cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30094525.v1
title: Core code and data related to AI-Blockchain Synergy for Resilient Agricultural Supply Chains An Evolutionary Game Analysis

description: Agricultural supply chain finance faces enduring challenges stemming from information asymmetry, seasonal fluctuations, and credit constraints. This study develops an integrated framework combining artificial intelligence and blockchain technologies to strengthen supply chain resilience. We establish a tripartite evolutionary game model engaging banks, core enterprises, and small and medium-sized enterprises, incorporating dynamic technical parameters alongside critical operational variables including inventory dynamics, seasonal financing patterns, and heterogeneous agent behaviors. Through evolutionary game theory and systematic numerical simulations, we examine how AI-blockchain synergy influences strategic evolution and stability conditions. The study introduces a quantifiable resilience index R and identifies dual threshold conditions governing effective technological collaboration. Based on these findings, we design coordinated financing and inventory management strategies. Our results demonstrate that the proposed mechanism reduces agency costs, enhances credit assessment and demand forecasting accuracy, and directs the system toward Pareto-optimal equilibrium. This creates a self-reinforcing cycle where technological advancement drives operational improvements that subsequently strengthen systemic resilience. These findings provide theoretical foundations, analytical tools, and practical guidance for developing data-enriched, resilient agricultural supply chains.
DOI: 10.15468/dl.ata26y
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Neolamprologus leleupi",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035304-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17616077
title: Underwater images collected by a Kite surf in One-Eye, Mauritius - 2019-08-03

description: This dataset was collected by a Kite surf in One-Eye, Mauritius - 2019-08-03. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/dl.pjxt72
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-109.3188 24.74913, -109.1688 24.74913, -109.1688 24.89913, -109.3188 24.89913, -109.3188 24.74913))"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035305-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qvsxxf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Neosalanx taihuensis",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035306-251025141854904/datasets/export for details.
DOI: 10.34740/kaggle/dsv/13741657
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.eq7nun
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-109.1688 24.74913, -109.0188 24.74913, -109.0188 24.89913, -109.1688 24.89913, -109.1688 24.74913))"
 ]
}

 The dataset includes 38 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035307-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14999050
title: Australia's terrestrial industrial footprint and ecological intactness

description: These datasets represent a Human Industrial Footprint (HIF) index map and an Ecological Intactness Index (EII) map for Australia circa 2020-2024. The datasets are distributed in raster format (.tif) and have a spatial resolution of 100 m, mapped on an Australian Albers Equal Area projection (EPSG:3577). 

The HIF was created by incorporating 16 nationally relevant pressure layers, also part of the dataset. The pressures used to compute the HIF were 1) intensive land uses, 2) buildings, 3) mining and quarrying, 4) human population density, 5) croplands, 6) pasturelands, 7) forestry plantations, 8) reservoirs and large dams, 9) farm dams, 10) roads, 11) railways, 12) energy transmission lines, 13) oil pipelines, 14) gas pipelines, 15) hiking trails, and 16)  navigable waterways. Each pressure layer was assigned a relative score between 0 and 10 to make them comparable. The scored (scaled) pressure layers were then summed to obtain the final HIF map.

The HIF was used to derive the Ecological Intactness Index (EII). The EII is calculated using the HIF, with the intactness index value for each cell parameterised to: a) be proportional to habitat area when there is no habitat fragmentation; b) decline mono-tonically as fragmentation increases, and be sensitive to both the number of nearby patches and the separation between patches, and (c) to be proportional to habitat quality for a given total area of habitat and degree of fragmentation.

In the pressure layer folder, native and modified pasturelands are merged in the "pastures" pressure layer and paved and unpaved roads are in the "roads" layer.

The code to create these maps is also available through this repository.  The code is an end‑to‑end GRASS GIS pipeline to rebuild the Human Industrial Footprint Index for continental Australia on a 100 m grid in Albers Australia Equal Area (EPSG:3577). It generates 16 pressure layers, applies hierarchical priority (Urban > Mining > Crops >Pasture), scales each 0–10, and exports individual layers plus the summed index as Cloud‑Optimised GeoTIFFs (COGs).

Acknowledgements

This research was funded by The Wilderness Society.

Contact

Further queries regarding these datasets can be directed to Ruben Venegas (r.venegas@uq.edu.au) and James Watson (james.watson@uq.edu.au).
DOI: 10.5281/zenodo.17606284
title: Australia's terrestrial industrial footprint and ecological intactness

description: These datasets represent a Human Industrial Footprint (HIF) index map and an Ecological Intactness Index (EII) map for Australia circa 2020-2024. The datasets are distributed in raster format (.tif) and have a spatial resolution of 100 m, mapped on an Australian Albers Equal Area projection (EPSG:3577). 

The HIF was created by incorporating 16 nationally relevant pressure layers, also part of the dataset. The pressures used to compute the HIF were 1) intensive land uses, 2) buildings, 3) mining and quarrying, 4) human population density, 5) croplands, 6) pasturelands, 7) forestry plantations, 8) reservoirs and large dams, 9) farm dams, 10) roads, 11) railways, 12) energy transmission lines, 13) oil pipelines, 14) gas pipelines, 15) hiking trails, and 16)  navigable waterways. Each pressure layer was assigned a relative score between 0 and 10 to make them comparable. The scored (scaled) pressure layers were then summed to obtain the final HIF map.

The HIF was used to derive the Ecological Intactness Index (EII). The EII is calculated using the HIF, with the intactness index value for each cell parameterised to: a) be proportional to habitat area when there is no habitat fragmentation; b) decline mono-tonically as fragmentation increases, and be sensitive to both the number of nearby patches and the separation between patches, and (c) to be proportional to habitat quality for a given total area of habitat and degree of fragmentation.

In the pressure layer folder, native and modified pasturelands are merged in the "pastures" pressure layer and paved and unpaved roads are in the "roads" layer.

The code to create these maps is also available through this repository.  The code is an end‑to‑end GRASS GIS pipeline to rebuild the Human Industrial Footprint Index for continental Australia on a 100 m grid in Albers Australia Equal Area (EPSG:3577). It generates 16 pressure layers, applies hierarchical priority (Urban > Mining > Crops >Pasture), scales each 0–10, and exports individual layers plus the summed index as Cloud‑Optimised GeoTIFFs (COGs).

Acknowledgements

This research was funded by The Wilderness Society.

Contact

Further queries regarding these datasets can be directed to Ruben Venegas (r.venegas@uq.edu.au) and James Watson (james.watson@uq.edu.au).
DOI: 10.15468/dl.ggpfs8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nimbochromis livingstonii",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035309-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17596512
title: Nitrogen stable isotope composition of typical industry emitted NOx and atmospheric NOx
DOI: 10.5281/zenodo.17616264
title: Nitrogen stable isotope composition of typical industry emitted NOx and atmospheric NOx
DOI: 10.15468/dl.kvxr6x
title: Occurrence Download

description: A dataset containing 6205201 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "Geometry POLYGON((87.06298 21.07991,98.06126 21.07991,98.06126 29.99807,87.06298 29.99807,87.06298 21.07991))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6205201 records from 803 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035287-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3zwzh2
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-109.0188 24.74913, -108.8688 24.74913, -108.8688 24.89913, -109.0188 24.89913, -109.0188 24.74913))"
 ]
}

 The dataset includes 103 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035310-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17616385
title: SMILES Biohack 2025 Munich

description: This is a part of biohack 2025 Munich Hackathon. Reproduction of https://www.nature.com/articles/s41592-025-02781-5 and https://www.nature.com/articles/s41467-018-06500-x papers with additional data cleaning and EDA.
DOI: 10.5281/zenodo.17616142
title: Underwater images collected by a Kite surf in Anakao, Madagascar - 2022-11-11

description: This dataset was collected by a Kite surf in Anakao, Madagascar - 2022-11-11. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/dl.qbrxf2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nimbochromis polystigma",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035311-251025141854904/datasets/export for details.
DOI: 10.15468/dl.4chm3h
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.8688 24.74913, -108.7188 24.74913, -108.7188 24.89913, -108.8688 24.89913, -108.8688 24.74913))"
 ]
}

 The dataset includes 27 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035312-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.73yasw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nimbochromis venustus",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035313-251025141854904/datasets/export for details.
DOI: 10.15468/dl.3jcgkp
title: Occurrence Download

description: A dataset containing 941 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Paratectonatica tigrina"
 ]
}

 The dataset includes 941 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035314-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c5pv4g
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.7188 24.74913, -108.5688 24.74913, -108.5688 24.89913, -108.7188 24.89913, -108.7188 24.74913))"
 ]
}

 The dataset includes 47 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035315-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3gj58z
title: Occurrence Download

description: A dataset containing 118836 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((32.90404 37.09311,31.46162 37.22084,29.85893 26.56647,43.80232 26.42303,44.3232 37.28462,32.90404 37.09311))",
 "HasGeospatialIssue is false",
 "MediaType is Image",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 118836 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035265-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14892499
title: Exposure datasets at multiple scales

description: The data is a gridded dataset of global exposure (population, gross domestic product and net fixed asset value) at multiple spatial scales, spanning years 1850 to 2100 at annual resolution, including five future trajectories consistent with the Shared Socio-economic Pathways (SSPs).

Due to large file sizes of the high-resolutino data, only a selection of resolutions and timesteps is provided in this repository: a spatial resolution of 30 arc seconds (approximately 0.93 km at the equator) and at 30 arc min (approximately 56km at the equator). The Python code and input data provided alongside the dataset enable users to generate different resolutions and timesteps as required by their research needs. See the description in documentation/ folder and the readme file in the code/ folder.

Note: the input data is divided into multiple zip files. They all need to be downloaded and unzipped together. In addition to the files here, running the model requires the following external datasets:



selected Global Human Settlement Layer files (population and build-up surface at 30” resolution, 1975-2030, Total RES+NRES, WGS84 coordination system) from https://human-settlement.emergency.copernicus.eu/download.php

HYDE 3.2 population count (‘popc’, decennial 1850 AD-1980 AD) in the ‘baseline’ scenario, https://geo.public.data.uu.nl/vault-hyde/HYDE%203.2%5B1710494848%5D/original/ 

gridded population from WorldPop (https://hub.worldpop.org/geodata/listing?id=137) and projections from FuturePop (https://hub.worldpop.org/doi/10.5258/SOTON/WP00849)


The dataset an update of Deliverable 3.1 of the European Union’s HORIZON project COMPASS (https://compass-climate.eu/)

A subset of this dataset, covering only population density, is one of 12 climate forcings in CMIP7 (https://input4mips-cvs.readthedocs.io/en/latest/dataset-overviews/population/).
DOI: 10.5281/zenodo.17572467
title: Exposure datasets at multiple scales

description: The data is a gridded dataset of global exposure (population, gross domestic product and net fixed asset value) at multiple spatial scales, spanning years 1850 to 2100 at annual resolution, including five future trajectories consistent with the Shared Socio-economic Pathways (SSPs).

Due to large file sizes of the high-resolutino data, only a selection of resolutions and timesteps is provided in this repository: a spatial resolution of 30 arc seconds (approximately 0.93 km at the equator) and at 30 arc min (approximately 56km at the equator). The Python code and input data provided alongside the dataset enable users to generate different resolutions and timesteps as required by their research needs. See the description in documentation/ folder and the readme file in the code/ folder.

Note: the input data is divided into multiple zip files. They all need to be downloaded and unzipped together. In addition to the files here, running the model requires the following external datasets:



selected Global Human Settlement Layer files (population and build-up surface at 30” resolution, 1975-2030, Total RES+NRES, WGS84 coordination system) from https://human-settlement.emergency.copernicus.eu/download.php

HYDE 3.2 population count (‘popc’, decennial 1850 AD-1980 AD) in the ‘baseline’ scenario, https://geo.public.data.uu.nl/vault-hyde/HYDE%203.2%5B1710494848%5D/original/ 

gridded population from WorldPop (https://hub.worldpop.org/geodata/listing?id=137) and projections from FuturePop (https://hub.worldpop.org/doi/10.5258/SOTON/WP00849)


The dataset an update of Deliverable 3.1 of the European Union’s HORIZON project COMPASS (https://compass-climate.eu/)

A subset of this dataset, covering only population density, is one of 12 climate forcings in CMIP7 (https://input4mips-cvs.readthedocs.io/en/latest/dataset-overviews/population/).
DOI: 10.15468/dl.42exte
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nipponocypris sieboldii",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035316-251025141854904/datasets/export for details.
DOI: 10.15468/dl.cpnyp9
title: Occurrence Download

description: A dataset containing 172 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.5688 24.74913, -108.4188 24.74913, -108.4188 24.89913, -108.5688 24.89913, -108.5688 24.74913))"
 ]
}

 The dataset includes 172 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035318-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.