cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7613106
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images</strong></em> Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of flooded buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes:<br> other<br> flooded building <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' 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. '.h5' 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. '_modelcard.json' 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. '_model_history.npz' 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. '.png' 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 References<br> *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 **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.
DOI: 10.5281/zenodo.7622733
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images</strong></em> Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of flooded buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes:<br> other<br> flooded building <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' 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. '.h5' 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. '_modelcard.json' 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. '_model_history.npz' 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. '.png' 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 References<br> *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 **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.
DOI: 10.5281/zenodo.7613105
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images</strong></em> Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of flooded buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes:<br> other<br> flooded building <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' 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. '.h5' 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. '_modelcard.json' 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. '_model_history.npz' 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. '.png' 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 References<br> *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 **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.
DOI: 10.5281/zenodo.7622742
title: lottia_pelta_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7622741
title: lottia_pelta_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7622544
title: Galaxy Training Data for "End-to-End Tissue Microarray Image Analysis with Galaxy-ME"

description: This dataset provides the inputs used in the Galaxy Training Network (GTN) training 'End-to-End Tissue Microarray Image Analysis with Galaxy-ME'. The tutorial demonstrates how to use the Galaxy-ME tool suite for primary image processing, data analysis, and interactive visualization of multiple tissue imaging datasets. Original data was published by Schapiro <em>et al</em>.
DOI: 10.57834/sruc.21408495
title: metagenomics of mixed cultures mapping to K4

description: metagenomics of mixed cultures mapping to K4
DOI: 10.57834/sruc.21408495.v1
title: metagenomics of mixed cultures mapping to K4

description: metagenomics of mixed cultures mapping to K4
DOI: 10.57834/sruc.21946901
title: Description of data collected about the Scottish camelid sector

description: This project involves an online questionnaire survey of Scotland's camelid keepers. We are seeking information on their current practices for recording information about the livestock they keep - llamas, alpacas, guanacos, vicunas and camels. We have asked about recording of births and deaths, purchases and sales, and what methods they use if they do record these data. The dataset contains individuals' responses to these questions, informaiton about numbers of livestock of each species, and in some cases personal email addresses that they have provided in order to allow us to make contact at future stages of the project, to discuss identification, registration and movement recording within this sector. We also ask for some high-level location information (first half of postcode).
DOI: 10.57834/sruc.21946901.v1
title: Description of data collected about the Scottish camelid sector

description: This project involves an online questionnaire survey of Scotland's camelid keepers. We are seeking information on their current practices for recording information about the livestock they keep - llamas, alpacas, guanacos, vicunas and camels. We have asked about recording of births and deaths, purchases and sales, and what methods they use if they do record these data. The dataset contains individuals' responses to these questions, informaiton about numbers of livestock of each species, and in some cases personal email addresses that they have provided in order to allow us to make contact at future stages of the project, to discuss identification, registration and movement recording within this sector. We also ask for some high-level location information (first half of postcode).
DOI: 10.57834/sruc.21946928
title: Description of data collected about the Scottish deer sector

description: These data are being gathered by questionnaire survey online, sent to deer keepers around Scotland. We are focusing on "managed" deer - meaning humans have influence over their range for at least part of the year. We are asking questions about recording of births, deaths, sales and purchases; we also ask how records are made and kept, when this happens. The dataset contains responses to these questions and may also contain personal email addresses; we have asked for these to be given, optionally, if respondents want to get involved in further discussion of this topic later in the project. We also request some location information - first half of postcode where deer premises is registered.
DOI: 10.57834/sruc.21946928.v1
title: Description of data collected about the Scottish deer sector

description: These data are being gathered by questionnaire survey online, sent to deer keepers around Scotland. We are focusing on "managed" deer - meaning humans have influence over their range for at least part of the year. We are asking questions about recording of births, deaths, sales and purchases; we also ask how records are made and kept, when this happens. The dataset contains responses to these questions and may also contain personal email addresses; we have asked for these to be given, optionally, if respondents want to get involved in further discussion of this topic later in the project. We also request some location information - first half of postcode where deer premises is registered.
DOI: 10.5281/zenodo.7622762
title: tyrannus_albogularis_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7622761
title: tyrannus_albogularis_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22056530
title: Explanation Strategy Experiment Dataset

description: Data used for explanation strategy experiment.
DOI: 10.6084/m9.figshare.22056530.v1
title: Explanation Strategy Experiment Dataset

description: Data used for explanation strategy experiment.
DOI: 10.21227/0d95-6y38
title: Age-gender Effect on Career Progression in American Universities

description: Anonymized data used in the study of "Administrative data processing, Clustering, classification, and association rules, Human factors and ergonomics, Machine learning"
DOI: 10.5281/zenodo.7622784
title: tyrannus_niveigularis_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7622783
title: tyrannus_niveigularis_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22056485
title: CodeData_AlonsoScheer_etal_NatComm_2023

description: Generated data and custom processing/analysis Matlab code.
DOI: 10.5281/zenodo.7622835
title: simalia_tracyae_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7622834
title: simalia_tracyae_spades_01.fasta.gz

description: .
DOI: 10.15482/usda.adc/1528521
title: Data from USDA ARS High Plains Grasslands Research Station (East Unit) near Cheyenne, WY: Yearling cattle weight gains managed in light, moderate and heavily stocked pastures (1982-2022)

description: The USDA-Agricultural Research Service High Plains Grasslands Research Station (HPGRS) is located in Cheyenne, Wyoming, USA.  In 1982, a long-term stocking rate study on northern mixed-grass prairie was initiated with season-long (early June to October) grazing. Stocking rates defined as light (35% below NRCS recommended rate, 15 yearlings per 80 ha), moderate (NRCS recommended rate, 4 yearlings per 12ha), and heavy (33% above NRCS recommended rate, 4 yearlings per 9 ha). British- and continental-breed yearling cattle were used throughout the study years. When forage supply was limited due to drought, grazing seasons were shortened or cattle were not grazed for that season. Individual raw data on cattle entry and exit weights are available from 1982 to 2022. No grazing occurred in the years 1989, 2000, and 2002 due to drought conditions. Weight gain outliers (± 2 sd of treatment mean) were removed from the dataset.
DOI: 10.15472/njjop6
title: Lista de mamíferos priorizados para su categorización según los lineamientos de la IUCN en el territorio nacional

description: Listado de especies que hacen parte de el conjunto de especies priorizadas para la actualización del libro rojo de mamíferos de Colombia, compilado y actualizado por medio de la recopilación de información online sobre cada una de las especies de mamíferos y posterior discusión de la misma durante los talleres de categorización realizados durante el año 2021 con los expertos en este grupo de especies. Contiene la información de 188 especies de mamíferos que tienen distribución en el territorio nacional.
DOI: 10.6084/m9.figshare.21992264
title: ct-uroliths-freiburg

description: Data related to the Publication "<strong>Effect of surface-partial-volume correction and adaptive threshold on segmentation of uroliths in computed tomography"</strong> <br> 101 Uroliths were ex-vivo (volume and diameter) and CT imaging was performed. Data consists of - measured ground truth volume - measured ground truth diameter - CT imaging of all stones - stone composition (when available) <br> <br> Data ist provided as a csv table, and as MATLAB .mat file. <br> <strong>CSV Table Data</strong> Table containing a row for each of the 101 uroliths with colums: ID, Volume, Diameter and Stone Type. <br> <strong>Matlab Data</strong> Matlab structure array of the 101 uroliths with the following fields: <br> gt_diam: diameter (ground truth) gt_vol: volume (ground truth) <em><strong> </strong></em> imgdata 3D data from CT imaging voxsiz voxel size in mm (x y z) voxvol_mL size per voxel in milliliter <br> Some uroliths have cavities, these are filled with NaN values in imgdata <br>
DOI: 10.5517/ccdc.csd.cc272frj
title: CCDC 2057927: Experimental Crystal Structure Determination

description: Related Article: Hannah van Dyk, Frederick J.F. Jacobs, Alice Brink|2022|CSD Communication|||
description: Related Article: Hannah van Dyk, Frederick Jacobus Francois Jacobs, Robin Edward Kroon, Tshepiso Jan Makhafola, Alice Brink|2022|J.Mol.Struct.|1276|134737|doi:10.1016/j.molstruc.2022.134737
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27xnzt
title: CCDC 2083137: Experimental Crystal Structure Determination

description: Related Article: Hao Chen, Kaikai Wen, Yu Lu, Xin Zhang, Yuhao Shi, Qinqin Shi, Han Ma, Qian Peng, Hui Huang|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27rlyk
title: CCDC 2078269: Experimental Crystal Structure Determination

description: Related Article: Emanuele Priola, Nadia Curetti, Domenica Marabello, Jacopo Andreo, Alessia Giordana, Luca Andreo, Piera Benna, Paulo de Tarso Cavalcante Freire, Paola Benzi, Lorenza Operti, Eliano Diana|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28fms4
title: CCDC 2098476: Experimental Crystal Structure Determination

description: Related Article: shengling li|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27rm1p
title: CCDC 2078272: Experimental Crystal Structure Determination

description: Related Article: Emanuele Priola, Nadia Curetti, Domenica Marabello, Jacopo Andreo, Alessia Giordana, Luca Andreo, Piera Benna, Paulo de Tarso Cavalcante Freire, Paola Benzi, Lorenza Operti, Eliano Diana|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27rm7w
title: CCDC 2078278: Experimental Crystal Structure Determination

description: Related Article: Emanuele Priola, Nadia Curetti, Domenica Marabello, Jacopo Andreo, Alessia Giordana, Luca Andreo, Piera Benna, Paulo de Tarso Cavalcante Freire, Paola Benzi, Lorenza Operti, Eliano Diana|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bwgj7
title: CCDC 2171349: Experimental Crystal Structure Determination

description: Related Article: Yu Zhang, Zi-Juan Yi, Ai-Mei Yang, Jia-Ming Guo, Xin Li, Bang-Guo Wei|2023|Org.Biomol.Chem.|21|1303|doi:10.1039/D2OB02213C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bwgk8
title: CCDC 2171350: Experimental Crystal Structure Determination

description: Related Article: Yu Zhang, Zi-Juan Yi, Ai-Mei Yang, Jia-Ming Guo, Xin Li, Bang-Guo Wei|2023|Org.Biomol.Chem.|21|1303|doi:10.1039/D2OB02213C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cl9cn
title: CCDC 2192331: Experimental Crystal Structure Determination

description: Related Article: Yui Fukuda, Yuki Akamatsu, Masataka Umetani, Koki Kise, Kenichi Kato, Atsuhiro Osuka, Takayuki Tanaka|2023|Org.Biomol.Chem.|21|1158|doi:10.1039/D2OB02125K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.