cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nhv3z4
title: Occurrence Download

description: A dataset containing 5422 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Eleutherodactylus planirostris (Cope, 1862))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5422 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086368-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pj9jx7
title: Occurrence Download

description: A dataset containing 11477 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parnassius mnemosyne (Linnaeus, 1758)",
 "HasCoordinate is true",
 "Geometry POLYGON((-11.64551 29.26758,48.20801 29.26758,48.20801 72.1582,-11.64551 72.1582,-11.64551 29.26758))",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1993",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 11477 records from 298 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011617-240425142415019/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.96wxzg
title: Occurrence Download

description: A dataset containing 3856 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dahlia coccinea Cav.",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3856 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064347-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17076445
title: Processed data for 'Recent European marine heatwaves are unprecedented but not unexpected'

description: NetCDF files containing post-processed data from model and/or observations output, ready to be used in plotting scripts (see GitHub repo for accompanying code [archived on Zenodo here]). Analysis and figures as submitted for publication in 'Recent European marine heatwaves are unprecedented but not unexpected. Communications Earth and Environment. 2025'.
DOI: 10.5281/zenodo.17076446
title: Processed data for 'Recent European marine heatwaves are unprecedented but not unexpected'

description: NetCDF files containing post-processed data from model and/or observations output, ready to be used in plotting scripts (see GitHub repo for accompanying code [archived on Zenodo here]). Analysis and figures as submitted for publication in 'Recent European marine heatwaves are unprecedented but not unexpected. Communications Earth and Environment. 2025'.
DOI: 10.6084/m9.figshare.27021316
title: <b>Data on thermophilization of the community of adults vs. juveniles trees of temperate forests across Europe</b>

description: With this database we quantify the long-term thermophilization of tree species across temperate European forests comparing adults vs. juvenile trees using layer-specific climatic data, species cover, and occurrence from plots located in 12 European countries surveyed at least twice between the 1930s and 2010s. We inferred species’ thermal profiles from species distribution maps matched to gridded above-canopy macroclimate temperature for adult trees and to below canopy microclimate temperature for juveniles. Based on that information we inferred the community temperature index per plot that was later used to calculate thermophilization.
DOI: 10.15468/cwn39k
title: Halophila hawaiiana ITS sequence data 2022

description: DNA sequence data for endemic species Halophila hawaiiana collected on Oahu.
DOI: 10.6084/m9.figshare.25266720
title: Dynamic Structural Equation Models with Missing Data: Data Requirements on <i>N</i> and <i>T</i>

description: Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in M<i>plus</i> to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM.
DOI: 10.6084/m9.figshare.25266720.v1
title: Dynamic Structural Equation Models with Missing Data: Data Requirements on <i>N</i> and <i>T</i>

description: Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in M<i>plus</i> to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM.
DOI: 10.15468/dl.7qzbfd
title: Occurrence Download

description: A dataset containing 17330 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Canidae"
 ]
}

 The dataset includes 17330 records from 152 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020290-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11581004
title: Data for 'Adaptive Particle Refinement for compressible Smoothed Particle Hydrodynamics'

description: Contains necessary files to recreate the simulations presented in Adaptive Particle Refinement for compressible Smoothed Particle Hydrodynamics (under review). We have also included the files to recreate the rendered plots presented in the paper. There are seven folders;



Circumbinary disc test

Planet-disc test

Flyby test

Periodic box tests

Firehose test

Nchild test

Kernel test with planet disc interaction


Within each of these folders there are sub-folders that refer to each specific simulation presented in the paper. Each folder contains the necessary .setup and .in file as well as the .ev files where applicable. Where rendered figures have been made, the corresponding dump files are also included here. All figures were made using sarracen (https://github.com/ttricco/sarracen/).
DOI: 10.5281/zenodo.11581005
title: Data for 'Adaptive Particle Refinement for compressible Smoothed Particle Hydrodynamics'

description: Contains necessary files to recreate the simulations presented in Adaptive Particle Refinement for compressible Smoothed Particle Hydrodynamics (under review). We have also included the files to recreate the rendered plots presented in the paper. There are seven folders;



Circumbinary disc test

Planet-disc test

Flyby test

Periodic box tests

Firehose test

Nchild test

Kernel test with planet disc interaction


Within each of these folders there are sub-folders that refer to each specific simulation presented in the paper. Each folder contains the necessary .setup and .in file as well as the .ev files where applicable. Where rendered figures have been made, the corresponding dump files are also included here. All figures were made using sarracen (https://github.com/ttricco/sarracen/).
DOI: 10.5281/zenodo.12747094
title: Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs

description: This dataset is associated with the study "Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs." Detailed instructions for installation, usage, and example scripts for the PACMOF2 models can be found on our GitHub repository.









The dataset includes the following files:



DDEC6_data.zip: Contains the crystal structures of MOFs with DDEC6 partial charges.

PACMOF2_prediction_cifs.zip: Contains the crystal structures of MOFs with DDEC6 charges as predicted by the PACMOF2 models.

PACMOF2_ionic.gz: A machine learning model designed to predict charges in ionic MOFs with a non-zero formal charge.

PACMOF2_neutral.gz: A machine learning model designed to predict charges in neutral MOFs.


For more details about each file, please refer to the accompanying README.md file.Updates: - September 2024 (version 1.0.1): Added README.md file.
DOI: 10.5281/zenodo.12747095
title: Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs

description: The dataset associated with the work "Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs".

 

This dataset contains the following files:



DDEC6_data.zip: The crystal structures of MOFs with DDEC6 partial charges.

PACMOF2_prediction_cifs.zip: The crystal structures of MOFs with DDEC6 charges as predicted by our PACMOF2 models.

PACMOF2_ionic.gz: A machine learning model for predicting charges in ionic MOFs with a non-zero formal charge.

PACMOF2_neutral.gz: A machine learning model for predicting charges in neutral MOFs.
DOI: 10.5281/zenodo.13776833
title: Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs

description: This dataset is associated with the study "Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs." Detailed instructions for installation, usage, and example scripts for the PACMOF2 models can be found on our GitHub repository.









The dataset includes the following files:



DDEC6_data.zip: Contains the crystal structures of MOFs with DDEC6 partial charges.

PACMOF2_prediction_cifs.zip: Contains the crystal structures of MOFs with DDEC6 charges as predicted by the PACMOF2 models.

PACMOF2_ionic.gz: A machine learning model designed to predict charges in ionic MOFs with a non-zero formal charge.

PACMOF2_neutral.gz: A machine learning model designed to predict charges in neutral MOFs.


For more details about each file, please refer to the accompanying README.md file.Updates: - September 2024 (version 1.0.1): Added README.md file.
DOI: 10.5281/zenodo.13776849
title: juniperus_oxycedrus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.13776850
title: juniperus_oxycedrus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.13776857
title: juniperus_phoenicea_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.13776858
title: juniperus_phoenicea_decontaminated_FSCR.fasta.gz
DOI: 10.25919/5mqs-5e27
title: Hydrodynamic model output for reefs of the Great Barrier Reef

description: Hourly surface and depth-averaged hydrodynamic model output in netcdf file format for short (~ 4 month) simulations of 25 individual reefs within the Great Barrier Reef marine park.

Files are named with the reef name as a pre-fix e.g. for Arlington Reef the relevant output can be found in file 'arlington_out_std_surf.nc'

Note that the model grid is staggered (Arakawa-C) and often rotated. Surface velocity vectors are labelled u1 (i component) and u2 (j component) and depth averaged velocity vectors are labelled u1av and u2av.
DOI: 10.15468/dl.6e9mfx
title: Occurrence Download

description: A dataset containing 6765 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Atelidae"
 ]
}

 The dataset includes 6765 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020292-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jcsbtg
title: Occurrence Download

description: A dataset listing the 172 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-73.04243 6.68129,-72.99246 6.68129,-72.99246 6.74548,-73.04243 6.74548,-73.04243 6.68129))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 172 records were derived from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020275-240906103802322/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/9422732
title: StarWars
DOI: 10.15468/dl.3c4bxr
title: Occurrence Download

description: A dataset containing 1104 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lupinus L."
 ]
}

 The dataset includes 1104 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020294-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mykzwp
title: Occurrence Download

description: A dataset listing the 107 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((174.79295 -37.20987,174.90303 -37.2095,174.90186 -37.11889,174.79233 -37.12024,174.79295 -37.20987))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chordata"
 ]
}

 The dataset's 107 records were derived from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020278-240906103802322/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/9422731
title: Indians Diabetes
DOI: 10.15468/dl.gqkbuf
title: Occurrence Download

description: A dataset containing 551517 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations, Pl@ntNet automatically identified occurrences)",
 "TaxonKey is Papaveraceae"
 ]
}

 The dataset includes 551517 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020205-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13776870
title: juniperus_thurifera_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.13776871
title: juniperus_thurifera_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.zua6zj
title: Occurrence Download

description: A dataset containing 426546 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1990",
 "TaxonKey is Chamaenerion angustifolium (L.) Scop."
 ]
}

 The dataset includes 426546 records from 1242 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020274-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13776879
title: kinnapotiguara_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.13776880
title: kinnapotiguara_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.13776835
title: Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"

description: This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications".
DOI: 10.5281/zenodo.13776836
title: Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"

description: This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications".
DOI: 10.5281/zenodo.10561457
title: Databases of human SARS-CoV-2 antibody peptides for bottom-up proteomics

description: Bottom-up proteomics approaches rely on database searches that compare experimental values of peptides to theoretical values derived from protein sequences in a database. While the human body can produce millions of distinct antibodies, current databases for human antibodies such as UniProtKB are limited to only 1095 sequences (as of 2024 January). This limitation may hinder the identification of new antibodies using bottom-up proteomics. Therefore, extending the databases is an important task for discovering new antibodies.

Herein, we adopted extensive collection of antibody sequences from Observed Antibody Space for conducting efficient database searches in publicly available proteomics data with a focus on the SARS-CoV-2 disease. Thirty million heavy antibody sequences from 146 SARS-CoV-2 patients in the Observed Antibody Space were in silico digested to obtain 18 million unique peptides. These peptides were then used to create six databases (DB1-DB6) for bottom-up proteomics. We used those databases for searching antibody peptides in publicly available SARS-CoV-2 human plasma samples in the Proteomics Identification Database (PRIDE), and we consistently found new antibody peptides in those samples. The database searching task was done by using Fragpipe softwares.

Table 1. Information of databases. In addition to human SARS-CoV-2 antibody peptides, every database also contains human protein sequences from UniProt database and contaminants from cRAP database.




File
Database
Number of human SARS-CoV-2 antibody peptides


DB1.fasta
DB1
100


DB2.fasta
DB2
1,000


DB3.fasta
DB3
10,000


DB4.fasta
DB4
100,000


DB5.fasta
DB5
1,000,000


DB6.fasta
DB6
10,000,000
DOI: 10.5281/zenodo.10566370
title: Databases of human SARS-CoV-2 antibody peptides for bottom-up proteomics

description: Bottom-up proteomics approaches rely on database searches that compare experimental values of peptides to theoretical values derived from protein sequences in a database. While the human body can produce millions of distinct antibodies, current databases for human antibodies such as UniProtKB are limited to only 1095 sequences (as of 2024 January). This limitation may hinder the identification of new antibodies using bottom-up proteomics. Therefore, extending the databases is an important task for discovering new antibodies.

Herein, we adopted extensive collection of antibody sequences from Observed Antibody Space for conducting efficient database searches in publicly available proteomics data with a focus on the SARS-CoV-2 disease. Thirty million heavy antibody sequences from 146 SARS-CoV-2 patients in the Observed Antibody Space were in silico digested to obtain 18 million unique peptides. These peptides were then used to create six databases (DB1-DB6) for bottom-up proteomics. We used those databases for searching antibody peptides in publicly available SARS-CoV-2 human plasma samples in the Proteomics Identification Database (PRIDE), and we consistently found new antibody peptides in those samples. The database searching task was done by using Fragpipe softwares.

Table 1. Information of databases. In addition to human SARS-CoV-2 antibody peptides, every database also contains human protein sequences from UniProt database and contaminants from cRAP database.




File
Database
Number of human SARS-CoV-2 antibody peptides
Number of covered antibodies


DB1.fasta
DB1
100
1.28E7


DB2.fasta
DB2
1E3
1.93E7


DB3.fasta
DB3
1E4
2.40E7


DB4.fasta
DB4
1E5
2.66E7


DB5.fasta
DB5
1E6
2.83E7


DB6.fasta
DB6
1E7
3.01E7
DOI: 10.15468/dl.b34g62
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "DatasetKey is one of (Fauna herpetológica de las Reservas Distritales de Humedal de Bogotá, D.C.)"
 ]
}

 The dataset includes 154 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020295-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dsj28c
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "DatasetKey is one of (Fauna herpetológica de las Reservas Distritales de Humedal de Bogotá, D.C.)"
 ]
}

 The dataset includes 154 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020297-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8e4nd7
title: Occurrence Download

description: A dataset containing 47721 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((5.53161 49.66889,13.4627 47.1016,14.02811 51.31929,11.01489 49.55061,5.53161 49.66889))",
 "OccurrenceStatus is Present",
 "TaxonKey is Carabidae"
 ]
}

 The dataset includes 47721 records from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020296-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.l6fntf
title: Occurrence Download

description: A dataset containing 10801 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "StateProvince is piauí",
 "HasCoordinate is true",
 "TaxonKey is Magnoliopsida",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 10801 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019577-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.up6uyp
title: Occurrence Download

description: A dataset containing 34139 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Misgurnus anguillicaudatus (Cantor, 1842)"
 ]
}

 The dataset includes 34139 records from 113 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0173853-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k2cv7t
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hoya bordenii Schltr."
 ]
}

 The dataset includes 24 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022370-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.