cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5747392
title: ChemTastesDB: A Curated Database of Molecular Tastants

description: ChemTastesDB is a database that includes curated information of 4075 molecular tastants. ChemTastesDB is distributed to the scientific community to expand the information of molecular tastants, which could assist the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR/QSPR) studies for taste prediction. Examples of QSPR approaches for the prediction of molecular taste are given in the following publication: Rojas, C., Abril-González, M., Ballabio, D. & García, F. (2025). ChemTastesPredictor: An ensemble of machine learning classifiers to predict the taste of molecular tastants. Chemometrics and Intelligent Laboratory Systems. 261, 105380. https://doi.org/10.1016/j.chemolab.2025.105380.

The 4075 molecular tastants are categorized into one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, non-bitter, multitaste and miscellaneous). The molecules are categorized into following ten classes: sweet (1313), bitter (1615), umami (220), sour (49), salty (16), multitaste (179), tasteless (232), non-sweet (304), non-bitter (28), and miscellaneous (119).

ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. In addition, the molecular structure in the HyperChem (.hin) format of each compound is provided.

This is version 2.1 of the ChemTastesDB. In this new version, 1131 newly curated compounds were added. These new molecules were retrieved from 52 new bibliographic references.
DOI: 10.5281/zenodo.5747393
title: ChemTastesDB: A Curated Database of Molecular Tastants

description: ChemTastesDB is a database that includes curated information of 2944 molecular tastants. ChemTastesDB constitutes a useful tool for the scientific community to expand the information of molecular tastants, which could assist in the analysis of the relationships between molecular structure and taste, as well as <em>in</em> <em>silico</em> (QSAR) studies for taste prediction by means of diverse machine learning approaches. Molecules are sequestered to one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other non-basic classes such as: tasteless, non-sweet, multitaste and miscellaneous. ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. Moreover, the molecular structure in the HyperChem (<em>.hin</em>) format of each chemical is provided.
DOI: 10.5281/zenodo.6300997
title: ChemTastesDB: A Curated Database of Molecular Tastants

description: ChemTastesDB is a database that includes curated information of 2944 molecular tastants. ChemTastesDB constitutes a useful tool for the scientific community to expand the information of molecular tastants, which could assist in the analysis of the relationships between molecular structure and taste, as well as <em>in</em> <em>silico</em> (QSAR) studies for taste prediction by means of diverse machine learning approaches. Molecules are labelled in one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, multitaste and miscellaneous). ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. Moreover, the molecular structure in the HyperChem (<em>.hin</em>) format of each chemical is provided. The database is freeware and may be used if proper reference is given to the authors. Preferably refer to the following paper:<br> Rojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & García, F. (2022). ChemTastesDB: A curated database of molecular tastants. Food Chemistry: Molecular Sciences, 4, 100090. https://doi.org/10.1016/j.fochms.2022.100090.
description: {"references": ["Rojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & Garc\u00eda, F. (2022). ChemTastesDB: A curated database of molecular tastants. Food Chemistry: Molecular Sciences, 4, 100090."]}
DOI: 10.5281/zenodo.6528835
title: ChemTastesDB: A Curated Database of Molecular Tastants

description: <strong>ChemTastesDB</strong> is a database that includes curated information of 2944 molecular tastants. ChemTastesDB constitutes a useful tool for the scientific community to expand the information of molecular tastants, which could assist in the analysis of the relationships between molecular structure and taste, as well as <em>in</em> <em>silico</em> (QSAR) studies for taste prediction by means of diverse machine learning approaches. Molecules are labelled in one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, multitaste and miscellaneous). ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. Moreover, the molecular structure in the HyperChem (<em>.hin</em>) format of each chemical is provided. This is version 1.2 of the ChemTastesDB. <em>What's new in version 1.2:</em> Chemical information (for instance, name, PubChem CID or CAS number) for some tastants has been included. The database is freeware and may be used if proper reference is given to the authors. Preferably refer to the following paper:<br> Rojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & García, F. (2022). ChemTastesDB: A curated database of molecular tastants. Food Chemistry: Molecular Sciences, 4, 100090. https://doi.org/10.1016/j.fochms.2022.100090.
description: {"references": ["Rojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & Garc\u00eda, F. (2022). ChemTastesDB: A curated database of molecular tastants. Food Chemistry: Molecular Sciences, 4, 100090."]}
DOI: 10.15468/dl.5g8fy8
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Caloplaca Th.Fr., 1860"
 ]
}

 The dataset includes 58 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018459-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wxr5vn
title: Occurrence Download

description: A dataset containing 366 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Faxonius virilis (Hagen, 1870))",
 "BasisOfRecord is one of (Human Observation)",
 "Year is greater than or equal to 1971",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 366 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018460-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y595v3
title: Occurrence Download

description: A dataset containing 1286722 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Magnoliopsida"
 ]
}

 The dataset includes 1286722 records from 313 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018453-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13899025
title: Advancing Microplastic Detection in Zebrafish with Micro Computed Tomography: A Novel Approach to Revealing Microplastic Accumulation in Organisms.

description: This dataset includes data from X-ray micro-computed tomography (microCT), which is used for non-destructive 3D imaging of microplastics in biological tissues, specifically zebrafish. Traditional spectroscopic and pyrolytic methods struggle to provide detailed spatial information, limiting the understanding of microplastic impacts on living organisms.

In this study, fish tissues spiked with polyethylene microplastics were analyzed. MicroCT results for particle size distribution closely matched those from laser diffraction size analysis, demonstrating its reliability. Additionally, microCT allowed for 3D visualization of microplastics in tissues, offering spatial detail beyond what 2D methods can achieve.

Data components include:



3D MicroCT Imaging of reference samples: Shows spatial distribution and polyethylene particle sizes in agarose gel.

Laser Diffraction Data: Particle sizer data - validates particle size findings.

Spiked Tissue Analysis: Demonstrates microCT’s detection capabilities in biological matrices - Zebrafish and extracted guts from zebrafish bith spiked with polyethylene microplastics. 


This dataset highlights microCT as a promising tool for future non-invasive microplastic research, with potential applications in human health studies.
DOI: 10.5281/zenodo.13899026
title: Advancing Microplastic Detection in Zebrafish with Micro Computed Tomography: A Novel Approach to Revealing Microplastic Accumulation in Organisms.

description: This dataset includes data from X-ray micro-computed tomography (microCT), which is used for non-destructive 3D imaging of microplastics in biological tissues, specifically zebrafish. Traditional spectroscopic and pyrolytic methods struggle to provide detailed spatial information, limiting the understanding of microplastic impacts on living organisms.

In this study, fish tissues spiked with polyethylene microplastics were analyzed. MicroCT results for particle size distribution closely matched those from laser diffraction size analysis, demonstrating its reliability. Additionally, microCT allowed for 3D visualization of microplastics in tissues, offering spatial detail beyond what 2D methods can achieve.

Data components include:



3D MicroCT Imaging of reference samples: Shows spatial distribution and polyethylene particle sizes in agarose gel.

Laser Diffraction Data: Particle sizer data - validates particle size findings.

Spiked Tissue Analysis: Demonstrates microCT’s detection capabilities in biological matrices - Zebrafish and extracted guts from zebrafish bith spiked with polyethylene microplastics. 


This dataset highlights microCT as a promising tool for future non-invasive microplastic research, with potential applications in human health studies.
DOI: 10.15468/dl.bh6c5x
title: Occurrence Download

description: A dataset containing 868 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Larix Mill.",
 "Year 1970-2025"
 ]
}

 The dataset includes 868 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018461-250310093411724/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.28558727
title: Optimisation of silane chemistry with machine learning and molecular dynamics

description: Among the numerous silane chemistries in commercial use and literature, only a few are well-parameterised and understood. This study presents a systematic approach using molecular dynamics (MD) and machine learning to rank and identify molecules with optimal mechanical strengths. MD simulations were conducted on silane molecules of varying functionalities and molecular weights to assess peak force performance in Mode I. The systems comprised a single silane molecule bonded to a glass surface through one or two bonds in either extended or relaxed configurations. Based on bond failure mechanisms, silane molecules were classified into three failure categories: (1) Silicon-Oxygen/Silicon-Carbon scission, (2) Nitrogen-Carbon scission, and (3) Oxygen-Carbon scission. In relaxed configurations, non-bonded interactions between silane molecules and the glass surface contributed significantly to energy absorption, with about 25% of total energy utilised to overcome these interactions. A machine learning approach was employed to predict peak forces for over 84,000 molecules by leveraging parameters from MD simulations. A Variational Autoencoder was used to reduce the molecular structure information to a low-dimensional, physically-informed space, followed by a quantile random forest regression model to predict the peak force performance for the broader set of silane molecules.
DOI: 10.6084/m9.figshare.28558727.v1
title: Optimisation of silane chemistry with machine learning and molecular dynamics

description: Among the numerous silane chemistries in commercial use and literature, only a few are well-parameterised and understood. This study presents a systematic approach using molecular dynamics (MD) and machine learning to rank and identify molecules with optimal mechanical strengths. MD simulations were conducted on silane molecules of varying functionalities and molecular weights to assess peak force performance in Mode I. The systems comprised a single silane molecule bonded to a glass surface through one or two bonds in either extended or relaxed configurations. Based on bond failure mechanisms, silane molecules were classified into three failure categories: (1) Silicon-Oxygen/Silicon-Carbon scission, (2) Nitrogen-Carbon scission, and (3) Oxygen-Carbon scission. In relaxed configurations, non-bonded interactions between silane molecules and the glass surface contributed significantly to energy absorption, with about 25% of total energy utilised to overcome these interactions. A machine learning approach was employed to predict peak forces for over 84,000 molecules by leveraging parameters from MD simulations. A Variational Autoencoder was used to reduce the molecular structure information to a low-dimensional, physically-informed space, followed by a quantile random forest regression model to predict the peak force performance for the broader set of silane molecules.
DOI: 10.15468/dl.4mhrbv
title: Occurrence Download

description: A dataset containing 962 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aleochara fumata Gravenhorst, 1802",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 962 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018462-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c7q29j
title: Occurrence Download

description: A dataset containing 982 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-11.20544 28.34024,-11.18601 28.48352,-11.3772 28.49256,-11.3998 28.34431,-11.20544 28.34024))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 982 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018463-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.apl.28559261
title: Supplement Number 1

description: See the supplementary material for calculation details, band gap variation, lattice symmetry operation and partly vibration mode
DOI: 10.15468/dl.wj9sqx
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Buellia De Not., 1846"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018465-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.apl.28559417
title: Supplement Number 1

description: The testing of U values in DFT+U, the convergence testing of K-points, and the stability analysis of MoSeF under strain conditions.
DOI: 10.60893/figshare.apl.28559417.v1
title: Supplement Number 1

description: The testing of U values in DFT+U, the convergence testing of K-points, and the stability analysis of MoSeF under strain conditions.
DOI: 10.15468/dl.bthhnu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bryconops Kner, 1858"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028637-240626123714530/datasets/export for details.
DOI: 10.4121/82985fe1-ff55-42ad-ae60-262e486288a9.v1
title: Data underlying the paper: Land use intensity and plant species diversity differentiate arthropod communities in permanent, semi-natural and multi-species grasslands

description: This datasets contains the results of both pollinator counts and pyramid catches. Data was collected in the East of the Netherlands in the region 'achterhoek'. All locations had sandy soils. Treatment types were semi-natural grasslands (SNG), multi-species grasslands (MSG), and permanent, conventional grasslands (PG).
DOI: 10.15468/dl.varejd
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Camelina sativa (L.) Crantz"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018466-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18150/ycuodu
title: Direct 3D mass spectrometry imaging analysis of environmental microorganisms

description: Following files contain the results of LARAPPI/CI (Laser Ablation Remote Atmospheric Pressure Photoionization/Chemical Ionization)-3D-MSI performed employing an ultrahigh resolution QToF (quadrupole-time-of-flight) mass spectrometer Bruker Impact II.2D_Fusarium_graminarum_Bacillus_cereus.d.zip: The .zip file contains raw data from LARAPPI/CI-MSI from the 2D measurement of the sample with Fusarium graminarum and Paenibacillus xylanexedens. The .d files can be accessed using the open-source R package available for free.Fusarium_graminarum_Paenibacillus_xylanexedens.zip: The .zip file contains raw data from LARAPPI/CI-MSI from the 3D measurement of the sample with Fusarium graminarum and Paenibacillus xylanexedens, including a .d file for each of the two steps. The .d files can be accessed using the open-source R package available for free.
DOI: 10.48597/aqjz-zxu8
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/2pjh-umwc
title: OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/5nqy-qarm
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/7rjd-ree6
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/bcg7-k79g
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/eb3v-8ne9
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/qd6q-rbvc
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/txtc-tew3
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/vefw-8kqk
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/zv5z-rhzn
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.48597/4m9q-93q4
title: Measurement of OC/EC at Pic du Midi

description: Measurement of OC/EC at Pic du Midi
DOI: 10.15468/dl.934gs5
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aspicilia A.Massal., 1852"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018467-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.jap.28515785
title: Supplement Data for Figs6,7

description: Rawdata for Figures 6 and 7
DOI: 10.60893/figshare.jap.28515785.v1
title: Supplement Data for Figs6,7

description: Rawdata for Figures 6 and 7
DOI: 10.15468/dl.cy4r55
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypogeococcus festerianus (Lizer y Trelles, 1942)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018470-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.jcp.28506635.v1
title: Supplement Number 1

description: OPTIMIZED GEOMETRIES OF CH +
4 ISOMERS
DOI: 10.15468/dl.krs2kr
title: Occurrence Download

description: A dataset containing 18919 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Norway",
 "OccurrenceStatus is Present",
 "TaxonKey is Alces Gray, 1821",
 "Year 2004-2025"
 ]
}

 The dataset includes 18919 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018469-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.jcp.28506635
title: Supplement Number 1

description: OPTIMIZED GEOMETRIES OF CH +
4 ISOMERS
DOI: 10.60893/figshare.jcp.28462637
title: Supplemental Material

description: Geometries of isomeri of acrilici acid and vinylimidazole optimized at the PCS2 level
DOI: 10.60893/figshare.jcp.28462637.v1
title: Supplemental Material

description: Geometries of isomeri of acrilici acid and vinylimidazole optimized at the PCS2 level
DOI: 10.15468/dl.3swzc3
title: Occurrence Download

description: A dataset containing 288 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "TaxonKey is Camelina sativa (L.) Crantz"
 ]
}

 The dataset includes 288 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018473-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.