cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28757051
title: Monitoring ‘frozen commons’ with MODIS in Google Earth Engine

description: Snow, ice, and permafrost constitute ‘frozen commons,’ or common-pool resources that are collectively used and managed. This study examines the state and uses of snow and ice commons in two remote communities: Bayanzürkh in Khövsgöl Aimag, Mongolia, and McGrath, Alaska. Regional climate analyses indicate air temperatures warmed more than 2.2°C in both locales since the mid-twentieth century, compounding their similar accessibility challenges and dependence on natural resources. Warming affects transit timing and duration over snow and ice, impacting Mongolian herding and Alaskan subsistence hunting. Snow cover duration in both communities was calculated by classifying MODIS Snow Cover imagery utilizing the Google Earth Engine JavaScript API. Annual lake and river ice breakup timing and safe travel days were quantified from winter MODIS imagery from 2002 to 2023 for Lake Khövsgöl in Mongolia and the Kuskokwim River near McGrath, Alaska. Snow and ice duration did not significantly change over the 21 years examined. Relatively high map accuracies allowed discussion of interannual variability impacts on subsistence, transportation, and tourism. Daily snow and ice mapping in Google Earth Engine is a cost-effective and rapid method for quantifying environmental change impacting frozen commons, and therefore a tool for community decision-making and communication.
DOI: 10.6084/m9.figshare.28757051.v1
title: Monitoring ‘frozen commons’ with MODIS in Google Earth Engine

description: Snow, ice, and permafrost constitute ‘frozen commons,’ or common-pool resources that are collectively used and managed. This study examines the state and uses of snow and ice commons in two remote communities: Bayanzürkh in Khövsgöl Aimag, Mongolia, and McGrath, Alaska. Regional climate analyses indicate air temperatures warmed more than 2.2°C in both locales since the mid-twentieth century, compounding their similar accessibility challenges and dependence on natural resources. Warming affects transit timing and duration over snow and ice, impacting Mongolian herding and Alaskan subsistence hunting. Snow cover duration in both communities was calculated by classifying MODIS Snow Cover imagery utilizing the Google Earth Engine JavaScript API. Annual lake and river ice breakup timing and safe travel days were quantified from winter MODIS imagery from 2002 to 2023 for Lake Khövsgöl in Mongolia and the Kuskokwim River near McGrath, Alaska. Snow and ice duration did not significantly change over the 21 years examined. Relatively high map accuracies allowed discussion of interannual variability impacts on subsistence, transportation, and tourism. Daily snow and ice mapping in Google Earth Engine is a cost-effective and rapid method for quantifying environmental change impacting frozen commons, and therefore a tool for community decision-making and communication.
DOI: 10.6084/m9.figshare.29146707
title: Putting brakes on Expedition Cruise Tourism in Svalbard: exercising power at the expense of legitimacy?

description: Ecosystems in the Arctic are affected by multiple stressors caused by climate and environmental changes, increased pollution and human activities. The changes are unprecedented, with considerable scientific uncertainties over their potential consequences. In such a situation the use of the precautionary principle can be justified in environmental management. The principle is still contested, and its implementation needs careful considerations to be experienced as legitimate. This article will use the new regulatory amendments in Svalbard as a case to examine what preconditions must be in place to maintain legitimacy in environmental policy making when the precautionary principle is being applied. Based on a compilation of ship positional data, we assess the spatio-temporal distribution of cruise ships in Svalbard from 2011 to 2022 and the potential impacts on selected species. We then discuss the legitimacy of the regulatory amendments and the application of the precautionary principle. Our results show significant growth in the amount, distribution, and mileage of expedition cruise vessels' length of travel. While a precautionary approach to management is wise in a situation in which more stressors than ever are threatening Arctic wildlife, a more transparent and inclusive regulatory process would have strengthened the legitimacy of regulations and reduced conflict.
DOI: 10.6084/m9.figshare.29146707.v1
title: Putting brakes on Expedition Cruise Tourism in Svalbard: exercising power at the expense of legitimacy?

description: Ecosystems in the Arctic are affected by multiple stressors caused by climate and environmental changes, increased pollution and human activities. The changes are unprecedented, with considerable scientific uncertainties over their potential consequences. In such a situation the use of the precautionary principle can be justified in environmental management. The principle is still contested, and its implementation needs careful considerations to be experienced as legitimate. This article will use the new regulatory amendments in Svalbard as a case to examine what preconditions must be in place to maintain legitimacy in environmental policy making when the precautionary principle is being applied. Based on a compilation of ship positional data, we assess the spatio-temporal distribution of cruise ships in Svalbard from 2011 to 2022 and the potential impacts on selected species. We then discuss the legitimacy of the regulatory amendments and the application of the precautionary principle. Our results show significant growth in the amount, distribution, and mileage of expedition cruise vessels' length of travel. While a precautionary approach to management is wise in a situation in which more stressors than ever are threatening Arctic wildlife, a more transparent and inclusive regulatory process would have strengthened the legitimacy of regulations and reduced conflict.
DOI: 10.15468/dl.9eaa82
title: Occurrence Download

description: A dataset containing 218010 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-10-22",
 "EventDate is less than or equal to 2025-11-21",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 218010 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038382-251025141854904/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/13760588
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.qusyye
title: Occurrence Download

description: A dataset containing 132926034 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is iNaturalist Research-grade Observations"
 ]
}

 The dataset includes 132926034 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038353-251025141854904/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/13760620
title: ITB Official News Dataset (Berita Resmi ITB)
DOI: 10.5281/zenodo.17626654
title: Underwater images collected by a Paddle in Le-Morne, Mauritius - 2018-02-24

description: This dataset was collected by a Paddle in Le-Morne, Mauritius - 2018-02-24. 

            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!
DOI: 10.5281/zenodo.17590061
title: Underwater images collected by a Paddle in Le-Morne, Mauritius - 2018-02-24

description: This dataset was collected by a Paddle in Le-Morne, Mauritius - 2018-02-24. 

            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!
DOI: 10.15468/dl.78s458
title: Occurrence Download

description: A dataset containing 3713 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations)",
 "Geometry POLYGON(( -105.601802164238 35.5325227840993 , -105.2372292897 35.5325227840993 , -105.2372292897 36.1779250141282 , -105.601802164238 36.1779250141282 , -105.601802164238 35.5325227840993 ))",
 "Year is greater than or equal to 2016"
 ]
}

 The dataset includes 3713 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038387-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17589924
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-01-14

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2018-01-14. 

            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!
DOI: 10.5281/zenodo.17626613
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-01-14

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2018-01-14. 

            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!
DOI: 10.15468/dl.kkbdwy
title: Occurrence Download

description: A dataset containing 11083685 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.633667 -32.459255, 148.639343 -32.458847, 150.292588 -32.283535, 150.295837 -32.282829, 150.749618 -32.179356, 150.752853 -32.177967, 150.756851 -32.174973, 150.76088 -32.169445, 150.764877 -32.163685, 150.7686 -32.156109, 150.772583 -32.14669, 152.365665 -28.321791, 152.391314 -28.177884, 152.384507 -28.082301, 152.382492 -28.059806, 151.185169 -23.407955, 151.086248 -23.153876, 150.585411 -22.330317, 150.551508 -22.283709, 150.549412 -22.281027, 148.2634 -19.970291, 147.418026 -19.314967, 147.416546 -19.314047, 147.395047 -19.303257, 146.882133 -19.108932, 146.863157 -19.102872, 146.833074 -19.105093, 146.753258 -19.116713, 146.601762 -19.150359, 146.552763 -19.199358, 146.552382 -19.199753, 146.5502 -19.202953, 146.477158 -19.341229, 146.131272 -21.105778, 146.131065 -21.106913, 145.964791 -24.054007, 145.94305 -24.549892, 145.943172 -24.551474, 147.281219 -29.859392, 147.309067 -29.936316, 148.272537 -32.223354, 148.274658 -32.22522, 148.341263 -32.271286, 148.611847 -32.454727, 148.615585 -32.456596, 148.621521 -32.458469, 148.633667 -32.459255))"
 ]
}

 The dataset includes 11083685 records from 804 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038364-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mg2qfp
title: Occurrence Download

description: A dataset containing 50587 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Meripilus",
 "HasCoordinate is true"
 ]
}

 The dataset includes 50587 records from 259 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038386-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nvznk5
title: Occurrence Download

description: A dataset containing 106 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Nymphaea sulphurea"
 ]
}

 The dataset includes 106 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038388-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.99ncvk
title: Occurrence Download

description: A dataset containing 15544 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gustavia"
 ]
}

 The dataset includes 15544 records from 418 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038389-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3dhj5m
title: Occurrence Download

description: A dataset containing 11119 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Observation, Human Observation, Machine Observation, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Spilosoma lubricipeda (Linnaeus, 1758)"
 ]
}

 The dataset includes 11119 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002597-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.