cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/776e311c-9ccf-4fab-886d-c1886ff15fcf
title: Trained Model and Data for Trajectory Predictions of Autonomous Surface Vessels in Urban Canals

description: This dataset contains artificial data consisting of trajectories of multiple vessels navigating in to-scale canal maps of Amsterdam and a trained prediction model on this data. This dataset is related to the IEEE 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) proceedings titled "Interaction-Aware Sampling-Based MPC with Learned Local Goal Prediction".
DOI: 10.4121/776e311c-9ccf-4fab-886d-c1886ff15fcf.v1
title: Trained Model and Data for Trajectory Predictions of Autonomous Surface Vessels in Urban Canals

description: This dataset contains artificial data consisting of trajectories of multiple vessels navigating in to-scale canal maps of Amsterdam and a trained prediction model on this data. This dataset is related to the IEEE 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) proceedings titled "Interaction-Aware Sampling-Based MPC with Learned Local Goal Prediction".
DOI: 10.15468/dl.ar6qkf
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Camellia pilosperma S.Y.Liang"
 ]
}

 The dataset includes 26 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025889-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p7fpxw
title: Occurrence Download

description: A dataset containing 543 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is one of (Chinese Taipei, China, Hong Kong)",
 "TaxonKey is Meliosma myriantha Siebold & Zucc."
 ]
}

 The dataset includes 543 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025890-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14197393
title: Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic

description: Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).

The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (https://hermes.acri.fr/) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).

The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.

Storage

All variables are stored in the same daily file: PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.

The PFT variables are stored using linear packing (see https://nco.sourceforge.net/nco.html#Linear-Packing) as 16-bits unsigned integers (NC_USHORT), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (=(2**16 - 2) * 1.54e-3), with a discretization step of 1.54e-3.As for the PFT variables, these values are in percent points.

References



El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,J. Geophys. Res. Oceans 124, 1357–1378, https://doi.org/10.1029/2018jc014450, 2019



El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”,Ocean Science 20 no. 1, 217−39. https://doi.org/10.5194/os-20-217-2024, 2024



Changelog

v1.2



[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).

[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.


v1.1



[2023-08-30] Store PFT data using linear packing. 


v1.0



[2023-08-03] those data are reorganised, and metadata is added, using the script https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads

[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`
DOI: 10.5281/zenodo.14197394
title: Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic

description: Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).

The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (https://hermes.acri.fr/) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).

The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.

Storage

All variables are stored in the same daily file: PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.

The PFT variables are stored using linear packing (see https://nco.sourceforge.net/nco.html#Linear-Packing) as 16-bits unsigned integers (NC_USHORT), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (=(2**16 - 2) * 1.54e-3), with a discretization step of 1.54e-3.As for the PFT variables, these values are in percent points.

References



El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,J. Geophys. Res. Oceans 124, 1357–1378, https://doi.org/10.1029/2018jc014450, 2019



El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”,Ocean Science 20 no. 1, 217−39. https://doi.org/10.5194/os-20-217-2024, 2024



Changelog

v1.2



[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).

[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.


v1.1



[2023-08-30] Store PFT data using linear packing. 


v1.0



[2023-08-03] those data are reorganised, and metadata is added, using the script https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads

[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`
DOI: 10.15468/dl.4kw99b
title: Occurrence Download

description: A dataset containing 21908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Agave americana L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 21908 records from 220 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025891-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.68e9na
title: Occurrence Download

description: A dataset containing 160 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Dominican Republic",
 "TaxonKey is Fuchsia triphylla L."
 ]
}

 The dataset includes 160 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025894-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4nvy83
title: Occurrence Download

description: A dataset listing the 10039 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-2.4939 50.61437,-2.43095 50.63966,-2.39947 50.64498,-2.2379 50.62502,-2.19594 50.637,-2.19594 50.80836,-2.27987 50.87461,-2.68485 50.86534,-2.78766 50.79908,-2.77717 50.72076,-2.4939 50.61437))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 10039 records were derived from 294 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025881-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4pkfj6
title: Occurrence Download

description: A dataset containing 12291 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Campylorhamphus pusillus (P.L.Sclater, 1860)"
 ]
}

 The dataset includes 12291 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025893-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14197648
title: Tables
DOI: 10.5281/zenodo.14197649
title: Tables
DOI: 10.15468/dl.6qxmps
title: Occurrence Download

description: A dataset containing 2445 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Medicago ciliaris (L.) All."
 ]
}

 The dataset includes 2445 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025896-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9qcuvs
title: Occurrence Download

description: A dataset containing 141 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Agave fourcroydes Lem.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 141 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025897-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5uc3d6
title: Occurrence Download

description: A dataset listing the 33712 species recorded in GBIF matching the query:
{
 "Country" : [
 "is Egypt"
 ]
}

 The dataset's 33712 records were derived from 1150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025886-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nw3f8e
title: Occurrence Download

description: A dataset containing 249081 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Invertebrate Site Register - England 1738-2005"
 ]
}

 The dataset includes 249081 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019863-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s29pjb
title: Occurrence Download

description: A dataset containing 249752 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Texas A&M University, S.M. Tracy Herbarium"
 ]
}

 The dataset includes 249752 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019862-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ue9v47
title: Occurrence Download

description: A dataset containing 247402 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Records of vascular plants, bryophytes and lichens from Ukrainian Grassland Database"
 ]
}

 The dataset includes 247402 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019861-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1285/i30350107n3p175
title: Colophon
DOI: 10.15468/dl.2cuhzs
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Picea farreri C.N.Page & Rushforth"
 ]
}

 The dataset includes 24 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016897-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r323jg
title: Occurrence Download

description: A dataset containing 1967 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Cutleria multifida (Turner) Grev.",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 1967 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016898-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/7p2897
title: New species of Phallus from a subtropical forest in Xishuangbanna, China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Li, Huili, Mortimer, Peter E., Karunarathna, Samantha C., Xu, Jianchu, Hyde, Kevin D. (2014): New species of Phallus from a subtropical forest in Xishuangbanna, China. Phytotaxa 163 (2): 91-103, DOI: 10.11646/phytotaxa.163.2.3, URL: http://dx.doi.org/10.11646/phytotaxa.163.2.3AbstractSeven collections of Phallus species were made in surveys of a subtropical forest in Xishuangbanna in Yunnan Province, China, during the wet season of 2012. Macro and micro characters, together with nrITS sequence data, were used to separate the collections into three species. Phallus mengsongensis and P. serrata are introduced as species new to science, while a further Phallus species is described, but not formally introduced, due to paucity of material. Macro and micro descriptions, colour photographs, line drawings and nrITS phylogenetic data for all three species are provided, which are discussed in relation to similar species in these genera.
DOI: 10.15156/bio/sh0199200.10fu
title: SH0199200.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199201.10fu
title: SH0199201.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199202.10fu
title: SH0199202.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199203.10fu
title: SH0199203.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199204.10fu
title: SH0199204.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199205.10fu
title: SH0199205.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199206.10fu
title: SH0199206.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.