cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17597251
title: Underwater images collected by a Kite surf in Pointe-Esny, Mauritius - 2018-03-03

description: This dataset was collected by a Kite surf in Pointe-Esny, Mauritius - 2018-03-03. 

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

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

            
                
                                       
                
                
 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.17595354
title: Underwater images collected by a Kite surf in Pointe-Esny, Mauritius - 2018-03-03

description: This dataset was collected by a Kite surf in Pointe-Esny, Mauritius - 2018-03-03. 

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

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

            
                
                                       
                
                
 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.j5jscu
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Field Museum of Natural History (Zoology) Bird Collection",
 "TaxonKey is Turnix suscitator plumbipes"
 ]
}

 The dataset includes 18 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030395-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hcnpy7
title: Occurrence Download

description: A dataset containing 221 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Turnix suscitator plumbipes"
 ]
}

 The dataset includes 221 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030396-251025141854904/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.30608345
title: Physical activity and academic performance

description: Physical activity and academic performance dataset
DOI: 10.6084/m9.figshare.30608345.v1
title: Physical activity and academic performance

description: Physical activity and academic performance dataset
DOI: 10.15468/dl.v5hszy
title: Occurrence Download

description: A dataset containing 310269 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Vespa velutina",
 "Year 2020-2025"
 ]
}

 The dataset includes 310269 records from 478 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030370-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.56yycx
title: Occurrence Download

description: A dataset containing 2880215 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cetacea"
 ]
}

 The dataset includes 2880215 records from 1846 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030368-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xjw2b6
title: Occurrence Download

description: A dataset containing 5863 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Undaria pinnatifida (Harv.) Suringar",
 "Year 1925-2025"
 ]
}

 The dataset includes 5863 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013662-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rebjg6
title: Occurrence Download

description: A dataset containing 114095 species occurrences available in GBIF matching the query:
{
 "PublishingCountry" : [
 "is Uzbekistan"
 ]
}

 The dataset includes 114095 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006023-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rkucmw
title: Occurrence Download

description: A dataset containing 123446 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is Gleditsia triacanthos"
 ]
}

 The dataset includes 123446 records from 554 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006018-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60555/4qc4-de67
title: Burning and Suppression of Solids - II
title: BASS-II

description: BASS-II results contribute to the combustion computational models used in the design of fire detection and suppression systems in microgravity and on Earth.
Objectives Include:
  1) Study the ignition, flame growth, flame spread, and extinction limits for solid fuels burning in low-velocity forced flows in microgravity.
  2) Begin to bridge the gap between the normal gravity NASA-STD-6001 Test #1 method, ground-based microgravity tests, and actual material flammability in microgravity.
  3) Provide SoFIE PIs with preliminary data to refine Science Requirements.
  4)  Practical, realistic (thicker) fuels in typical geometries will be examined, including slabs, cylinders, and spherical sections.

The primary variables include:
     • Forced flow velocity (speed and direction)
     • Ambient oxygen concentration (via working volume nitrogen vitiation)
     • Sample geometry (rods, spherical section, slabs, films, and fabric sheets
DOI: 10.5281/zenodo.17405857
title: Light-Driven Radical Catch-and-Release with BODIPY Photocages

description: Data associated to the study "Light-Driven Radical Catch-and-Release with BODIPY Photocages"

The raw data are separated into four main foulders according to the chemical nature of compounds: BODIPY-Cl, BODIPY-TEMPO, BODIPY-Cl-TEMPO-QYfl, polymers.Withing these folders the data are arranged into subfolders based on the technique used for the measurements (NMR, HR-MS, LC-MS, GPC, EPR, UV-Vis and fluorescence spectroscopy). Withing these folders the data are summarized in the subfolders with the numbers of the corresponding Figures.Overall structure is following: structural type -> characterization techniques -> Figure number -> dataThe data for the EPR measurements of the X-BODIPY-Cl can be found on zenodo platform under the following link: https://zenodo.org/records/17408124The codes of experiments in the file names correspond to the following compounds:BODIPY-TEMPO: AP-215, APB-13CH3-BODIPY-TEMPO: APB-14I-BODIPY-TEMPO: APB-16BODIPY-Cl: BDP-ClCH3-BODIPY-Cl: APB-10I-BODIPY-Cl: APB-24
DOI: 10.5281/zenodo.17405856
title: Light-Driven Radical Catch-and-Release with BODIPY Photocages

description: Data associated to the study "Light-Driven Radical Catch-and-Release with BODIPY Photocages"

The raw data are separated into four main foulders according to the chemical nature of compounds: BODIPY-Cl, BODIPY-TEMPO, BODIPY-Cl-TEMPO-QYfl, polymers.Withing these folders the data are arranged into subfolders based on the technique used for the measurements (NMR, HR-MS, LC-MS, GPC, EPR, UV-Vis and fluorescence spectroscopy). Withing these folders the data are summarized in the subfolders with the numbers of the corresponding Figures.Overall structure is following: structural type -> characterization techniques -> Figure number -> dataThe data for the EPR measurements of the X-BODIPY-Cl can be found on zenodo platform under the following link: https://zenodo.org/records/17408124The codes of experiments in the file names correspond to the following compounds:BODIPY-TEMPO: AP-215, APB-13CH3-BODIPY-TEMPO: APB-14I-BODIPY-TEMPO: APB-16BODIPY-Cl: BDP-ClCH3-BODIPY-Cl: APB-10I-BODIPY-Cl: APB-24
DOI: 10.15468/dl.sqvzza
title: Occurrence Download

description: A dataset containing 174 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chama croceata"
 ]
}

 The dataset includes 174 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006048-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25573/serc.22025612
title: 2023 Meteorological Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2023 meteorological data collected on WXT536, PTB110, and LI-190R instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA).
Location: 38.886928, -76.540172
DOI: 10.25573/serc.22025612.v4
title: 2023 Meteorological Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2023 meteorological data collected on WXT536, PTB110, and LI-190R instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA).
Location: 38.886928, -76.540172
DOI: 10.25573/serc.22025612.v5
title: 2023 Meteorological Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2023 meteorological data collected on WXT536, PTB110, and LI-190R instrumentation at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA).
Location: 38.886928, -76.540172
DOI: 10.15468/dl.zmyjbv
title: Occurrence Download

description: A dataset containing 10155346 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Panama"
 ]
}

 The dataset includes 10155346 records from 2135 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006022-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f5aptb
title: Occurrence Download

description: A dataset containing 27673 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Orectolobus maculatus (Bonnaterre, 1788)"
 ]
}

 The dataset includes 27673 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001127-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a76nb5
title: Occurrence Download

description: A dataset containing 485 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Pelagic amphipods (Crustacea: Amphipoda: Hyperiidea) from the southern Gulf of Mexico"
 ]
}

 The dataset includes 485 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011964-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ejgun
title: Occurrence Download

description: A dataset containing 24582 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scarus guacamaia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 24582 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003728-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14722700
title: Microbial iron cycling illuminates the biological contribution and potential climate drivers of deep-sea rare earth element enrichment

description: Table S1. Raw data of maior and trace elements of the GC112 core discussed in this study.

Table S2. Raw data of Fe isotope and two standard deviations (2sd) of the GC112 core discussed in this study.

Table S3. lron species distribution in REY normal and rich layers (n = 3 per layer)

Table S4. Raw data of susceptibility (χ), frequency-dependent susceptibility (χfd%), anhysteresis remanent magnetization (ARM), and soft magnetic component (IRMsoft) of GC112.

Table S5. Raw data of ZFC and FC in the REY-enriched layer of core GC112 discussed in this study.

Table S6. Raw data of κ-T in the REY-enriched layer of core GC112 discussed in this study.

Table S7. Mass percentage of fish teeth in the whole rock and the contribution of fish teeth P to the total P at different depths of core GC112.
DOI: 10.5281/zenodo.17684759
title: Microbial iron cycling illuminates the biological contribution and potential climate drivers of deep-sea rare earth element enrichment

description: Table S1. Raw data of maior and trace elements of the GC112 core discussed in this study.

Table S2. Raw data of Fe isotope and two standard deviations (2sd) of the GC112 core discussed in this study.

Table S3. lron species distribution in REY normal and rich layers (n = 3 per layer)

Table S4. Raw data of susceptibility (χ), frequency-dependent susceptibility (χfd%), anhysteresis remanent magnetization (ARM), and soft magnetic component (IRMsoft) of GC112.

Table S5. Raw data of ZFC and FC in the REY-enriched layer of core GC112 discussed in this study.

Table S6. Raw data of κ-T in the REY-enriched layer of core GC112 discussed in this study.

Table S7. Mass percentage of fish teeth in the whole rock and the contribution of fish teeth P to the total P at different depths of core GC112.
DOI: 10.15468/dl.2yuq8n
title: Occurrence Download

description: A dataset containing 36833 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.27059 10.984,-74.1346 10.984,-74.1346 11.07989,-74.27059 11.07989,-74.27059 10.984))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 36833 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003729-251120083545085/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.29132843
title: <b>A molecular inventory of the faecal microbiomes of 23 marsupial species</b>

description: Despite the recent expansion of culture-independent analyses of animal faecal microbiomes, many lineages remain understudied. Marsupials represent one such group, where despite their iconic status, direct sequencing-based analyses remain limited. Here we present a metagenomic and metabolomic exploration of the faecal microbiomes of 23 <i>Diprotodontia </i>marsupials, producing a reference set of 3,868 prokaryotic and 12,142 viral metagenome-assembled genomes, the majority (&gt;80%) of which represent novel species. As with other animals, host phylogeny is the primary driver of microbiome composition, including distinct profiles for two eucalyptus folivore specialists (koalas and southern greater gliders), suggesting independent solutions to this challenging diet. Expansion of several bacterial and viral lineages were observed in these and other marsupial hosts that likely provide adaptive benefits. Antimicrobial resistance genes were significantly more prevalent in captive than wild animals likely reflecting human interaction. This molecular dataset contributes to our ongoing understanding of animal faecal microbiomes.
DOI: 10.6084/m9.figshare.29132843.v1
title: <b>A molecular inventory of the faecal microbiomes of 23 marsupial species</b>

description: Despite the recent expansion of culture-independent analyses of animal faecal microbiomes, many lineages remain understudied. Marsupials represent one such group, where despite their iconic status, direct sequencing-based analyses remain limited. Here we present a metagenomic and metabolomic exploration of the faecal microbiomes of 23 <i>Diprotodontia </i>marsupials, producing a reference set of 3,868 prokaryotic and 12,142 viral metagenome-assembled genomes, the majority (&gt;80%) of which represent novel species. As with other animals, host phylogeny is the primary driver of microbiome composition, including distinct profiles for two eucalyptus folivore specialists (koalas and southern greater gliders), suggesting independent solutions to this challenging diet. Expansion of several bacterial and viral lineages were observed in these and other marsupial hosts that likely provide adaptive benefits. Antimicrobial resistance genes were significantly more prevalent in captive than wild animals likely reflecting human interaction. This molecular dataset contributes to our ongoing understanding of animal faecal microbiomes.
DOI: 10.5281/zenodo.17684785
title: THE CURE FOR DEATH
DOI: 10.5281/zenodo.17684786
title: THE CURE FOR DEATH
DOI: 10.5281/zenodo.17684799
title: SSA-SDT
DOI: 10.13026/85hv-ct26
title: InReDD-Dataset-PAN924

description: InReDD-Dataset-PAN924 is a collection of 924 radiographic images annotated
with mouth and teeth labels by specialists from the InReDD research group.

InReDD (Interdisciplinary Research Group in Digital Dentistry) is a
collaborative research initiative at the University of Sao Paulo's Ribeirao
Preto Campus (USP-RP), uniting the Department of Computation and Mathematics
(DCM-USP-RP) and the School of Dentistry of Ribeirao Preto (FORP-USP-RP). The
group is dedicated to developing applied technologies for the field of
Odontology.

In this context, the InReDD-Dataset-PAN924 is an image collection from the
field of Odontology. It was developed to support descriptive analyses and to
facilitate the creation and validation of artificial intelligence models. The
data were collected primarily through clinical work at FORP-RP.

This manuscript draws upon a previously published work, _" Development of a
dental digital dataset for research in artificial intelligence: the importance
of labeling performed by radiologists."_ However, certain details have been
adjusted or updated to account for temporal adaptations and contextual
revisions. As a result, portions of the content may not correspond verbatim to
the original publication, although the scientific essence and core
contributions remain preserved.