cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.b86wqr
title: Occurrence Download

description: A dataset containing 7931 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Viguiera Kunth"
 ]
}

 The dataset includes 7931 records from 240 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0203037-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4nrj63
title: Occurrence Download

description: A dataset containing 28784 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Asclepias L.",
 "BasisOfRecord is one of (Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (United States of America, Canada)"
 ]
}

 The dataset includes 28784 records from 137 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0268043-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r9zxrn
title: Occurrence Download

description: A dataset containing 589 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((11.59486 50.87337,11.59273 50.8914,11.56886 50.89111,11.5602 50.88181,11.57099 50.87212,11.59486 50.87337))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Arthropoda"
 ]
}

 The dataset includes 589 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056857-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mz4dze
title: Occurrence Download

description: A dataset containing 24142 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Wedelia Jacq."
 ]
}

 The dataset includes 24142 records from 429 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0203038-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2r8hku
title: Occurrence Download

description: A dataset containing 109 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gerbilliscus inclusus (Thomas & Wroughton, 1908)"
 ]
}

 The dataset includes 109 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0354225-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vzhyq2
title: Occurrence Download

description: A dataset containing 7923 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Peromyscus difficilis (J.A.Allen, 1891)"
 ]
}

 The dataset includes 7923 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061483-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kgzu2t
title: Occurrence Download

description: A dataset containing 1035 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ellipanthus Hook.f."
 ]
}

 The dataset includes 1035 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0203050-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3fwzrs
title: Occurrence Download

description: A dataset containing 727 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Glossina palpalis (Robineau-Desvoidy, 1830)"
 ]
}

 The dataset includes 727 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058359-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pz9uxy
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mus cypriacus Cucchi, Orth, Auffray, Renaud, Fabre, Catalan, Hadjisterkotis, Bonhomme & Vigne, 2006"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0354222-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10794636
title: An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea

description: The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).

The following Earth System Models were used in building the ensemble:



CMCC-ESM2 (Lovato et al. 2022)

CMCC-CM2-SR5 (Cherchi et al. 2019)

GFDL-ESM4 (Dunne et al., 2020)

MPI-ESM1-2-LR (Mauritsen et al., 2020)

IPSL-CM6A-LR (Boucher et al. 2020)


A description of the downscaling approach and evaluation of the datasets over the European regions is published in Kristiansen et al. 2024.

 Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.

We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00021,  https://doi.org/10.48670/moi-00019.

This data is distributed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
DOI: 10.15468/dl.9hnvja
title: Occurrence Download

description: A dataset containing 24199 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Zinnia L."
 ]
}

 The dataset includes 24199 records from 466 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0203044-210914110416597/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.28240010
title: TEs consensus library and annotation for 80 species of arthropods.

description: In this project we performed Transposable Elements (TEs) identification and analysis for 80 species of arthropods belongs to four major subphyla (Hexapod, Chelicerata, Crustacea, Myriapoda). The aim of the project was to highlight the impact of TEs on genomes evolution of arthropods, and their role in evolution of new genes and expansion of gene families. We produced a detailed curated TEs libraries for each of the analyzed species and has deposited here.
DOI: 10.6084/m9.figshare.28240010.v1
title: TEs consensus library and annotation for 80 species of arthropods.

description: In this project we performed Transposable Elements (TEs) identification and analysis for 80 species of arthropods belongs to four major subphyla (Hexapod, Chelicerata, Crustacea, Myriapoda). The aim of the project was to highlight the impact of TEs on genomes evolution of arthropods, and their role in evolution of new genes and expansion of gene families. We produced a detailed curated TEs libraries for each of the analyzed species and has deposited here.
DOI: 10.15468/dl.2f8zqe
title: Occurrence Download

description: A dataset containing 2399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "DatasetKey is Herbarium specimens of Université de Montpellier 2, Institut de Botanique (MPU))"
 ]
}

 The dataset includes 2399 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004635-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rzn293
title: Occurrence Download

description: A dataset containing 15197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Scleroptila afra (Latham, 1790))",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 15197 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004636-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n35nhp
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.2126 -18.18111,-69.02624 -18.18111,-69.02624 -14.95984,-75.2126 -14.95984,-75.2126 -18.18111))",
 "HasGeospatialIssue is false",
 "TaxonKey is Oreopholus ruficollis (Wagler, 1829)",
 "Year 2021-2025"
 ]
}

 The dataset includes 48 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004639-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h62xeu
title: Occurrence Download

description: A dataset containing 6803 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Acrocephalus arundinaceus (Linnaeus, 1758))",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 6803 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004640-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14392548
title: IGN Train and Validation Data for ICDAR'25 MapText Competition

description: Data set of 2Kx2K image tiles cropped from Napoleonic Cadastre maps of the Val de Marne Archive for the ICDAR'25 Competition on Historical Map Text Detection, Recognition, and Linking.

Annotations and images follow the format described at the competition website and can be evaluated using the official evaluation repository script.

This dataset is a superset of the dataset used in the 2024 edition: the first 80 image from the training set, and the first 15 images from the validation set are the same as the version 1.1 of the IGN Train and Validation Data for ICDAR'24 MapText Competition. However, minor issues in their annotations may have been fixed, so you should use this new dataset instead.

Please note the we also provide an extra synthetic dataset for training, which is released under a different record: "IGN Synthetic Train Data for ICDAR'25 MapText Competition" (10.5281/zenodo.14394546).




 
Train
Validation


Annotations
ign25_train.json
ign25_val.json


Images
train.zip
val.zip


Files
ign25/train/*.jpg
ign25/val/*.jpg


Tiles
228
25


Map Sheets
78
12


Words
25,564
2,725


Label Groups
23,542
2,413


Illegible Words
1,684
274


Truncated Words
1,351
129


Valid Words
23,880
2,451




Original images available at https://archives.valdemarne.fr/recherches/archives-en-ligne/cadastre-napoleonien as of 11 Dec. 2024.
DOI: 10.5281/zenodo.14394545
title: IGN Synthetic Train Data for ICDAR'25 MapText Competition

description: Data set of 2Kx2K synthetic image tiles for the ICDAR'25 Competition on Historical Map Text Detection, Recognition, and Linking.

Annotations and images follow the format described at the competition website and can be evaluated using the official evaluation repository script.

This synthetic dataset is supplementary to the dataset of real tiles IGN Train and Validation Data for ICDAR'25 MapText Competition.

This synthetic training set mimics the style (background and fonts) of the original maps, and leverages the actual, modern land use database from the French government to generate realistic geometries and names from similar geographic areas (both in terms of vocabulary and urban density). This synthetic data is meant to be used as a supplementary training set, and is organized as such.

We also provide a sample for fast download and code testing, containing only the images and ground truth for the first 10 images of the dataset.




 
Synthetic Train
Sample (in sample.zip)


Annotations
ign25synth_train.json
(same)


Images
synthtrain.zip
(same)


Files
ign25synth/train/*.jpg
(same)


Tiles
18,073
10


Map Sheets
a dozen of different styles
1 style


Words
1,622,398
114


Label Groups
1,489,072
91


Illegible Words
33
0


Truncated Words
79,972
2


Valid Words
1,542,426
112




 All data used to generate this dataset is public domain.

Finally, a style_sample.zip file provides some examples for each rendering style. The images it contains are extracted from the main dataset and should not be added to it.

 

ℹ️ This version 3 features some improvements which impact all ZIP files:



Some text region were rendered but not added to the ground truth — this is now fixed.

Truncation detection was improved, but this should not change the actual content.

Some images were generated with wrong shapes, leading to them being discarded in the final dataset. They are now exported correctly and included in the new version. As a result, the new dataset is larger.

Finally, we added some sample images for each style in a new style_sample.zip file.


ℹ️ Version 2 added a fix in the  ign25synth_train.json file from which very small regions (<1 square pixel) were removed to mitigate evaluation issues. This results in a smaller number of total words and groups, but the number of valid words remains the same compared to version 1. The sample in sample.zip and the images in ign25synth_train.zip were not changed and are identical to version 1.
DOI: 10.5281/zenodo.14394546
title: IGN Synthetic Train Data for ICDAR'25 MapText Competition

description: Data set of 2Kx2K synthetic image tiles for the ICDAR'25 Competition on Historical Map Text Detection, Recognition, and Linking.

Annotations and images follow the format described at the competition website and can be evaluated using the official evaluation repository script.

This synthetic dataset is supplementary to the dataset of real tiles IGN Train and Validation Data for ICDAR'25 MapText Competition.

This synthetic training set mimics the style (background and fonts) of the original maps, and leverages the actual, modern land use database from the French government to generate realistic geometries and names from similar geographic areas (both in terms of vocabulary and urban density). This synthetic data is meant to be used as a supplementary training set, and is organized as such.

We also provide a sample for fast download and code testing, containing only the images and ground truth for the first 10 images of the dataset.




 
Synthetic Train
Sample (in sample.zip)


Annotations
ign25synth_train.json
(same)


Images
synthtrain.zip
(same)


Files
ign25synth/train/*.jpg
(same)


Tiles
18,072
10


Map Sheets
a dozen of different styles
1 style


Words
1,615,354
111


Label Groups
1,485,898
90


Illegible Words
34
0


Truncated Words
75,569
2


Valid Words
1,539,785
109




 All data used to generate this dataset is public domain.
DOI: 10.5281/zenodo.14608821
title: IGN Synthetic Train Data for ICDAR'25 MapText Competition

description: Data set of 2Kx2K synthetic image tiles for the ICDAR'25 Competition on Historical Map Text Detection, Recognition, and Linking.

Annotations and images follow the format described at the competition website and can be evaluated using the official evaluation repository script.

This synthetic dataset is supplementary to the dataset of real tiles IGN Train and Validation Data for ICDAR'25 MapText Competition.

This synthetic training set mimics the style (background and fonts) of the original maps, and leverages the actual, modern land use database from the French government to generate realistic geometries and names from similar geographic areas (both in terms of vocabulary and urban density). This synthetic data is meant to be used as a supplementary training set, and is organized as such.

We also provide a sample for fast download and code testing, containing only the images and ground truth for the first 10 images of the dataset.




 
Synthetic Train
Sample (in sample.zip)


Annotations
ign25synth_train.json
(same)


Images
synthtrain.zip
(same)


Files
ign25synth/train/*.jpg
(same)


Tiles
18,072
10


Map Sheets
a dozen of different styles
1 style


Words
1,614,631
111


Label Groups
1,485,245
90


Illegible Words
34
0


Truncated Words
74,846
2


Valid Words
1,539,785
109




 All data used to generate this dataset is public domain.

 

ℹ️ This version 2 contains a fix in the  ign25synth_train.json file from which very small regions (<1 square pixel) were removed to mitigate evaluation issues. This results in a smaller number of total words and groups, but the number of valid words remains the same compared to version 1. The sample in sample.zip and the images in ign25synth_train.zip were not changed and are identical to version 1.
DOI: 10.5281/zenodo.14704475
title: IGN Synthetic Train Data for ICDAR'25 MapText Competition

description: Data set of 2Kx2K synthetic image tiles for the ICDAR'25 Competition on Historical Map Text Detection, Recognition, and Linking.

Annotations and images follow the format described at the competition website and can be evaluated using the official evaluation repository script.

This synthetic dataset is supplementary to the dataset of real tiles IGN Train and Validation Data for ICDAR'25 MapText Competition.

This synthetic training set mimics the style (background and fonts) of the original maps, and leverages the actual, modern land use database from the French government to generate realistic geometries and names from similar geographic areas (both in terms of vocabulary and urban density). This synthetic data is meant to be used as a supplementary training set, and is organized as such.

We also provide a sample for fast download and code testing, containing only the images and ground truth for the first 10 images of the dataset.




 
Synthetic Train
Sample (in sample.zip)


Annotations
ign25synth_train.json
(same)


Images
synthtrain.zip
(same)


Files
ign25synth/train/*.jpg
(same)


Tiles
18,073
10


Map Sheets
a dozen of different styles
1 style


Words
1,622,398
114


Label Groups
1,489,072
91


Illegible Words
33
0


Truncated Words
79,972
2


Valid Words
1,542,426
112




 All data used to generate this dataset is public domain.

Finally, a style_sample.zip file provides some examples for each rendering style. The images it contains are extracted from the main dataset and should not be added to it.

 

ℹ️ This version 3 features some improvements which impact all ZIP files:



Some text region were rendered but not added to the ground truth — this is now fixed.

Truncation detection was improved, but this should not change the actual content.

Some images were generated with wrong shapes, leading to them being discarded in the final dataset. They are now exported correctly and included in the new version. As a result, the new dataset is larger.

Finally, we added some sample images for each style in a new style_sample.zip file.


ℹ️ Version 2 added a fix in the  ign25synth_train.json file from which very small regions (<1 square pixel) were removed to mitigate evaluation issues. This results in a smaller number of total words and groups, but the number of valid words remains the same compared to version 1. The sample in sample.zip and the images in ign25synth_train.zip were not changed and are identical to version 1.
DOI: 10.5281/zenodo.14704660
title: Horizontal to vertical spectral ratios (HVNSR) on ambient noise recordings of stations of the 6N network during the activities of the EMERSITO INGV emergency group in Ancona (Italy) following the 2022 MW 5.5 Costa Marchigiana-Pesarese earthquake

description: These files contain the results of the horizontal-to-vertical spectral ratios on the ambient noise (HVNSR) recorded by stations of the temporary network 6N, installed by the INGV EMERSITO emergency group at Ancona (Central Italy, Marche Region) for about 4 months after the start of a seismic sequence in the Adriatic Sea in 2022. The mainshock of the sequence occured the 9th of November 2022 and its moment magnitude was 5.5. Eleven stations, equipped with velocimeters and accelerometers, were deployed at Ancona, at about 45-50km from the epicenters of the sequence. They recorded both ambient noise and aftershocks of the sequence from November 2022 to February 2023; the complete dataset can be accessed under the European Integrated Data Archive (EIDA) using the link: 10.13127/sd/qctgd6c-3a.

The files are formatted as four column separated by commas; the first column is the frequency in Hz, the second the average HVNSR, the third is the average HVNSR minus one standard deviation, the fourth is the average HVNSR plus one standard deviation.

The stations are named as: CMA05, CMA06, CMA07, CMA08, CMA09, CMA10, CMA11, CMA12, CMA13, CMA14 and CMA15.

These HVNSR are presented as figures inserted in the station sheets that is going to be published in a summary paper in Earth System Science Data (ESSD) journal with the doi: 10.5194/essd-2024-162.