma, R., Pang, G., Chen, L., van den Hengel, A.: Deep graph-level anomaly detection by glocal knowledge distillation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 704\u2013714 (2022)","DOI":"10.1145\/3488560.3498473"},{"issue":"8","key":"366_CR44","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Lett. Pattern Recogn. 27(8), 861\u2013874 (2006)","journal-title":"Lett. Pattern Recogn."},{"key":"366_CR45","doi-asserted-by":"crossref","unstructured":"Cl\u00e9men\u00e7on, S., Vayatis, N.: Nonparametric estimation of the precision-recall curve. In: ICML \u201909: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 185\u2013192 (2009)","DOI":"10.1145\/1553374.1553398"},{"issue":"3","key":"366_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv.: CSUR 41(3), 1\u201358 (2009)","journal-title":"ACM Comput. Surv.: CSUR"},{"key":"366_CR47","unstructured":"Segaert, P., Hubert, M., Rousseeuw, P., Raymaekers, J.: mrfdepth: depth measures in multivariate, regression and functional settings. R package version 1.0.11 (2019)"},{"key":"366_CR48","unstructured":"Tarabelloni, N., Arribas-Gil, A., Ieva, F., Paganoni, A.M., Romo, J.: Roahd: robust analysis of high dimensional data. R package version 1.4.1 (2018)"},{"key":"366_CR49","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"issue":"1","key":"366_CR50","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1198\/jcgs.2009.08158","volume":"19","author":"RJ Hyndman","year":"2010","unstructured":"Hyndman, R.J., Shang, H.L.: Rainbow plots, bagplots, and boxplots for functional data. J. Comput. Graph. Stat. 19(1), 29\u201345 (2010)","journal-title":"J. Comput. Graph. Stat."},{"issue":"2","key":"366_CR51","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1198\/jcgs.2011.09224","volume":"20","author":"Y Sun","year":"2011","unstructured":"Sun, Y., Genton, M.G.: Functional boxplots. J. Comput. Graph. Stat. 20(2), 316\u2013334 (2011)","journal-title":"J. Comput. Graph. Stat."},{"issue":"519","key":"366_CR52","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1080\/01621459.2016.1256813","volume":"112","author":"W Xie","year":"2017","unstructured":"Xie, W., Kurtek, S., Bharath, K., Sun, Y.: A geometric approach to visualization of variability in functional data. J. Am. Stat. Assoc. 112(519), 979\u2013993 (2017)","journal-title":"J. Am. Stat. Assoc."},{"issue":"4","key":"366_CR53","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1093\/biostatistics\/kxu006","volume":"15","author":"A Arribas-Gil","year":"2014","unstructured":"Arribas-Gil, A., Romo, J.: Shape outlier detection and visualization for functional data: the outliergram. Biostatistics 15(4), 603\u2013619 (2014)","journal-title":"Biostatistics"},{"issue":"2","key":"366_CR54","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1080\/10618600.2017.1366912","volume":"27","author":"PJ Rousseeuw","year":"2018","unstructured":"Rousseeuw, P.J., Raymaekers, J., Hubert, M.: A measure of directional outlyingness with applications to image data and video. J. Comput. Graph. Stat. 27(2), 345\u2013359 (2018)","journal-title":"J. Comput. Graph. Stat."},{"key":"366_CR55","doi-asserted-by":"crossref","unstructured":"Dai, W., Genton, M.: Multivariate functional data visualization and outlier detection. J. Comput. Graph. Stat. 27, 923\u2013934 (2017)","DOI":"10.1080\/10618600.2018.1473781"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-022-00366-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-022-00366-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-022-00366-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T04:17:59Z","timestamp":1683865079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-022-00366-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,20]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["366"],"URL":"https:\/\/doi.org\/10.1007\/s41060-022-00366-5","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,20]]},"assertion":[{"value":"18 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2022","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 have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}