{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T23:28:59Z","timestamp":1778110139730,"version":"3.51.4"},"publisher-location":"Cham","reference-count":56,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031167874","type":"print"},{"value":"9783031167881","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16788-1_31","type":"book-chapter","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T20:35:56Z","timestamp":1663878956000},"page":"513-528","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["I-MuPPET: Interactive Multi-Pigeon Pose Estimation and\u00a0Tracking"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1626-9253","authenticated-orcid":false,"given":"Urs","family":"Waldmann","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7627-1726","authenticated-orcid":false,"given":"Hemal","family":"Naik","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8817-087X","authenticated-orcid":false,"given":"Nagy","family":"M\u00e1t\u00e9","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4534-6630","authenticated-orcid":false,"given":"Fumihiro","family":"Kano","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8556-4558","authenticated-orcid":false,"given":"Iain D.","family":"Couzin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5803-2185","authenticated-orcid":false,"given":"Oliver","family":"Deussen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3427-4029","authenticated-orcid":false,"given":"Bastian","family":"Goldl\u00fccke","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,20]]},"reference":[{"issue":"3\u20134","key":"31_CR1","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1163\/156853974X00534","volume":"49","author":"J Altmann","year":"1974","unstructured":"Altmann, J.: Observational study of behavior: sampling methods. Behaviour 49(3\u20134), 227\u2013266 (1974)","journal-title":"Behaviour"},{"issue":"1","key":"31_CR2","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.neuron.2014.09.005","volume":"84","author":"D Anderson","year":"2014","unstructured":"Anderson, D., Perona, P.: Toward a science of computational ethology. Neuron 84(1), 18\u201331 (2014)","journal-title":"Neuron"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Badger, M., et al.: 3d bird reconstruction: a dataset, model, and shape recovery from a single view. In: ECCV, pp. 1\u201317 (2020)","DOI":"10.1007\/978-3-030-58523-5_1"},{"key":"31_CR4","doi-asserted-by":"publisher","first-page":"4560","DOI":"10.1038\/s41467-020-18441-5","volume":"11","author":"PC Bala","year":"2020","unstructured":"Bala, P.C., Eisenreich, B.R., Yoo, S.B.M., Hayden, B.Y., Park, H.S., Zimmermann, J.: Automated markerless pose estimation in freely moving macaques with openMonkeyStudio. Nat. Commun. 11, 4560 (2020)","journal-title":"Nat. Commun."},{"issue":"23","key":"31_CR5","first-page":"1","volume":"16","author":"GJ Berman","year":"2018","unstructured":"Berman, G.J.: Measuring behavior across scales. BMC Biol. 16(23), 1\u201311 (2018)","journal-title":"BMC Biol."},{"key":"31_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2008\/246309","volume":"2008","author":"K Bernardin","year":"2008","unstructured":"Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. 2008, 1\u201310 (2008)","journal-title":"EURASIP J. Image Video Process."},{"key":"31_CR7","unstructured":"Bernshtein, N.: The Co-ordination and Regulation of Movements. Pergamon Press (1967)"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: ICIP, pp. 3464\u20133468 (2016)","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"31_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-20873-8_1","volume-title":"Computer Vision \u2013 ACCV 2018","author":"B Biggs","year":"2019","unstructured":"Biggs, B., Roddick, T., Fitzgibbon, A., Cipolla, R.: Creatures great and SMAL: recovering the shape and motion of animals from video. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11365, pp. 3\u201319. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20873-8_1"},{"key":"31_CR10","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1038\/s41592-021-01103-9","volume":"18","author":"LA Bola\u00f1os","year":"2021","unstructured":"Bola\u00f1os, L.A., et al.: A three-dimensional virtual mouse generates synthetic training data for behavioral analysis. Nat. Methods 18, 378\u2013381 (2021)","journal-title":"Nat. Methods"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhai, H., Liu, D., Li, W., Ding, C., Xie, Q., Han, H.: SiamBOMB: a real-time AI-based system for home-cage animal tracking, segmentation and behavioral analysis. In: IJCAI, pp. 5300\u20135302 (2020)","DOI":"10.24963\/ijcai.2020\/776"},{"issue":"7","key":"31_CR12","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.tree.2014.05.004","volume":"29","author":"AI Dell","year":"2014","unstructured":"Dell, A.I., et al.: Automated image-based tracking and its application in ecology. Trends Ecol. Evol. 29(7), 417\u2013428 (2014)","journal-title":"Trends Ecol. Evol."},{"issue":"4","key":"31_CR13","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1007\/s11263-020-01393-0","volume":"129","author":"P Dendorfer","year":"2020","unstructured":"Dendorfer, P., et al.: MOTChallenge: a benchmark for single-camera multiple target tracking. Int. J. Comput. Vis. 129(4), 845\u2013881 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01393-0","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"5","key":"31_CR15","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1038\/s41592-021-01106-6","volume":"18","author":"TW Dunn","year":"2021","unstructured":"Dunn, T.W., et al.: Geometric deep learning enables 3D kinematic profiling across species and environments. Nat. Methods 18(5), 564\u2013573 (2021)","journal-title":"Nat. Methods"},{"issue":"3","key":"31_CR16","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1002\/rse2.195","volume":"7","author":"I Duporge","year":"2021","unstructured":"Duporge, I., Isupova, O., Reece, S., Macdonald, D.W., Wang, T.: Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes. Remote Sens. Ecol. Conserv. 7(3), 369\u2013381 (2021)","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"31_CR17","unstructured":"Ferrero, F.R., Bergomi, M.G., Heras, F.J., Hinz, R., de Polavieja, G.G.: The champalimaud foundation: idtracker.ai (2017). https:\/\/idtrackerai.readthedocs.io\/en\/latest"},{"key":"31_CR18","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1038\/nn.3812","volume":"17","author":"A Gomez-Marin","year":"2014","unstructured":"Gomez-Marin, A., Paton, J.J., Kampff, A.R., Costa, R.M., Mainen, Z.F.: Big behavioral data: psychology, ethology and the foundations of neuroscience. Nat. Neurosci. 17, 1455\u20131462 (2014)","journal-title":"Nat. Neurosci."},{"key":"31_CR19","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1038\/s41592-021-01226-z","volume":"18","author":"A Gosztolai","year":"2021","unstructured":"Gosztolai, A., et al.: Liftpose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals. Nat. Methods 18, 975\u2013981 (2021)","journal-title":"Nat. Methods"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Graving, J.M., et al.: Deepposekit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 8, e47994 (2019)","DOI":"10.7554\/eLife.47994"},{"key":"31_CR21","doi-asserted-by":"crossref","unstructured":"G\u00fcnel, S., Rhodin, H., Morales, D., Campagnolo, J., Ramdya, P., Fua, P.: Deepfly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila. eLife 8, e48571 (2019)","DOI":"10.7554\/eLife.48571"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"9","key":"31_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1007354","volume":"15","author":"FJH Heras","year":"2019","unstructured":"Heras, F.J.H., Romero-Ferrero, F., Hinz, R.C., de Polavieja, G.G.: Deep attention networks reveal the rules of collective motion in zebrafish. PLOS Comput. Biol. 15(9), 1\u201323 (2019)","journal-title":"PLOS Comput. Biol."},{"issue":"7","key":"31_CR25","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2014","unstructured":"Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325\u20131339 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Iskakov, K., Burkov, E., Lempitsky, V., Malkov, Y.: Learnable triangulation of human pose. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00781"},{"key":"31_CR27","unstructured":"Jonathon Luiten, A.H.: Trackeval. https:\/\/github.com\/JonathonLuiten\/TrackEval (2020)"},{"key":"31_CR28","doi-asserted-by":"publisher","unstructured":"Joska, D., et al.: AcinoSet: a 3D pose estimation dataset and baseline models for cheetahs in the wild. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13901\u201313908 (2021). https:\/\/doi.org\/10.1109\/ICRA48506.2021.9561338","DOI":"10.1109\/ICRA48506.2021.9561338"},{"issue":"1","key":"31_CR29","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1115\/1.3662552","volume":"82","author":"RE Kalman","year":"1960","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35\u201345 (1960)","journal-title":"J. Basic Eng."},{"issue":"13","key":"31_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2021.109730","volume":"36","author":"P Karashchuk","year":"2021","unstructured":"Karashchuk, P., et al.: Anipose: a toolkit for robust markerless 3D pose estimation. Cell Rep. 36(13), 109730 (2021)","journal-title":"Cell Rep."},{"key":"31_CR31","doi-asserted-by":"crossref","unstructured":"Kays, R., Crofoot, M.C., Jetz, W., Wikelski, M.: Terrestrial animal tracking as an eye on life and planet. Science 348(6240), aaa2478 (2015)","DOI":"10.1126\/science.aaa2478"},{"issue":"1\u20132","key":"31_CR32","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1\u20132), 83\u201397 (1955)","journal-title":"Naval Res. Logist. Q."},{"key":"31_CR33","doi-asserted-by":"publisher","first-page":"268","DOI":"10.3389\/fnbeh.2020.581154","volume":"14","author":"R Labuguen","year":"2021","unstructured":"Labuguen, R., et al.: MacaquePose: a novel \u201cin the wild\u201d macaque monkey pose dataset for markerless motion capture. Front. Behav. Neurosci. 14, 268 (2021)","journal-title":"Front. Behav. Neurosci."},{"key":"31_CR34","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1038\/s41592-022-01443-0","volume":"19","author":"J Lauer","year":"2022","unstructured":"Lauer, J., et al.: Multi-animal pose estimation, identification and tracking with DeepLabCut. Nat. Methods 19, 496\u2013504 (2022)","journal-title":"Nat. Methods"},{"key":"31_CR35","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, C., Nevatia, R.: Learning to associate: HybridBoosted multi-target tracker for crowded scene. In: CVPR, pp. 2953\u20132960 (2009)","DOI":"10.1109\/CVPR.2009.5206735"},{"key":"31_CR36","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.106"},{"issue":"2","key":"31_CR37","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","volume":"129","author":"P Dendorfer","year":"2021","unstructured":"Dendorfer, P., et al.: HOTA: a higher order metric for evaluating multi-object tracking. Int. J. Comput. Vis. 129(2), 548\u2013578 (2021). https:\/\/doi.org\/10.1007\/s11263-020-01375-2","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR38","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1038\/s41593-018-0209-y","volume":"21","author":"A Mathis","year":"2018","unstructured":"Mathis, A., et al.: DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281\u20131289 (2018)","journal-title":"Nat. Neurosci."},{"key":"31_CR39","unstructured":"Naik, H.: XR For all: Closed-loop Visual Stimulation Techniques for Human and Non-Human Animals. Dissertation, Technische Universit\u00e4t M\u00fcnchen, M\u00fcnchen (2021)"},{"key":"31_CR40","doi-asserted-by":"publisher","first-page":"2152","DOI":"10.1038\/s41596-019-0176-0","volume":"14","author":"T Nath","year":"2019","unstructured":"Nath, T., Mathis, A., Chen, A.C., Patel, A., Bethge, M., Mathis, M.W.: Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nat. Protoc. 14, 2152\u20132176 (2019)","journal-title":"Nat. Protoc."},{"key":"31_CR41","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: ECCV, pp. 483\u2013499 (2016)","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"31_CR42","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1038\/s41592-020-0961-2","volume":"17","author":"A Nourizonoz","year":"2020","unstructured":"Nourizonoz, A., et al.: EthoLoop: automated closed-loop neuroethology in naturalistic environments. Nat. Methods 17, 1052\u20131059 (2020)","journal-title":"Nat. Methods"},{"key":"31_CR43","unstructured":"Park, H.S., Rhodin, H., Kanazawa, A., Neverova, N., Nobuhara, S., Black, M.: Cv4Animals: computer vision for animal behavior tracking and modeling (2021). https:\/\/www.cv4animals.com\/"},{"key":"31_CR44","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)"},{"key":"31_CR45","doi-asserted-by":"crossref","unstructured":"Pedersen, M., Haurum, J.B., Bengtson, S.H., Moeslund, T.B.: 3D-ZeF: a 3D zebrafish tracking benchmark dataset. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00250"},{"key":"31_CR46","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1038\/s41592-018-0234-5","volume":"16","author":"TD Pereira","year":"2019","unstructured":"Pereira, T.D., et al.: Fast animal pose estimation using deep neural networks. Nat. Methods 16, 117\u2013125 (2019)","journal-title":"Nat. Methods"},{"key":"31_CR47","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1038\/s41592-022-01426-1","volume":"19","author":"TD Pereira","year":"2022","unstructured":"Pereira, T.D., et al.: SLEAP: a deep learning system for multi-animal pose tracking. Nat. Methods 19, 486\u2013495 (2022)","journal-title":"Nat. Methods"},{"key":"31_CR48","doi-asserted-by":"crossref","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: ECCV, pp. 17\u201335 (2016)","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"31_CR49","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1038\/s41592-018-0295-5","volume":"16","author":"F Romero-Ferrero","year":"2019","unstructured":"Romero-Ferrero, F., Bergomi, M.G., Hinz, R.C., Heras, F.J.H., de Polavieja, G.G.: idtracker.ai: tracking all individuals in small or large collectives of unmarked animals. Nat. Methods 16, 179\u2013182 (2019)","journal-title":"Nat. Methods"},{"key":"31_CR50","doi-asserted-by":"crossref","unstructured":"Van Horn, G., et al.: Building a bird recognition app and large scale dataset with citizen scientists: the fine print in fine-grained dataset collection. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298658"},{"key":"31_CR51","doi-asserted-by":"crossref","unstructured":"Walter, T., Couzin, I.D.: Trex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields. eLife 10, e64000 (2021)","DOI":"10.7554\/eLife.64000"},{"key":"31_CR52","doi-asserted-by":"crossref","unstructured":"Wang, J., Yuille, A.L.: Semantic part segmentation using compositional model combining shape and appearance. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298788"},{"key":"31_CR53","doi-asserted-by":"crossref","unstructured":"Wang, P., Shen, X., Lin, Z., Cohen, S., Price, B., Yuille, A.L.: Joint object and part segmentation using deep learned potentials. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.184"},{"key":"31_CR54","unstructured":"Welinder, P., et al.: Caltech-UCSD Birds 200. Tech. Rep. CNS-TR-2010-001, California Institute of Technology (2010)"},{"key":"31_CR55","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01231-1_29"},{"issue":"12","key":"31_CR56","doi-asserted-by":"publisher","first-page":"2878","DOI":"10.1109\/TPAMI.2012.261","volume":"35","author":"Y Yang","year":"2013","unstructured":"Yang, Y., Ramanan, D.: Articulated human detection with flexible mixtures of parts. IEEE Trans. Pattern Anal. Mech. Intell. 35(12), 2878\u20132890 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mech. Intell."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16788-1_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T20:42:54Z","timestamp":1663879374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16788-1_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031167874","9783031167881"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16788-1_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Konstanz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/gcpr-vmv-2022.uni-konstanz.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}