cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ym2wmc
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magnolia champaca",
 "Country is Philippines",
 "HasCoordinate is true"
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007300-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jz5w26
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magnolia champaca",
 "Country is Seychelles",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007301-251009101135966/datasets/export for details.
DOI: 10.5281/zenodo.17317051
title: Dataset for Shipwreck Susceptibility Predictive Modeling

description: README

1. DATASET OVERVIEWThis dataset is compiled for advanced Shipwreck Susceptibility Mapping research using Machine Learning (ML) models (ANN, RF, SVM) in the Chinese adjacent seas. It provides a balanced set of historical shipwreck locations and non-shipwreck locations, alongside 16 high-resolution marine environmental conditioning factors.

The primary objective is to offer a standardized, model-ready resource for researchers to train, validate, and benchmark novel spatial prediction algorithms.

2. FILE ORGANIZATIONThe dataset is organized into the following main folders:

shipwreckData: Contains the historical shipwreck inventory.conditioningData: Stores the 16 conditioning factors and the geographic extent of the study area.susceptibilityMapping: Includes the final susceptibility maps and model assessment metrics.code: Contains the Python scripts used for data processing and analysis.README:Guide to file organization, providing concise descriptions of each file's purpose and usage.

3. DATA CONTENT DESCRIPTION

A. shipwreckData:

Format: CSV and GeoDatabase (.gdb).

Content: Critical variables for 856 historical shipwreck sites, including vessel name, nationality, lost date, longitude, and latitude.

B. conditioningData:

Format: GeoDatabase (.gdb) folders and GeoTIFF raster files.

Structure: Organized into four GDBs:

studyArea.gdb: Contains the geographic extent of the dataset.

geospatial conditions.gdb: Includes Underwater depth, distance to mainland coastline, and ship density.

hydrodynamic conditions.gdb: Includes Rotation of surface wind stress, Atmosphere relative vorticity, Swell wave direction, Swell wave height, and Wave height.

depositional conditions.gdb: Includes Net Primary Productivity (Nppv), pH, temperature, Salinity, Total zooplankton, Total chlorophyll, Dissolved oxygen, and Total phytoplankton.

C. susceptibilityMapping:

Format: Raster maps (e.g., GeoTIFF) and tabular metrics (e.g., CSV).

Content: Contains three subfolders (one for ANN, RF, and SVM results). Each subfolder includes the final Susceptibility Map and a file listing model assessment metrics (Accuracy, Precision, Sensitivity, Specificity, Kappa, and AUC) for training and validation data.

4. CODE EXECUTION (code folder)The code folder provides Python scripts for the three predictive algorithms (ANN, RF, SVM).

Prerequisites: You must first install the necessary Python environment listed in the requirements.txt file.

Input Data: The scripts utilize the pre-processed and generalized raster data located in the training2025new_FR.gdb GeoDatabase, along with the trainingset and validatingset CSV files.

Purpose: The code ensures the replicability of the susceptibility analysis and serves as a baseline benchmark for comparing new algorithms.
DOI: 10.5281/zenodo.17317052
title: Dataset for Shipwreck Susceptibility Predictive Modeling

description: README

1. DATASET OVERVIEWThis dataset is compiled for advanced Shipwreck Susceptibility Mapping research using Machine Learning (ML) models (ANN, RF, SVM) in the Chinese adjacent seas. It provides a balanced set of historical shipwreck locations and non-shipwreck locations, alongside 16 high-resolution marine environmental conditioning factors.

The primary objective is to offer a standardized, model-ready resource for researchers to train, validate, and benchmark novel spatial prediction algorithms.

2. FILE ORGANIZATIONThe dataset is organized into the following main folders:

shipwreckData: Contains the historical shipwreck inventory.conditioningData: Stores the 16 conditioning factors and the geographic extent of the study area.susceptibilityMapping: Includes the final susceptibility maps and model assessment metrics.code: Contains the Python scripts used for data processing and analysis.README:Guide to file organization, providing concise descriptions of each file's purpose and usage.

3. DATA CONTENT DESCRIPTION

A. shipwreckData:

Format: CSV and GeoDatabase (.gdb).

Content: Critical variables for 856 historical shipwreck sites, including vessel name, nationality, lost date, longitude, and latitude.

B. conditioningData:

Format: GeoDatabase (.gdb) folders and GeoTIFF raster files.

Structure: Organized into four GDBs:

studyArea.gdb: Contains the geographic extent of the dataset.

geospatial conditions.gdb: Includes Underwater depth, distance to mainland coastline, and ship density.

hydrodynamic conditions.gdb: Includes Rotation of surface wind stress, Atmosphere relative vorticity, Swell wave direction, Swell wave height, and Wave height.

depositional conditions.gdb: Includes Net Primary Productivity (Nppv), pH, temperature, Salinity, Total zooplankton, Total chlorophyll, Dissolved oxygen, and Total phytoplankton.

C. susceptibilityMapping:

Format: Raster maps (e.g., GeoTIFF) and tabular metrics (e.g., CSV).

Content: Contains three subfolders (one for ANN, RF, and SVM results). Each subfolder includes the final Susceptibility Map and a file listing model assessment metrics (Accuracy, Precision, Sensitivity, Specificity, Kappa, and AUC) for training and validation data.

4. CODE EXECUTION (code folder)The code folder provides Python scripts for the three predictive algorithms (ANN, RF, SVM).

Prerequisites: You must first install the necessary Python environment listed in the requirements.txt file.

Input Data: The scripts utilize the pre-processed and generalized raster data located in the training2025new_FR.gdb GeoDatabase, along with the trainingset and validatingset CSV files.

Purpose: The code ensures the replicability of the susceptibility analysis and serves as a baseline benchmark for comparing new algorithms.
DOI: 10.5281/zenodo.15337046
title: Deliverable D5.1 - Mapping of PM(T) concerns in EU, including essential uses -  Report and Annexes 1 and 2 (database and documentation)

description: Deliverable 5.1 from the PROMISCES dives into the mapping results and proposes ways to improve vPvM/PMT substances categorization for diagnosis, identifying the principal uses of these substances and contributing to appropriate regulatory management in the context of a circular economy in the soil-water-sediment system.

The database used for mapping PM(T) substances, i.e., creating connections, associations, or representations between substances for analysis, classification, modeling, or data visualization, is included.

Quick version description: 

V1 includes a dataset as a CSV file.

V2 includes an updated dataset and the accepted report describing the mapping results.  

V3 includes The PROMISCES_D5-1_Mapping-Annex-I_v3.csv document, a dataset where all substances listed in the NORMAN SustDat dataset were categorized.
DOI: 10.5281/zenodo.11543653
title: Deliverable D5.1 - Mapping of PM(T) concerns in EU, including essential uses -  Report and Annexes 1 and 2 (database and documentation)

description: Deliverable 5.1 from the PROMISCES dives into the mapping results and proposes ways to improve vPvM/PMT substances categorization for diagnosis, identifying the principal uses of these substances and contributing to appropriate regulatory management in the context of a circular economy in the soil-water-sediment system.

The database used for mapping PM(T) substances, i.e., creating connections, associations, or representations between substances for analysis, classification, modeling, or data visualization, is included.

Quick version description: 

V1 includes a dataset as a CSV file.

V2 includes an updated dataset and the accepted report describing the mapping results.  

V3 includes The PROMISCES_D5-1_Mapping-Annex-I_v3.csv document, a dataset where all substances listed in the NORMAN SustDat dataset were categorized.
DOI: 10.25582/data.2024-04.3081077/2545998
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3081078/2545999
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3081079/2546000
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3081080/2546001
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3081081/2546002
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3081082/2546003
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3081083/2546004
title: Data for EMSL Project 51287 from April 2024
DOI: 10.15468/dl.dsvwew
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-73.91957 40.91972,-73.96557 40.82418,-74.01637 40.75972,-74.02667 40.70196,-74.05413 40.65197,-74.08915 40.64989,-74.09876 40.6478,-74.13789 40.64416,-74.16124 40.64728,-74.17772 40.64728,-74.19351 40.64364,-74.20312 40.62748,-74.20518 40.61185,-74.19969 40.59986,-74.20724 40.58891,-74.21479 40.55709,-74.23333 40.5597,-74.24569 40.5524,-74.25049 40.54248,-74.24775 40.52265,-74.25804 40.51169,-74.26148 40.49916,-74.25942 40.48872,-74.22509 40.47566,-73.97861 40.51952,-73.9024 40.49081,-73.89279 40.5049,-73.80422 40.53152,-73.76234 40.53257,-73.75891 40.56179,-73.73351 40.58526,-73.74586 40.60611,-73.76852 40.61497,-73.76989 40.62488,-73.75067 40.63061,-73.74106 40.64624,-73.72252 40.64885,-73.72939 40.66656,-73.71771 40.71446,-73.69849 40.7488,-73.75067 40.7826,-73.77882 40.81015,-73.78088 40.82574,-73.75754 40.84496,-73.74724 40.87197,-73.91957 40.91972))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Medicago lupulina L."
 ]
}

 The dataset includes 24 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005566-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-04.3081085/2546005
title: Data for EMSL Project 51287 from April 2024
DOI: 10.15468/dl.my92tf
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Entedon ergias Walker, 1839",
 "HasCoordinate is true"
 ]
}

 The dataset includes 90 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005565-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-04.3081086/2546006
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3081089/2546007
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3081090/2546008
title: Data for EMSL Project 51287 from April 2024
DOI: 10.15468/dl.gsycuc
title: Occurrence Download

description: A dataset containing 1029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Solanum scabrum Mill.",
 "Continent is Africa"
 ]
}

 The dataset includes 1029 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005567-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1207055.10fu
title: SH1207055.10FU

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/sh1207056.10fu
title: SH1207056.10FU

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/sh1207057.10fu
title: SH1207057.10FU

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/sh1207058.10fu
title: SH1207058.10FU

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/sh1207060.10fu
title: SH1207060.10FU

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/sh1207061.10fu
title: SH1207061.10FU

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/sh1207064.10fu
title: SH1207064.10FU

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/sh1207068.10fu
title: SH1207068.10FU

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: 10.15468/dl.8c225k
title: Occurrence Download

description: A dataset containing 2902 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hyptis alata (Raf.) Shinners"
 ]
}

 The dataset includes 2902 records from 97 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0251650-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5qzy4q
title: Occurrence Download

description: A dataset containing 134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Massaria inquinans (Tode) De Not.)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 134 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0245201-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc112036751
title: Cuneiform Tablet 1913.14.1685, Spurlock 1913.14.1685

description: [Text Description:] Administrative tablet ❧ [Text or Publication Number Note:] Spurlock 1913.14.1685; CDLI P421536 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Yellowish White (10YR 9/1.5) ❧ [Physical Object Note:] 3.2 x 2.2 x 1.6 cm (10 g)