cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7969559
title: Rib CT fracture

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the detection and classification of rib fractures on computed tomography (CT) scans and was previously introduced and described by Jin, L. et al (2020). The original dataset was modified to classify center-cropped rib fractures and no images or patient information were added. Data was restructured in compliance with the AUC23 challenge format. Images are 3D tensors: 0: 3D center-cropped CT scan Fracture classification labels: 0: Displaced 1: Non-displaced 2: Buckle 3: Segmental imagesTr (root folder with all patients and studies)<br> ├── ribfrac_0001001_0000.mha (3D CT for fracture 0001001)<br> ├── ribfrac_0001002_0000.mha (3D CT for fracture 0001002)<br> ├── ... Please cite the following article if you are using the Rib Fracture Detection and Classification dataset: <pre><code>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106</code></pre>
description: {"references": ["Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106"]}
DOI: 10.5281/zenodo.7974548
title: Rib CT fracture

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the detection and classification of rib fractures on computed tomography (CT) scans and was previously introduced and described by Jin, L. et al (2020). The original dataset was modified to classify center-cropped rib fractures and no images or patient information were added. Data was restructured in compliance with the AUC23 challenge format. Images are 3D tensors: 0: 3D center-cropped CT scan Fracture classification labels: 0: Displaced 1: Non-displaced 2: Buckle 3: Segmental Folder structure: imagesTr (root folder with all patients and studies)<br> ├── ribfrac_0001001_0000.mha (3D CT for fracture 0001001)<br> ├── ribfrac_0001002_0000.mha (3D CT for fracture 0001002)<br> ├── ... Please cite the following article if you are using the Rib Fracture Detection and Classification dataset: <pre><code>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106</code></pre>
description: {"references": ["Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106"]}
DOI: 10.5281/zenodo.8042125
title: Rib CT fracture

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the detection and classification of rib fractures on computed tomography (CT) scans and was previously introduced and described by Jin, L. et al (2020). The original dataset was modified to classify center-cropped rib fractures and no images or patient information were added. Data was restructured in compliance with the AUC23 challenge format. Images are 3D tensors: 0: 3D center-cropped CT scan Fracture classification labels: 0: Displaced 1: Non-displaced 2: Buckle 3: Segmental Folder structure: imagesTr (root folder with all patients and studies)<br> ├── ribfrac_0001001_0000.mha (3D CT for fracture 0001001)<br> ├── ribfrac_0001002_0000.mha (3D CT for fracture 0001002)<br> ├── ... Please cite the following article if you are using the Rib Fracture Detection and Classification dataset: <pre><code>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106</code></pre>
description: {"references": ["Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106"]}
DOI: 10.5281/zenodo.7969558
title: Rib CT fracture

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the detection and classification of rib fractures on computed tomography (CT) scans and was previously introduced and described by Jin, L. et al (2020). The original dataset was modified to classify center-cropped rib fractures and no images or patient information were added. Data was restructured in compliance with the AUC23 challenge format. Images are 3D tensors: 0: 3D center-cropped CT scan Fracture classification labels: 0: Displaced 1: Non-displaced 2: Buckle 3: Segmental Folder structure: imagesTr (root folder with all patients and studies)<br> ├── ribfrac_0001001_0000.mha (3D CT for fracture 0001001)<br> ├── ribfrac_0001002_0000.mha (3D CT for fracture 0001002)<br> ├── ... Please cite the following article if you are using the Rib Fracture Detection and Classification dataset: <pre><code>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106</code></pre>
description: {"references": ["Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106"]}
DOI: 10.5281/zenodo.8054740
title: Rib CT fracture

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the detection and classification of rib fractures on computed tomography (CT) scans and was previously introduced and described by Jin, L. et al (2020). The original dataset was modified to classify center-cropped rib fractures and no images or patient information were added. Data was restructured in compliance with the AUC23 challenge format. Images are 3D tensors: 0: 3D center-cropped CT scan Fracture classification labels: 0: Displaced 1: Non-displaced 2: Buckle 3: Segmental Folder structure: imagesTr (root folder with all patients and studies)<br> ├── ribfrac_0001001_0000.mha (3D CT for fracture 0001001)<br> ├── ribfrac_0001002_0000.mha (3D CT for fracture 0001002)<br> ├── ... Please cite the following article if you are using the Rib Fracture Detection and Classification dataset: <pre><code>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106</code></pre>
description: {"references": ["Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106"]}
DOI: 10.5281/zenodo.8055070
title: Parameters & Complexity Indexes of 3D CAD models from "Unleashing the Potential of Artificial Reefs Design: A Purpose-Driven Evaluation of Structural Complexity"

description: You'll find here data frames related to the paper <strong>"Unleashing the Potential of Artificial Reefs Design: A Purpose-Driven Evaluation of Structural Complexity"</strong>: df_parameters: displays parameters from 3D CAD models of the study df_complexity_Indexes: displays the complexity indexes computed with the parameters of the 3D CAD models
DOI: 10.5281/zenodo.8055069
title: Parameters & Complexity Indexes of 3D CAD models from "Unleashing the Potential of Artificial Reefs Design: A Purpose-Driven Evaluation of Structural Complexity"

description: You'll find here data frames related to the paper <strong>"Unleashing the Potential of Artificial Reefs Design: A Purpose-Driven Evaluation of Structural Complexity"</strong>: df_parameters: displays parameters from 3D CAD models of the study df_complexity_Indexes: displays the complexity indexes computed with the parameters of the 3D CAD models
DOI: 10.5281/zenodo.8055073
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Pierre Marie Édouard Lamy de la Chapelle, http://www.wikidata.org/entity/Q7192353. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q7192353
DOI: 10.5281/zenodo.8055072
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Pierre Marie Édouard Lamy de la Chapelle, http://www.wikidata.org/entity/Q7192353. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q7192353
DOI: 10.5281/zenodo.8055077
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Poul Edvard Poulsson, http://www.wikidata.org/entity/Q6055113. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q6055113
DOI: 10.5281/zenodo.8055076
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Poul Edvard Poulsson, http://www.wikidata.org/entity/Q6055113. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q6055113
DOI: 10.5281/zenodo.8055083
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Felipe Poey, http://www.wikidata.org/entity/Q500649. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q500649
DOI: 10.5281/zenodo.8055089
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by František Polášek, http://www.wikidata.org/entity/Q84609764. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q84609764
DOI: 10.5281/zenodo.8055088
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by František Polášek, http://www.wikidata.org/entity/Q84609764. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q84609764
DOI: 10.5281/zenodo.8055095
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Elisha Newton Plank, http://www.wikidata.org/entity/Q111694215. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q111694215
DOI: 10.5281/zenodo.8055094
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Elisha Newton Plank, http://www.wikidata.org/entity/Q111694215. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q111694215
DOI: 10.5281/zenodo.8055099
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Conrad Platou, http://www.wikidata.org/entity/Q107619554. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q107619554
DOI: 10.5281/zenodo.8055098
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Conrad Platou, http://www.wikidata.org/entity/Q107619554. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q107619554
DOI: 10.5281/zenodo.8055101
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Victor von Plessen, http://www.wikidata.org/entity/Q107276. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q107276
DOI: 10.5281/zenodo.8055100
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Victor von Plessen, http://www.wikidata.org/entity/Q107276. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q107276
DOI: 10.5281/zenodo.8055108
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Robert Pinchon, http://www.wikidata.org/entity/Q110526737. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q110526737
DOI: 10.5281/zenodo.8055107
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Robert Pinchon, http://www.wikidata.org/entity/Q110526737. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q110526737
DOI: 10.34740/kaggle/dsv/5969764
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.8055112
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Santiago Díaz Piedrahita, http://www.wikidata.org/entity/Q21510338. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21510338
DOI: 10.5281/zenodo.8055111
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Santiago Díaz Piedrahita, http://www.wikidata.org/entity/Q21510338. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21510338
DOI: 10.5281/zenodo.8055114
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Viktor Pietschmann, http://www.wikidata.org/entity/Q84868. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q84868
DOI: 10.5281/zenodo.8055113
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Viktor Pietschmann, http://www.wikidata.org/entity/Q84868. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q84868
DOI: 10.34740/kaggle/dsv/5969770
title: Egypt Gold Prices | Daily Updated
DOI: 10.6084/m9.figshare.23538738
title: ZeoDiff

description: This is a dataset used for the training of ZeoDiff, a diffusion model generating pure silica zeolite. This was the first work to generate porous materials using diffusion models for the first time. Structures for the training were retrieved from IZA and PCOD databases. <br> training.tar.gz : 31,173 zeolite structures used as training dataset test.tar.gz : 31,173 zeolite structures used as test dataset <br> for more information, please check 1. https://github.com/parkjunkil/ZeoDiff/
DOI: 10.5281/zenodo.8055120
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Marian H. Pettibone, http://www.wikidata.org/entity/Q55264645. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55264645
DOI: 10.5281/zenodo.8055119
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Marian H. Pettibone, http://www.wikidata.org/entity/Q55264645. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55264645
DOI: 10.5281/zenodo.8047231
title: GSLW - Greek Sign Language in the Wild Dataset

description: GSLW: The dataset has been recorded under various background and lighting variations and with different smartphones. The camera position and orientation are gently varied among subsequent recordings to increase video diversity. <strong>15 cases</strong> of hearing-impaired people dealing with public services have been recorded. The resulting test dataset has <strong>1,736 videos</strong> that were annotated both at individual gloss and sentence level.<br> <br> Instructions:<br> <br> <strong><em> GSLW.csv</em></strong> file has the paths, gloss and sentence annotations of the videos. <strong><em> load_gslw.py</em></strong> has a python function to load the GSLW.csv file for training or inference.<br> <br> Citation: <br> If you use our dataset please cite our work : <pre>@article{gsl, author={N. M. {Adaloglou} and T. {Chatzis} and I. {Papastratis} and A. {Stergioulas} and G. T. {Papadopoulos} and V. {Zacharopoulou} and G. {Xydopoulos} and K. {Antzakas} and D. {Papazachariou} and P. n. {Daras}}, journal={IEEE Transactions on Multimedia}, title={A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TMM.2021.3070438}}</pre>
description: Acknowledgments This work was supported by the Greek General Secretariat of Research and Technology under contract Τ1ΕΔΚ-02469 EPIKOINONO.
DOI: 10.5281/zenodo.8047230
title: GSLW - Greek Sign Language in the Wild Dataset

description: GSLW: The dataset has been recorded under various background and lighting variations and with different smartphones. The camera position and orientation are gently varied among subsequent recordings to increase video diversity. <strong>15 cases</strong> of hearing-impaired people dealing with public services have been recorded. The resulting test dataset has <strong>1,736 videos</strong> that were annotated both at individual gloss and sentence level.<br> <br> Instructions:<br> <br> <strong><em> GSLW.csv</em></strong> file has the paths, gloss and sentence annotations of the videos. <strong><em> load_gslw.py</em></strong> has a python function to load the GSLW.csv file for training or inference.<br> <br> Citation: <br> If you use our dataset please cite our work : <pre>@article{gsl, author={N. M. {Adaloglou} and T. {Chatzis} and I. {Papastratis} and A. {Stergioulas} and G. T. {Papadopoulos} and V. {Zacharopoulou} and G. {Xydopoulos} and K. {Antzakas} and D. {Papazachariou} and P. n. {Daras}}, journal={IEEE Transactions on Multimedia}, title={A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TMM.2021.3070438}}</pre>
description: Acknowledgments This work was supported by the Greek General Secretariat of Research and Technology under contract Τ1ΕΔΚ-02469 EPIKOINONO.
DOI: 10.5281/zenodo.8055124
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Albert Peter, http://www.wikidata.org/entity/Q53623. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q53623
DOI: 10.5281/zenodo.8055123
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Albert Peter, http://www.wikidata.org/entity/Q53623. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q53623
DOI: 10.5281/zenodo.8055128
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Thomas Edward Penard, http://www.wikidata.org/entity/Q3525087. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q3525087