cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6964830
title: GPS fixes for foraging Southern Giant Petrels (Macronectes giganteus) breeding in Nelson Island, Maritime Antarctic Peninsula (December 2019-January 2020)

description: In the ZIP file there are two files. One is a .csv with GPS fixes processed using the adehabitatLT to identify events of low speed and high turn movements categorized as foraging. Points within a 250m radius from the breeding colony were excluded, and foraging trips were estimated based on the moment the animals crossed such a radius. Each foraging point was classified as being on land, within 5kms of coastline or on pelagic waters, and on different levels of Sea Ice Cover (from Bremen Sea Ice Remote Sensing data browser https://seaice.uni-bremen.de/databrowser/, Spreen, G., L. Kaleschke, and G.Heygster (2008), Sea ice remote sensing using AMSR-E 89 GHz channels J. Geophys. Res.,vol. 113, C02S03, doi:10.1029/2005JC003384). Second file is a R Script with details of the methods for processing the data. Two types of devices were used: Kite-M (Ecotone, NAV individuals) and Axy Trek Marine (Techno SmArt, HP individuals) configurated to collect points each 5 and 10 minutes, respectively. Funded by Fondecyt Iniciación (111 801 75) and Programa Áreas Marinas Protegidas del Instituto Antártico Chileno (24 03 052).
DOI: 10.5281/zenodo.6950479
title: Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 and Landsat RGB images of coasts. CoastTrain-only version

description: <strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 and Landsat RGB images of coasts.</strong> <strong>Based on Coast Train*** data</strong> These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 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` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 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>'_history.npz'</strong> files contain model training metrics One additional file, BEST_MODEL.txt, contains the name of the model with the highest validation accuracy References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://dbuscombe-usgs.github.io/CoastTrain/docs/Version%201:%20March%202022/data
DOI: 10.5281/zenodo.6950478
title: Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 and Landsat RGB images of coasts. CoastTrain-only version

description: <strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 and Landsat RGB images of coasts.</strong> <strong>Based on Coast Train*** data</strong> These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 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` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 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>'_history.npz'</strong> files contain model training metrics One additional file, BEST_MODEL.txt, contains the name of the model with the highest validation accuracy References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://dbuscombe-usgs.github.io/CoastTrain/docs/Version%201:%20March%202022/data
DOI: 10.5281/zenodo.6837154
title: Data for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"

description: Data for the main figures for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"
DOI: 10.5281/zenodo.6837153
title: Data for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"

description: Data for the main figures for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"
DOI: 10.6084/m9.figshare.20432277
title: Input raster datasets for an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle

description: These raster datasets are the inputs for a spatial water yield model applied to an 11 county area in the state of Florida's panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in the associated publication. The five input datasets required for this water yield analysis are: 1) a model of pine species basal area, named "ARSA_PineBA_10m" 2) a binary depth to water table raster named "DTW_cm_binary2" , and 3) three spatial aridity index raster dataset named "Aridity_Min", "Aridity_Max" and "Aridity_Mean", created from potential evapotranspiration, and precipitation raster datasets. The min max and mean codifiers relate to the range of aridity values found in our dataset of 7 year temporal range, from MODIS PET and PRISM percipitation yearly data. All input and output data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution.
DOI: 10.6084/m9.figshare.20432277.v1
title: Input raster datasets for an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle

description: These raster datasets are the inputs for a spatial water yield model applied to an 11 county area in the state of Florida's panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in the associated publication. The five input datasets required for this water yield analysis are: 1) a model of pine species basal area, named "ARSA_PineBA_10m" 2) a binary depth to water table raster named "DTW_cm_binary2" , and 3) three spatial aridity index raster dataset named "Aridity_Min", "Aridity_Max" and "Aridity_Mean", created from potential evapotranspiration, and precipitation raster datasets. The min max and mean codifiers relate to the range of aridity values found in our dataset of 7 year temporal range, from MODIS PET and PRISM percipitation yearly data. All input and output data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution.
DOI: 10.6084/m9.figshare.3761562.v2300
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.6964866
title: Neural Network for Determination of the Substrate Activation in Enzymes

description: 1. Multiwfn_lap.sh -- a Linux script to run calculation of the Laplacian of electron density grid in the plain of a nucleophile atom and a carbonyl group. 2. lapZero150DPI.py - a Python script for visualization of the 2D Laplacian of the electron density map 3. crop_image.py - a Python script that crops the image 4. 2500_crop_dataset_MolInf.ipynb - a Python notebook that trains the CNN with the dataset_crop_2500-MolInf 5. dataset_crop_2500-MolInf.7z - a dataset to train the CNN 6. model_crop_2500-MolInf.h5 - trained CNN, ready for utilization 7. validation_datasets.zip -- an archive that includes additional datasets for the neural network validation (complexes of the Mpro with substrates containing Ser, Thr and Pro at P2 and a complex of the NDM-1 and imipenem)
DOI: 10.5281/zenodo.6964865
title: Neural Network for Determination of the Substrate Activation in Enzymes

description: 1. Multiwfn_lap.sh -- a Linux script to run calculation of the Laplacian of electron density grid in the plain of a nucleophile atom and a carbonyl group. 2. lapZero150DPI.py - a Python script for visualization of the 2D Laplacian of the electron density map 3. crop_image.py - a Python script that crops the image 4. 2500_crop_dataset_MolInf.ipynb - a Python notebook that trains the CNN with the dataset_crop_2500-MolInf 5. dataset_crop_2500-MolInf.7z - a dataset to train the CNN 6. model_crop_2500-MolInf.h5 - trained CNN, ready for utilization 7. validation_datasets.zip -- an archive that includes additional datasets for the neural network validation (complexes of the Mpro with substrates containing Ser, Thr and Pro at P2 and a complex of the NDM-1 and imipenem)
DOI: 10.5281/zenodo.6964917
title: Global wetland CH4 emissions estimated by LPJ-wsl model for 1980-2021

description: This dataset contains two files that summarize the regional wetland methane emissions simulated by LPJ-wsl model for the period of 2000-2021. two estimates based on two climate forcing datasets, ground-based CRU and reanalysis-based MERRA2. The ground-based input dataset is a monthly climatic observation based on meteorological stations developed by Climatic Research Unit, University of East Anglia. The reanalysis-based climate dataset is a daily climatic dataset from 1 hourly reanalysis Modern-Era Retrospective analysis for Research and Applications Version 2. For more details about LPJ-wsl model, see Zhang et al., (2017, 2018). References: Zhang, Z., Zimmermann, N.E., Stenke, A., Li, X., Hodson, E.L., Zhu, G., Huang, C., Poulter, B., 2017. Emerging role of wetland methane emissions in driving 21st century climate change. Proceedings of the National Academy of Sciences 114, 9647–9652. https://doi.org/10.1073/pnas.1618765114 Zhang, Z., Zimmermann, N.E., Calle, L., Hurtt, G., Chatterjee, A., Poulter, B., 2018. Enhanced response of global wetland methane emissions to the 2015–2016 El Niño-Southern Oscillation event. Environmental Research Letters 13, 074009. https://doi.org/10.1088/1748-9326/aac939
DOI: 10.6069/s9mt-2j06
title: Maltodextrin binding protein (MBP) DEER data set
DOI: 10.7303/syn21513145
title: MSBB ATAC-seq
DOI: 10.5281/zenodo.6963421
title: GPI-anchored protein homolog IcFBR1 functions directly in morphogenesis of Isaria cicadae

description: <em>Isaria cicadae</em> is a famous edible and medicinal fungus in China and Asia. The molecular basis of morphogenesis and synnemal formation needs to be understood in more detail, because this is the main source of biomass production in <em>I. cicadae</em>. In the present study, a fruiting body formation-related gene with a glycosylphosphatidylinositol (GPI) anchoring protein (GPI-Ap) gene homolog, <em>IcFBR1</em>, was identified by screening random insertion mutants. Targeted deletion of <em>IcFBR1</em> resulted in abnormal, non-formation of synnemata, impairing aerial hyphae growth and sporulation. The <em>IcFBR1</em> mutants were defective in utilization of carbon sources with reduced polysaccharide contents and in the regulation of amylase and protease activities. Transcriptome analysis of <em>ΔIcfbr1</em> showed that <em>IcFBR1</em> deletion influenced 49 gene ontology terms, including 23 biological processes, nine molecular functions, and 14 cellular components. IcFBR1 is therefore necessary for regulating synnemal development, secondary metabolism, and nutrient utilization in this important edible and medicinal fungus. This is the first report illustrating that the function of IcFBR1 is associated with the synnemata in <em>I. cicadae</em>.
DOI: 10.5281/zenodo.6963420
title: GPI-anchored protein homolog IcFBR1 functions directly in morphogenesis of Isaria cicadae

description: <em>Isaria cicadae</em> is a famous edible and medicinal fungus in China and Asia. The molecular basis of morphogenesis and synnemal formation needs to be understood in more detail, because this is the main source of biomass production in <em>I. cicadae</em>. In the present study, a fruiting body formation-related gene with a glycosylphosphatidylinositol (GPI) anchoring protein (GPI-Ap) gene homolog, <em>IcFBR1</em>, was identified by screening random insertion mutants. Targeted deletion of <em>IcFBR1</em> resulted in abnormal, non-formation of synnemata, impairing aerial hyphae growth and sporulation. The <em>IcFBR1</em> mutants were defective in utilization of carbon sources with reduced polysaccharide contents and in the regulation of amylase and protease activities. Transcriptome analysis of <em>ΔIcfbr1</em> showed that <em>IcFBR1</em> deletion influenced 49 gene ontology terms, including 23 biological processes, nine molecular functions, and 14 cellular components. IcFBR1 is therefore necessary for regulating synnemal development, secondary metabolism, and nutrient utilization in this important edible and medicinal fungus. This is the first report illustrating that the function of IcFBR1 is associated with the synnemata in <em>I. cicadae</em>.
DOI: 10.34740/kaggle/dsv/4030147
title: Tales from the Pizza Guy
DOI: 10.6084/m9.figshare.19759195
title: Heart inflammation and piscine orthoreovirus genotype-1 in Pacific Canada Atlantic salmon net-pen farms: 2016-2019 - collected and generated data

description: Data supporting the study entitled "Heart inflammation and piscine orthoreovirus genotype-1 in Pacific Canada Atlantic salmon net-pen farms: 2016-2019" BMC Veterinary Research, 2022
DOI: 10.6084/m9.figshare.19759195.v1
title: Heart inflammation and piscine orthoreovirus genotype-1 in Pacific Canada Atlantic salmon net-pen farms: 2016-2019 - collected and generated data

description: Data supporting the study entitled "Heart inflammation and piscine orthoreovirus genotype-1 in Pacific Canada Atlantic salmon net-pen farms: 2016-2019" BMC Veterinary Research, 2022
DOI: 10.6084/m9.figshare.20436168.v1
title: GPI-anchored protein homolog IcFBR1 functions directly in morphogenesis of Isaria cicadae

description: <em>Isaria cicadae</em> is a famous edible and medicinal fungus in China and Asia. The molecular basis of morphogenesis and synnemal formation needs to be understood in more detail, because this is the main source of biomass production in <em>I. cicadae</em>. In the present study, a fruiting body formation-related gene with a glycosylphosphatidylinositol (GPI) anchoring protein (GPI-Ap) gene homolog, <em>IcFBR1</em>, was identified by screening random insertion mutants. Targeted deletion of <em>IcFBR1</em> resulted in abnormal, non-formation of synnemata, impairing aerial hyphae growth and sporulation. The <em>IcFBR1</em> mutants were defective in utilization of carbon sources with reduced polysaccharide contents and in the regulation of amylase and protease activities. Transcriptome analysis of <em>ΔIcfbr1</em> showed that <em>IcFBR1</em> deletion influenced 49 gene ontology terms, including 23 biological processes, nine molecular functions, and 14 cellular components. IcFBR1 is therefore necessary for regulating synnemal development, secondary metabolism, and nutrient utilization in this important edible and medicinal fungus. This is the first report illustrating that the function of IcFBR1 is associated with the synnemata in <em>I. cicadae</em>.
DOI: 10.7284/122907
title: Multiplexed data collected during research cruise WS16015 using a Miami VIDS instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122909
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise WS16015 using a Hawaii UHDAS instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122906
title: Anemometer data collected during research cruise WS16015 using a RMYoung 05106 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122904
title: Fluorometer data collected during research cruise WS16015 using a Turner Cyclops-7 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122905
title: Hygrometer data collected during research cruise WS16015 using a RMYoung 41372 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122897
title: Radiometer data collected during research cruise WS16015 using a Eppley PSP instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122898
title: Radiometer data collected during research cruise WS16015 using a Eppley TUV instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122902
title: Radiometer data collected during research cruise WS16015 using a Eppley PIR instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122901
title: Rain Gauge data collected during research cruise WS16015 using a OSI ORG-815 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122908
title: Singlebeam Echosounder data collected during research cruise WS16015 using a Knudsen 3260 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122899
title: Water Temperature data collected during research cruise WS16015 using a SeaBird SBE-38 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122900
title: TSG (Thermosalinograph) data collected during research cruise WS16015 using a SeaBird SBE-45 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122956
title: Multiplexed data collected during research cruise WS16108 using a Miami VIDS instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122958
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise WS16108 using a Hawaii UHDAS instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122955
title: Anemometer data collected during research cruise WS16108 using a RMYoung 05106 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122953
title: Fluorometer data collected during research cruise WS16108 using a Turner Cyclops-7 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122954
title: Hygrometer data collected during research cruise WS16108 using a RMYoung 41372 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122946
title: Radiometer data collected during research cruise WS16108 using a Eppley PSP instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122947
title: Radiometer data collected during research cruise WS16108 using a Eppley TUV instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122951
title: Radiometer data collected during research cruise WS16108 using a Eppley PIR instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122950
title: Rain Gauge data collected during research cruise WS16108 using a OSI ORG-815 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122957
title: Singlebeam Echosounder data collected during research cruise WS16108 using a Knudsen 3260 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122948
title: Water Temperature data collected during research cruise WS16108 using a SeaBird SBE-38 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/122949
title: TSG (Thermosalinograph) data collected during research cruise WS16108 using a SeaBird SBE-45 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/123048
title: Multiplexed data collected during research cruise WS16200 using a Miami VIDS instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/123050
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise WS16200 using a Hawaii UHDAS instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/123047
title: Anemometer data collected during research cruise WS16200 using a RMYoung 05106 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/123045
title: Fluorometer data collected during research cruise WS16200 using a Turner Cyclops-7 instrument system onboard the platform RV F.G. Walton Smith

description: Original (unprocessed) data collected during a Field_expedition