cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15128/r18k71nh13g
title: Anti-Biofouling Functional Surfaces for Marine Aquaculture [Dataset]

description: Aquaculture is a global scale industry providing sustainable production of protein-rich foods required to feed the growing world population. Microalgae cultivation in aquaculture bioreactors can exude extracellular polymeric substances leading to biofouling of infrastructure and increased disease risk. A structure–behaviour relationship was developed by examining how the surface wettability of a range of functional nanocoatings impacts the extent of biofouling during exposure to continuous microalgae culture under normal hydrodynamic conditions. High wetting (hydrophilic) surfaces were found to reduce biofouling better than water repelling (hydrophobic) surfaces. Low toxicity in conjunction with antifouling behaviour was found for pulsed plasma poly(4-vinylpyridine) coated bioreactor surfaces (water contact angle = 38 ± 5 °) towards the marine microalgal species Chaetoceros calcitrans, Chaetoceros mulleri, and Tisochrysis lutea (T-Iso), which are commonly grown as aquaculture food.
DOI: 10.5281/zenodo.7448285
title: Homeostatic regulation through strengthening of neuronal network correlated synaptic inputs

description: Data from "Homeostatic regulation through strengthening of neuronal network correlated synaptic inputs" by Barnes SJ, Keller GB & Keck T.
DOI: 10.6084/m9.figshare.21731090.v2
title: Identification and Validation of Basement Membrane Related LncRNA Signatures as A Novel Prognostic Model for Hepatocellular Carcinoma

description: These data sets are used in the benchmark study presented in the paper "Identification and Validation of Basement Membrane Related LncRNA Signatures as A Novel Prognostic Model for Hepatocellular Carcinoma" by Huajian Gu.
DOI: 10.5281/zenodo.7448324
title: Direct fabrication of arrays of Cu(In,Ga)Se2 micro solar cells by sputtering for micro-concentrator photovoltaics

description: Dataset for the article entitled "Direct fabrication of arrays of Cu(In,Ga)Se2 micro solar cells by sputtering for micro-concentrator photovoltaics".
DOI: 10.5281/zenodo.7448325
title: Direct fabrication of arrays of Cu(In,Ga)Se2 micro solar cells by sputtering for micro-concentrator photovoltaics

description: Dataset for the article entitled "Direct fabrication of arrays of Cu(In,Ga)Se2 micro solar cells by sputtering for micro-concentrator photovoltaics".
DOI: 10.5281/zenodo.6461437
title: Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda

description: Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda": <strong>Figure 6: </strong>Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country. <strong>Figure 8:</strong> Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).
description: The CARAMAL Project was funded by Unitaid (grant reference XM-DAC-30010-CHAIRAS)
DOI: 10.5281/zenodo.6461438
title: Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda

description: Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda": <strong>Figure 6: </strong>Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country. <strong>Figure 8:</strong> Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).
description: The CARAMAL Project was funded by Unitaid (grant reference XM-DAC-30010-CHAIRAS)
DOI: 10.6073/pasta/0e4d75453560ab5c085c9b547be68731
title: Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013

description: Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is just a catalog, please cite the original GenBank data, journal article, or related Arctic Data Center dataset as appropriate. Citation guidance for the journal article and related Arctic Data Center dataset is available on the respective publishers' websites.
DOI: 10.34740/kaggle/dsv/4732793
title: Correlation Comparison in Visualizations Dataset
DOI: 10.34740/kaggle/dsv/4732789
title: Correlation Comparison in Visualizations Dataset
DOI: 10.6084/m9.figshare.21735797.v1
title: Data Set from Evaluating Stage Motion for Automated Electron Microscopy

description: Dataset includes 311 *.bmp images acquired by the PNNL AutoEM system and calculations of various types of stage precision. Associated with the manuscript "Evaluating Stage Motion for Automated Electron Microscopy" by Kevin Fiedler, Matthew Olszta, Kayla Yano, Christina Doty, Derek Hopkins, Sarah Akers, and Steven Spurgeon.
DOI: 10.5281/zenodo.7388007
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) 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 5-band (RGB+NIR+SWIR) images of coasts.</strong></em> These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) 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.7388008
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) 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 5-band (RGB+NIR+SWIR) images of coasts.</strong></em> These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) 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.7448390
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) 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 5-band (RGB+NIR+SWIR) images of coasts.</strong></em> These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) 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.7384254
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) 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 3-band (RGB) images of coasts.</strong></em> These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7384242 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, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384242
DOI: 10.5281/zenodo.7384255
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) 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 3-band (RGB) images of coasts.</strong></em> These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7384242 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, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384242