cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.gfb4nj
title: Occurrence Download

description: A dataset containing 307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is one of (North America, South America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Technomyrmex Mayr, 1872"
 ]
}

 The dataset includes 307 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078986-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yavpap
title: Occurrence Download

description: A dataset containing 323 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lynx pardinus (Temminck, 1827)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 323 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078987-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ke6h6p
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Ommatoiulus terulensis Ceuca, 1974",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078988-240626123714530/datasets/export for details.
DOI: 10.15468/dl.yjn65e
title: Occurrence Download

description: A dataset containing 1671 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Caracterización de fauna y flora asociada a bosque seco tropical en la OMEC Las Guacharacas, municipio de Piojó, Atlántico"
 ]
}

 The dataset includes 1671 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078989-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m2zn7t
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Ommatoiulus armatus (Verhoeff, 1910)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078991-240626123714530/datasets/export for details.
DOI: 10.15468/dl.5zpxr7
title: Occurrence Download

description: A dataset containing 11583 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capra pyrenaica Schinz, 1838",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 11583 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078990-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q5482q
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Ommatoiulus demangei Vicente & Rodriguez, 1992",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078992-240626123714530/datasets/export for details.
DOI: 10.15468/dl.jupfmx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capra pyrenaica pyrenaica",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078993-240626123714530/datasets/export for details.
DOI: 10.15468/dl.bq9vsc
title: Occurrence Download

description: A dataset containing 310 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is one of (North America, South America)",
 "TaxonKey is Technomyrmex Mayr, 1872"
 ]
}

 The dataset includes 310 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078995-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rqn8ye
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Ommatoiulus tridentifer Ceuca, 1972",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078996-240626123714530/datasets/export for details.
DOI: 10.15468/dl.38ucwc
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pipistrellus maderensis (Dobson, 1878)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078997-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13346120
title: Data and code for "Design Principles for Energy Transfer in the Photosystem II Supercomplex from Kinetic Transition Networks"

description: Data and code for "Design Principles for Energy Transfer in the Photosystem II Supercomplex from Kinetic Transition Networks".
DOI: 10.5281/zenodo.13346121
title: Data and code for "Design Principles for Energy Transfer in the Photosystem II Supercomplex from Kinetic Transition Networks"

description: Data and code for "Design Principles for Energy Transfer in the Photosystem II Supercomplex from Kinetic Transition Networks".
DOI: 10.15468/dl.ucgpzx
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Belize",
 "DatasetKey is one of (Tropicos MO Specimen Data, NMNH Extant Specimen Records (USNM, US), Digitalización y Sistematización de las Colecciones Biológicas Nacionales del Instituto de Biología, UNAM (Plantas vasculares), iNaturalist Research-grade Observations, Árboles de Chiapas: registro georreferenciado de los ejemplares depositados en el herbario de la Academia de Ciencias de California (CAS), Tropicos Specimens Non-MO, CAS Botany (BOT), Computarización del Herbario ENCB, IPN. Fase IV. Base de datos de la familia Pinaceae y de distintas familias de la clase Magnoliopsida depositadas en el Herbario de la Escuela Nacional de Ciencias Biológicas, IPN, Natural History Museum (London) Collection Specimens, Digitalización y sistematización de las colecciones biológicas del INECOL (Herbario))",
 "OccurrenceStatus is Present",
 "TaxonKey is Louteridium donnell-smithii S.Watson"
 ]
}

 The dataset includes 52 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078998-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yuka3x
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Ommatoiulus ilicis (Brölemann, 1897)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 15 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078999-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6yk3pn
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alytes dickhilleni Arntzen & García-París, 1995",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 137 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079000-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.23788104
title: Solar wind implantation

description: This item includes the figures and original data files for the JGR-planet manuscript [2023JE008024]. There are 15 data files in all that are in .dat and .plt format, which are obtained by the global MHD simulations and can be opened and visualized by Tecplot software. In particular, the first data file (3D_LMM.plt) is related to the panel of Figure 1a, and the second and third data files are assocaited with the panels of Figure 1b-1f. The 4th data file is used for Figure 2, and the 6th to the 12th data files are associated with Figure 3. The 13th and 14the data files are related to Figure 4, and the 15the data file is used for Figure 5.
DOI: 10.25442/hku.23682528
title: Supporting data for "Machine learning for natural hazard data analyses and data-driven geotechnical engineering applications"

description: Extensive exploration of novel machine learning (ML) technologies within the domain of geoscience has been actively pursued. However, the integration of ML with geoscience is in its early stages and unevenly advancing. My Ph.D. thesis entitled "Machine learning for natural hazard data analyses and data-driven geotechnical engineering applications" aims to contribute to this interdisciplinary research field by taking a modest step forward. The thesis concentrates on five specific research topics: (1) classic ML for classification, (2) classic ML for regression, (3) supervised learning by convolutional neural network (CNN), (4) unsupervised learning by CNN, and (5) deep learning (DL) with 3D input data. These five research topics cover a wide range of contents, including (1) boulder fall volume range prediction, (2) seismic source localization, (3) rock type classification, (4) low-light rock image enhancement, and (5) 3D point cloud filtering. They collectively contribute to the integration of ML in geoscience. This dataset serves the abovementioned studies. Specifically, Dataset (Chapter 3) contains the datasets used for boulder fall volume range prediction by machine learning algorithms. Dataset (Chapter 4) contains the datasets used for source localization of acoustic emission events using the support vector machine and iterative methods. (Chapter 5) contains the datasets used for automatic rock type classification using HKUDES_Net, a Python program. (Chapter 6) contains the datasets used for low-light rock image enhancement. (Chapter 7) contains the datasets used for automatic and point-wise noise filtering for 3D point clouds. More details are found in the README.txt file.
DOI: 10.25442/hku.23682528.v1
title: Supporting data for "Machine learning for natural hazard data analyses and data-driven geotechnical engineering applications"

description: Extensive exploration of novel machine learning (ML) technologies within the domain of geoscience has been actively pursued. However, the integration of ML with geoscience is in its early stages and unevenly advancing. My Ph.D. thesis entitled "Machine learning for natural hazard data analyses and data-driven geotechnical engineering applications" aims to contribute to this interdisciplinary research field by taking a modest step forward. The thesis concentrates on five specific research topics: (1) classic ML for classification, (2) classic ML for regression, (3) supervised learning by convolutional neural network (CNN), (4) unsupervised learning by CNN, and (5) deep learning (DL) with 3D input data. These five research topics cover a wide range of contents, including (1) boulder fall volume range prediction, (2) seismic source localization, (3) rock type classification, (4) low-light rock image enhancement, and (5) 3D point cloud filtering. They collectively contribute to the integration of ML in geoscience. This dataset serves the abovementioned studies. Specifically, Dataset (Chapter 3) contains the datasets used for boulder fall volume range prediction by machine learning algorithms. Dataset (Chapter 4) contains the datasets used for source localization of acoustic emission events using the support vector machine and iterative methods. (Chapter 5) contains the datasets used for automatic rock type classification using HKUDES_Net, a Python program. (Chapter 6) contains the datasets used for low-light rock image enhancement. (Chapter 7) contains the datasets used for automatic and point-wise noise filtering for 3D point clouds. More details are found in the README.txt file.
DOI: 10.6084/m9.figshare.23788235
title: Additional file 1 of OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

description: Additional file 1. Benchmark script. R code used for benchmarking four overlap tools in terms of processing time and number of overlaps.
DOI: 10.6084/m9.figshare.23788235.v1
title: Additional file 1 of OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

description: Additional file 1. Benchmark script. R code used for benchmarking four overlap tools in terms of processing time and number of overlaps.
DOI: 10.6084/m9.figshare.23788247
title: Additional file 5 of OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

description: Additional file 5: Table S2. OGRE tabular output 1. OGRE’s tabular output of gene- TFBS overlap, where each gene is listed with all overlapping TFBS using input data from [23].
DOI: 10.6084/m9.figshare.23788247.v1
title: Additional file 5 of OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

description: Additional file 5: Table S2. OGRE tabular output 1. OGRE’s tabular output of gene- TFBS overlap, where each gene is listed with all overlapping TFBS using input data from [23].
DOI: 10.6084/m9.figshare.23788250
title: Additional file 6 of OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

description: Additional file 6: Table S3. OGRE tabular output 2. Detailed information on each occurring overlap including genomic coordinates for TFBS using input data from [23].
DOI: 10.6084/m9.figshare.23788250.v1
title: Additional file 6 of OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

description: Additional file 6: Table S3. OGRE tabular output 2. Detailed information on each occurring overlap including genomic coordinates for TFBS using input data from [23].
DOI: 10.6084/m9.figshare.23788252.v1
title: Additional file 1 of Homeostasis of cellular amino acids in Acanthamoeba castellanii exposed to different media under amoeba-bacteria coculture conditions

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.23788252
title: Additional file 1 of Homeostasis of cellular amino acids in Acanthamoeba castellanii exposed to different media under amoeba-bacteria coculture conditions

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.23788255
title: Additional file 2 of Homeostasis of cellular amino acids in Acanthamoeba castellanii exposed to different media under amoeba-bacteria coculture conditions

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.23788255.v1
title: Additional file 2 of Homeostasis of cellular amino acids in Acanthamoeba castellanii exposed to different media under amoeba-bacteria coculture conditions

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.23788283
title: Additional file 2 of Sociodemographic, mental health, and physical health factors associated with participation within re-contactable mental health cohorts: an investigation of the GLAD Study

description: Additional file 2. Output from the statistical models.
DOI: 10.6084/m9.figshare.23788283.v1
title: Additional file 2 of Sociodemographic, mental health, and physical health factors associated with participation within re-contactable mental health cohorts: an investigation of the GLAD Study

description: Additional file 2. Output from the statistical models.
DOI: 10.6084/m9.figshare.23788188
title: <strong>Data for the paper</strong> upload in July 2023

description: Mainly including: 1) The forecasting errors of the streamflow into Hongze Lake and Luoma Lake; 2) The forecasting errors of the water demand in the water receiving areas inside Jiangsu Province of the South-to-North Water Transfer East Route Project in China. The streamflow forecast was performed using the method with a hybrid physical process (SWAT) and mathematical statistic (vine copula function). The water demand forecast was performed using the Penman-Monteith equation and LSTM.
DOI: 10.26188/23597013.v1
title: <strong>Data available for "Identification of herbarium specimen sheet components from high-resolution images using deep learning": Annotations for selected MELU specimen sheet digital images</strong>

description: Data Available for the paper: "<strong>Identification of herbarium specimen sheet components from high-resolution images using deep learning</strong>", by Karen M Thompson, Robert Turnbull, Emily Fitzgerald, Joanne L Birch <br> These are specific annotations of selected specimen sheet digital images from the MELU collection (Melbourne University Herbarium). MELU collection images are available: https://online.herbarium.unimelb.edu.au/ <br> These annotations for use in a YOLO object detection model. <br> The format of this file is a .ZIP containing a .TXT for each image annotated. Each .TXT file will have a row for each annotated element. Eg. "4 0.064133 0.414363 0.072186 0.309392" (i) first element is an integer identifying the object type: 0 small database label 1 handwritten data<br> 2 stamp<br> 3 annotation label<br> 4 scale<br> 5 swing tag<br> 6 full database label<br> 7 database label<br> 8 swatch<br> 9 institutional label<br> 10 number (ii) then the following four elements are the corner coordinates for the bounding box <br> Other information available to support this paper: (1) annotations for benchmark dataset (noting these are specific to the MELU trained model) (2) MELU-trained sheet-component object detection model weights (for application in YOLOv5)
DOI: 10.26188/23597013
title: <strong>Data available for "Identification of herbarium specimen sheet components from high-resolution images using deep learning": Annotations for selected MELU specimen sheet digital images</strong>

description: Data Available for the paper: "<strong>Identification of herbarium specimen sheet components from high-resolution images using deep learning</strong>", by Karen M Thompson, Robert Turnbull, Emily Fitzgerald, Joanne L Birch <br> These are specific annotations of selected specimen sheet digital images from the MELU collection (Melbourne University Herbarium). MELU collection images are available: https://online.herbarium.unimelb.edu.au/ <br> These annotations for use in a YOLO object detection model. <br> The format of this file is a .ZIP containing a .TXT for each image annotated. Each .TXT file will have a row for each annotated element. Eg. "4 0.064133 0.414363 0.072186 0.309392" (i) first element is an integer identifying the object type: 0 small database label 1 handwritten data<br> 2 stamp<br> 3 annotation label<br> 4 scale<br> 5 swing tag<br> 6 full database label<br> 7 database label<br> 8 swatch<br> 9 institutional label<br> 10 number (ii) then the following four elements are the corner coordinates for the bounding box <br> Other information available to support this paper: (1) annotations for benchmark dataset (noting these are specific to the MELU trained model) (2) MELU-trained sheet-component object detection model weights (for application in YOLOv5)
DOI: 10.26188/23597034
title: <strong>Data available for "Identification of herbarium specimen sheet components from high-resolution images using deep learning": YOLOv5 Best model weights for MELU trained object detection model</strong>

description: Data Available for the paper: "<strong>Identification of herbarium specimen sheet components from high-resolution images using deep learning</strong>", by Karen M Thompson, Robert Turnbull, Emily Fitzgerald, Joanne L Birch <br> This is the 'best' weights for use in a YOLOv5 object detection model. <br> Other information available to support this paper: (1) annotations for selected MELU specimen sheet digital images (2) annotations for benchmark dataset (noting these are specific to the MELU trained model)
DOI: 10.26188/23597034.v1
title: <strong>Data available for "Identification of herbarium specimen sheet components from high-resolution images using deep learning": YOLOv5 Best model weights for MELU trained object detection model</strong>

description: Data Available for the paper: "<strong>Identification of herbarium specimen sheet components from high-resolution images using deep learning</strong>", by Karen M Thompson, Robert Turnbull, Emily Fitzgerald, Joanne L Birch <br> This is the 'best' weights for use in a YOLOv5 object detection model. <br> Other information available to support this paper: (1) annotations for selected MELU specimen sheet digital images (2) annotations for benchmark dataset (noting these are specific to the MELU trained model)
DOI: 10.6084/m9.figshare.23558628
title: Data for the study on epidemology of maxillofacial trauma

description: Data includes various types of facial fractures that are seen in monsoon and non monsoon season in a hospital in manipal.
DOI: 10.6084/m9.figshare.23558628.v1
title: Data for the study on epidemology of maxillofacial trauma

description: Data includes various types of facial fractures that are seen in monsoon and non monsoon season in a hospital in manipal.