cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15182980
title: Landslide susceptibility maps – Dominica, 2025

description: Science Case Name



 Multi-hazards in the Caribbean SIDS





Dataset Name/Title



Landslide susceptibility maps – Dominica, 2025 





Dataset Description



Landslide susceptibility maps





Key Methodologies



As the basis for the landslide susceptibility assessment a data layer was generated that represents homogeneous terrain units. Initially an attempt was made to generate these automatically, using the r.slopeunits application, which generates Terrain Units using a set of parameters from a Digital Elevation Model. To compute the susceptibility, we opted for a terrain unit partition and for the implementation of statistical models, in combination with expert-based mapping. These models learn from past events (and specifically from past landslide occurrences) to find patterns with respect to a set of predisposing factors. On the basis of these patterns a prediction is then made on the expected unstable locations in the future. To assess the susceptibility, we used a statistical model known as binomial Generalized Linear Model or Logistic Regression. 





Temporal Domain



The maps are generated for the current situation (2025).





Spatial Domain



The maps cover the country of Dominica.





Key indicators



Landslide susceptibility classes (Very low, Low, Moderate, High, Very high).  





Data format



GeoTIF





Source data



The maps were generated by the University of Twente 





Accessibility



Zenodo, https://doi.org/10.5281/zenodo.15182981





Stakeholder Relevance



Identifying area affected by landslide and expected unstable area for future planning of landuse and mitigation measures





Limitations/Assumptions



The maps does not provide information on the actual landslide runout, and focuses on the initiation zones, based on the Geomorphological units





Additional Output/Information

NA




Contact Information



University of Twente, Cees van Westen & Luigi Lombardo
DOI: 10.5281/zenodo.15182981
title: Landslide susceptibility maps – Dominica, 2025

description: Science Case Name



 Multi-hazards in the Caribbean SIDS





Dataset Name/Title



Landslide susceptibility maps – Dominica, 2025 





Dataset Description



Landslide susceptibility maps





Key Methodologies



As the basis for the landslide susceptibility assessment a data layer was generated that represents homogeneous terrain units. Initially an attempt was made to generate these automatically, using the r.slopeunits application, which generates Terrain Units using a set of parameters from a Digital Elevation Model. To compute the susceptibility, we opted for a terrain unit partition and for the implementation of statistical models, in combination with expert-based mapping. These models learn from past events (and specifically from past landslide occurrences) to find patterns with respect to a set of predisposing factors. On the basis of these patterns a prediction is then made on the expected unstable locations in the future. To assess the susceptibility, we used a statistical model known as binomial Generalized Linear Model or Logistic Regression. 





Temporal Domain



The maps are generated for the current situation (2025).





Spatial Domain



The maps cover the country of Dominica.





Key indicators



Landslide susceptibility classes (Very low, Low, Moderate, High, Very high).  





Data format



GeoTIF





Source data



The maps were generated by the University of Twente 





Accessibility



Zenodo, https://doi.org/10.5281/zenodo.15182981





Stakeholder Relevance



Identifying area affected by landslide and expected unstable area for future planning of landuse and mitigation measures





Limitations/Assumptions



The maps does not provide information on the actual landslide runout, and focuses on the initiation zones, based on the Geomorphological units





Additional Output/Information

NA




Contact Information



University of Twente, Cees van Westen & Luigi Lombardo
DOI: 10.15468/dl.2zxsu6
title: Occurrence Download

description: A dataset containing 3847 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Vincetoxicum rossicum (Kleopow) Barbar.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Canada",
 "Year is one of (1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024)"
 ]
}

 The dataset includes 3847 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001286-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14778116
title: An extended POLIPHON conversion factor dataset

description: This is an extended dataset of POLIPHON (POlarization LIdar PHOtometer Networking) conversion factors for retrieving dust-related CCN and INP concentration profiles over the world (Mamouri and Ansmann, 2015, 2016). The dataset is derived based on the historical AERONET (Aerosol RObotic NETwork) (Holben et al., 1998) database worldwide using the method from He et al. (2023). The converison factors include C_v_d, C_250_d, C_s_d, C_s_100_d, C_100_d, and X_d, while the corresponding linear Pearson correlation coefficients R_v_d, R_250,d, R_s_d, R_s, and R_s_100_d are also provide. In addition, aerosol optical depth (AOD) at 532 nm and Angstrom exponent between 440 nm and 870 nm are provided. The name, latitude, and longitude of each site is provided according to the information from AERONET. Overall, there are 198 AERONET sites included in this data set. L15 and L20 data set is derived using AERONET level-1.5 and level-2.0 AOD data product, respectively.
DOI: 10.5281/zenodo.14969598
title: An extended POLIPHON conversion factor dataset for dust

description: This is an extended dataset of POLIPHON (POlarization LIdar PHOtometer Networking) conversion factors for retrieving dust-related CCN and INP concentration profiles over the world (Mamouri and Ansmann, 2015, 2016). The dataset is derived based on the historical AERONET (Aerosol RObotic NETwork) (Holben et al., 1998) database worldwide using the method from He et al. (2023). The converison factors include C_v_d, C_250_d, C_s_d, C_s_100_d, C_100_d, and X_d, while the corresponding linear Pearson correlation coefficients R_v_d, R_250,d, R_s_d, R_s, and R_s_100_d are also provide. In addition, aerosol optical depth (AOD) at 532 nm and Ångström exponent between 440 nm and 870 nm are also provided. The name, latitude, and longitude of each site is provided according to the information from AERONET. Overall, there are 198 AERONET sites included in this data set. Here the L15 conversion factor data set is derived using AERONET level-2.0 AOD product together with level-1.5 aerosol inversion product. Here the L20 conversion factor data set is derived using AERONET level-2.0 AOD product together with level-2.0 aerosol inversion product, which makes much fewer sites with conversion factors available.
DOI: 10.5281/zenodo.15025306
title: An extended POLIPHON conversion factor dataset for dust

description: This is an extended dataset of POLIPHON (POlarization LIdar PHOtometer Networking) conversion factors for retrieving dust-related CCN and INP concentration profiles over the world (Mamouri and Ansmann, 2015, 2016). The dataset is derived based on the historical AERONET (Aerosol RObotic NETwork) (Holben et al., 1998) database worldwide using the method from He et al. (2023). The converison factors include C_v_d, C_250_d, C_s_d, C_s_100_d, C_100_d, and X_d, while the corresponding linear Pearson correlation coefficients R_v_d, R_250,d, R_s_d, R_s, and R_s_100_d are also provide. In addition, aerosol optical depth (AOD) at 532 nm and Ångström exponent between 440 nm and 870 nm are also provided. The name, latitude, and longitude of each site is provided according to the information from AERONET. Overall, there are 198 AERONET sites included in this data set. Here the L15 conversion factor data set is derived using AERONET level-2.0 AOD product together with level-1.5 aerosol inversion product. Here the L20 conversion factor data set is derived using AERONET level-2.0 AOD product together with level-2.0 aerosol inversion product, which makes much fewer sites with conversion factors available.
DOI: 10.5281/zenodo.15227823
title: An extended POLIPHON conversion factor dataset for dust

description: This is an extended dataset of POLIPHON (POlarization LIdar PHOtometer Networking) conversion factors for retrieving dust-related CCN and INP concentration profiles over the world (Mamouri and Ansmann, 2015, 2016). The dataset is derived based on the historical AERONET (Aerosol RObotic NETwork) (Holben et al., 1998) database worldwide using the method from He et al. (2023). The converison factors include C_v_d, C_250_d, C_s_d, C_s_100_d, C_100_d, and X_d, while the corresponding linear Pearson correlation coefficients R_v_d, R_250,d, R_s_d, R_s, and R_s_100_d are also provide. In addition, aerosol optical depth (AOD) at 532 nm and Ångström exponent between 440 nm and 870 nm are also provided. The name, latitude, and longitude of each site is provided according to the information from AERONET. Overall, there are 198 AERONET sites included in this data set. Here the L15 conversion factor data set is derived using AERONET level-2.0 AOD product together with level-1.5 aerosol inversion product. Here the L20 conversion factor data set is derived using AERONET level-2.0 AOD product together with level-2.0 aerosol inversion product, which makes much fewer sites with conversion factors available.
DOI: 10.5281/zenodo.15281078
title: An extended POLIPHON conversion factor dataset for dust

description: This is an extended dataset of POLIPHON (POlarization LIdar PHOtometer Networking) conversion factors for retrieving dust-related CCN and INP concentration profiles over the world (Mamouri and Ansmann, 2015, 2016). The dataset is derived based on the historical AERONET (Aerosol RObotic NETwork) (Holben et al., 1998) database worldwide using the method from He et al. (2023). The converison factors include C_v_d, C_250_d, C_s_d, C_s_100_d, C_100_d, and X_d, while the corresponding linear Pearson correlation coefficients R_v_d, R_250,d, R_s_d, R_s, and R_s_100_d are also provide. In addition, aerosol optical depth (AOD) at 532 nm and Ångström exponent between 440 nm and 870 nm are also provided. The name, latitude, and longitude of each site is provided according to the information from AERONET. Overall, there are 198 AERONET sites included in this data set. Here the L15 conversion factor data set is derived using AERONET level-2.0 AOD product together with level-1.5 aerosol inversion product. Here the L20 conversion factor data set is derived using AERONET level-2.0 AOD product together with level-2.0 aerosol inversion product, which makes much fewer sites with conversion factors available.
DOI: 10.5281/zenodo.8246489
title: An extended POLIPHON conversion factor dataset

description: This is an extended dataset of POLIPHON (POlarization LIdar PHOtometer Networking) conversion factors for retrieving dust-related CCN and INP concentration profiles over the world (Mamouri and Ansmann, 2015, 2016). The dataset is derived based on the historical AERONET (Aerosol RObotic NETwork) (Holben et al., 1998) database worldwide using the method from He et al. (2023). The converison factors include C_v_d, C_250_d, C_s_d, C_s_100_d, C_100_d, and X_d, while the corresponding linear Pearson correlation coefficients R_v_d, R_250,d, R_s_d, R_s, and R_s_100_d are also provide. In addition, aerosol optical depth (AOD) at 532 nm and Angstrom exponent between 440 nm and 870 nm are provided. The name, latitude, and longitude of each site is provided according to the information from AERONET. Overall, there are 198 AERONET sites included in this data set.
DOI: 10.15468/dl.xmpcta
title: Occurrence Download

description: A dataset containing 1398030 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 7a070ba0-bafb-11d9-8e53-b8a03c50a862",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 1398030 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001275-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pqysgg
title: Occurrence Download

description: A dataset containing 229 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nasua narica (Linnaeus, 1766)",
 "Year 2025-2025"
 ]
}

 The dataset includes 229 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001285-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7artcx
title: Occurrence Download

description: A dataset containing 47518 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cornus sanguinea",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 47518 records from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049113-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nu6nry
title: Occurrence Download

description: A dataset containing 2247 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sideroxylon obtusifolium (Roem. & Schult.) T.D.Penn.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2247 records from 149 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009436-250515123054153/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.29125403
title: Sacred sites shelter native plant species in a strongly urbanized landscape - reproducible analysis

description: The code, data and documentation in this folder should allow you to replicate the analysis and figures produced in the corresponding paper titled "<b>Sacred sites shelter native plant species in a strongly urbanized landscape</b>". If questions arise, please contact me under l.schmid@nus.edu.sg or lucvalschmid@gmail.com.
DOI: 10.17632/9f985t4wfc
title: miR-21 Target Prediction

description: Output of miR-21 Target Prediction using TargetScan
DOI: 10.17632/9f985t4wfc.1
title: miR-21 Target Prediction

description: Output of miR-21 Target Prediction using TargetScan
DOI: 10.25378/janelia.13122509
title: Automatic segmentation of organelles in isotropic electron microscopy data of mouse choroid plexus (jrc_choroid-plexus-2)

description: The cell interior contains hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demand. Obtaining a complete understanding of this organization is challenging and requires nanometer-level, three-dimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To overcome this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing all known cellular organelles from entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from ER to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures and their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. The data, computer code, and trained models are all shared through an open data and open source web platform OpenOrganelle, enabling scientists everywhere to query and further reconstruct the datasets.Sample: Mouse choroid plexusDataset ID: jrc_choroid-plexus-2EM Data DOI: 10.25378/janelia.13123427EM voxel size (nm): 8.0 x 8.0 x 8.0 (X, Y, Z)Segmentation voxel size (nm): 4.0 x 4.0 x 4.0 (X, Y, Z)Dataset URL: s3://janelia-cosem-datasets/jrc_choroid-plexus-2/jrc_choroid-plexus-2.zarr/recon-1/labels/inference/segmentations/Visualization Website: https://openorganelle.janelia.org/datasets/jrc_choroid-plexus-2Publication: Heinrich et al., 2020Segmented Organelles: Endo (M625k), ER (M650k), Mito (M875k), Nucleus (M1100k), PM (M975k), Vesicle (M800k)Included in Dataset: predictions, segmentations
DOI: 10.25378/janelia.13122509.v1
title: Automatic segmentation of organelles in isotropic electron microscopy data of mouse choroid plexus (jrc_choroid-plexus-2)

description: The cell interior contains hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demand. Obtaining a complete understanding of this organization is challenging and requires nanometer-level, three-dimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To overcome this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing all known cellular organelles from entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from ER to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures and their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. The data, computer code, and trained models are all shared through an open data and open source web platform OpenOrganelle, enabling scientists everywhere to query and further reconstruct the datasets.Sample: Mouse choroid plexusDataset ID: jrc_choroid-plexus-2EM Data DOI: 10.25378/janelia.13123427EM voxel size (nm): 8.0 x 8.0 x 8.0 (X, Y, Z)Segmentation voxel size (nm): 4.0 x 4.0 x 4.0 (X, Y, Z)Dataset URL: s3://janelia-cosem-datasets/jrc_choroid-plexus-2/jrc_choroid-plexus-2.zarr/recon-1/labels/inference/segmentations/Visualization Website: https://openorganelle.janelia.org/datasets/jrc_choroid-plexus-2Publication: Heinrich et al., 2020Segmented Organelles: Endo (M625k), ER (M650k), Mito (M875k), Nucleus (M1100k), PM (M975k), Vesicle (M800k)Included in Dataset: predictions, segmentations
DOI: 10.15468/dl.py8u3u
title: Occurrence Download

description: A dataset containing 3420 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Estrilda melpoda (Vieillot, 1817))",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 3420 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015604-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hmxntd
title: Occurrence Download

description: A dataset containing 553 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Estrilda melpoda (Vieillot, 1817))",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 553 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015606-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12038081
title: Peer-to-Peer Boliviano (BOB) Exchange Data