cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.g67c4n
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-4.08301 52.56032,-4.08277 52.5591,-4.07606 52.54892,-4.0717 52.54266,-4.06662 52.54115,-4.0613 52.54068,-4.05791 52.54119,-4.05404 52.54178,-4.04932 52.54193,-4.04557 52.54255,-4.044 52.54314,-4.04249 52.5438,-4.03976 52.54395,-4.03607 52.54446,-4.03601 52.54553,-4.0388 52.54538,-4.04097 52.54472,-4.04333 52.54494,-4.04424 52.54428,-4.04642 52.54454,-4.04708 52.54572,-4.04962 52.54627,-4.05114 52.54509,-4.05398 52.54461,-4.05694 52.54597,-4.05948 52.54597,-4.06257 52.54686,-4.06517 52.548,-4.06529 52.54925,-4.0671 52.55009,-4.06807 52.55138,-4.0688 52.55237,-4.06819 52.55377,-4.06868 52.55495,-4.06995 52.55601,-4.07128 52.55708,-4.0717 52.55793,-4.07231 52.56006,-4.07406 52.56285,-4.07666 52.56352,-4.08301 52.56032)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "Year 2015-2025"
 ]
}

 The dataset includes 16 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057235-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qbpyuz
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "Geometry POLYGON((-96.64723 -11.25579,-60.35277 -11.25579,-60.35277 3.25579,-96.64723 3.25579,-96.64723 -11.25579))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is 9fff025a-f223-4b26-8ee1-2266b219934f",
 "TaxonKey is Pristimantis metabates (Duellman & Pramuk, 1999)"
 ]
}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057236-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hngtez
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acapoeta tanganicae (Boulenger, 1900)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((55.6087 -21.8876,56.1388 -21.7124,56.3374 -21.1838,56.0195 -20.5719,55.451 -20.3718,55.1382 -20.4922,55.1382 -21.6954,55.6087 -21.8876))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057237-250525065834625/datasets/export for details.
DOI: 10.15468/dl.qa3c42
title: Occurrence Download

description: A dataset containing 6388 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Butastur rufipennis (Sundevall, 1850)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 6388 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057238-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data426
title: iDGP

description: Database of human genes prioritized for their probability of involvement in dominant or recessive hereditary diseases.
description: # Content
 dom_rec_prediction.sql -&gt; MySQL database
 web/* -&gt; PHP website
DOI: 10.5281/zenodo.15698382
title: Electromyographic activity of the orbicularis oris muscle in children with and without lip competence: A cross-sectional study

description: This is the supplementary tables for the above paper, including the results of tests used to check the assumptions of the statistical methods.

The package includes the following files:

Supplementary Table 1. Tests of distributional assumptions for variables in Table 1.

Supplementary Table 2. Tests of distributional assumptions for variables in Table 2.

Supplementary Table 3.  Tests of distributional assumptions for variables in Table 3.
DOI: 10.5281/zenodo.14878508
title: Ligandable pockets for different E3 ligases predicted by mixed solvent molecular dynamics

description: E3 ligases are crucial components of the ubiquitin-proteasome system (UPS) that confer substrate specificity for proteasomal degradation. E3 ligases have been linked to various pathogenic mechanisms, and for this reason, these protein complexes have emerged as attractive drug target candidates for precise therapeutic intervention. Although there is a lack of clear-cut design strategies, a variety of potential activities can be envisioned from directly pharmacologically modulating E3 ligases, such as promoting a higher rate of (neo)substrate degradation or, in contrast, inhibiting their degradation. However, despite the tremendous clinical significance of this large protein family, only a single E3 ligase has FDA approved drugs.

To expand the current approaches, and to increase the arsenal of targeted E3 ligases, we present an efficient E3 ligase-based computational approach, where we used the PDB structure of a diverse set of 23 E3 ligases to identify and characterize their pockets. We encourage the scientific community to use all the ligandable pockets identified in thes work, to facilitate the development of small molecules targeting new, uncharted, E3 ligases.

The prediction of pockets has been performed using mixed-solvent molecular dynamics, using the pyMDMix setup. We employed a mixture of ethanol:water at 1:4 concentration. These systems where simulated for 3 replicas of 50ns each in AMBER, and the joint occupancy of the ethanol converted into energy grids following the standard pyMDMix protocol. Clusterization of the energy grids led to the energy hotspots (saved as ETA_hotspots.pdb in the subdirectory of each E3). Grouping those hotspots by surface distance, coupled by volume, balance between hydrophobic and polar hotspots led to the prediction of ligandable pockets. All pockets, ligandable and not, are represented in the pockets.pdb files in each subdirectory corresponding to their E3 ligase.

A compiled information about the pockets (energy efficiency, location, volume), coupled with their ligandablility prediction is detailed in predicted_pockets_information.xlsx. Finally, a pre-generated visualization session is stored for the ease of analyzing the pockets.
DOI: 10.5281/zenodo.14878509
title: Ligandable pockets for different E3 ligases predicted by mixed solvent molecular dynamics

description: E3 ligases are crucial components of the ubiquitin-proteasome system (UPS) that confer substrate specificity for proteasomal degradation. E3 ligases have been linked to various pathogenic mechanisms, and for this reason, these protein complexes have emerged as attractive drug target candidates for precise therapeutic intervention. Although there is a lack of clear-cut design strategies, a variety of potential activities can be envisioned from directly pharmacologically modulating E3 ligases, such as promoting a higher rate of (neo)substrate degradation or, in contrast, inhibiting their degradation. However, despite the tremendous clinical significance of this large protein family, only a single E3 ligase has FDA approved drugs.

To expand the current approaches, and to increase the arsenal of targeted E3 ligases, we present an efficient E3 ligase-based computational approach, where we used the PDB structure of a diverse set of 23 E3 ligases to identify and characterize their pockets. We encourage the scientific community to use all the ligandable pockets identified in thes work, to facilitate the development of small molecules targeting new, uncharted, E3 ligases.

The prediction of pockets has been performed using mixed-solvent molecular dynamics, using the pyMDMix setup. We employed a mixture of ethanol:water at 1:4 concentration. These systems where simulated for 3 replicas of 50ns each in AMBER, and the joint occupancy of the ethanol converted into energy grids following the standard pyMDMix protocol. Clusterization of the energy grids led to the energy hotspots (saved as ETA_hotspots.pdb in the subdirectory of each E3). Grouping those hotspots by surface distance, coupled by volume, balance between hydrophobic and polar hotspots led to the prediction of ligandable pockets. All pockets, ligandable and not, are represented in the pockets.pdb files in each subdirectory corresponding to their E3 ligase.

A compiled information about the pockets (energy efficiency, location, volume), coupled with their ligandablility prediction is detailed in predicted_pockets_information.xlsx. Finally, a pre-generated visualization session is stored for the ease of analyzing the pockets.
DOI: 10.15468/dl.jgrgdu
title: Occurrence Download

description: A dataset containing 85 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cocconeis C.G.Ehrenberg, 1837",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.2855 -34.7533,20.509 -34.5676,20.509 -32.9007,17.7723 -32.9007,17.8156 -33.0786,18.3471 -33.7176,18.3748 -33.8287,18.2181 -34.0492,18.3197 -34.3408,18.4452 -34.429,18.5435 -34.4213,18.5872 -34.3667,18.5363 -34.167,18.7345 -34.1508,18.7216 -34.3637,18.7808 -34.4521,19.0289 -34.4206,19.0831 -34.4835,19.2655 -34.5017,19.2032 -34.6157,19.2394 -34.6926,19.375 -34.6963,19.6091 -34.8524,19.8251 -34.8396,19.9798 -34.9064,20.1081 -34.8731,20.1517 -34.7847,20.2855 -34.7533))"
 ]
}

 The dataset includes 85 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057239-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nrpua6
title: Occurrence Download

description: A dataset containing 121183 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((6.19946 46.26721,6.14473 46.27942,6.08823 46.23303,6.05645 46.17804,6.10942 46.15114,6.19593 46.17804,6.23831 46.19882,6.22595 46.23913,6.19946 46.26721))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 2000-2025"
 ]
}

 The dataset includes 121183 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057165-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2gb6du
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-3.61131 53.2905,-3.61263 53.28968,-3.61258 53.28953,-3.60987 53.28941,-3.60489 53.28992,-3.59936 53.29075,-3.58988 53.29297,-3.58601 53.2942,-3.5823 53.295,-3.58167 53.29553,-3.58255 53.29596,-3.58579 53.29544,-3.58731 53.29525,-3.59005 53.29434,-3.5932 53.29364,-3.59768 53.29216,-3.60033 53.29159,-3.60398 53.29087,-3.60686 53.29039,-3.60901 53.29037,-3.61064 53.29062,-3.61131 53.2905)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "Year 2015-2025"
 ]
}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057243-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zyfvgq
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Liolaemus paulinae Donoso-Barros, 1961"
 ]
}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057240-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.59yrtm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acapoeta tanganicae (Boulenger, 1900)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((58.3101 -20.2751,58.2432 -19.5708,57.8054 -19.3157,57.2737 -19.5421,56.8082 -20.4638,57.1232 -20.9316,57.7947 -20.9681,58.1423 -20.734,58.3101 -20.2751))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057242-250525065834625/datasets/export for details.
DOI: 10.15468/dl.d8uy8n
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-4.92435 51.61869,-4.9232 51.61618,-4.92532 51.61584,-4.9255 51.61392,-4.92671 51.61392,-4.92955 51.61358,-4.93276 51.61272,-4.93367 51.61159,-4.93391 51.60926,-4.9327 51.60817,-4.93082 51.60847,-4.93004 51.6076,-4.9278 51.60678,-4.9278 51.60618,-4.9278 51.60527,-4.92725 51.60448,-4.92834 51.60336,-4.92871 51.60219,-4.92937 51.60065,-4.92913 51.59948,-4.92622 51.59847,-4.92447 51.59847,-4.92501 51.59994,-4.92447 51.60118,-4.92217 51.60182,-4.92187 51.60385,-4.92114 51.60561,-4.91987 51.60648,-4.92168 51.60753,-4.92047 51.60945,-4.92011 51.60967,-4.91823 51.60776,-4.91618 51.6073,-4.91285 51.60847,-4.91369 51.61088,-4.91212 51.61001,-4.90964 51.61027,-4.90752 51.61136,-4.90595 51.61129,-4.90322 51.61193,-4.90268 51.60997,-4.9008 51.60997,-4.9002 51.61095,-4.89759 51.60971,-4.89608 51.61016,-4.89535 51.61076,-4.89626 51.61317,-4.89735 51.61373,-4.89989 51.61448,-4.90013 51.61509,-4.89989 51.61633,-4.90128 51.61681,-4.90365 51.6173,-4.90389 51.61881,-4.90086 51.61918,-4.89959 51.62057,-4.89862 51.62076,-4.89711 51.62185,-4.89862 51.62377,-4.89923 51.62467,-4.89687 51.62643,-4.89917 51.62677,-4.9011 51.62602,-4.90292 51.62403,-4.90165 51.62241,-4.90298 51.62177,-4.90395 51.6217,-4.90552 51.62279,-4.9074 51.62317,-4.90873 51.62283,-4.90897 51.62226,-4.91158 51.62147,-4.91236 51.6202,-4.91381 51.62016,-4.91551 51.6199,-4.9163 51.62031,-4.91799 51.62106,-4.9195 51.62155,-4.92072 51.62129,-4.9218 51.62012,-4.92417 51.61944,-4.92435 51.61869)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "Year 2015-2025"
 ]
}

 The dataset includes 41 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057245-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mus6z9
title: Occurrence Download

description: A dataset containing 38703 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Buteo augur (Ruppell, 1836)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 38703 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057244-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15696464
title: NMR and MS dataset for project: Manool disarms Streptococcus mutans by targeting ABC transporters

description: This dataset contains NMR and MS raw data for analysing glucose, glycolysis products, amino acids consumption and kanamycin levels in Streptococcus mutans culture supernants before and after treatment with the diterpene manool.
DOI: 10.6084/m9.figshare.29069525.v3
title: Automated_interaction_rates

description: Data and code to reproduce the analysis in: Automated quantification of ecological interactions from video. See README file for description of each file.
DOI: 10.15468/dl.jmy5qa
title: Occurrence Download

description: A dataset containing 258155 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Calamagrostis epigejos (L.) Roth, Chamaecytisus ruthenicus (Fisch. ex Wol.) Klásk., Gypsophila altissima L., Onosma simplicissima L., Phleum phleoides (L.) H.Karst.)",
 "Geometry POLYGON ((-180 45.53015, 180 45.53015, 180 83.62313, -180 83.62313, -180 45.53015))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "CoordinateUncertaintyInMeters is less than or equal to 50000"
 ]
}

 The dataset includes 258155 records from 806 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057225-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18028878
title: The Influence of Information Trust, Information Accuracy, and Mobile App Design on User Shopping Loyalty in the Context of Shopee Users in Indonesia with E-satisfaction as a Mediator

description:  


Abstract— This study examines the effects of information trust, information accuracy, and mobile app design on e-satisfaction and user shopping loyalty among Shopee users in Indonesia. As mobile-based e-commerce continues to grow rapidly, user satisfaction and loyalty have become key determinants of platform sustainability, particularly in mobile-first environments where users rely heavily on digital information and application interfaces. This study employs a quantitative approach using cross-sectional survey data collected from 421 Shopee users in Indonesia and analyzes the proposed research model using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The results show that information trust, information accuracy, and mobile app design have positive and significant effects on e-satisfaction. In turn, e-satisfaction significantly influences user shopping loyalty, indicating its important role in driving repeat purchase intentions and platform preference. The findings confirm that high-quality information and user-centered mobile app design reduce perceived risk, enhance the shopping experience, and strengthen long-term loyalty in mobile e-commerce contexts. This study provides practical implications for e-commerce platforms to improve customer retention by prioritizing trustworthy information, accurate product details, and effective mobile app design.

Keywords— E-Commerce, Information Trust, Information Accuracy, Mobile App Design, E-Satisfaction, User Shopping Loyalty, Shopee, PLS-SEM
DOI: 10.5281/zenodo.18028877
title: The Influence of Information Trust, Information Accuracy, and Mobile App Design on User Shopping Loyalty in the Context of Shopee Users in Indonesia with E-satisfaction as a Mediator

description:  


Abstract— This study examines the effects of information trust, information accuracy, and mobile app design on e-satisfaction and user shopping loyalty among Shopee users in Indonesia. As mobile-based e-commerce continues to grow rapidly, user satisfaction and loyalty have become key determinants of platform sustainability, particularly in mobile-first environments where users rely heavily on digital information and application interfaces. This study employs a quantitative approach using cross-sectional survey data collected from 421 Shopee users in Indonesia and analyzes the proposed research model using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The results show that information trust, information accuracy, and mobile app design have positive and significant effects on e-satisfaction. In turn, e-satisfaction significantly influences user shopping loyalty, indicating its important role in driving repeat purchase intentions and platform preference. The findings confirm that high-quality information and user-centered mobile app design reduce perceived risk, enhance the shopping experience, and strengthen long-term loyalty in mobile e-commerce contexts. This study provides practical implications for e-commerce platforms to improve customer retention by prioritizing trustworthy information, accurate product details, and effective mobile app design.

Keywords— E-Commerce, Information Trust, Information Accuracy, Mobile App Design, E-Satisfaction, User Shopping Loyalty, Shopee, PLS-SEM