cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23520384.v1
title: <strong>Machine learning and phylogenetic models identify predictors of genetic variation in Neotropical amphibians - Supplementary Material</strong>

description: Supplementary Tables: Table S1.1. The data set used in this study. Nucleotide diversity (<em>π</em>) and Isolation-by-Distance (IBD) were used as dependent variables in the analyses. Lar=Larvae, Dir=Direct, DD= Data Deficient, LC=Least Concern, NT=Near Threatened, VU= Vulnerable, EN=Endangered, CR= Critically Endangered, EX= Extinct, NE= Not Evaluated. Table S2.1. Results of Phylogenetic Linear (pi) and Logistic (IBD – in blue) regression models using <em>phylolm</em> R package <br>
DOI: 10.5281/zenodo.8040642
title: Comprehensive Set of Structural Comparisons of Transport Domains from Elevator Transporters

description: This upload is a supplementary dataset for the following preprint: N. Trebesch and E. Tajkhorshid. "Structure Reveals Homology in Elevator Transporters." <em>bioRxiv</em>. (2023). DOI: 10.1101/2023.06.14.544989. Please see the preprint for the methods, analysis, and discussion associated with this dataset.
DOI: 10.5281/zenodo.8040643
title: Comprehensive Set of Structural Comparisons of Transport Domains from Elevator Transporters

description: This upload is a supplementary dataset for the following preprint: N. Trebesch and E. Tajkhorshid. "Structure Reveals Homology in Elevator Transporters." <em>bioRxiv</em>. (2023). DOI: 10.1101/2023.06.14.544989. Please see the preprint for the methods, analysis, and discussion associated with this dataset.
DOI: 10.5281/zenodo.7944501
title: Micromet data from AWS 00000E88 from 21.4.2010 to 31.12.2022

description: Micromet data from AWS 0000E88 from 21.4.2010 to 31.12.2022
DOI: 10.5281/zenodo.7944500
title: Micromet data from AWS 00000E88 from 21.4.2010 to 31.12.2022

description: Micromet data from AWS 0000E88 from 21.4.2010 to 31.12.2022
DOI: 10.5281/zenodo.8043218
title: Lung CT COVID-19 batch 6

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 2: https://zenodo.org/record/8042589 Batch 3: https://zenodo.org/record/8042817 Batch 4: https://zenodo.org/record/8043089 Batch 5: https://zenodo.org/record/8043216
DOI: 10.5281/zenodo.8043217
title: Lung CT COVID-19 batch 6

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 2: https://zenodo.org/record/8042589 Batch 3: https://zenodo.org/record/8042817 Batch 4: https://zenodo.org/record/8043089 Batch 5: https://zenodo.org/record/8043216
DOI: 10.5281/zenodo.6809580
title: jwildeboer/MUCNumbers: 2022-07-06-MUCNumbers

description: Munich numbers 2022-07-06
DOI: 10.5281/zenodo.8043216
title: Lung CT COVID-19 batch 5

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 2: https://zenodo.org/record/8042589 Batch 3: https://zenodo.org/record/8042817 Batch 4: https://zenodo.org/record/8043089 Batch 6: https://zenodo.org/record/8043218
DOI: 10.5281/zenodo.8043215
title: Lung CT COVID-19 batch 5

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 2: https://zenodo.org/record/8042589 Batch 3: https://zenodo.org/record/8042817 Batch 4: https://zenodo.org/record/8043089 Batch 6: https://zenodo.org/record/8043218
DOI: 10.5281/zenodo.8043089
title: Lung CT COVID-19 batch 4

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 2: https://zenodo.org/record/8042589 Batch 3: https://zenodo.org/record/8042817 Batch 5: https://zenodo.org/record/8043216 Batch 6: https://zenodo.org/record/8043218
DOI: 10.5281/zenodo.8043088
title: Lung CT COVID-19 batch 4

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 2: https://zenodo.org/record/8042589 Batch 3: https://zenodo.org/record/8042817 Batch 5: https://zenodo.org/record/8043216 Batch 6: https://zenodo.org/record/8043218
DOI: 10.5066/p9q5eku1
title: Forest canopy gap dynamics: quantifying forest gaps and understanding gap &amp;ndash; level forest regeneration in Upper Mississippi River floodplain forests

description: Forest canopy gap metrics for select floodplain forest canopy gaps in the Upper Mississippi River System (UMRS).
DOI: 10.5281/zenodo.8042817
title: Lung CT COVID-19 batch 3

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 2: https://zenodo.org/record/8042589 Batch 4: https://zenodo.org/record/8043089 Batch 5: https://zenodo.org/record/8043216 Batch 6: https://zenodo.org/record/8043218
DOI: 10.5281/zenodo.8042816
title: Lung CT COVID-19 batch 3

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 2: https://zenodo.org/record/8042589 Batch 4: https://zenodo.org/record/8043089 Batch 5: https://zenodo.org/record/8043216 Batch 6: https://zenodo.org/record/8043218
DOI: 10.5281/zenodo.8042589
title: Lung CT COVID-19 batch 2

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 3: https://zenodo.org/record/8042817 Batch 4: https://zenodo.org/record/8043089 Batch 5: https://zenodo.org/record/8043216 Batch 6: https://zenodo.org/record/8043218
DOI: 10.5281/zenodo.8042588
title: Lung CT COVID-19 batch 2

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge. Images are 3D tensors: 0: 3D CT scan Classification labels: COVID-19: 0: Negative RT-PCR 1: Positive RT-PCR<br> Severe COVID-19: 0: Alive and no intubation after one month 1: Death or intubation after one month imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ... Please cite the following article if you are using the STOIC2021 training dataset: <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> Due to upload size limits, the data set was split into six batches. Batch 1: https://zenodo.org/record/7969800 Batch 3: https://zenodo.org/record/8042817 Batch 4: https://zenodo.org/record/8043089 Batch 5: https://zenodo.org/record/8043216 Batch 6: https://zenodo.org/record/8043218
DOI: 10.5883/ds-lasioch
title: Integrative taxonomy of the pseudoscorpion family Chernetidae (Pseudoscorpiones: Cheliferoidea): evidence for new range restricted species in the Dinaric Karst

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/ds-bacspn1
title: Genomic data reveals new species and the limits of mtDNA barcode diagnostics to contain a global pest species complex (Diptera: Tephritidae: Dacinae)

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.