cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2q020v
title: CCDC 2502506: Experimental Crystal Structure Determination

description: Related Article: Bohan Jin, Bruce C. Noll, Allen G Oliver, Marvin J. Miller|2025|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2q023y
title: CCDC 2502509: Experimental Crystal Structure Determination

description: Related Article: Yifei Zhou|2025|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.zu845b
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Checklist and key for identification of larvae of Hydropsyche species (Hydropsychidae, Trichoptera) of the Maghreb"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038489-251025141854904/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/13761577
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.z2tdz2
title: Occurrence Download

description: A dataset containing 807 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Melia azedarach",
 "Year 1980-2025"
 ]
}

 The dataset includes 807 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038492-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17532305
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-01-01

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

            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.17627245
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-01-01

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

            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.4v5sjx
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Pelagic tunicates (Appendicularia and Thaliacea) of Sri Lanka: two first records with an annotated checklist"
 ]
}

 The dataset includes 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038495-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17549402
title: Underwater images collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-08-18

description: This dataset was collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-08-18. 

            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.17627225
title: Underwater images collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-08-18

description: This dataset was collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-08-18. 

            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.4uzx7f
title: Occurrence Download

description: A dataset containing 215278 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Sylviidae"
 ]
}

 The dataset includes 215278 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038484-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17627404
title: Ipseity Daily Data

description: At what rate do individuals say they are X?

Begining on 2025-07-08, I surveyed a small random sample of American adults on a daily cadence.  They responded to many items of the form: Does <signifier> describe you today?

The file ipseity-daily-aggregated.csv in this repository contains the aggregated results through 2025-07-28.  The columns are as follows:



signifier - a word, phrase or emoji

yes_count - the number of respondents who answered yes (aggregated all-time).

no_count - the number of respondents who answered no (aggregated all-time).

prevalence_per_10k - an estimate of the prevalence per 10,000.  If we were to survey 10,000 American adults, how many would we expect to respond yes to this signifier?  The number is calculated as yes_count / (yes_count + no_count) * 10,000.

earliest_obs_date - this date represents the first time this particular signifier was ever displayed to a respondent.  Signifiers are chosen with stochasticity from a long list; new signifiers are sometimes added, old signifiers are sometimes deleted, and a signifier may be moved higher or lower in priority for sampling.

latest_obs_date - the date closest to the present date on which signifier was most recently presented to a respondent.


Version created on 2025-11-17 — cumulative data.
DOI: 10.5281/zenodo.17626913
title: GRAVITY: Dynamic gene regulatory network-enhanced RNA velocity modeling for trajectory inference and biological discovery
DOI: 10.5281/zenodo.17626914
title: GRAVITY: Dynamic gene regulatory network-enhanced RNA velocity modeling for trajectory inference and biological discovery
DOI: 10.15468/dl.vj2bmw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (, , , , , Phoebe glabrifolia, , , )",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038497-251025141854904/datasets/export for details.
DOI: 10.15468/dl.5ss8a2
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nymphaea dimorpha"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038498-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qx23a6
title: Occurrence Download

description: A dataset containing 207458 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Pellorneidae"
 ]
}

 The dataset includes 207458 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038485-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17549287
title: Reconstructed NDVI Dataset for the CASA (Carnegie-Ames-Stanford Approach) Model (2019–2023)

description: This dataset provides a reconstructed Normalized Difference Vegetation Index (NDVI) time series covering the period 2019–2023, developed to support ecological and carbon-cycle modeling applications. The dataset integrates multi-source satellite observations from Landsat 8 OLI and MODIS (MOD13Q1) products and applies a Gap-Filling and Savitzky–Golay (GF–SG) reconstruction method to correct for cloud contamination, sensor noise, and missing observations. It contains two main components: (1) model-ready NDVI composites standardized for direct input into the CASA model for NPP and NEP estimation, and (2) monthly reconstructed NDVI data for vegetation dynamics, phenological monitoring, and long-term trend analysis. All files are provided in GeoTIFF format with a spatial resolution of 30 meters and projected in WGS 84 / UTM Zone 50N. This dataset enables accurate assessment of vegetation productivity, ecological restoration effectiveness, and carbon source/sink variations in regions such as Beijing and similar urban–rural transition zones. The data are made freely available for academic and non-commercial purposes; users must provide proper attribution when using the dataset in research, teaching, or publications.
DOI: 10.5281/zenodo.17549288
title: Reconstructed NDVI Dataset for the CASA (Carnegie-Ames-Stanford Approach) Model (2019–2023)

description: This dataset provides a reconstructed Normalized Difference Vegetation Index (NDVI) time series covering the period 2019–2023, developed to support ecological and carbon-cycle modeling applications. The dataset integrates multi-source satellite observations from Landsat 8 OLI and MODIS (MOD13Q1) products and applies a Gap-Filling and Savitzky–Golay (GF–SG) reconstruction method to correct for cloud contamination, sensor noise, and missing observations. It contains two main components: (1) model-ready NDVI composites standardized for direct input into the CASA model for NPP and NEP estimation, and (2) monthly reconstructed NDVI data for vegetation dynamics, phenological monitoring, and long-term trend analysis. All files are provided in GeoTIFF format with a spatial resolution of 30 meters and projected in WGS 84 / UTM Zone 50N. This dataset enables accurate assessment of vegetation productivity, ecological restoration effectiveness, and carbon source/sink variations in regions such as Beijing and similar urban–rural transition zones. The data are made freely available for academic and non-commercial purposes; users must provide proper attribution when using the dataset in research, teaching, or publications.
DOI: 10.15468/dl.w8xns3
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Nymphaea minuta"
 ]
}

 The dataset includes 10 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038500-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17627432
title: COVID-19-Todesfälle in Deutschland

description: Im Datensatz "COVID-19-Todesfälle in Deutschland" des Robert Koch-Instituts werden täglich aktualisierte Informationen zu COVID-19-bedingten Todesfällen in Deutschland bereitgestellt. Die Datenerhebung erfolgt im Rahmen des Infektionsschutzgesetzes (IfSG). Der Datensatz umfasst aggregierte Todesfallzahlen nach Berichtsdatum, Altersgruppen und Bundesländern und wird ergänzt durch den Fall-Verstorbenen-Anteil. Der Datensatz ermöglicht fundierte Analysen zur Bewertung der Krankheitsschwere und Verbreitung von COVID-19 in Deutschland.
DOI: 10.15468/dl.akrrcg
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Athelidium aurantiacum"
 ]
}

 The dataset includes 43 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038501-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j6eskk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anguilla anguilla",
 "Country is Viet Nam",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036355-250811113504898/datasets/export for details.