cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive100841.20180622.3
title: Microbacterium sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive100842.20180622.3
title: Microbacterium sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive100843.20180622.3
title: Microbacterium sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive100844.20180622.3
title: Microbacterium sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive101708.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive101709.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive101710.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive101711.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive101712.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive101713.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive101714.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102533.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102534.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102535.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102536.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102537.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102538.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102539.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102540.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive102541.20180622.3
title: Arthrobacter sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive103344.20180622.3
title: Agromyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive103345.20180622.3
title: Agromyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.5281/zenodo.7559015
title: chrysichthys_thysi_decontamination_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7559014
title: chrysichthys_thysi_decontamination_gx.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/4884836
title: Dota 2 Pro League Matches 2023
DOI: 10.5281/zenodo.7557080
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI images of coasts.

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI images of coasts.</strong></em> These Residual-UNet model data are based on MNDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7384263 Classes: {0=other, 1=water} <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384263
DOI: 10.5281/zenodo.7557079
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI images of coasts.

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI images of coasts.</strong></em> These Residual-UNet model data are based on MNDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7384263 Classes: {0=other, 1=water} <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384263
DOI: 10.5281/zenodo.7559041
title: clarias_angolensis_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7559040
title: clarias_angolensis_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7207350
title: Supplementary Information Materials for the G-Cubed submission by Zakharov et al.

description: This is an upload for the purposes of review at the G-Cubed journal by AGU. The supporting information is provided for the MGL opal-CT, as well as the results of the SIMS and EMPA measurements. The Secondary Ion Probe Mass Spectrometry (SIMS) measurements are included as the .xslx table (Data Set S1) with analytical conditions, raw measurements and VSMOW-calibrated values. The Electron Microprobe (EMPA) analyses are provided in the .xslx file (Data Set S2). The Data Set S2 is separated by tabs for individual sample. Images feature the analyzed areas, including petrographic image, reflected light and the SIMS points.
DOI: 10.5281/zenodo.7558928
title: Supplementary Information Materials for the G-Cubed submission by Zakharov et al.

description: This is an upload for the purposes of review at the G-Cubed journal by AGU. The supporting information is provided for the MGL opal-CT, as well as the results of the SIMS and EMPA measurements. The Secondary Ion Probe Mass Spectrometry (SIMS) measurements are included as the .xslx table (Data Set S1) with analytical conditions, raw measurements and VSMOW-calibrated values. The Electron Microprobe (EMPA) analyses are provided in the .xslx file (Data Set S2). The Data Set S2 is separated by tabs for individual sample. Images feature the analyzed areas, including petrographic image, reflected light and the SIMS points.
DOI: 10.5281/zenodo.7559044
title: Supplementary Information Materials for the G-Cubed submission by Zakharov et al.

description: This is an upload for the purposes of review at the G-Cubed journal by AGU. The supporting information is provided for the MGL opal-CT, as well as the results of the SIMS and EMPA measurements. The Secondary Ion Probe Mass Spectrometry (SIMS) measurements are included as the .xslx table (Data Set S1) with analytical conditions, raw measurements and VSMOW-calibrated values. The Electron Microprobe (EMPA) analyses are provided in the .xslx file (Data Set S2). The Data Set S2 is separated by tabs for individual sample. Images feature the analyzed areas, including petrographic image, reflected light and the SIMS points.
DOI: 10.5281/zenodo.7207349
title: Supplementary Information Materials for the G-Cubed submission by Zakharov et al.

description: This is an upload for the purposes of review at the G-Cubed journal by AGU. The supporting information is provided for the MGL opal-CT, as well as the results of the SIMS and EMPA measurements. The Secondary Ion Probe Mass Spectrometry (SIMS) measurements are included as the .xslx table (Data Set S1) with analytical conditions, raw measurements and VSMOW-calibrated values. The Electron Microprobe (EMPA) analyses are provided in the .xslx file (Data Set S2). The Data Set S2 is separated by tabs for individual sample. Images feature the analyzed areas, including petrographic image, reflected light and the SIMS points.
DOI: 10.5281/zenodo.7559063
title: Supplementary Information Materials for the G-Cubed submission by Zakharov et al.

description: This is an upload for the purposes of review at the G-Cubed journal by AGU. The supporting information is provided for the MGL opal-CT, as well as the results of the SIMS and EMPA measurements. The Secondary Ion Probe Mass Spectrometry (SIMS) measurements are included as the .xslx table (Data Set S1) with analytical conditions, raw measurements and VSMOW-calibrated values. The Electron Microprobe (EMPA) analyses are provided in the .xslx file (Data Set S2). The Data Set S2 is separated by tabs for individual sample. Images feature the analyzed areas, including petrographic image, reflected light and the SIMS points.
DOI: 10.5281/zenodo.7331973
title: HRPlanesv2 - High Resolution Satellite Imagery for Aircraft Detection

description: The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size.
description: {"references": ["https://eod-grss-ieee.com/dataset-detail/ak1BclhJbkpuUkh5Uitmd3B5L2hNQT09", "https://github.com/dilsadunsal/HRPlanesv2-Data-Set"]}
DOI: 10.5281/zenodo.7331974
title: HRPlanesv2 - High Resolution Satellite Imagery for Aircraft Detection

description: The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size.
description: {"references": ["https://eod-grss-ieee.com/dataset-detail/ak1BclhJbkpuUkh5Uitmd3B5L2hNQT09", "https://github.com/dilsadunsal/HRPlanesv2-Data-Set"]}
DOI: 10.5281/zenodo.7315090
title: 10,000 Crop Field Boundaries across India

description: Release of dataset and neural network weights accompanying the paper "Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision" (forthcoming in <em>Remote Sensing)</em>. Ten thousand crop fields in India were delineated manually through inspection of high-resolution satellite imagery (Airbus SPOT). We also provide the weights of the highest performing neural network (FracTAL ResUNet architecture) pre-trained in France and fine-tuned on Airbus SPOT images in India. The model was trained in MXNet 1.6.0 and can be loaded with the "model.load_parameters()" function.
DOI: 10.5281/zenodo.7315089
title: 10,000 Crop Field Boundaries across India

description: Release of dataset and neural network weights accompanying the paper "Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision" (forthcoming in <em>Remote Sensing)</em>. Ten thousand crop fields in India were delineated manually through inspection of high-resolution satellite imagery (Airbus SPOT). We also provide the weights of the highest performing neural network (FracTAL ResUNet architecture) pre-trained in France and fine-tuned on Airbus SPOT images in India. The model was trained in MXNet 1.6.0 and can be loaded with the "model.load_parameters()" function.
DOI: 10.5281/zenodo.7100326
title: KappaSet: Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks

description: <strong>General information</strong> The dataset consists of 9251 labelled sub-tiles from 1038 Sentinel-2 (S2) Level-1C (L1C) products distributed over the globe. In terms of seasonal distribution, S2 products can be divided into the following groups: Winter products: 29 austral and 142 boreal S2 products Sprint products: 45 austral and 257 boreal S2 products Summer products: 30 austral and 293 boreal S2 products Autumn products: 29 austral and 213 boreal S2 products Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels sub-tiles. From each S2 product, the most challenging ~5 sub-tiles per product were selected for labelling. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations around the world. The classification pixel-wise map consists of the following categories: 0 – UNDEFINED: pixels that the labeler is not sure which class they belong to; 1 – CLEAR: pixels without clouds or cloud shadows; 2 – CLOUD SHADOW: pixels with cloud shadows; 3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km). 4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km). 5 – MISSING: missing or invalid pixels. The dataset was labelled using Computer Vision Annotation Tool (CVAT) and Segments.ai. With the possibility of integrating an active learning process in Segments.ai, the labelling was performed semi-automatically. The distribution of the dataset is presented in the Figure below. Color represents the season from which the product was chosen. The dataset limitations must be considered: the data mostly covers terrestrial regions (around 91%) and includes some water areas (around 9%); only around 7% of the dataset contains snow. Current sub-tiles do not have georeferencing. <strong>Contributions and Acknowledgements</strong> The data were annotated by Olga Wold, Mariana Rohtsalu, Nikita Murin, Joosep Truupõld and Fariha Harun. The data verification and Software Development were performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold and Tetiana Shtym. The methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich and Tetiana Shtym. The data were collected, processed, and checked as a part of “KappaMask: AI-based Cloudmask Processor for Sentinel-2” project. We thank Segments.ai team for providing a wonderful annotation tool that was actively used to prepare the dataset. In the end, we thank European Space Agency (ESA) for supporting, advising, and funding the project. The project was funded by <em><strong>European Space Agency,</strong></em> Contract No. 4000132124/20/I-DT.
DOI: 10.5281/zenodo.7100327
title: KappaSet: Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks

description: <strong>General information</strong> The dataset consists of 9251 labelled sub-tiles from 1038 Sentinel-2 (S2) Level-1C (L1C) products distributed over the globe. In terms of seasonal distribution, S2 products can be divided into the following groups: Winter products: 29 austral and 142 boreal S2 products Sprint products: 45 austral and 257 boreal S2 products Summer products: 30 austral and 293 boreal S2 products Autumn products: 29 austral and 213 boreal S2 products Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels sub-tiles. From each S2 product, the most challenging ~5 sub-tiles per product were selected for labelling. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations around the world. The classification pixel-wise map consists of the following categories: 0 – UNDEFINED: pixels that the labeler is not sure which class they belong to; 1 – CLEAR: pixels without clouds or cloud shadows; 2 – CLOUD SHADOW: pixels with cloud shadows; 3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km). 4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km). 5 – MISSING: missing or invalid pixels. The dataset was labelled using Computer Vision Annotation Tool (CVAT) and Segments.ai. With the possibility of integrating an active learning process in Segments.ai, the labelling was performed semi-automatically. The distribution of the dataset is presented in the Figure below. Color represents the season from which the product was chosen. The dataset limitations must be considered: the data mostly covers terrestrial regions (around 91%) and includes some water areas (around 9%); only around 7% of the dataset contains snow. Current sub-tiles do not have georeferencing. <strong>Contributions and Acknowledgements</strong> The data were annotated by Olga Wold, Mariana Rohtsalu, Nikita Murin, Joosep Truupõld and Fariha Harun. The data verification and Software Development were performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold and Tetiana Shtym. The methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich and Tetiana Shtym. The data were collected, processed, and checked as a part of “KappaMask: AI-based Cloudmask Processor for Sentinel-2” project. We thank Segments.ai team for providing a wonderful annotation tool that was actively used to prepare the dataset. In the end, we thank European Space Agency (ESA) for supporting, advising, and funding the project. The project was funded by <em><strong>European Space Agency,</strong></em> Contract No. 4000132124/20/I-DT.