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Cryo-electron tomography (cryo-ET) has the ability to three-dimensionally visualize the cellular architecture as well as the structural details of macro-molecular assemblies under near-native conditions. Due to beam sensitivity of biological samples, an inidividual tomogram has a maximal resolution of 5 nanometers. By averaging volumes, each depicting copies of the same type of a molecule, resolutions beyond 4 Å have been achieved. Key in this process is the ability to localize and classify the components of interest, which is challenging due to the low signal-to-noise ratio. Innovation in computational methods remains key to mine biological information from the tomograms.\n\nTo promote such innovation, we organize this SHREC track and provide a simulated dataset with the goal of establishing a benchmark in localization and classification of biological particles in cryo-electron tomograms. The publicly available dataset contains ten reconstructed tomograms obtained from a simulated cell-like volume. Each volume contains twelve different types of proteins, varying in size and structure. Participants had access to 9 out of 10 of the cell-like ground-truth volumes for learning-based methods, and had to predict protein class and location in the test tomogram.\n\nYou can find more details in <a href=\"https://diglib.eg.org/handle/10.2312/3dor20191061\">the related publication</a> and on the <a href=\"https://www.shrec.net/cryo-et/2019/\">contest webpage</a>."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2019-05-05"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Medicine, Health and Life Sciences"]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Gubins, I., Schot, G., Veltkamp, R., Förster, F., Du, X., Zeng, X., Zhu, Z., Chang, L., Xu, M., Moebel, E., Martinez-Sanchez, A., Kervrann, C., Lai, T., Han, X., Terashi, G., Kihara, D., Himes, B., Wan, X., Zhang, J., Gao, S., Hao, Y., Lv, Z., Wan, X., Yang, Z., Ding, Z., Cui, X., & Zhang, F. (2019). Classification in Cryo-Electron Tomograms. In Eurographics Workshop on 3D Object Retrieval. The Eurographics Association."},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.2312/3dor.20191061"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://diglib.eg.org/handle/10.2312/3dor20191061"}}]},{"typeName":"notesText","multiple":false,"typeClass":"primitive","value":"This entry includes contest dataset (without test ground truth), additional dataset (test ground truth and evaluation script), as well as the full dataset."},{"typeName":"distributionDate","multiple":false,"typeClass":"primitive","value":"2019-02-20"},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Gubins, Ilja"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2022-04-20"},{"typeName":"kindOfData","multiple":true,"typeClass":"primitive","value":["Simulated cryo-electron tomography data"]}]},"dansDataVaultMetadata":{"displayName":"Data Vault Metadata","name":"dansDataVaultMetadata","fields":[{"typeName":"dansDataversePid","multiple":false,"typeClass":"primitive","value":"doi:10.34894/XSKKQV"},{"typeName":"dansDataversePidVersion","multiple":false,"typeClass":"primitive","value":"2.1"},{"typeName":"dansBagId","multiple":false,"typeClass":"primitive","value":"urn:uuid:6069e9f1-edf7-4fd3-9692-7374fc721789"},{"typeName":"dansNbn","multiple":false,"typeClass":"primitive","value":"urn:nbn:nl:ui:13-da9014f9-09d5-4f22-bc41-28b863d3fec1"}]}},"files":[{"description":"Contest dataset without test ground truth data","label":"shrec19_cryoet_contest_dataset.zip","restricted":false,"version":1,"datasetVersionId":20341,"dataFile":{"id":285772,"persistentId":"","filename":"shrec19_cryoet_contest_dataset.zip","contentType":"application/zip","friendlyType":"ZIP Archive","filesize":6836692488,"description":"Contest dataset without test ground truth data","storageIdentifier":"surf://store:1805699c07e-b8fa2d5f8d27","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"80700ad124f7939e0e1f9c1d198deee3565460b8"},"tabularData":false,"creationDate":"2022-04-23","publicationDate":"2022-04-25","lastUpdateTime":"2022-06-22T08:32:49Z","fileAccessRequest":false}},{"description":"Additional difference dataset containing test ground truth and evaluation script","label":"shrec19_cryoet_diff_dataset.zip","restricted":false,"version":1,"datasetVersionId":20341,"dataFile":{"id":285771,"persistentId":"","filename":"shrec19_cryoet_diff_dataset.zip","contentType":"application/zip","friendlyType":"ZIP Archive","filesize":199102928,"description":"Additional difference dataset containing test ground truth and evaluation script","storageIdentifier":"surf://store:180569bcf89-9a649ae8560e","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"f995dc4ace3e6b0cb3f57b2d16b3d6c15e4d9943"},"tabularData":false,"creationDate":"2022-04-23","publicationDate":"2022-04-25","lastUpdateTime":"2022-06-22T08:32:49Z","fileAccessRequest":false}},{"description":"Full dataset","label":"shrec19_cryoet_full_dataset.zip","restricted":false,"version":1,"datasetVersionId":20341,"dataFile":{"id":285770,"persistentId":"","filename":"shrec19_cryoet_full_dataset.zip","contentType":"application/zip","friendlyType":"ZIP Archive","filesize":7027892581,"description":"Full dataset","storageIdentifier":"surf://store:180569e8c0f-9cda034e5bb9","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"f41376374e53b111fb254d9b8155e43f37f059ce"},"tabularData":false,"creationDate":"2022-04-23","publicationDate":"2022-04-25","lastUpdateTime":"2022-06-22T08:32:49Z","fileAccessRequest":false}}]}}}