cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.28v8an
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Echinops nanus",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 29 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009441-260120142942310/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.31144330.v1
title: XRD data of solid products

description: XRD data of solid products
DOI: 10.15468/dl.nrhan3
title: Occurrence Download

description: A dataset containing 1378 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Echinops sphaerocephalus",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 1378 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009442-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7fb4kd
title: Occurrence Download

description: A dataset containing 5152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tetragonisca angustula, Puma concolor)",
 "HasCoordinate is true",
 "Country is Brazil"
 ]
}

 The dataset includes 5152 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009443-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wzzs3r
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Prunus cerasifera",
 "Year 1950-2020"
 ]
}

 The dataset includes 40 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009444-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.23yb7r
title: Occurrence Download

description: A dataset containing 287 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Echinops strigosus",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 287 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009445-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6y5nt6
title: Occurrence Download

description: A dataset containing 12556 records in GBIF matching the query:
SELECT
 '302' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= 24.552553756517728
 AND decimalLatitude &lt;= 28.773895969281842
 AND decimalLongitude &gt;= -108.26001776192012
 AND decimalLongitude &lt;= -100.51087295937347
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 12556 records were derived from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009439-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gdk6ud
title: Occurrence Download

description: A dataset listing the 4138420 species recorded in GBIF matching the query:
{
 "OccurrenceStatus" : [
 "is Present"
 ]
}

 The dataset's 4138420 records were derived from 88400 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009435-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kbd5ea
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is Abelmoschus esculentus",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009446-260120142942310/datasets/export for details.
DOI: 10.5281/zenodo.18360936
title: sarana dan prasarana
DOI: 10.5281/zenodo.18360937
title: sarana dan prasarana
DOI: 10.15468/dl.3uyhk3
title: Occurrence Download

description: A dataset containing 10180 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eclipta prostrata",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 10180 records from 471 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009448-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mnqjf7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypentelium nigricans",
 "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/0034737-251025141854904/datasets/export for details.
DOI: 10.15468/dl.4hupw2
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-107.8188 23.54913, -107.6688 23.54913, -107.6688 23.69913, -107.8188 23.69913, -107.8188 23.54913))"
 ]
}

 The dataset includes 21 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034738-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17611372
title: Spatial Single-Cell Atlas Reveals KSHV-Driven Broad Cellular Reprogramming, Progenitor Expansion, Immune and Vascular Remodeling in Kaposi's Sarcoma

description: Kaposi’s sarcoma (KS) is a highly inflammatory, angiogenic tumor driven by Kaposi’s sarcoma-associated herpesvirus (KSHV), yet the origins of tumor cells and mechanisms of progression remain unclear. Here, we present the first spatial single-cell atlas of KS, profiling 256 samples across patch, plaque, and nodular lesions and normal controls. We identify CD34⁺ progenitor lymphatic endothelial cells (LECs) as the primary targets of KSHV, whose clonal expansion drives tumor growth. KSHV infection induces widespread cellular reprogramming across the tumor microenvironment, including LECs, vascular endothelial cells, fibroblasts, and macrophages, generating hybrid phenotypes that support angiogenesis, inflammation, and immune modulation. KSHV⁺ macrophages are enriched in tumor-proximal niches, further promoting a proangiogenic, immunosuppressive environment. Spatial analysis reveals evolving tumor-associated niches, with a core-to-periphery gradient correlating with infection, immune modulation, and cellular remodeling. We identify disease progression predictive signatures, offering mechanistic insights into KS pathogenesis and potential new therapeutic strategies by reprogramming the tumor microenvironment.
DOI: 10.5281/zenodo.17611373
title: Spatial Single-Cell Atlas Reveals KSHV-Driven Broad Cellular Reprogramming, Progenitor Expansion, Immune and Vascular Remodeling in Kaposi's Sarcoma

description: Kaposi’s sarcoma (KS) is a highly inflammatory, angiogenic tumor driven by Kaposi’s sarcoma-associated herpesvirus (KSHV), yet the origins of tumor cells and mechanisms of progression remain unclear. Here, we present the first spatial single-cell atlas of KS, profiling 256 samples across patch, plaque, and nodular lesions and normal controls. We identify CD34⁺ progenitor lymphatic endothelial cells (LECs) as the primary targets of KSHV, whose clonal expansion drives tumor growth. KSHV infection induces widespread cellular reprogramming across the tumor microenvironment, including LECs, vascular endothelial cells, fibroblasts, and macrophages, generating hybrid phenotypes that support angiogenesis, inflammation, and immune modulation. KSHV⁺ macrophages are enriched in tumor-proximal niches, further promoting a proangiogenic, immunosuppressive environment. Spatial analysis reveals evolving tumor-associated niches, with a core-to-periphery gradient correlating with infection, immune modulation, and cellular remodeling. We identify disease progression predictive signatures, offering mechanistic insights into KS pathogenesis and potential new therapeutic strategies by reprogramming the tumor microenvironment.
DOI: 10.15468/dl.crh4ub
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypentelium roanokense",
 "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/0034739-251025141854904/datasets/export for details.
DOI: 10.15468/dl.gnzpxc
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-107.6688 23.54913, -107.5188 23.54913, -107.5188 23.69913, -107.6688 23.69913, -107.6688 23.54913))"
 ]
}

 The dataset includes 18 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034740-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17614156
title: Underwater images collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-06-16

description: This dataset was collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-06-16. 

            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.

            
                
                                       
                
                
 GPS information: 

                No GPS.
            
                    
                

                
            

            
 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: 10.5281/zenodo.17614224
title: Underwater images collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-06-16

description: This dataset was collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-06-16. 

            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.

            
                
                                       
                
                
 GPS information: 

                No GPS.
            
                    
                

                
            

            
 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.x6mvhn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyphessobrycon anisitsi",
 "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/0034741-251025141854904/datasets/export for details.
DOI: 10.15468/dl.6j73h7
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-107.5188 23.54913, -107.3688 23.54913, -107.3688 23.69913, -107.5188 23.69913, -107.5188 23.54913))"
 ]
}

 The dataset includes 34 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034742-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17614144
title: Dataset 0.1 — Formulário de Entrevista Estruturada para Validação Inicial do Sistema Cognitivo Modular Nemosine Nous

description: NOTA DE CORREÇÃO — Versão Obsoleta (0.1)

Esta versão contém um arquivo publicado inadvertidamente com dados pessoais não anonimizados, decorrente de erro operacional durante o envio.

Esta versão está depreciada e não deve ser utilizada para leitura, citação, análise ou referência.

A versão correta, totalmente anonimizada e revisada, encontra-se disponível em:

👉 Dataset 0.1.1 – Formulário de Entrevista Estruturada para Validação Inicial do Sistema Cognitivo Modular Nemosine NousDOI: 10.5281/zenodo.17614209

Por favor, utilize exclusivamente a versão 0.1.1 ou superiores (v0.2, v0.3…) para qualquer finalidade científica, acadêmica ou documental.

A integridade científica e a proteção dos respondentes foram restabelecidas na versão corrigida.
description: CORRECTION NOTICE — Deprecated Version (0.1)

This version contains a file that was inadvertently published with non-anonymized personal data, due to an operational error during upload.

This version is deprecated and must not be used for reading, citation, analysis, or reference.

The fully anonymized and corrected version is available at:

👉 Dataset 0.1.1 – Structured Interview Form for Initial Validation of the Nemosine Nous Modular Cognitive SystemDOI: 10.5281/zenodo.17614209

Please use only version 0.1.1 or any subsequent versions (v0.2, v0.3…) for scientific, academic, or documentary purposes.

Scientific integrity and participant protection have been fully restored in the corrected version.
DOI: 10.5281/zenodo.17614120
title: Underwater images collected by a Kite surf in Le-Morne-With-Aerial, Mauritius - 2018-04-29

description: This dataset was collected by a Kite surf in Le-Morne-With-Aerial, Mauritius - 2018-04-29. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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!