cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5kmpgfykjf
title: Dataset on Gen Z Consumer Preferences toward Influencer Marketing, Product Reviews, and AI-Based Recommendation Systems

description: This dataset is an Excel file containing questionnaire responses from Gen Z participants regarding their preferences toward influencer marketing, product reviews, and AI-based recommendation systems. The data includes structured scale responses that reflect consumer perceptions, trust levels, and behavioral tendencies in the context of online cosmetic purchases.
DOI: 10.17632/5kmpgfykjf.1
title: Dataset on Gen Z Consumer Preferences toward Influencer Marketing, Product Reviews, and AI-Based Recommendation Systems

description: This dataset is an Excel file containing questionnaire responses from Gen Z participants regarding their preferences toward influencer marketing, product reviews, and AI-based recommendation systems. The data includes structured scale responses that reflect consumer perceptions, trust levels, and behavioral tendencies in the context of online cosmetic purchases.
DOI: 10.17632/vfvv9dv6fp
title: Monitoring maize phenology using multi-source data by integrating Convolutional Neural Networks and Transformers，code，data&Readme

description: This repository serves as the comprehensive supplementary dataset and code archive for the associated research article published in Remote Sensing, aimed at ensuring the full transparency and reproducibility of the study's findings. The archive is systematically organized to include the raw in-situ field measurement data collected during the agricultural experiments, providing the essential ground truth values and physiological parameters necessary for validating the remote sensing results. Furthermore, it contains the complete source code for the deep learning framework developed in the study, encompassing the specific Python scripts used for data preprocessing, model training, and inference to replicate the proposed methodology. Additionally, the repository includes the data visualization and plotting scripts utilized to generate the statistical charts and result figures presented in the manuscript, allowing researchers to verify the analysis pipelines and reconstruct the visual outputs directly from the source data.
DOI: 10.17632/vfvv9dv6fp.1
title: Monitoring maize phenology using multi-source data by integrating Convolutional Neural Networks and Transformers，code，data&Readme

description: This repository serves as the comprehensive supplementary dataset and code archive for the associated research article published in Remote Sensing, aimed at ensuring the full transparency and reproducibility of the study's findings. The archive is systematically organized to include the raw in-situ field measurement data collected during the agricultural experiments, providing the essential ground truth values and physiological parameters necessary for validating the remote sensing results. Furthermore, it contains the complete source code for the deep learning framework developed in the study, encompassing the specific Python scripts used for data preprocessing, model training, and inference to replicate the proposed methodology. Additionally, the repository includes the data visualization and plotting scripts utilized to generate the statistical charts and result figures presented in the manuscript, allowing researchers to verify the analysis pipelines and reconstruct the visual outputs directly from the source data.
DOI: 10.58132/umadit
title: In search for new mechanisms of maize adaptation to cool spring conditions

description: Maize cultivation has spread far beyond its place of origin in Central America. This expansion was made possible by breaking the crop’s photoperiodic requirements and by selecting early‑maturing genotypes. In this study we explore the traits that could enable maize to grow at the low temperatures typical of temperate climates. Building on previous work, we identified four phenotypic responses to cold. The aim of this investigation is to describe the mechanisms underlying these phenotypes. The dataset consists of automated phenotyping measurements. Phenotyping was performed on three groups of maize lines that differ in growth speed grown in cold with subsequent regrowth period in control conditions. The metadata_ISA_tab.zip file contains detailed metadata in ISA‑tab format (https://doi.org/10.5281/zenodo.161355).
DOI: 10.6084/m9.figshare.30514319.v1
title: Lactate-to-Albumin Ratio and Albumin-Corrected Anion Gap as Predictors of Outcome in Methanol Poisoning: A Retrospective Observational Study

description: Lactate-to-Albumin Ratio and Albumin-Corrected Anion Gap as Predictors of Outcome in Methanol Poisoning: A Retrospective Observational Study
DOI: 10.5285/7ee55609-d6b1-4693-8b36-2bf84fef76c2
title: Scots pine single nucleotide polymorphism (SNP) genotypes for Axiom array validation

description: The dataset contains  genotypes for samples used to validate a 50K single nucleotide polymorphism (SNP, DNA mutation) Axiom array for Scots pine (Pinus sylvestris) and closely related members of the Pinus mugo complex.
description: Samples &  Validation of the array was carried out using a subset of 87 samples, which included four species of pine (Pinus sylvestris: SY; P. mugo: MU; P. uncinata: UN; P. uliginosa: UL). DNA was extracted from needles using a Qiagen DNeasy 96 kit following the manufacturer’s instructions. Needles were dried on silica gel prior to extraction and DNA was quantified using a Qubit spectrophotometer to ensure a minimum standardized concentration of 35ng/µl. The quality of genomic DNA was also checked visually for fragmentation on 1%  agarose gel.

 Genotyping was done at Edinburgh Genomics following DNA amplification, fragmentation, chip hybridisation, single-base extension through DNA ligation and signal amplification performed according to the Affymetrix Axiom® Assay protocol. Genotyping was performed in 384-well format on a GeneTitan according to the manufacturer’s procedure. Genotype calls were performed using Axiom Analysis Suite software as recommended by the manufacturer (ThermoFisher). Samples were assigned to an analysis group based on their call rate (CR) and dish QC (DQC: a metric provided by ThermoFisher which is generated by measuring signals at multiple sites in the genome known not to vary among individuals), using the following thresholds: DQC 'high' ≥ 0.82; DQC 'low' < 0.82; CR 'high' ≥ 96; CR 'low' < 96. Analyses: 1) DQC high + CR high; 2) DQC high + CR low; 3) DQC low + CR low. High quality samples (N=529), with high CR and DQC, were used to set thresholds for allele calls. Posteriors for allele calls were subsequently used as priors for analyses 2 (N = 753) and 3 (N = 251).
DOI: 10.5281/zenodo.17670176
title: Supplementary material 4. Test results according to the faculty.
DOI: 10.5281/zenodo.17670177
title: Supplementary material 4. Test results according to the faculty.
DOI: 10.17632/gpx9jkf5vf
title: ENHANCING NETWORK SECURITY WITH A HYBRID NEURAL FUSION NETWORK FOR SYBIL ATTACK DETECTION

description: A VANET (Vehicular Ad Hoc Network) Malicious Node Dataset contains communication data generated from vehicles and roadside units where both normal and malicious nodes are present. It is mainly used to develop and test intrusion detection systems (IDS) for intelligent transportation.
DOI: 10.17632/gpx9jkf5vf.1
title: ENHANCING NETWORK SECURITY WITH A HYBRID NEURAL FUSION NETWORK FOR SYBIL ATTACK DETECTION

description: A VANET (Vehicular Ad Hoc Network) Malicious Node Dataset contains communication data generated from vehicles and roadside units where both normal and malicious nodes are present. It is mainly used to develop and test intrusion detection systems (IDS) for intelligent transportation.
DOI: 10.17632/smcr58spsx
title: Dataset of spectra and biochemical profiles of shallots under water deficit stress

description: This dataset contains Vis/NIR spectroscopy data collected from 144 samples (three varieties). Data are available as .xlsx and .unsb file extension formats.
DOI: 10.17632/smcr58spsx.2
title: Dataset of spectra and biochemical profiles of shallots under water deficit stress

description: This dataset contains Vis/NIR spectroscopy data collected from 144 samples (three varieties). Data are available as .xlsx and .unsb file extension formats.
DOI: 10.17632/3tcfbww9k2
title: Questionnaire and consent form for the data set onLearner based factors and academic achievement data uploaded by Stella

description: These files contain the questionnaire and consent
DOI: 10.5285/80abddf7-3feb-43f8-9244-c5fdb6980075
title: Land Cover Map 2022 (land parcels, GB)

description: This is a vector data set representing the land surface of Great Britain, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. This vector Land Parcel dataset is the result of intersecting the 10m raster classified pixel datasets with the UKCEH Land Parcel Spatial Framework to generate summary land parcel attributes for each land cover parcel. A full description of this and all UKCEH LCM2022 products are available from the LCM2022 product documentation.
description: UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2019 to 2021. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.  
Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2022 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation.
The 25 m rasterised land parcels product is created by pixelating the corresponding land parcel product.
DOI: 10.17632/3tcfbww9k2.1
title: Questionnaire and consent form for the data set onLearner based factors and academic achievement data uploaded by Stella

description: These files contain the questionnaire and consent
DOI: 10.17632/yjmdhjnwx7.1
title: Remittance inflows, democracy and economic freedom in developing countries: An approach based on democratic regimes

description: These are the replication files for the paper entitled "Remittance inflows, democracy and economic freedom in developing countries: An approach based on democratic regimes"
DOI: 10.17632/mmzttzt9vs.1
title: A Self-Calibrating Retrieval Algorithm for Three-phase water Raman Lidar Incorporating Temperature-Dependent Spectral Characteristics

description: Three-phase water,Temperature and depolarization ratio data from from 20:00 LST on August 21th to 2:00 LST on August 22th. The lidar is located at at Boluo station in Huizhou, Guangdong Province, China.
DOI: 10.17632/wzdx55br3x
title: GCA_OXYGEN_PRESERVATION

description: data associated with article——Constraining Conditions for the Preservation of Oxygen Atoms from Hydroxyl Radicals in the Oxidation Products
DOI: 10.15468/dl.r9skqx
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Gentiana sierrae"
 ]
}

 The dataset includes 70 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002028-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wzdx55br3x.1
title: GCA_OXYGEN_PRESERVATION

description: data associated with article——Constraining Conditions for the Preservation of Oxygen Atoms from Hydroxyl Radicals in the Oxidation Products
DOI: 10.17632/ftxkg53yhj
title: The Impact of Different Land Use Scenarios and (C) Factor Extraction Methods on Structural Sediment Connectivity
DOI: 10.5285/0ff4958d-47bc-4260-9aaf-399de3b83b6f
title: Land Cover Map 2023 (land parcels, N. Ireland)

description: This is a vector data set representing the land surface of Northern Ireland, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. This vector Land Parcel dataset is the result of intersecting the 10m raster classified pixel datasets with the UKCEH Land Parcel Spatial Framework to generate summary land parcel attributes for each land cover parcel. A full description of this and all UKCEH LCM2023 products are available from the LCM2023 product documentation.
description: UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2020 to 2022. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.  

Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2023 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation.
The 25 m rasterised land parcels product is created by pixelating the corresponding land parcel product.
DOI: 10.17632/ftxkg53yhj.1
title: The Impact of Different Land Use Scenarios and (C) Factor Extraction Methods on Structural Sediment Connectivity
DOI: 10.17632/6hznjbj82h
title: DATA ON DFIs THULANI DLAMINI

description: THE DATA INCLUDES THAT THE DATA REORGNAISED IN NVIVO -ALL OF IT. THE OPEN CODES DOCUMENT AND THE SPSS DATA FILE.
DOI: 10.17632/6hznjbj82h.1
title: DATA ON DFIs THULANI DLAMINI

description: THE DATA INCLUDES THAT THE DATA REORGNAISED IN NVIVO -ALL OF IT. THE OPEN CODES DOCUMENT AND THE SPSS DATA FILE.
DOI: 10.17632/sztmnwvsd9
title: PERK orchestrates MERCS formation and mitochondrial remodelling promoting physiological adaptations during adaptive UPR signalling

description: Source data for all experiments contained in this manuscript.
DOI: 10.15468/dl.szre22
title: Occurrence Download

description: A dataset containing 20585 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 GBIF_EEARGCODE(1000, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)) eeacellcode,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, GBIF_EEARGCODE(1000, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)), "year")) classcount,
 COUNT(*) occurrences,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.basisofrecord = 'HUMAN_OBSERVATION'
 AND occurrence.countrycode = 'BE'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 2479226 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 2479226 OR CAST(occurrence.kingdomkey AS INTEGER) = 2479226 OR CAST(occurrence.phylumkey AS INTEGER) = 2479226 OR CAST(occurrence.classkey AS INTEGER) = 2479226 OR CAST(occurrence.orderkey AS INTEGER) = 2479226 OR CAST(occurrence.familykey AS INTEGER) = 2479226 OR CAST(occurrence.genuskey AS INTEGER) = 2479226 OR CAST(occurrence.specieskey AS INTEGER) = 2479226)
 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 occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence.hascoordinate = TRUE)
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",
 GBIF_EEARGCODE(1000, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000))

 The dataset's 20585 records were derived from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002014-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sztmnwvsd9.2
title: PERK orchestrates MERCS formation and mitochondrial remodelling promoting physiological adaptations during adaptive UPR signalling

description: Source data for all experiments contained in this manuscript.
DOI: 10.17632/dbxkn8sys5
title: THE INFLUENCE OF DIGITAL PRODUCT UTILIZATION IN FOOD AND BAVERAGE BUSINESS PROCESS WITH BIBLIOGRAPHY METHOD

description: This dataset contains the comprehensive bibliographic records used for the research article titled "The Influence of Digital Product Utilization in Food and Beverage Business Process." The study aims to map the evolution, intellectual structure, and current trends of digital product adoption (such as blockchain, IoT, and AI) within the global food and beverage industry. The primary hypothesis of the study is that digital transformation has shifted from basic digitization to complex, data-driven ecosystems dominated by supply chain transparency and efficiency.
DOI: 10.17632/dbxkn8sys5.1
title: THE INFLUENCE OF DIGITAL PRODUCT UTILIZATION IN FOOD AND BAVERAGE BUSINESS PROCESS WITH BIBLIOGRAPHY METHOD

description: This dataset contains the comprehensive bibliographic records used for the research article titled "The Influence of Digital Product Utilization in Food and Beverage Business Process." The study aims to map the evolution, intellectual structure, and current trends of digital product adoption (such as blockchain, IoT, and AI) within the global food and beverage industry. The primary hypothesis of the study is that digital transformation has shifted from basic digitization to complex, data-driven ecosystems dominated by supply chain transparency and efficiency.
DOI: 10.5285/50b344eb-8343-423b-8b2f-0e9800e34bbd
title: Land Cover Map 2023 (land parcels, GB)

description: This is a vector data set representing the land surface of Great Britain, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. This vector Land Parcel dataset is the result of intersecting the 10m raster classified pixel datasets with the UKCEH Land Parcel Spatial Framework to generate summary land parcel attributes for each land cover parcel. A full description of this and all UKCEH LCM2023 products are available from the LCM2023 product documentation.
description: UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2020 to 2022. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.  
Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2023 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation.
The 25 m rasterised land parcels product is created by pixelating the corresponding land parcel product.
DOI: 10.15468/dl.fzr8h8
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Phylloscopus herberti",
 "Year 2024-2025"
 ]
}

 The dataset includes 25 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002029-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16572630
title: Datasets and Trained Machine Learning Models for Potential Energy Surfaces of Hydrogen Atom Transfer in Peptides

description: Datasets and trained machine learning models for the paper: 'Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides' (Neubert, Gräter, Friederich, 2025).  It includes DFT (bmk/def2-TZVPD) and semiempirical (xTB) configurations, along with corresponding energies and forces for a diverse set of hydrogen atom transfer reactions in peptide environments. Trained graph neural network models (SchNet, Allegro, and MACE) are included, which have been trained on the full dataset and subsets.
DOI: 10.5281/zenodo.17670180
title: Datasets and Trained Machine Learning Models for Potential Energy Surfaces of Hydrogen Atom Transfer in Peptides

description: Datasets and trained machine learning models for the paper: 'Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides' (Neubert, Gräter, Friederich, 2025).  It includes DFT (bmk/def2-TZVPD) and semiempirical (xTB) configurations, along with corresponding energies and forces for a diverse set of hydrogen atom transfer reactions in peptide environments. Trained graph neural network models (SchNet, Allegro, and MACE) are included, which have been trained on the full dataset and subsets.
DOI: 10.15468/dl.a9aqh7
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Material sample)",
 "Geometry POLYGON((76.95332 34.51079,76.5899 33.39375,77.64956 32.18107,78.28459 32.21167,78.35727 33.48556,77.94794 33.99052,77.49654 34.52609,76.95332 34.51079))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Panthera uncia",
 "Year 2000-2022"
 ]
}

 The dataset includes 48 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002030-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.