cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2d7lkt
title: CCDC 2211836: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
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.cc2dcp1j
title: CCDC 2215757: Experimental Crystal Structure Determination

description: Related Article: yusuke yoshigoe|2022|CSD Communication|||
description: Related Article: Yusuke Yoshigoe, Keiichiro Hashizume, Shinichi Saito|2022|Dalton Trans.|51|17035|doi:10.1039/D2DT03303H
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.cc2dlxsq
title: CCDC 2222724: Experimental Crystal Structure Determination

description: Related Article: Lia̅na Orola, Anatoly Mishnev, Dmitrijs Stepanovs, Agris Be̅rziņš|2022|Cryst.Growth Des.|23|873|doi:10.1021/acs.cgd.2c01114
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: 10.15783/c7d01r
title: CRAWDAD dataset sunysb/mobisteer (v. 2007-06-30)

description: This data set includes data traces that were collected from a moving car 
equipped with an electronically steerable directinal antenna. We drove 
the car in two different environments in Stony Brook University campus 
- Apartment Complex and Parking lot.
description: Changelog: the initial version
DOI: 10.15783/c73s3n
title: CRAWDAD dataset cambridge/haggle (v. 2006-09-15)

description: This data includes a number of traces of Bluetooth sightings by groups of users carrying small devices (iMotes) for a number of days - in office environments, conference environments, and city environments.
description: Changelog: The trace cambridge/haggle/imote/content was added.
DOI: 10.5281/zenodo.7268083
title: Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon

description: The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022). All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.) The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times. Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right. <br> The resize command used was: <br> #to resize and then quarter<br> #Dir structure is:<br> # --Desktop<br> # |- originals<br> # |- resized<br> # |- quarters `cd originals`<br> `mogrify -resize 2000x2000 -path ../resized *.jpg` #then quarter them<br> `cd ..`<br> `cd resized` `mogrify -crop 2x2@ +repage -path ../quarters *.jpg` For full size images, please download the jpegs directly from NOAA.
DOI: 10.5281/zenodo.7628732
title: Doodleverse/Segmentation Zoo Res-UNet model for NOAA ERI/4-class segmentation of RGB 512x512 images

description: This Residual-UNet model is trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. The dataset is available here: https://doi.org/10.5281/zenodo.7268082 Models have been created using Segmentation Gym: Code - https://github.com/Doodleverse/segmentation_gym Paper - https://doi.org/10.1029/2022EA002332 The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 4, corresponding to 4 classes: water bare sediment vegetation development (roads, buildings, power lines, parking lots, etc.) Included here are 6 files with the same root name: '.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. '.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`. '_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` '.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` '.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py` '_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 Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU
description: {"references": ["https://doi.org/10.5281/zenodo.7268083", "https://doi.org/10.1029/2022EA002332"]}
DOI: 10.5281/zenodo.7628733
title: Doodleverse/Segmentation Zoo Res-UNet model for NOAA ERI/4-class segmentation of RGB 512x512 images

description: This Residual-UNet model is trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. The dataset is available here: https://doi.org/10.5281/zenodo.7268082 Models have been created using Segmentation Gym: Code - https://github.com/Doodleverse/segmentation_gym Paper - https://doi.org/10.1029/2022EA002332 The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 4, corresponding to 4 classes: water bare sediment vegetation development (roads, buildings, power lines, parking lots, etc.) Included here are 5 files with the same root name: '.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. '.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`. '_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` '.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` '.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py`
description: {"references": ["https://doi.org/10.5281/zenodo.7268083", "https://doi.org/10.1029/2022EA002332"]}
DOI: 10.5281/zenodo.7631354
title: Doodleverse/Segmentation Zoo Res-UNet model for NOAA ERI/4-class segmentation of RGB 512x512 images

description: This Residual-UNet model is trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. The dataset is available here: https://doi.org/10.5281/zenodo.7268082 Models have been created using Segmentation Gym: Code - https://github.com/Doodleverse/segmentation_gym Paper - https://doi.org/10.1029/2022EA002332 The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 4, corresponding to 4 classes: water bare sediment vegetation development (roads, buildings, power lines, parking lots, etc.) Included here are 6 files with the same root name: '.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. '.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`. '_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` '.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` '.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py` '_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 Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU
description: {"references": ["https://doi.org/10.5281/zenodo.7268083", "https://doi.org/10.1029/2022EA002332"]}
DOI: 10.5281/zenodo.3814451
title: Coral and substratum monitoring Cayo Arcas database

description: English<br> Coral and substratum monitoring data collected on April 26th to May 4th, 2019 in Cayo Arcas reef, southern Gulf of Mexico. Were sampled 17 sites with depths between 0.8-12.0 m. At each site, two transects (25 m) were placed. The benthic coverage category below the line every 25 cm was registered. Eighteen categories were used to describe the benthic coverage, which were established according to the categories of the Atlantic and Gulf Rapid Reef Assessment (AGRRA) project. At each sampling site, two-band transects 10 m long by 1 m wide were made to scleractinian coral monitoring. Were sampled a total area of 340 m2. All colonies greater than 4 cm in length were recorded. We recorded a total of 25 scleractinian coral species.<br> This is part of BDMY (Biodiversidad Marina de Yucatan) lab research.<br> Please, cite this data set as: "Padierna-López M, Hernández-Díaz YQ and Simões N (2020). Coral and substratum monitoring Cayo Arcas 2019 database.” Español<br> Datos de monitoreo de corales y sustrato recolectados del 26 de abril al 4 de mayo en el arrecife Cayo Arcas, sur del Golfo de México. Se muestrearon 17 sitios con profundidades entre 0.8-12.0 m. Se muestrearon dos transectos (25 m) por sitio. La categoría de cubierta bentónica bajo la línea fue registrada cada 25 cm. Se utilizaron dieciocho categorías para describir la cubierta bentónica, que fueron establecidos de acuerdo con las categorías del proyecto Atlantic and Gulf Reef Assessment (AGRRA). El monitoreo de corales escleractinios para cada sitio de muestreo se realizó mediante dos transectos de banda de 10 m de largo por 1 m de ancho. Se muestreó un área total de 340m2. Se registraron todas las colonias mayores a 4 cm de longitud. Registramos un total de 25 especies de coral escleractinio.<br> Esta información es parte del laboratorio de investigación BDMY (Biodiversidad Marina de Yucatán).<br> Por favor, cite la información de esta base de datos como: "Padierna-López M, Hernández-Díaz YQ and Simões N (2020). Coral and substratum monitoring Cayo Arcas 2019 database.”<br> Agradecimientos: Universidad Nacional Autónoma de México (UNAM).<br> Unidad Multidisciplinaria de Docencia e Investigación-Sisal, Facultad de Ciencias, UNAM.<br> Harte Charitable Foundation.<br> Harte Research Institute for Gulf of Mexico Studies.<br> Estación de Investigación Oceanográfica Carmen. 7ma Zona Naval. Dirección General de Investigación y Desarrollo. Secretaría de Marina.<br> BI-04 Antares, Secretaría de Marina.<br> Capitán Antares BI-04: Cap. Frag. C. G. Jorge Abraham Martínez Vázquez, Secretaría de Marina.<br> Jefe de Campaña Oceanográfica: Tte. Nav. SMAM. Edwalt Hernández Torralba, Secretaría de Marina.
DOI: 10.5281/zenodo.3814452
title: Coral and substratum monitoring Cayo Arcas database

description: English<br> Coral and substratum monitoring data collected on April 26th to May 4th, 2019 in Cayo Arcas reef, southern Gulf of Mexico. Were sampled 17 sites with depths between 0.8-12.0 m. At each site, two transects (25 m) were placed. The benthic coverage category below the line every 25 cm was registered. Eighteen categories were used to describe the benthic coverage, which were established according to the categories of the Atlantic and Gulf Rapid Reef Assessment (AGRRA) project. At each sampling site, two-band transects 10 m long by 1 m wide were made to scleractinian coral monitoring. Were sampled a total area of 340 m2. All colonies greater than 4 cm in length were recorded. We recorded a total of 25 scleractinian coral species.<br> This is part of BDMY (Biodiversidad Marina de Yucatan) lab research.<br> Please, cite this data set as: "Padierna-López M, Hernández-Díaz YQ and Simões N (2020). Coral and substratum monitoring Cayo Arcas 2019 database.” Español<br> Datos de monitoreo de corales y sustrato recolectados del 26 de abril al 4 de mayo en el arrecife Cayo Arcas, sur del Golfo de México. Se muestrearon 17 sitios con profundidades entre 0.8-12.0 m. Se muestrearon dos transectos (25 m) por sitio. La categoría de cubierta bentónica bajo la línea fue registrada cada 25 cm. Se utilizaron dieciocho categorías para describir la cubierta bentónica, que fueron establecidos de acuerdo con las categorías del proyecto Atlantic and Gulf Reef Assessment (AGRRA). El monitoreo de corales escleractinios para cada sitio de muestreo se realizó mediante dos transectos de banda de 10 m de largo por 1 m de ancho. Se muestreó un área total de 340m2. Se registraron todas las colonias mayores a 4 cm de longitud. Registramos un total de 25 especies de coral escleractinio.<br> Esta información es parte del laboratorio de investigación BDMY (Biodiversidad Marina de Yucatán).<br> Por favor, cite la información de esta base de datos como: "Padierna-López M, Hernández-Díaz YQ and Simões N (2020). Coral and substratum monitoring Cayo Arcas 2019 database.”<br> Agradecimientos: Universidad Nacional Autónoma de México (UNAM).<br> Unidad Multidisciplinaria de Docencia e Investigación-Sisal, Facultad de Ciencias, UNAM.<br> Harte Charitable Foundation.<br> Harte Research Institute for Gulf of Mexico Studies.<br> Estación de Investigación Oceanográfica Carmen. 7ma Zona Naval. Dirección General de Investigación y Desarrollo. Secretaría de Marina.<br> BI-04 Antares, Secretaría de Marina.<br> Capitán Antares BI-04: Cap. Frag. C. G. Jorge Abraham Martínez Vázquez, Secretaría de Marina.<br> Jefe de Campaña Oceanográfica: Tte. Nav. SMAM. Edwalt Hernández Torralba, Secretaría de Marina.
DOI: 10.34740/kaggle/dsv/4979618
title: OntoUML/UFO Catalog
DOI: 10.6084/m9.figshare.20236458.v1
title: A Theoretical Model on the Generation and Transport of Aerosol in Rain

description: The experimental data, observation data and discussion and analysis data involved in the research are listed here.
DOI: 10.5517/ccdc.csd.cc2dlxtr
title: CCDC 2222725: Experimental Crystal Structure Determination

description: Related Article: Lia̅na Orola, Anatoly Mishnev, Dmitrijs Stepanovs, Agris Be̅rziņš|2022|Cryst.Growth Des.|23|873|doi:10.1021/acs.cgd.2c01114
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.cc2dly98
title: CCDC 2222740: Experimental Crystal Structure Determination

description: Related Article: Lia̅na Orola, Anatoly Mishnev, Dmitrijs Stepanovs, Agris Be̅rziņš|2022|Cryst.Growth Des.|23|873|doi:10.1021/acs.cgd.2c01114
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.cc2d7llv
title: CCDC 2211837: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
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.cc2dnb2h
title: CCDC 2224066: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
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.cc2f47pj
title: CCDC 2238407: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, M.J. Carr|2023|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.cc2f47qk
title: CCDC 2238408: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, M.J. Carr|2023|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.cc2f47rl
title: CCDC 2238409: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, M.J. Carr|2023|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.cc2f47sm
title: CCDC 2238410: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, M.J. Carr|2023|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.