cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.30530
title: Research Data Source Platform
title: 研究数据来源平台

description: Open-source data sources for:Public Health and Social Measures (PHSM) stringency dataVaccination dataData on SARS-CoV-2 variantsCOVID-19 effective reproduction number (Rt) dataPopulation densityProportion of population aged 65 and aboveGDP per capitaHuman Development Index (HDI)Age-standardized mortality rate from cardiovascular diseasesAge-standardized mortality rate from diabetes mellitusPrevalence of overweightLife expectancyHospital beds per thousand peopleGovernment health expenditure as a percentage of total health spendingPer capita health expenditure
description: Open-source data sources for:Public Health and Social Measures (PHSM) stringency dataVaccination dataData on SARS-CoV-2 variantsCOVID-19 effective reproduction number (Rt) dataPopulation densityProportion of population aged 65 and aboveGDP per capitaHuman Development Index (HDI)Age-standardized mortality rate from cardiovascular diseasesAge-standardized mortality rate from diabetes mellitusPrevalence of overweightLife expectancyHospital beds per thousand peopleGovernment health expenditure as a percentage of total health spendingPer capita health expenditure
DOI: 10.57760/sciencedb.30478
title: Driving Risk of Female Drivers Under Different In-vehicle Interaction Modalities
title: Driving Risk of Female Drivers Under Different In-vehicle Interaction Modalities

description: This study employed a 2×2 multifactorial within-subjects design. The two key factors were interaction mode (touchscreen, physical buttons) and task difficulty (Skip a track, Text entry). This study aimed to examine the effects of these two factors on task completion performance and cognitive load for female drivers in a driving context.Prior to the formal experiment, a static test session was conducted. The experimenter briefed each participant driver on the test content and requirements, allowing them to fully familiarize themselves with the operational steps and procedures. Participants performed one complete trial run of the experimental procedure while the vehicle was stationary.Following the static test, the formal experiment commenced. In the formal experiment, each participant performed a 10-minute car-following driving task. Voice prompts were provided during the drive. While maintaining driving safety, each participant experienced two different interaction modes (touchscreen and physical buttons). Each interaction mode required the completion of two task types (skip a track and text entry). A 10-minute break was provided between different interaction modes and tasks. After completing all four task rounds, participants filled out the NASA-TLX scale four times. The experimental procedure is illustrated in
description: This study employed a 2×2 multifactorial within-subjects design. The two key factors were interaction mode (touchscreen, physical buttons) and task difficulty (Skip a track, Text entry). This study aimed to examine the effects of these two factors on task completion performance and cognitive load for female drivers in a driving context.Prior to the formal experiment, a static test session was conducted. The experimenter briefed each participant driver on the test content and requirements, allowing them to fully familiarize themselves with the operational steps and procedures. Participants performed one complete trial run of the experimental procedure while the vehicle was stationary.Following the static test, the formal experiment commenced. In the formal experiment, each participant performed a 10-minute car-following driving task. Voice prompts were provided during the drive. While maintaining driving safety, each participant experienced two different interaction modes (touchscreen and physical buttons). Each interaction mode required the completion of two task types (skip a track and text entry). A 10-minute break was provided between different interaction modes and tasks. After completing all four task rounds, participants filled out the NASA-TLX scale four times. The experimental procedure is illustrated in
DOI: 10.57760/sciencedb.30557
title: Data for “Exciton-Driven Two-Step Charge Transfer in MoS2-WSe2 Heterostructures Revealed by Valley-Resolved Broadband Pump-Probe Spectroscopy”
title: Data for “Exciton-Driven Two-Step Charge Transfer in MoS2-WSe2 Heterostructures Revealed by Valley-Resolved Broadband Pump-Probe Spectroscopy”

description: Data for “Exciton-Driven Two-Step Charge Transfer in MoS2-WSe2 Heterostructures Revealed by Valley-Resolved Broadband Pump-Probe Spectroscopy”,The data include steady-state absorption spectra, Raman spectra, photoluminescence spectra, and transient absorption spectra, among others.
description: Data for “Exciton-Driven Two-Step Charge Transfer in MoS2-WSe2 Heterostructures Revealed by Valley-Resolved Broadband Pump-Probe Spectroscopy”,The data include steady-state absorption spectra, Raman spectra, photoluminescence spectra, and transient absorption spectra, among others.
DOI: 10.57760/sciencedb.24216
title: BD Sports-10 Dataset
title: BD Sports-10 Dataset

description: The BD Sports-10 Dataset is a comprehensive collection of 3,000 high-resolution videos (1920×1080 pixels at 30 frames per second) showcasing ten culturally and traditionally significant Bangladeshi sports. It is designed to support research in action recognition, cultural heritage preservation, sports video classification, and machine learning applications. The BD_Sports_10 folder contains two subfolders: Annotation and Dataset. The Dataset folder includes 10 subfolders, each corresponding to a sports class. Each sports category comprises 300 videos, ensuring a balanced distribution for supervised learning tasks.The dataset includes the following Bangladeshi sports:Hari VangaJoldangaKanamachiLathimMorog LoraiToilakto Kolagach Arohon (Kolagach)Nouka BaichKabaddiKho KhoLathi Khela
description: The BD Sports-10 Dataset is a comprehensive collection of 3,000 high-resolution videos (1920×1080 pixels at 30 frames per second) showcasing ten culturally and traditionally significant Bangladeshi sports. It is designed to support research in action recognition, cultural heritage preservation, sports video classification, and machine learning applications. The BD_Sports_10 folder contains two subfolders: Annotation and Dataset. The Dataset folder includes 10 subfolders, each corresponding to a sports class. Each sports category comprises 300 videos, ensuring a balanced distribution for supervised learning tasks.The dataset includes the following Bangladeshi sports:Hari VangaJoldangaKanamachiLathimMorog LoraiToilakto Kolagach Arohon (Kolagach)Nouka BaichKabaddiKho KhoLathi Khela
DOI: 10.57760/sciencedb.30264
title: MuSyQ SHG-LCC: A high-resolution global leaf chlorophyll content dataset derived from Sentinel-2 imagery (2019)
title: MuSyQ SHG-LCC:全球高分辨率叶片叶绿素含量产品(2019)

description: Leaf chlorophyll content (LCC) is a critical indicator of plant photosynthetic capacity and plays a key role in understanding the terrestrial carbon cycle. However, existing global LCC products, primarily derived from coarse-resolution remote sensing data, are unable to accurately capture the spatiotemporal variations of LCC in heterogeneous regions. Currently, high-resolution global LCC products remain limited, significantly constraining their application at fine scales. To address this gap, this dataset developed a high-resolution global LCC product (MuSyQ SHG-LCC) with 100-meter/10-day resolution from Sentinel-2 MSI data for the 2019-2023. The dataset was generated using the leaf-scale MERIS Terrestrial Chlorophyll Index (MTCIleaf), which effectively corrects for canopy structural effects on LCC retrieval. Validation results demonstrated that the dataset achieved high retrieval accuracy across various vegetation types, providing important data support for global carbon cycle and ecological research.
description: Leaf chlorophyll content (LCC) is a critical indicator of plant photosynthetic capacity and plays a key role in understanding the terrestrial carbon cycle. However, existing global LCC products, primarily derived from coarse-resolution remote sensing data, are unable to accurately capture the spatiotemporal variations of LCC in heterogeneous regions. Currently, high-resolution global LCC products remain limited, significantly constraining their application at fine scales. To address this gap, this dataset developed a high-resolution global LCC product (MuSyQ SHG-LCC) with 100-meter/10-day resolution from Sentinel-2 MSI data for the 2019-2023. The dataset was generated using the leaf-scale MERIS Terrestrial Chlorophyll Index (MTCIleaf), which effectively corrects for canopy structural effects on LCC retrieval. Validation results demonstrated that the dataset achieved high retrieval accuracy across various vegetation types, providing important data support for global carbon cycle and ecological research.
DOI: 10.57760/sciencedb.10725
title: Direct de novo design of picomolar miniproteins against PD-L1
title: Direct de novo design of picomolar miniproteins against PD-L1

description: The design scripts and main PDB models, computational protocol, the entire miniprotein scaffold library and all the design models used in this paper.
description: The design scripts and main PDB models, computational protocol, the entire miniprotein scaffold library and all the design models used in this paper.
DOI: 10.15468/dl.qe36yy
title: Occurrence Download

description: A dataset containing 11908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Korea, Republic of",
 "Year is 2018",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 11908 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012234-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bf4p57
title: Occurrence Download

description: A dataset containing 6051 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.continent IN ('NORTH_AMERICA', 'SOUTH_AMERICA')
 AND occurrence.countrycode IN ('US', 'MX', 'BR', 'CA', 'CO', 'AR', 'PA', 'EC', 'BO', 'PY')
 AND (CAST(occurrence.taxonkey AS INTEGER) = 9368 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 9368 OR CAST(occurrence.kingdomkey AS INTEGER) = 9368 OR CAST(occurrence.phylumkey AS INTEGER) = 9368 OR CAST(occurrence.classkey AS INTEGER) = 9368 OR CAST(occurrence.orderkey AS INTEGER) = 9368 OR CAST(occurrence.familykey AS INTEGER) = 9368 OR CAST(occurrence.genuskey AS INTEGER) = 9368 OR CAST(occurrence.specieskey AS INTEGER) = 9368)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 6051 records were derived from 476 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012219-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eheet8
title: Occurrence Download

description: A dataset containing 28683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((61.52487 35.25806,62.13574 32.8192,65.43525 30.98288,64.25106 30.30649,61.1454 30.71859,59.8759 29.24475,61.67247 27.61076,60.74092 25.57456,61.68271 24.80799,65.41545 24.6437,67.90727 25.52982,68.39312 27.68685,70.49363 28.57444,72.44407 32.8567,80.98744 27.14283,86.16975 25.62307,89.04903 26.0095,88.74933 24.82891,90.52724 24.3422,90.06478 23.29551,91.03887 20.66572,93.42061 17.87605,93.1616 15.96719,93.89526 15.18054,95.83251 16.49895,95.81423 18.88373,96.39316 18.47345,98.87368 18.96896,101.47837 20.55262,102.27374 21.94828,100.49257 22.85578,100.53278 23.67343,103.96621 24.93764,104.54136 25.56759,104.73085 26.17354,104.10241 27.30708,105.00753 29.39687,104.11397 30.60069,105.42037 32.20441,106.1748 34.7684,103.30227 38.41545,100.08066 39.85855,93.00629 39.97377,89.9109 39.03537,88.85681 38.17749,89.10603 37.31957,84.21138 36.22857,80.10264 36.84447,78.05782 36.35705,75.71181 38.18624,72.78709 38.03729,72.40386 39.0425,70.94208 39.27737,68.69605 37.98985,66.20038 38.42204,64.59397 37.89534,61.52487 35.25806),(95.12017 22.31185,95.43108 21.76189,95.15338 20.81699,95.12017 22.31185))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Myxini, Petromyzonti, Elasmobranchii, Holocephali, Acipenseriformes, Lepisosteiformes, Amiiformes, Osteoglossiformes, Elopiformes, Albuliformes, Notacanthiformes, Anguilliformes, Clupeiformes, Gonorynchiformes, Cypriniformes, Characiformes, Gymnotiformes, Siluriformes, Esociformes, Salmoniformes, Osmeriformes, Stomiiformes, Ateleopodiformes, Aulopiformes, Myctophiformes, Lampriformes, Percopsiformes, Zeiformes, Gadiformes, Polymixiiformes, Beryciformes, Ophidiiformes, Batrachoidiformes, Syngnathiformes, Synbranchiformes, Pleuronectiformes, Atheriniformes, Cyprinodontiformes, Beloniformes, Mugiliformes, Gobiesociformes, Lophiiformes, Perciformes, Coelacanthi, Dipneusti, Polypteriformes)"
 ]
}

 The dataset includes 28683 records from 151 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012236-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.88gb3k
title: Occurrence Download

description: A dataset containing 5158 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Polygonia c-album",
 "Year 1910-1955"
 ]
}

 The dataset includes 5158 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012237-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17510160
title: code
DOI: 10.5281/zenodo.17510159
title: code
DOI: 10.15468/dl.kcw8q4
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Monitoreos preliminares de la fauna silvestre en planta La Garita, ICE 2024"
 ]
}

 The dataset includes 56 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012239-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x3c2by
title: Occurrence Download

description: A dataset containing 17156 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Korea, Republic of",
 "Year is 2019",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 17156 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012238-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vg3qw6
title: Occurrence Download

description: A dataset containing 21794 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Korea, Republic of",
 "Year is 2020",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 21794 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012241-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ydu3mb
title: Occurrence Download

description: A dataset containing 1777 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Puma concolor"
 ]
}

 The dataset includes 1777 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012242-251025141854904/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.30061038
title: The structural basis of drugs targeting protein–protein interactions uncovered with the protein–ligand interaction profiler PLIP

description: Promiscuity of drugs and targets plays an important role in drug-target prediction, ranging from the explanation of side effects to their exploitation in drug repositioning. A specific form of promiscuity concerns drugs, which interfere with protein–protein interactions. With the rising importance of such drugs in drug discovery and with the large-scale availability of structural data, the question arises on the structural basis of this form of promiscuity and the commonalities of the underlying protein–ligand (PLI) and protein–protein interactions (PPI). The authors applied the protein–ligand interaction profiler, PLIP, to experimental and predicted structures and characterize drugs in clinical trials, which target PPI. PPIs generally involve more non-covalent interactions than PLI with overlapping interaction patterns and key binding site residues. In contrast to experimental structures, predicted structures fall short in accurately capturing interaction details at the interface. Taken together, our analysis shows that PPIs and PLIs have sufficient commonalities to merit future work into computational screenings for drugs targeting PPIs. It will be key to further improve structure prediction, specifically for binding site details.
DOI: 10.6084/m9.figshare.30061038.v1
title: The structural basis of drugs targeting protein-protein interactions uncovered with the protein-ligand interaction profiler PLIP

description: Promiscuity of drugs and targets plays an important role in drug-target prediction, ranging from the explanation of side effects to their exploitation in drug repositioning. A specific form of promiscuity concerns drugs, which interfere with protein-protein interactions. With the rising importance of such drugs in drug discovery and with the large-scale availability of structural data, the question arises on the structural basis of this form of promiscuity and the commonalities of the underlying protein-ligand (PLI) and protein-protein interactions (PPI). The authors applied the protein-ligand interaction profiler, PLIP, to experimental and predicted structures and characterize drugs in clinical trials, which target PPI. PPIs generally involve more non-covalent interactions than PLI with overlapping interaction patterns and key binding site residues. In contrast to experimental structures, predicted structures fall short in accurately capturing interaction details at the interface. Taken together, our analysis shows that PPIs and PLIs have sufficient commonalities to merit future work into computational screenings for drugs targeting PPIs. It will be key to further improve structure prediction, specifically for binding site details.
DOI: 10.6084/m9.figshare.30061038.v2
title: The structural basis of drugs targeting protein–protein interactions uncovered with the protein–ligand interaction profiler PLIP

description: Promiscuity of drugs and targets plays an important role in drug-target prediction, ranging from the explanation of side effects to their exploitation in drug repositioning. A specific form of promiscuity concerns drugs, which interfere with protein–protein interactions. With the rising importance of such drugs in drug discovery and with the large-scale availability of structural data, the question arises on the structural basis of this form of promiscuity and the commonalities of the underlying protein–ligand (PLI) and protein–protein interactions (PPI). The authors applied the protein–ligand interaction profiler, PLIP, to experimental and predicted structures and characterize drugs in clinical trials, which target PPI. PPIs generally involve more non-covalent interactions than PLI with overlapping interaction patterns and key binding site residues. In contrast to experimental structures, predicted structures fall short in accurately capturing interaction details at the interface. Taken together, our analysis shows that PPIs and PLIs have sufficient commonalities to merit future work into computational screenings for drugs targeting PPIs. It will be key to further improve structure prediction, specifically for binding site details.
DOI: 10.15468/dl.25szca
title: Occurrence Download

description: A dataset containing 4695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (North America, Asia)",
 "Geometry POLYGON((11.459 -16.12342,357.06205 -16.12342,357.06205 79.66056,11.459 79.66056,11.459 -16.12342))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Callitriche palustris"
 ]
}

 The dataset includes 4695 records from 233 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012243-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cmeseq
title: Occurrence Download

description: A dataset containing 21392 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Korea, Republic of",
 "Year is 2021",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 21392 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012244-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cb5fa2
title: Occurrence Download

description: A dataset containing 14880 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 0553362c-9393-411f-bec2-116d0c343c4c",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 14880 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012245-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.799rpq
title: Occurrence Download

description: A dataset listing the 21025 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Honduras",
 "TaxonKey is Arthropoda"
 ]
}

 The dataset's 21025 records were derived from 324 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012240-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.