cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17717964
title: Time Series of Kelp Canopy Maps from Planet Dove (Raster)

description: This dataset contains monthly kelp canopy extent maps for the California coastline. Products are provided as GeoTIFFs (rasters) and were originally generated from 3 m Planet Dove GeoTIFF classifications. For each month, at least five cloud-free Planet Dove images were acquired and classified using a VGG16-U-Net  model trained to identify four classes: kelp, seawater, land, and waves. Monthly kelp canopy extent was produced with a majority filter: pixels labeled as kelp in at least 50% of the input images were retained in the final composite. For full methodological details, see Cavanaugh et al., “High-resolution Planet Dove data identify local drivers of kelp canopy persistence.” Users should cite this publication when using these data.

Contents and file structure



One ZIP file per year-month (e.g., YYYY_MM_PlanetDove_KelpRaster.zip)

Each ZIP contains one GeoTIFF and one shapefile (with associated .shx, .dbf, .prj, etc.):



YYYY_MM_California_KelpCanopy_Raster.tif (GeoTIFF where a value of 1 indicates mapped kelp canopy for that month).

YYYY_MM_California_KelpCanopy_AreaSurveyed.shp (polygons delineating the area that was surveyed by the model (i.e., locations that were covered by at least 1 Planet Dove image and that were not masked for land or clouds). Areas with no data are excluded from this layer.




Spatial & temporal extent



Spatial extent: California coastline

Temporal extent: September for years 2017-2024
DOI: 10.15468/dl.p2w844
title: Occurrence Download

description: A dataset containing 7897 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((9.623457 46.318592, 9.629000 46.368651, 9.561326 46.368651, 9.555822 46.318592, 9.623457 46.318592))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 7897 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042543-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbwnck
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((100.244 2.455178, 100.173673 2.449609, 100.10508 2.43304, 100.03991 2.40588, 99.979769 2.368797, 99.92614 2.322706, 99.880342 2.268741, 99.843503 2.208234, 99.816529 2.142674, 99.800084 2.073675, 99.79456999999999 2.002938, 99.800122 1.932204, 99.816602 1.863214, 99.843603 1.797667, 99.880459 1.737177, 99.92626300000001 1.683231, 99.97988599999999 1.637158, 100.040009 1.600093, 100.105152 1.572945, 100.173711 1.556385, 100.244 1.55082, 100.314289 1.556385, 100.382848 1.572945, 100.447991 1.600093, 100.508114 1.637158, 100.561737 1.683231, 100.607541 1.737177, 100.644397 1.797667, 100.671398 1.863214, 100.687878 1.932204, 100.69343 2.002938, 100.687916 2.073675, 100.671471 2.142674, 100.644497 2.208234, 100.607658 2.268741, 100.56186 2.322706, 100.508231 2.368797, 100.44809 2.40588, 100.38292 2.43304, 100.314327 2.449609, 100.244 2.455178))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042544-251120083545085/datasets/export for details.
DOI: 10.15468/dl.2ymdt7
title: Occurrence Download

description: A dataset containing 413 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dasybranchus lumbricoides",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 413 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042545-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17873155
title: Coarse-Grained Molecular Dynamics Simulation Dataset for Peptide-Lipid Interaction

description: This dataset contains the coarse-grained molecular dynamics simulations used to analyze peptide-lipid interactions using the Martini 3 force field. The system was constructed and coarse-grained following standard Martini workflows, and simulations were performed with GROMACS 2023. After energy minimization with a steepest-descent algorithm, the system underwent several equilibration stages designed to stabilize temperature, pressure, and membrane mechanics. Early equilibration used a gentle thermostat-barostat combination to relax the system, followed by a final equilibration phase employing more accurate coupling schemes suitable for production. The production simulation was run with a 20-fs timestep under semi-isotropic pressure coupling and a temperature-controlled ensemble appropriate for Martini CG simulations. The dataset includes all files required to reproduce the simulation workflow: the mdp parameter file, full topologies (top + itps), the compiled tpr with the complete setup, the initial configuration, the final configuration, and the full trajectory.
DOI: 10.15468/dl.4y57qf
title: Occurrence Download

description: A dataset containing 1109916 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Belgium, Germany, Netherlands)",
 "TaxonKey is one of (Ondatra zibethicus, Myocastor coypus)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1109916 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042513-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17873154
title: Coarse-Grained Molecular Dynamics Simulation Dataset for Peptide-Lipid Interaction

description: This dataset contains the coarse-grained molecular dynamics simulations used to analyze peptide-lipid interactions using the Martini 3 force field. The system was constructed and coarse-grained following standard Martini workflows, and simulations were performed with GROMACS 2023. After energy minimization with a steepest-descent algorithm, the system underwent several equilibration stages designed to stabilize temperature, pressure, and membrane mechanics. Early equilibration used a gentle thermostat-barostat combination to relax the system, followed by a final equilibration phase employing more accurate coupling schemes suitable for production. The production simulation was run with a 20-fs timestep under semi-isotropic pressure coupling and a temperature-controlled ensemble appropriate for Martini CG simulations. The dataset includes all files required to reproduce the simulation workflow: the mdp parameter file, full topologies (top + itps), the compiled tpr with the complete setup, the initial configuration, the final configuration, and the full trajectory.
DOI: 10.17026/ar/eskswu
title: PAN-00006261 - coin/coin-related, Constans (337-350), nummus

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00006261&lt;/p&gt;
description: Modified: 2025-05-26T07:41:27+02:00
DOI: 10.5281/zenodo.17716355
title: California Drone Kelp Canopy Maps for Validation of Planet Dove Products

description: This dataset contains millimeter-scale drone classifications of kelp canopy presence/absence collected along the California coastline. Surveys were conducted during the fall months (August-October) and were designed for use as validation for the Planet Dove kelp canopy maps presented in Cavanaugh et al., “High-resolution Planet Dove data identify local drivers of kelp canopy persistence.” Users should cite this publication when using these data.

Each drone image was classified using spectral-based methods described in Cavanaugh et al. (2021), An automated method for mapping giant kelp canopy dynamics from UAVs, or using Kelp-O-Matic, an open-source deep learning tool for kelp canopy extraction (https://kelp-o-matic.readthedocs.io/en/stable/). The classification method varied by site and year depending on image quality and sensor configuration, but both approaches generated binary presence/absence outputs suitable for validating satellite-derived kelp canopy products. Values of 0 represent “no data,” 1 represent “no kelp,” and 3 represent “kelp.”
DOI: 10.5281/zenodo.17716356
title: California Drone Kelp Canopy Maps for Validation of Planet Dove Products

description: This dataset contains millimeter-scale drone classifications of kelp canopy presence/absence collected along the California coastline. Surveys were conducted during the fall months (August-October) and were designed for use as validation for the Planet Dove kelp canopy maps presented in Cavanaugh et al., “High-resolution Planet Dove data identify local drivers of kelp canopy persistence.” Users should cite this publication when using these data.

Each drone image was classified using spectral-based methods described in Cavanaugh et al. (2021), An automated method for mapping giant kelp canopy dynamics from UAVs, or using Kelp-O-Matic, an open-source deep learning tool for kelp canopy extraction (https://kelp-o-matic.readthedocs.io/en/stable/). The classification method varied by site and year depending on image quality and sensor configuration, but both approaches generated binary presence/absence outputs suitable for validating satellite-derived kelp canopy products. Values of 0 represent “no data,” 1 represent “no kelp,” and 3 represent “kelp.”
DOI: 10.18126/46bb-sz19
title: Videos and front speeds of frontal ring-opening metathesis polymerization (FROMP) of DCPD/ENB with norbornene-functionalized PDMS comonomers
DOI: 10.15468/dl.fe9n86
title: Occurrence Download

description: A dataset containing 1916 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polycarpaea corymbosa",
 "Continent is Africa"
 ]
}

 The dataset includes 1916 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042546-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/xigwe0
title: PAN-00018256 - smelt waste or lump of copper-alloy

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00018256&lt;/p&gt;
description: Modified: 2025-07-11T10:06:53+02:00
DOI: 10.5281/zenodo.17644634
title: Time Series of Kelp Canopy Maps from Planet Dove (Polygon)

description: This dataset contains monthly kelp canopy extent maps for the California coastline. Products are provided as shapefiles and were originally generated from 3 m Planet Dove GeoTIFF classifications. For each month, at least five cloud-free Planet Dove images were acquired and classified using a VGG16-U-Net model trained to identify four classes: kelp, seawater, land, and waves. Monthly kelp canopy extent was produced with a majority filter: pixels labeled as kelp in at least 50% of the input images were retained in the final composite. For full methodological details, see Cavanaugh et al., “High-resolution Planet Dove data identify local drivers of kelp canopy persistence.” Users should cite this publication when using these data.

Contents and file structure



One ZIP file per year-month (e.g., YYYY_MM_PlanetDove_KelpPolygon.zip)

Each ZIP contains two shapefiles (with their associated .shx, .dbf, .prj, etc.):



YYYY_MM_California_KelpCanopy_KelpPresence.shp (polygons indicating mapped kelp canopy for that month).

YYYY_MM_California_KelpCanopy_AreaSurveyed.shp (polygons delineating the area that was surveyed by the model (i.e., locations that were covered by at least 1 Planet Dove image and that were not masked for land or clouds). Areas with no data are excluded from this layer.




Spatial & temporal extent



Spatial extent: California coastline

Temporal extent: September for years 2017-2024
DOI: 10.5281/zenodo.17644635
title: Time Series of Kelp Canopy Maps from Planet Dove (Polygon)

description: This dataset contains monthly kelp canopy extent maps for the California coastline. Products are provided as shapefiles and were originally generated from 3 m Planet Dove GeoTIFF classifications. For each month, at least five cloud-free Planet Dove images were acquired and classified using a VGG16-U-Net model trained to identify four classes: kelp, seawater, land, and waves. Monthly kelp canopy extent was produced with a majority filter: pixels labeled as kelp in at least 50% of the input images were retained in the final composite. For full methodological details, see Cavanaugh et al., “High-resolution Planet Dove data identify local drivers of kelp canopy persistence.” Users should cite this publication when using these data.

Contents and file structure



One ZIP file per year-month (e.g., YYYY_MM_PlanetDove_KelpPolygon.zip)

Each ZIP contains two shapefiles (with their associated .shx, .dbf, .prj, etc.):



YYYY_MM_California_KelpCanopy_KelpPresence.shp (polygons indicating mapped kelp canopy for that month).

YYYY_MM_California_KelpCanopy_AreaSurveyed.shp (polygons delineating the area that was surveyed by the model (i.e., locations that were covered by at least 1 Planet Dove image and that were not masked for land or clouds). Areas with no data are excluded from this layer.




Spatial & temporal extent



Spatial extent: California coastline

Temporal extent: September for years 2017-2024
DOI: 10.15468/dl.rntswu
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((103.301 2.458178, 103.230673 2.452609, 103.162079 2.43604, 103.096909 2.40888, 103.036769 2.371797, 102.983139 2.325706, 102.937341 2.271741, 102.900502 2.211234, 102.873528 2.145673, 102.857083 2.076675, 102.851569 2.005938, 102.857121 1.935204, 102.873601 1.866214, 102.900602 1.800667, 102.937459 1.740177, 102.983263 1.686231, 103.036886 1.640158, 103.097009 1.603093, 103.162152 1.575945, 103.230711 1.559385, 103.301 1.55382, 103.371289 1.559385, 103.439848 1.575945, 103.504991 1.603093, 103.565114 1.640158, 103.618737 1.686231, 103.664541 1.740177, 103.701398 1.800667, 103.728399 1.866214, 103.744879 1.935204, 103.750431 2.005938, 103.744917 2.076675, 103.728472 2.145673, 103.701498 2.211234, 103.664659 2.271741, 103.618861 2.325706, 103.565231 2.371797, 103.505091 2.40888, 103.439921 2.43604, 103.371327 2.452609, 103.301 2.458178))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042547-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fpaxax
title: Occurrence Download

description: A dataset containing 571 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((10.726162 46.270634, 10.732634 46.323159, 10.661643 46.323159, 10.655214 46.270634, 10.726162 46.270634))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 571 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042548-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zym6cb
title: Occurrence Download

description: A dataset containing 8125 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-50.50226 -0.08769,-50.7696 -0.30214,-50.82846 -1.09412,-50.80463 -1.45309,-50.61729 -1.71377,-50.56857 -1.85385,-50.24577 -1.85385,-49.99606 -1.80513,-49.80725 -1.86603,-49.0852 -1.62724,-48.82667 -1.52496,-48.33451 -0.95087,-48.23257 -0.21876,-49.78028 0.53192,-50.22514 0.29097,-50.40545 0.20645,-50.50226 -0.08769))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 8125 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042549-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ynxfh
title: Occurrence Download

description: A dataset containing 1274 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polycarpaea eriantha",
 "Continent is Africa"
 ]
}

 The dataset includes 1274 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042551-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17872993
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2025-11-28

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2025-11-28. 

            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.

            

            
 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!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.15468/dl.kzuh7q
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dasybranchus lunulatus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 154 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042552-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xy63ck
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((111.352 2.459178, 111.281673 2.453609, 111.213079 2.43704, 111.147909 2.40988, 111.087769 2.372797, 111.034139 2.326706, 110.988341 2.272741, 110.951502 2.212234, 110.924528 2.146673, 110.908082 2.077675, 110.902569 2.006938, 110.908121 1.936204, 110.924601 1.867214, 110.951602 1.801667, 110.988458 1.741176, 111.034262 1.687231, 111.087886 1.641158, 111.148009 1.604093, 111.213152 1.576945, 111.281711 1.560385, 111.352 1.55482, 111.422289 1.560385, 111.490848 1.576945, 111.555991 1.604093, 111.616114 1.641158, 111.669738 1.687231, 111.715542 1.741176, 111.752398 1.801667, 111.779399 1.867214, 111.795879 1.936204, 111.801431 2.006938, 111.795918 2.077675, 111.779472 2.146673, 111.752498 2.212234, 111.715659 2.272741, 111.669861 2.326706, 111.616231 2.372797, 111.556091 2.40988, 111.490921 2.43704, 111.422327 2.453609, 111.352 2.459178))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042554-251120083545085/datasets/export for details.
DOI: 10.17026/ar/jszcay
title: PAN-00130367 - coin/coin-related, Constantius II (337-361), voor Julianus, aes III

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00130367&lt;/p&gt;
description: Modified: 2025-10-21T09:22:14+02:00
DOI: 10.15468/dl.y2zkbc
title: Occurrence Download

description: A dataset containing 2422 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polycarpaea linearifolia",
 "Continent is Africa"
 ]
}

 The dataset includes 2422 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042557-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.