cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16680271.v1
title: Additional file 2 of Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases

description: Additional file 2: Table S3. Clinical Drug Effect Vector. This table presents 392 kinds of existing drugs or drug combinations involved in our experiment and their influences on the 35 kinds of laboratory results
DOI: 10.6084/m9.figshare.16680277.v1
title: Additional file 4 of Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases

description: Additional file 4: Table S4. Repurposing Possibility Score. This table include the repurposing possibility score of each drug-disease pair in our experiment
DOI: 10.25345/c5d27k
title: MassIVE Reanalysis RMSV000000256.87 - CCMS ProteoCarta MS-GF+ reanalysis of Methylomicrobium alcaliphilum from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Methylomicrobium alcaliphilum (MSV000083150/PXD010000).
DOI: 10.25345/c5885n
title: MassIVE Reanalysis RMSV000000256.88 - CCMS ProteoCarta MS-GF+ reanalysis of Micrococcus luteus from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Micrococcus luteus (MSV000083150/PXD010000).
DOI: 10.25345/c54k10
title: MassIVE Reanalysis RMSV000000256.89 - CCMS ProteoCarta MS-GF+ reanalysis of Mycobacterium smegmatis from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Mycobacterium smegmatis (MSV000083150/PXD010000).
DOI: 10.25345/c50v7s
title: MassIVE Reanalysis RMSV000000256.90 - CCMS ProteoCarta MS-GF+ reanalysis of Paracoccus denitrificans from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Paracoccus denitrificans (MSV000083150/PXD010000).
DOI: 10.25345/c5w255
title: MassIVE Reanalysis RMSV000000256.91 - CCMS ProteoCarta MS-GF+ reanalysis of Myxococcus xanthus DZ2 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Myxococcus xanthus DZ2 (MSV000083150/PXD010000).
DOI: 10.25345/c5mk0z
title: MassIVE Reanalysis RMSV000000256.95 - CCMS ProteoCarta MS-GF+ reanalysis of Prevotella ruminicola 23 ATCC 19189 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Prevotella ruminicola 23 ATCC 19189 (MSV000083150/PXD010000).
DOI: 10.25345/c5rc2w
title: MassIVE Reanalysis RMSV000000256.93 - CCMS ProteoCarta MS-GF+ reanalysis of Paenibacillus polymyxa ATCC842 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Paenibacillus polymyxa ATCC842 (MSV000083150/PXD010000).
DOI: 10.25345/c5gv8h
title: MassIVE Reanalysis RMSV000000256.92 - CCMS ProteoCarta MS-GF+ reanalysis of Rhodobacteraceae bacterium HL-91 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Rhodobacteraceae bacterium HL-91 (MSV000083150/PXD010000).
DOI: 10.25345/c5c25h
title: MassIVE Reanalysis RMSV000000256.94 - CCMS ProteoCarta MS-GF+ reanalysis of Pseudomonas putida KT2440 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Pseudomonas putida KT2440 (MSV000083150/PXD010000).
DOI: 10.25345/c57c3m
title: MassIVE Reanalysis RMSV000000256.96 - CCMS ProteoCarta MS-GF+ reanalysis of Rhodococcus jostii RHA1 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Rhodococcus jostii RHA1 (MSV000083150/PXD010000).
DOI: 10.25345/c53k09
title: MassIVE Reanalysis RMSV000000256.97 - CCMS ProteoCarta MS-GF+ reanalysis of Rhodopseudomonas palustris from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Rhodopseudomonas palustris (MSV000083150/PXD010000).
DOI: 10.25345/c5zv97
title: MassIVE Reanalysis RMSV000000256.99 - CCMS ProteoCarta MS-GF+ reanalysis of Ruminococcus gnavus from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Ruminococcus gnavus (MSV000083150/PXD010000).
DOI: 10.25345/c5qc3z
title: MassIVE Reanalysis RMSV000000256.98 - CCMS ProteoCarta MS-GF+ reanalysis of Stigmatella aurantiaca DW4_3-1 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Stigmatella aurantiaca DW4_3-1 (MSV000083150/PXD010000).
DOI: 10.25345/c5v267
title: MassIVE Reanalysis RMSV000000256.100 - CCMS ProteoCarta MS-GF+ reanalysis of Streptococcus agalactiae from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Streptococcus agalactiae (MSV000083150/PXD010000).
DOI: 10.25345/c5kk11
title: MassIVE Reanalysis RMSV000000256.101 - CCMS ProteoCarta MS-GF+ reanalysis of Shewanella oneidensis MR-1 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Shewanella oneidensis MR-1 (MSV000083150/PXD010000).
DOI: 10.25345/c5fv86
title: MassIVE Reanalysis RMSV000000256.102 - CCMS ProteoCarta MS-GF+ reanalysis of Streptomyces griseorubens from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Streptomyces griseorubens (MSV000083150/PXD010000).
DOI: 10.25345/c5b26k
title: MassIVE Reanalysis RMSV000000256.103 - CCMS ProteoCarta MS-GF+ reanalysis of Streptomyces venezuelae from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Streptomyces venezuelae (MSV000083150/PXD010000).
DOI: 10.25345/c56c39
title: MassIVE Reanalysis RMSV000000256.104 - CCMS ProteoCarta MS-GF+ reanalysis of Sulfobacillus thermosulfidooxidans from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Sulfobacillus thermosulfidooxidans (MSV000083150/PXD010000).
DOI: 10.25345/c52n93
title: MassIVE Reanalysis RMSV000000256.105 - CCMS ProteoCarta MS-GF+ reanalysis of Synechococcus elongatus PCC7942 from DeNovo Peptide Identification Deep Learning Training Set [launched by 8a5f304ae46e47af8c32c3cc6c71a310]
title: CCMS ProteoCarta database search (MSGF+) reanalysis of Synechococcus elongatus PCC7942 (MSV000083150/PXD010000).
DOI: 10.6084/m9.figshare.16680382
title: Total_LS_Training_Program.xlsx

description: The data has been extracted from the LMS dataset for finding out relationship between LMS hits, Evaluation Score and Learners Response Time. The data is heterogeneous in nature and includes details of three category of learners i.e., Students, Professionals and Academicians.
DOI: 10.6084/m9.figshare.16680382.v1
title: Total_LS_Training_Program.xlsx

description: The data has been extracted from the LMS dataset for finding out relationship between LMS hits, Evaluation Score and Learners Response Time. The data is heterogeneous in nature and includes details of three category of learners i.e., Students, Professionals and Academicians.
DOI: 10.5281/zenodo.5527172
title: Wikidata Dump Olympics

description: RDF dump of wikidata produced with wdumps. Getting all the data related to Olympics<br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.5527171
title: Wikidata Dump Olympics

description: RDF dump of wikidata produced with wdumps. Getting all the data related to Olympics<br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.15151/esrf-es-496408264
title: Russian BAG for Xtallography and BioSAXS
DOI: 10.5517/ccdc.csd.cc27pxc7
title: CCDC 2076639: Experimental Crystal Structure Determination

description: Related Article: Zheng Li, Yu Lu, You-Ping Tian, Xiao-Xue Han, Xiang-Wei Liu, Ying Zhou, Xiong-Li Liu|2021|Tetrahedron|98|132297|doi:10.1016/j.tet.2021.132297
DOI: 10.5517/ccdc.csd.cc27pxd8
title: CCDC 2076640: Experimental Crystal Structure Determination

description: Related Article: Zheng Li, Yu Lu, You-Ping Tian, Xiao-Xue Han, Xiang-Wei Liu, Ying Zhou, Xiong-Li Liu|2021|Tetrahedron|98|132297|doi:10.1016/j.tet.2021.132297
DOI: 10.5517/ccdc.csd.cc28bsyc
title: CCDC 2095753: Experimental Crystal Structure Determination

description: Related Article: Zhongwei Chen, Yu Wang, XingYue Wang, Zicheng Xiao, Pingfan Wu, Kanghong Hu|2021|J.Mol.Struct.|1248|131451|doi:10.1016/j.molstruc.2021.131451
DOI: 10.5517/ccdc.csd.cc26f651
title: CCDC 2038472: Experimental Crystal Structure Determination

description: Related Article: D. M. Ramitha Y. P. Rupasinghe, Himanshu Gupta, Makayla R. Baxter, Robert F. Higgins, Matthias Zeller, Eric J. Schelter, Suzanne C. Bart|2021|Inorg.Chem.|60|14302|doi:10.1021/acs.inorgchem.1c01859
DOI: 10.5517/ccdc.csd.cc26f662
title: CCDC 2038473: Experimental Crystal Structure Determination

description: Related Article: D. M. Ramitha Y. P. Rupasinghe, Himanshu Gupta, Makayla R. Baxter, Robert F. Higgins, Matthias Zeller, Eric J. Schelter, Suzanne C. Bart|2021|Inorg.Chem.|60|14302|doi:10.1021/acs.inorgchem.1c01859
DOI: 10.5517/ccdc.csd.cc26f673
title: CCDC 2038474: Experimental Crystal Structure Determination

description: Related Article: D. M. Ramitha Y. P. Rupasinghe, Himanshu Gupta, Makayla R. Baxter, Robert F. Higgins, Matthias Zeller, Eric J. Schelter, Suzanne C. Bart|2021|Inorg.Chem.|60|14302|doi:10.1021/acs.inorgchem.1c01859
DOI: 10.5517/ccdc.csd.cc26f684
title: CCDC 2038475: Experimental Crystal Structure Determination

description: Related Article: D. M. Ramitha Y. P. Rupasinghe, Himanshu Gupta, Makayla R. Baxter, Robert F. Higgins, Matthias Zeller, Eric J. Schelter, Suzanne C. Bart|2021|Inorg.Chem.|60|14302|doi:10.1021/acs.inorgchem.1c01859
DOI: 10.5517/ccdc.csd.cc26f695
title: CCDC 2038476: Experimental Crystal Structure Determination

description: Related Article: D. M. Ramitha Y. P. Rupasinghe, Himanshu Gupta, Makayla R. Baxter, Robert F. Higgins, Matthias Zeller, Eric J. Schelter, Suzanne C. Bart|2021|Inorg.Chem.|60|14302|doi:10.1021/acs.inorgchem.1c01859
DOI: 10.5517/ccdc.csd.cc26f6b6
title: CCDC 2038477: Experimental Crystal Structure Determination

description: Related Article: D. M. Ramitha Y. P. Rupasinghe, Himanshu Gupta, Makayla R. Baxter, Robert F. Higgins, Matthias Zeller, Eric J. Schelter, Suzanne C. Bart|2021|Inorg.Chem.|60|14302|doi:10.1021/acs.inorgchem.1c01859
DOI: 10.5517/ccdc.csd.cc26f6c7
title: CCDC 2038478: Experimental Crystal Structure Determination

description: Related Article: D. M. Ramitha Y. P. Rupasinghe, Himanshu Gupta, Makayla R. Baxter, Robert F. Higgins, Matthias Zeller, Eric J. Schelter, Suzanne C. Bart|2021|Inorg.Chem.|60|14302|doi:10.1021/acs.inorgchem.1c01859
DOI: 10.5517/ccdc.csd.cc26w47h
title: CCDC 2051866: Experimental Crystal Structure Determination

description: Related Article: Abhishek Koner, Tetiana Sergeieva, Bernd Morgenstern, Diego M. Andrada|2021|Inorg.Chem.|60|14202|doi:10.1021/acs.inorgchem.1c01583
DOI: 10.5517/ccdc.csd.cc270ydm
title: CCDC 2056490: Experimental Crystal Structure Determination

description: Related Article: Abhishek Koner, Tetiana Sergeieva, Bernd Morgenstern, Diego M. Andrada|2021|Inorg.Chem.|60|14202|doi:10.1021/acs.inorgchem.1c01583
DOI: 10.5517/ccdc.csd.cc270yfn
title: CCDC 2056491: Experimental Crystal Structure Determination

description: Related Article: Abhishek Koner, Tetiana Sergeieva, Bernd Morgenstern, Diego M. Andrada|2021|Inorg.Chem.|60|14202|doi:10.1021/acs.inorgchem.1c01583
DOI: 10.5517/ccdc.csd.cc27wn1v
title: CCDC 2082147: Experimental Crystal Structure Determination

description: Related Article: Aeri J. Gosselin, Alexandra M. Antonio, Kyle J. Korman, Meaghan M. Deegan, Glenn P. A. Yap, Eric D. Bloch|2021|J.Am.Chem.Soc.|143|14956|doi:10.1021/jacs.1c05613
DOI: 10.5517/ccdc.csd.cc283y5j
title: CCDC 2089157: Experimental Crystal Structure Determination

description: Related Article: Qinzhen Li, Baoyu Huang, Sha Yang, Hui Zhang, Jinsong Chai, Yong Pei, Manzhou Zhu|2021|J.Am.Chem.Soc.|143|15224|doi:10.1021/jacs.1c06354
DOI: 10.5517/ccdc.csd.cc283ybp
title: CCDC 2089162: Experimental Crystal Structure Determination

description: Related Article: Qinzhen Li, Baoyu Huang, Sha Yang, Hui Zhang, Jinsong Chai, Yong Pei, Manzhou Zhu|2021|J.Am.Chem.Soc.|143|15224|doi:10.1021/jacs.1c06354
DOI: 10.5517/ccdc.csd.cc283ykx
title: CCDC 2089169: Experimental Crystal Structure Determination

description: Related Article: Qinzhen Li, Baoyu Huang, Sha Yang, Hui Zhang, Jinsong Chai, Yong Pei, Manzhou Zhu|2021|J.Am.Chem.Soc.|143|15224|doi:10.1021/jacs.1c06354
DOI: 10.5517/ccdc.csd.cc286vjw
title: CCDC 2091958: Experimental Crystal Structure Determination

description: Related Article: Bahman Golesorkhi, Soroush Naseri, Laure Guénée, Inès Taarit, Filipe Alves, Homayoun Nozary, Claude Piguet|2021|J.Am.Chem.Soc.|143|15326|doi:10.1021/jacs.1c06865
DOI: 10.5517/ccdc.csd.cc286vkx
title: CCDC 2091959: Experimental Crystal Structure Determination

description: Related Article: Bahman Golesorkhi, Soroush Naseri, Laure Guénée, Inès Taarit, Filipe Alves, Homayoun Nozary, Claude Piguet|2021|J.Am.Chem.Soc.|143|15326|doi:10.1021/jacs.1c06865
DOI: 10.5517/ccdc.csd.cc28c8kh
title: CCDC 2096206: Experimental Crystal Structure Determination

description: Related Article: Guoping Liu, Lulu Liu, Tiantian Gong, Yanzhou Li, Lijuan Chen, Junwei Zhao|2021|Inorg.Chem.|60|14457|doi:10.1021/acs.inorgchem.1c02248
DOI: 10.5517/ccdc.csd.cc28c8lj
title: CCDC 2096207: Experimental Crystal Structure Determination

description: Related Article: Guoping Liu, Lulu Liu, Tiantian Gong, Yanzhou Li, Lijuan Chen, Junwei Zhao|2021|Inorg.Chem.|60|14457|doi:10.1021/acs.inorgchem.1c02248
DOI: 10.5517/ccdc.csd.cc28c8mk
title: CCDC 2096208: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc28c8nl
title: CCDC 2096209: Experimental Crystal Structure Determination

description: Related Article: Guoping Liu, Lulu Liu, Tiantian Gong, Yanzhou Li, Lijuan Chen, Junwei Zhao|2021|Inorg.Chem.|60|14457|doi:10.1021/acs.inorgchem.1c02248
DOI: 10.6084/m9.figshare.16680355
title: MicroarrayDataset

description: Microarray dataset
DOI: 10.6084/m9.figshare.16680355.v1
title: Microarray dataset

description: Microarray dataset
DOI: 10.48512/xcv8465723
title: 2019 Total Station data

description: Total station data points for artifacts, bones, charcoal, features, etc. from the 2019 excavation season at Lapa do Picareiro.
DOI: 10.6067/xcv8458798
title: Agricultural Temple Communities at Angkor, Cambodia

description: Data from: "Klassen, Sarah, et al. (2021), 'Provisioning an Early City: Spatial Equilibrium in the Agricultural Economy at Angkor, Cambodia', Journal of Archaeological Method and Theory."
DOI: 10.7910/dvn/xbor16
title: Taurus Raw NIKA2 IMBfits

description: This dataset contains all Taurus related raw data files to be ingested by any data reduction pipeline associated to the NIKA2 instrument.
DOI: 10.5281/zenodo.5526880
title: ICFHR 2016 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset

description: The ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) provided a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition. If you use this dataset, please cite: Florence Cloppet, Véronique Eglin, Van Cuong Kieu, Dominique Stutzmann, and Nicole Vincent, "ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script", <em>Proceedings of International Conference on Frontiers in Handwriting Recognition</em>, Los Alamos : IEEE, 2016, p. 590-595. [https://doi.org/10.1109/ICFHR.2016.0113] At this competition, we proposed two independent classification tasks which attracted five participants with seven submitted classifiers. Those classifiers are trained on a set of 2000 images with their ground truths. In the first task of script crisp classification, the classifiers have been evaluated on a test set of 1000 single-type manuscripts. In the second task of “Fuzzy Classification”, the classifiers have been carried out on a set of 2000 multi-script-type manuscripts. The present dataset contains the training dataset, both test datasets (task 1 and task 2) and the matrices provided by the competitors. It was first published on a https://clamm.irht.cnrs.fr/icfhr2016-clamm/ in Oct. 2016.
description: {"references": ["Florence Cloppet, V\u00e9ronique Eglin, Van Cuong Kieu, Dominique Stutzmann, and Nicole Vincent, \"ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script\", Proceedings of International Conference on Frontiers in Handwriting Recognition, Los Alamos : IEEE, 2016, p. 590-595. https://doi.org/10.1109/ICFHR.2016.0113"]}
DOI: 10.5281/zenodo.5526879
title: ICFHR 2016 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset

description: The ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) provided a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition. If you use this dataset, please cite: Florence Cloppet, Véronique Eglin, Van Cuong Kieu, Dominique Stutzmann, and Nicole Vincent, "ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script", <em>Proceedings of International Conference on Frontiers in Handwriting Recognition</em>, Los Alamos : IEEE, 2016, p. 590-595. [https://doi.org/10.1109/ICFHR.2016.0113] At this competition, we proposed two independent classification tasks which attracted five participants with seven submitted classifiers. Those classifiers are trained on a set of 2000 images with their ground truths. In the first task of script crisp classification, the classifiers have been evaluated on a test set of 1000 single-type manuscripts. In the second task of “Fuzzy Classification”, the classifiers have been carried out on a set of 2000 multi-script-type manuscripts. The present dataset contains the training dataset, both test datasets (task 1 and task 2) and the matrices provided by the competitors. It was first published on a https://clamm.irht.cnrs.fr/icfhr2016-clamm/ in Oct. 2016.
description: {"references": ["Florence Cloppet, V\u00e9ronique Eglin, Van Cuong Kieu, Dominique Stutzmann, and Nicole Vincent, \"ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script\", Proceedings of International Conference on Frontiers in Handwriting Recognition, Los Alamos : IEEE, 2016, p. 590-595. https://doi.org/10.1109/ICFHR.2016.0113"]}
DOI: 10.5281/zenodo.5527690
title: ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset

description: The ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) followed a competition at ICFHR2016 and provided a rich annotated database of European medieval manuscripts to the community on Handwriting Analysis and Recognition, containing information on date of production and class of script. If you use this upload, please cite: Florence Cloppet, Véronique Eglin, Marlène Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, "ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script", in <em>14th IAPR International Conference on Document Analysis and Recognition</em>. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. https://doi.org/10.1109/ICDAR.2017.224 We proposed four independent classification tasks which attracted 10 registered teams, with 6 submitted classifiers from 4 participants. Those classifiers are trained on a set of 3540 images with their ground<br> truths. In task 1 (Script classification) and task 3 (Date classification), the classifiers have been evaluated by a test set of 2000 greyscale, tiff, 300 dpi images. In task 2 (Script classification) and task 4 (Date classification), the test set consists of 1000 images in different formats, resolutions and color<br> representation. The present dataset contains the training dataset, both test datasets (tasks 1 and 3, and tasks 2 and 4) and the matrices provided by the competitors. It was first published on https://clamm.irht.cnrs.fr/icdar-2017/ in Nov. 2017.
description: {"references": ["Florence Cloppet, V\u00e9ronique Eglin, Marl\u00e8ne Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, \"ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script\", in 14th IAPR International Conference on Document Analysis and Recognition. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. https://doi.org/10.1109/ICDAR.2017.224"]}
DOI: 10.5281/zenodo.5527689
title: ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset

description: The ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) followed a competition at ICFHR2016 and provided a rich annotated database of European medieval manuscripts to the community on Handwriting Analysis and Recognition, containing information on date of production and class of script. If you use this upload, please cite: Florence Cloppet, Véronique Eglin, Marlène Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, "ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script", in <em>14th IAPR International Conference on Document Analysis and Recognition</em>. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. https://doi.org/10.1109/ICDAR.2017.224 We proposed four independent classification tasks which attracted 10 registered teams, with 6 submitted classifiers from 4 participants. Those classifiers are trained on a set of 3540 images with their ground<br> truths. In task 1 (Script classification) and task 3 (Date classification), the classifiers have been evaluated by a test set of 2000 greyscale, tiff, 300 dpi images. In task 2 (Script classification) and task 4 (Date classification), the test set consists of 1000 images in different formats, resolutions and color<br> representation. The present dataset contains the training dataset, both test datasets (tasks 1 and 3, and tasks 2 and 4) and the matrices provided by the competitors. It was first published on https://clamm.irht.cnrs.fr/icdar-2017/ in Nov. 2017.
description: {"references": ["Florence Cloppet, V\u00e9ronique Eglin, Marl\u00e8ne Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, \"ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script\", in 14th IAPR International Conference on Document Analysis and Recognition. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. https://doi.org/10.1109/ICDAR.2017.224"]}