cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7824013
title: Data of industrial heat source between 2012 and 2021 using long-term Active Fire/Hotspot data in China

description: Industrial heat sources serve as crucial indicators of energy consumption levels and air pollution. Energy-intensive industries are facing substantial challenges in transforming and upgrading in China. Therefore, accurately identifying industrial heat source locations and monitoring their temporal patterns are becoming utmost importance. In this study, a long-term industrial heat source datasets between 2012 and 2021 in China using long-term Active Fire/Hotspots (ACF) data has been constructed to monitor and analyze large-scale industrial heat sources. Firstly, density segmentation method based on an improved k-means algorithm using long-term ACF data and spatial topological correlation analysis was conducted to build industrial heat sources. Then, 4410 industrial heat sources were obtained between 2012 and 2021 in China, with an identification accuracy of 95.08% by manual verification using high-resolution remote sensing images and point of interest (POI) data. Finally, the trend in the spatio-temporal variation of industrial heat sources was analyzed using long-term series. The results from 2012 to 2021 showed that the spatial distribution of industrial heat sources in China exhibits local aggregation and a gradual shift from east to west.And,the number of industrial heat sources in China has followed a trend of an initial increase from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform-related policies.The result of this study indicated the temporal variation of industrial heat sources, enhanced the accuracy of fire points category identification, and demonstrated potential for advancing energy efficiency, emission reduction, and sustainable development in China.
DOI: 10.5281/zenodo.10976313
title: SMCAD pretraining dataset

description: This dataset is the first part of the sheet metal part machining feature dataset (named the SMCAD dataset) from the paper "Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy".

It is a synthetic dataset. For more information, please refer to the original paper.
DOI: 10.15468/dl.7czy8y
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cipura insularis Ravenna"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137983-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eechdg
title: Occurrence Download

description: A dataset containing 172 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Langes Tannen"
 ]
}

 The dataset includes 172 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004441-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10963406
title: Simulations from a max-stable process on a 64 x 64 grid

description: Simulations from a max-stable process to reproduce the results in Section 5.2 of "Spatial Bayesian Neural Networks", https://doi.org/10.1016/j.spasta.2024.100825. For details on the process, spatial locations, etc., please refer to the paper.
DOI: 10.5281/zenodo.10976481
title: Dataset: Digital Transformation in Developing Countries (A Case Study)

description: This data represents the availability of data to support the publication of researchers' papers on digital transformation in developing countries (case study: Indonesia).
DOI: 10.15468/dl.f97h66
title: Occurrence Download

description: A dataset containing 1703418 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is FloraCAT: Banco de datos de los cormófitos de Cataluña"
 ]
}

 The dataset includes 1703418 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082545-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10963407
title: Simulations from a max-stable process on a 64 x 64 grid

description: Simulations from a max-stable process to reproduce the results in Section 5.2 of "Spatial Bayesian Neural Networks", https://doi.org/10.1016/j.spasta.2024.100825. For details on the process, spatial locations, etc., please refer to the paper.
DOI: 10.5281/zenodo.10976312
title: SMCAD pretraining dataset

description: This dataset is the first part of the sheet metal part machining feature dataset (named the SMCAD dataset) from the paper "Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy".

It is a synthetic dataset. For more information, please refer to the original paper.
DOI: 10.15468/dl.jpx2nt
title: Occurrence Download

description: A dataset containing 399 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cienfuegosia digitata Cav."
 ]
}

 The dataset includes 399 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137886-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10815560
title: Results from simulated drift trajectories of driting Fish Aggregating Devices used in Figures for paper Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles

description: Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtleshttp://doi.org/10.5281/zenodo.10815559

Results data descriptionThe above doi contains simulation output data generated from passive drift simulations of drifting FADs in the Pacific ocean. We refer the reader to the main text of the paper for details and definitions of the different zones and simulation experiments.

All data files are matrices stored in comma-delimited files (.csv), where the first provides the column names of the matrix, and the first column provides the row names. The first cell in row 1, column 1 is simply a placement value that describes the nature of the matrix. 

When this value is a hash symbol (#), it denotes that the matrix represents a spatial density of virtual particles under a particular drift scenario and over a particular period of time. Column names provide the latitude indices of each cell, and row names are the longitude indices. Values are the proportion of all particules in the domain that passed through this cell, during the drift-time window of this results file (see below).

When the first cell value is a hash followed by a code (e.g. # EqZ), it denotes that the file represents a connectivity matrix between the zones given by the code (e.g. Equatorial Zones EqZ or Fishing Zones FZ) and defined in the row names of the matrix, and the turtle zones defined by the column names. Values are the proportion of particules beginning the zone defined by the row name at the start of the simulation, which are now present in the zone defined by the column name.

Files name follow a convention describing the deployment and drift-time scenario they represent.

For spatial density matrices:[Origin Zones]_[Subset]_Density_[Drift Time]_....csv

For connectivity/transition matrices:[Origin Zones]_[Drift Time]_[Number of Simulations Run]_TransitionMatrix_....csv
DOI: 10.15468/dl.rxogif
title: Occurrence Download

description: A dataset containing 3557 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Specimen"
 ]
 },
 "Country is Mexico",
 "Year 1970-2017",
 "StateProvince is tabasco",
 "TaxonKey is Chordata"
 ]
}

 The dataset includes 3557 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004441-171025204727650/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10815559
title: Results from simulated drift trajectories of driting Fish Aggregating Devices used in Figures for paper Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles

description: Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtleshttps://zenodo.org/doi/10.5281/zenodo.10815559

Results data descriptionThe above doi contains simulation output data generated from passive drift simulations of drifting FADs in the Pacific ocean. We refer the reader to the main text of the paper for details and definitions of the different zones and simulation experiments.

All data files are matrices stored in comma-delimited files (.csv), where the first provides the column names of the matrix, and the first column provides the row names. The first cell in row 1, column 1 is simply a placement value that describes the nature of the matrix. 

When this value is a hash symbol (#), it denotes that the matrix represents a spatial density of virtual particles under a particular drift scenario and over a particular period of time. Column names provide the latitude indices of each cell, and row names are the longitude indices. Values are the proportion of all particules in the domain that passed through this cell, during the drift-time window of this results file (see below).

When the first cell value is a hash followed by a code (e.g. # EqZ), it denotes that the file represents a connectivity matrix between the zones given by the code (e.g. Equatorial Zones EqZ or Fishing Zones FZ) and defined in the row names of the matrix, and the turtle zones defined by the column names. Values are the proportion of particules beginning the zone defined by the row name at the start of the simulation, which are now present in the zone defined by the column name.

Files name follow a convention describing the deployment and drift-time scenario they represent.

For spatial density matrices:[Origin Zones]_[Subset]_Density_[Drift Time]_....csv

For connectivity/transition matrices:[Origin Zones]_[Drift Time]_[Number of Simulations Run]_TransitionMatrix_....csv
DOI: 10.15468/dl.mwamns
title: Occurrence Download

description: A dataset listing the 4448 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Zimbabwe",
 "Geometry POLYGON((25.16065 -17.784,25.83868 -18.87049,26.26347 -19.6057,27.24375 -20.10401,28.5508 -19.80176,27.87277 -18.88682,26.37783 -18.40485,26.03473 -17.9964,25.63445 -17.80851,25.16065 -17.784))"
 ]
}

 The dataset's 4448 records were derived from 160 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137879-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10889056
title: Data from "Toward vanishing droplet friction on repellent surfaces"

description: Data, examples of raw experimental movies, and MATLAB codes from "Toward vanishing droplet friction on repellent surfaces", Backholm et al. PNAS (2024). Please see the README files for all details about the files. The datasets needed to plot Figs. 1C, 2C, 3C, 4F, 4G, 5A, 5D, and 5E of the article are published in the SI file of the PNAS paper.
DOI: 10.5281/zenodo.10889057
title: Data from "Toward vanishing droplet friction on repellent surfaces"

description: Data, examples of raw experimental movies, and MATLAB codes from "Toward vanishing droplet friction on repellent surfaces", Backholm et al. PNAS (2024). Please see the README files for all details about the files. The datasets needed to plot Figs. 1C, 2C, 3C, 4F, 4G, 5A, 5D, and 5E of the article are published in the SI file of the PNAS paper.
DOI: 10.5281/zenodo.10976656
title: Results from simulated drift trajectories of driting Fish Aggregating Devices used in Figures for paper Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles

description: Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtleshttps://zenodo.org/doi/10.5281/zenodo.10815559

Results data descriptionThe above doi contains simulation output data generated from passive drift simulations of drifting FADs in the Pacific ocean. We refer the reader to the main text of the paper for details and definitions of the different zones and simulation experiments.

All data files are matrices stored in comma-delimited files (.csv), where the first provides the column names of the matrix, and the first column provides the row names. The first cell in row 1, column 1 is simply a placement value that describes the nature of the matrix. 

When this value is a hash symbol (#), it denotes that the matrix represents a spatial density of virtual particles under a particular drift scenario and over a particular period of time. Column names provide the latitude indices of each cell, and row names are the longitude indices. Values are the proportion of all particules in the domain that passed through this cell, during the drift-time window of this results file (see below).

When the first cell value is a hash followed by a code (e.g. # EqZ), it denotes that the file represents a connectivity matrix between the zones given by the code (e.g. Equatorial Zones EqZ or Fishing Zones FZ) and defined in the row names of the matrix, and the turtle zones defined by the column names. Values are the proportion of particules beginning the zone defined by the row name at the start of the simulation, which are now present in the zone defined by the column name.

Files name follow a convention describing the deployment and drift-time scenario they represent.

For spatial density matrices:[Origin Zones]_[Subset]_Density_[Drift Time]_....csv

For connectivity/transition matrices:[Origin Zones]_[Drift Time]_[Number of Simulations Run]_TransitionMatrix_....csv
DOI: 10.5281/zenodo.10976832
title: AI research space conditions country academic performance

description: Data used for generating AI research space
DOI: 10.15468/dl.pnk1nd
title: Occurrence Download

description: A dataset containing 895 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "TaxonKey is Tetrastigma (Miq.) Planch."
 ]
}

 The dataset includes 895 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004441-171124123535762/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10976412
title: Modelación de la distribución espacial de recursos marinos de Baja California y Yucatán.

description: Se generaron modelos de distribución de 235 especies de interés pesquero en ambas penínsulas y zonas aledañas además de proyecciones de distribución bajo los escenarios SSP126 y SSP585 para identificar que especies son las más vulnerables a cambiar su distribución por el cambio climático y determinar espacialmente las regiones pesqueras que tendrán los mayores impactos por la llegada de nuevas especies y la migración de otras.
DOI: 10.5281/zenodo.10976411
title: Modelación de la distribución espacial de recursos marinos de Baja California y Yucatán.

description: Se generaron modelos de distribución de 235 especies de interés pesquero en ambas penínsulas y zonas aledañas además de proyecciones de distribución bajo los escenarios SSP126 y SSP585 para identificar que especies son las más vulnerables a cambiar su distribución por el cambio climático y determinar espacialmente las regiones pesqueras que tendrán los mayores impactos por la llegada de nuevas especies y la migración de otras.
DOI: 10.5281/zenodo.10976709
title: Ecological niche models for focal SARA-listed species in British Columbia
DOI: 10.15156/bio/sh0964582.10fu
title: SH0964582.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0964583.10fu
title: SH0964583.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0964584.10fu
title: SH0964584.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0964585.10fu
title: SH0964585.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17633/rd.brunel.24551791
title: Validity of a wrist-worn consumer-grade wearable device for measuring energy expenditure, sedentary behaviour and physical activity in manual wheelchair users with spinal cord injury

description: This dataset underpins the analysis within a project that evaluated the validity of a consumer-grade wearable for estimating energy expenditure, sedentary behaviour and physical activity in manual wheelchair users with spinal cord injury. The dataset includes raw accelerometry data from the consumer-based device (zTrack) and a research grade device (ActiGraph GT9X), in addition to raw energy expenditure data as measured by the Cosmed K5.
DOI: 10.17633/rd.brunel.24551791.v1
title: Validity of a wrist-worn consumer-grade wearable device for measuring energy expenditure, sedentary behaviour and physical activity in manual wheelchair users with spinal cord injury

description: This dataset underpins the analysis within a project that evaluated the validity of a consumer-grade wearable for estimating energy expenditure, sedentary behaviour and physical activity in manual wheelchair users with spinal cord injury. The dataset includes raw accelerometry data from the consumer-based device (zTrack) and a research grade device (ActiGraph GT9X), in addition to raw energy expenditure data as measured by the Cosmed K5.
DOI: 10.15156/bio/sh0964586.10fu
title: SH0964586.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.