cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17129052
title: MSG SEVIRI datasets to deduce VNIR / MIR / TIR directional hot spot signatures

description: The datasets released aim to investigate directional anisotropy due to different solar-view geometries for the measured target in VNIR / MIR / TIR domains deduced from satellite observations of geostationary MSG SEVIRI instrument. This release contains monthly timeseries with specific daily time interval for a number of 13 land surface groups observed in March 2016, 2018 and 2024 by MSG SEVIRI full disk images, and reformatted accordingly into 13 HDF5 files then compressed into .bz2 files. All files include the land surfaces SEVIRI image coordinates, i.e. pixel line number and column number. In terms of physical quantities, the 2016 and 2018 datasets contain MSG SEVIRI VIS0.6, VIS0.8, NIR1.6 µm TOA reflectances and IR3.9, IR8.7, IR9.7, IR10.8, IR12.0 µm TOA brightness temperatures (except for March 2018 the dataset of IR8.7 µm channel is TOA radiance instead of TOA brightness temperature), as well as LST retrieved. The 2024 dataset file includes only LST as physical quantity. The time series start on 1st March and ended on 31st March with each daily interval ranging from 11:00 to 14:45 with 15 minutes SEVIRI sampling rate. In addition, the auxiliary data within all files contain SEVIRI view zenith angles and view azimuth angles associated with the land surface selected. 2018 and 2024 datasets include as well the associated pixel latitudes and longitudes for optional use. In addition, all data files include the associated IR10.8, IR12.0 µm and broadband emissivities of daily sampling rate from 1st March to 31st March in 2016, 2018 and 2024 respectively. Information describing each dataset within the HDF5 files is stored as its attributes, for instance, the dataset identity or name, units of measured quantities, scaling factor, calibration slope and offset, default value, definition of each dimension of the dataset array, the starting day & time and ending day & time of the time series, etc.
DOI: 10.15468/dl.c4tskw
title: Occurrence Download

description: A dataset containing 1304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Longitarsus jacobaeae"
 ]
}

 The dataset includes 1304 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006682-250914085247600/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.29513534
title: Mapping predicted dung beetle diversity in the lowland tropical forests of Borneo by utilizing indices derived from a satellite image

description: In tropical regions, assessing biodiversity across expansive areas through direct data collection presents significant challenges. The advancement of remote sensing techniques for mapping predicted biodiversity offers a viable solution. To map predicted dung beetle diversity and its associated ecological functions using satellite images, we selected optimal multivariate regression models to predict overall species richness and abundance, forest species richness and abundance, biomass (logarithmic dry weight) associating ecological functions of the beetle, Simpson’s diversity index (1/D), and forest community integrity (measured as the score on axis 1 of nonmetric multidimensional scaling ordination analysis), utilizing the Akaike information criterion (AIC). The explanatory variables were extracted from a SPOT-5 satellite image utilizing varying grid cell configurations up to 290 m<sup>2</sup>. The objective variables consisted of dung beetle collection data from intact, burnt, or artificially degraded natural forests; plantation forests; and grasslands within a 20 × 20 km area near Balikpapan, Indonesia. When the predicted and observed values were compared, the coefficients of determination of the linear model exhibited marked significance, with all relations having an <i>R</i><sub><i>2</i></sub> exceeding 0.25. Mapping these predicted values revealed that the areas with high values generally coincided with the large intact natural forests and the remnants surrounding burnt areas, except for overall and forest species abundances, which displayed higher values along the burnt ridges surrounding the intact natural forests. These findings demonstrate that the mapping of predicted dung beetle diversity and its associated ecological functions within tropical regions can be effectively achieved through the use of satellite images.
DOI: 10.6084/m9.figshare.29513534.v1
title: Mapping predicted dung beetle diversity in the lowland tropical forests of Borneo by utilizing indices derived from a satellite image

description: In tropical regions, assessing biodiversity across expansive areas through direct data collection presents significant challenges. The advancement of remote sensing techniques for mapping predicted biodiversity offers a viable solution. To map predicted dung beetle diversity and its associated ecological functions using satellite images, we selected optimal multivariate regression models to predict overall species richness and abundance, forest species richness and abundance, biomass (logarithmic dry weight) associating ecological functions of the beetle, Simpson’s diversity index (1/D), and forest community integrity (measured as the score on axis 1 of nonmetric multidimensional scaling ordination analysis), utilizing the Akaike information criterion (AIC). The explanatory variables were extracted from a SPOT-5 satellite image utilizing varying grid cell configurations up to 290 m<sup>2</sup>. The objective variables consisted of dung beetle collection data from intact, burnt, or artificially degraded natural forests; plantation forests; and grasslands within a 20 × 20 km area near Balikpapan, Indonesia. When the predicted and observed values were compared, the coefficients of determination of the linear model exhibited marked significance, with all relations having an <i>R</i><sub><i>2</i></sub> exceeding 0.25. Mapping these predicted values revealed that the areas with high values generally coincided with the large intact natural forests and the remnants surrounding burnt areas, except for overall and forest species abundances, which displayed higher values along the burnt ridges surrounding the intact natural forests. These findings demonstrate that the mapping of predicted dung beetle diversity and its associated ecological functions within tropical regions can be effectively achieved through the use of satellite images.
DOI: 10.6084/m9.figshare.30137422
title: Borrowed Voices, Shared Debt: Plagiarism, Idea Recombination, and the Knowledge Commons in Large Language Models

description: Large language models generate fluent text by recombining the language and ideas of prior authors at scale. This process produces plagiarism-like harms in three dimensions: direct wording leakage, imitation of distinctive styles, and appropriation of argument structures or conceptual syntheses without provenance. At the same time, their capacity to provide insight or novel-seeming combinations depends entirely on the accumulated labor of millions of human writers, editors, teachers, and curators who built the knowledge commons. This paper argues that denunciation and recognition must proceed together: the harms of extraction must be exposed, yet the debt to the commons must also be acknowledged. The article proposes a framework that defines the scope of plagiarism in this context, diagnoses the mechanisms of recombination, and sets out operational remedies, including dataset governance, attribution layers, compensation pools, and measurable audit thresholds. The goal is to establish a system that restricts illegitimate appropriation while reinvesting in the infrastructures of shared knowledge that make such synthesis possible.<b>DOI</b>Primary archive: https://doi.org/<b>10.5281/zenodo.17132004</b>Secondary archive: https://doi.org/10.6084/m9.figshare.30137422SSRN: Pending assignment (ETA: Q3 2025)
DOI: 10.6084/m9.figshare.30137422.v1
title: Borrowed Voices, Shared Debt: Plagiarism, Idea Recombination, and the Knowledge Commons in Large Language Models

description: Large language models generate fluent text by recombining the language and ideas of prior authors at scale. This process produces plagiarism-like harms in three dimensions: direct wording leakage, imitation of distinctive styles, and appropriation of argument structures or conceptual syntheses without provenance. At the same time, their capacity to provide insight or novel-seeming combinations depends entirely on the accumulated labor of millions of human writers, editors, teachers, and curators who built the knowledge commons. This paper argues that denunciation and recognition must proceed together: the harms of extraction must be exposed, yet the debt to the commons must also be acknowledged. The article proposes a framework that defines the scope of plagiarism in this context, diagnoses the mechanisms of recombination, and sets out operational remedies, including dataset governance, attribution layers, compensation pools, and measurable audit thresholds. The goal is to establish a system that restricts illegitimate appropriation while reinvesting in the infrastructures of shared knowledge that make such synthesis possible.<b>DOI</b>Primary archive: https://doi.org/<b>10.5281/zenodo.17132004</b>Secondary archive: https://doi.org/10.6084/m9.figshare.30137422SSRN: Pending assignment (ETA: Q3 2025)
DOI: 10.15468/dl.ansps6
title: Occurrence Download

description: A dataset containing 97 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lunularia cruciata",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 97 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006683-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u87bj8
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lunularia cruciata",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 18 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006684-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60635/c3004f
title: Monthly summary_Sea Surface Temperature

description: Monthly summary of sea surface temperature data collected from near shore temperature sensors at depths ranging from 1 meter to 20 meters.
 Sensors are located in the following areas: Bay of Panama, Galeta (and locations to the east), Bocas del Torro, Gulf of Chiriqui (no longer active), Guna Yala (no longer active).
 Historical datasets can be located here: https://smithsonian.figshare.com/articles/dataset/Monthly_summary_Sea_Surface_Temperature/10059542
DOI: 10.15468/dl.zhpje3
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lunularia cruciata",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 29 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006685-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13077162
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.x6fc49
title: Occurrence Download

description: A dataset containing 93694 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lagodon rhomboides)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 93694 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006657-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q3edjq
title: Occurrence Download

description: A dataset containing 1064 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lunularia cruciata",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1064 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006690-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vqc7br
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lunularia cruciata",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006691-250914085247600/datasets/export for details.
DOI: 10.15468/dl.9q7v5y
title: Occurrence Download

description: A dataset containing 746 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lunularia cruciata",
 "Country is Germany",
 "HasCoordinate is true"
 ]
}

 The dataset includes 746 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006689-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17131655
title: 7-day change in Vegetation Health Index - Kent (UK), 2022

description: Demonstration Case Name



Multi-hazards in the Southeast UK region





Dataset Title



7-day change in Vegetation Health Index - Kent (UK), 2022





Dataset Description



This dataset consists of:



NESDIS_STAR_7day_VHI_Kent2022_1km.tif a multi-layer (52-band) image where each layer represents the 7-day Vegetation Health Index (VHI) in 2022 for the wider Kent region in Southeast UK.

NESDIS_STAR_7day_change_VHI_Kent2022_1km.tif: a multi-layer (51-band) image where each layer represents the 7-day change in Vegetation Health Index (VHI) in 2022 for the wider Kent region in Southeast UK.


Data was generated using code developed within the Google Earth Engine online platform and output in GeoTIFF formats for interoperability and comparative analysis within GIS.





Key Methodologies



Calculation of pixel threshold where the 7-day change in vegetation health index is negative (declining) or positive (improving) over each 7-day window in the NESDIS STAR Vegetation Health Index dataset (https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_ftp.php), calculated across the 2022 multi-date image stack. Data extracted from source then imported and analysis coded into Google Earth Engine online platform. Output in raster format.





Temporal Domain



Daily: 7th January 2022 to 31st December 2022 (7-day temporal resolution)





Spatial Domain



Geographic extent covering the region of Kent, Southeast UK.

Projected Coordinate System: British National Grid (EPSG:27700)

Spatial Extent Bounding Box: Upper Left (182000N, 509000E), Lower Right (95000N, 641000E)

Spatial Resolution: 1km x 1km (resampled from original 4km data in GEE)





Key Indicators



7-day image stack indicating where pixels indicate a decline (-) or improvement (+) in vegetation health, the decline associated with preconditioning for high heat events.





Data Format



GeoTIFF (*.tif)





Source Data



Primary source: National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Application and Research (STAR) Vegetation Health Index dataset (https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_ftp.php), extracted and coded into the Google Earth Engine online platform (GEE).





Accessibility



10.5281/zenodo.17131655





Stakeholder Relevance



Urban planning departmentsEmergency response organizationsInfrastructure development teamsDisaster risk assessment analysts





Limitations/Assumptions



Original spatial resolution of data 4km (GeoTIFF).





Additional Information



Technical specifications: GeoTIFF format require use of a multi-band image visualisation package

Pixel values represent the 7-day decline (-) or improvement (+) in vegetation health within that pixel.





Contact Information



kmcm@bgs.ac.uk
DOI: 10.5281/zenodo.17131654
title: 7-day change in Vegetation Health Index - Kent (UK), 2022

description: Demonstration Case Name



Multi-hazards in the Southeast UK region





Dataset Title



7-day change in Vegetation Health Index - Kent (UK), 2022





Dataset Description



This dataset consists of:



NESDIS_STAR_7day_VHI_Kent2022_1km.tif a multi-layer (52-band) image where each layer represents the 7-day Vegetation Health Index (VHI) in 2022 for the wider Kent region in Southeast UK.

NESDIS_STAR_7day_change_VHI_Kent2022_1km.tif: a multi-layer (51-band) image where each layer represents the 7-day change in Vegetation Health Index (VHI) in 2022 for the wider Kent region in Southeast UK.


Data was generated using code developed within the Google Earth Engine online platform and output in GeoTIFF formats for interoperability and comparative analysis within GIS.





Key Methodologies



Calculation of pixel threshold where the 7-day change in vegetation health index is negative (declining) or positive (improving) over each 7-day window in the NESDIS STAR Vegetation Health Index dataset (https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_ftp.php), calculated across the 2022 multi-date image stack. Data extracted from source then imported and analysis coded into Google Earth Engine online platform. Output in raster format.





Temporal Domain



Daily: 7th January 2022 to 31st December 2022 (7-day temporal resolution)





Spatial Domain



Geographic extent covering the region of Kent, Southeast UK.

Projected Coordinate System: British National Grid (EPSG:27700)

Spatial Extent Bounding Box: Upper Left (182000N, 509000E), Lower Right (95000N, 641000E)

Spatial Resolution: 1km x 1km (resampled from original 4km data in GEE)





Key Indicators



7-day image stack indicating where pixels indicate a decline (-) or improvement (+) in vegetation health, the decline associated with preconditioning for high heat events.





Data Format



GeoTIFF (*.tif)





Source Data



Primary source: National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Application and Research (STAR) Vegetation Health Index dataset (https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_ftp.php), extracted and coded into the Google Earth Engine online platform (GEE).





Accessibility



10.5281/zenodo.17131655





Stakeholder Relevance



Urban planning departmentsEmergency response organizationsInfrastructure development teamsDisaster risk assessment analysts





Limitations/Assumptions



Original spatial resolution of data 4km (GeoTIFF).





Additional Information



Technical specifications: GeoTIFF format require use of a multi-band image visualisation package

Pixel values represent the 7-day decline (-) or improvement (+) in vegetation health within that pixel.





Contact Information



kmcm@bgs.ac.uk
DOI: 10.21227/wk2r-nm82
title: "Fluid-structure interaction simulation data files"

description: "The data files include 3D models, a Python script, and Ansys programs for fluid-structure interaction simulations. "
DOI: 10.15468/dl.33qa96
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Buxus megistophylla H.Lév."
 ]
}

 The dataset includes 137 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0118713-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sybjus
title: Occurrence Download

description: A dataset containing 203172 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Patagioenas flavirostris (Wagler, 1831)"
 ]
}

 The dataset includes 203172 records from 112 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0370139-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kphhvs
title: Occurrence Download

description: A dataset containing 12282 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "TaxonKey is Chenopodium L.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12282 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000885-200127171203522/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pr7md2
title: Occurrence Download

description: A dataset containing 639 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Macrocephalon maleo S.Müller, 1846"
 ]
}

 The dataset includes 639 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0200531-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fvyn42
title: Occurrence Download

description: A dataset containing 1403 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Melanerpes chrysogenys (Vigors, 1839)"
 ]
}

 The dataset includes 1403 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012208-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a4fd67
title: Occurrence Download

description: A dataset containing 5208 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vitis vinifera subsp. sylvestris (Willd.) Hegi"
 ]
}

 The dataset includes 5208 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0403060-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7dqghc
title: Occurrence Download

description: A dataset containing 1147638 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cerastium L."
 ]
}

 The dataset includes 1147638 records from 1289 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015562-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.