cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17615989
title: Swiss Legislation modelled as a Property Graph

description: Property Graph of the federal Swiss Legislation
DOI: 10.15468/dl.8gfbkr
title: Occurrence Download

description: A dataset containing 491 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Albizzia julibrissin"
 ]
}

 The dataset includes 491 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035207-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uxxxpx
title: Occurrence Download

description: A dataset containing 1349 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-110.8188 24.59913, -110.6688 24.59913, -110.6688 24.74913, -110.8188 24.74913, -110.8188 24.59913))"
 ]
}

 The dataset includes 1349 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035206-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6zfer3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Morone americana",
 "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/0035208-251025141854904/datasets/export for details.
DOI: 10.15468/dl.d5q24t
title: Occurrence Download

description: A dataset containing 493 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-110.6688 24.59913, -110.5188 24.59913, -110.5188 24.74913, -110.6688 24.74913, -110.6688 24.59913))"
 ]
}

 The dataset includes 493 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035209-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nb6qft
title: Occurrence Download

description: A dataset containing 56205 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Ameiurus melas"
 ]
}

 The dataset includes 56205 records from 341 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035178-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9qqvcc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Morone chrysops",
 "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/0035210-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17615944
title: Underwater images collected by an No key for platform in Ilot-Sancho, Mauritius - 2019-05-19

description: This dataset was collected by an No key for platform in Ilot-Sancho, Mauritius - 2019-05-19. 

            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.17615811
title: Underwater images collected by an No key for platform in Ilot-Sancho, Mauritius - 2019-05-19

description: This dataset was collected by an No key for platform in Ilot-Sancho, Mauritius - 2019-05-19. 

            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.fzsfmg
title: Occurrence Download

description: A dataset containing 6004 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Spain, Portugal)",
 "DatasetKey is one of (FloraCAT: Banco de datos de los cormófitos de Cataluña, CSIC-Real Jardín Botánico-Anthos. Sistema de Información de las Plantas de España, Sistema de Información de la vegetación Ibérica y Macaronésica, Banco de Datos de la Biodiversidad de la Comunitat Valenciana, Base de datos de plantas vasculares del País Vasco: ARAN-EH, Herbarium JACA. Instituto Pirenaico de Ecología-CSIC, CSIC-Real Jardín Botánico-Colección de Plantas Vasculares (MA), Catálogo Florístico Histórico de Navarra. Gobierno de Navarra)",
 "TaxonKey is Asplenium ruta-muraria"
 ]
}

 The dataset includes 6004 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035212-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gamfwa
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Morone mississippiensis",
 "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/0035215-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17615963
title: Underwater images collected by a Kite surf in Le-Morne-La-Pointe, Mauritius - 2019-07-12

description: This dataset was collected by a Kite surf in Le-Morne-La-Pointe, Mauritius - 2019-07-12. 

            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.17615998
title: Underwater images collected by a Kite surf in Le-Morne-La-Pointe, Mauritius - 2019-07-12

description: This dataset was collected by a Kite surf in Le-Morne-La-Pointe, Mauritius - 2019-07-12. 

            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.6mjr3d
title: Occurrence Download

description: A dataset containing 1114 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-110.5188 24.59913, -110.3688 24.59913, -110.3688 24.74913, -110.5188 24.74913, -110.5188 24.59913))"
 ]
}

 The dataset includes 1114 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035214-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/3m8b-ra49
title: Parkes observations for project P595 semester 2025OCTS_13

description: We propose to continue the PULSE@Parkes project in which high school students from Australia and around the World use the Parkes radio telescope in a remote observing model to observe and analyse pulsar data. The data from some PULSE@Parkes observations are used to support the space missions, other observations supplement the P456 Pulsar Timing Array project and the remainder were chosen in order to make a detailed analysis of pulsar timing irregularities and intermittency.
DOI: 10.25919/p08v-tk40
title: Long-term project observations for project P1321 semester 2025OCTS_06

description: The atomic interstellar medium shows tiny-scale optical depth fluctuations on the scale of 0.1~10,000 AU, whose origin and nature are poorly understood. The existence of this Tiny-Scale Atomic Structure (TSAS) has significant implications, potentially calling into question our fundamental understanding of heating, cooling and dynamical processes in the interstellar medium. Yet observations remain sparse. This long-term project plans to search for temporal variations in HI absorption spectra seen against background pulsars to characterise TSAS in the Milky Way interstellar medium (ISM). These observations constitute the largest number of sightlines and densest temporal sampling ever performed in a single experiment, and will test predictions that TSAS is the tail end of a turbulent cascade, constrain its minimum size scale (down to resolutions of ~0.05 AU) and potentially provide the first direct measurements of pressures in "large" TSAS features of &gt; 1000 AU. We make use of the phase-resolved spectral line mode that we have recently implemented on Parkes, which has cut data rates and processing times by factors of ~1000 compared to past studies. This is an expansion of our pilot P1321 to a long term study.
DOI: 10.25919/zs73-hx44
title: Parkes observations for project P1183 semester 2025OCTS_08

description: After nearly three years of monitoring, comprising with the Parkes telescope and the FAST telescopes, FRB 20220529 is proven to be an extremely interesting repeater. It exhibits one of the longest activity durations and a potential ~200-day RM period. Due to frequent observations scheduled with both Parkes and FAST, an abrupt rotation measure (RM) flare has been observed in this source. This is the first detection of such an ``RM flare'' in a fast radio burst (FRB), suggesting that the source is in an environment with occasionally erupting coronal mass ejection. This presents a unique opportunity to study the eruption environment of FRBs and investigate the relation between burst activity and other burst parameters. If confirmed, the periodicity and the ``RM flare'' of FRB 20220529 would corroborate each other and become a ``smoking gun'' of the binary origin of FRB. In previous analysis, both Parkes and FAST observations have proven essential. While FAST's higher sensitivity has enabled the capture of many bursts, Parkes' wideband receiver has provided a good burst detection rate even during low-rate phases. Notably, during the RM flare, two high signal-to-noise ratio bursts from Parkes have been crucial in understanding the RM variations. To further investigate FRB 20220529, we have scheduled regular FAST observations every fortnight, monitoring the source for 20 minutes each time. Therefore, we also propose to monitor FRB 20220529 using the Parkes UWL receiver, ensuring high-time resolution and employing full-polarization observations.
DOI: 10.15468/dl.5z8hbf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Morone saxatilis",
 "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/0035218-251025141854904/datasets/export for details.
DOI: 10.15468/dl.ftkb2j
title: Occurrence Download

description: A dataset containing 271 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-110.3688 24.59913, -110.2188 24.59913, -110.2188 24.74913, -110.3688 24.74913, -110.3688 24.59913))"
 ]
}

 The dataset includes 271 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035217-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ejasgk
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-110.2188 24.59913, -110.0688 24.59913, -110.0688 24.74913, -110.2188 24.74913, -110.2188 24.59913))"
 ]
}

 The dataset includes 47 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035219-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r8w64e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Moxostoma anisurum",
 "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/0035220-251025141854904/datasets/export for details.
DOI: 10.15468/dl.accb2b
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-110.0688 24.59913, -109.9188 24.59913, -109.9188 24.74913, -110.0688 24.74913, -110.0688 24.59913))"
 ]
}

 The dataset includes 71 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035221-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5fgdf7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Moxostoma cervinum",
 "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/0035222-251025141854904/datasets/export for details.