cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/c7xhmd79zy.1
title: Declining Nipping of Alcohol and Increasing Sipping of Coffee

description: Dataset Description

The study uses a cross-national, multilevel dataset combining firm-level, city-level, and country-level data to examine whether rising coffee consumption substitutes for declining alcohol consumption, and how this relationship is moderated by national culture. The dataset covers 112 countries and nearly 50,000 coffee firms.

At the firm level, the data include coffee-producing firms with variables such as firm size, firm age, listing status, subsidiary status, World Compliance (WOCO), technological sector (SIC-based), performance, inter-sector technological distance, and city cluster density. All firm-level variables are log-transformed and standardized and serve as Level-1 predictors in the multilevel models.

Firms are nested within cities (Level 2), allowing the analysis to control for shared local conditions such as infrastructure, market size, and regional consumption environments through city-level random and fixed effects.

At the country level (Level 3), the dataset includes:

Alcohol consumption per capita (liters of pure alcohol per person aged 15+), from the World Health Organization.

Coffee consumption per capita (kilograms per person per year), from the International Coffee Organization.

Economic and demographic controls, including GDP per capita and the share of the Muslim population.

National culture indicators, measured using Hofstede’s four dimensions: Power Distance (PDI), Individualism (IDV), Masculinity (MAS), and Uncertainty Avoidance (UAI).

The main dependent variable is alcohol consumption per capita, while coffee consumption per capita is the key independent variable. Cultural dimensions enter the models both as direct predictors and as interaction terms with coffee consumption, allowing the study to test cultural moderation of the substitution effect.

The dataset is designed for multilevel mixed-effects and fixed-effects estimation, enabling robust cross-level inference that links firm-level industrial dynamics to national consumption outcomes within distinct cultural and institutional contexts.
DOI: 10.17632/hksbd9v6d5
title: Research data

description: Research data
DOI: 10.15468/dl.a7pwza
title: Occurrence Download

description: A dataset containing 18794131 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to 49.085278",
 "DecimalLatitude is less than or equal to 54.085278",
 "DecimalLongitude is greater than or equal to -5.0",
 "DecimalLongitude is less than or equal to 0.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 18794131 records from 535 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053684-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hksbd9v6d5.1
title: Research data

description: Research data
DOI: 10.5281/zenodo.17940707
title: MAKTABGACHA YOSHDAGI BOLALARNING KONSENTRATIV KOMPONENTLIGINI SHAKLLANTIRISH METODIKASI

description: Ushbu ilmiy maqolada maktabgacha yoshdagi bolalarda konsentrativ komponent — diqqatning barqaror ushlab turilishi, ixtiyoriy boshqarilishi va maqsadga yo‘naltirilganligi shakllanish jarayoni keng qamrovda yoritiladi. Maktabgacha ta’lim tashkilotlarida e’tiborni rivojlantirishga xizmat qiluvchi o‘yin texnologiyalari, sensor-motor mashqlar, konstruktiv faoliyat, muammoli vaziyatlar hamda pedagogik motivatsiya tizimining o‘rni ilmiy asosda tahlil qilinadi. Tadqiqot O‘zbekiston Respublikasining ta’lim sohasiga oid me’yoriy-huquqiy hujjatlar, davlat ta’lim standartlari va xalqaro psixologik nazariyalar asosida amalga oshirilgan. Mazkur yondashuvlar konsentrativ diqqatni mustahkamlash hamda bolalarning kognitiv rivojlanishini qo‘llab-quvvatlashda muhim omil sifatida baholanadi.
DOI: 10.5281/zenodo.17940708
title: MAKTABGACHA YOSHDAGI BOLALARNING KONSENTRATIV KOMPONENTLIGINI SHAKLLANTIRISH METODIKASI

description: Ushbu ilmiy maqolada maktabgacha yoshdagi bolalarda konsentrativ komponent — diqqatning barqaror ushlab turilishi, ixtiyoriy boshqarilishi va maqsadga yo‘naltirilganligi shakllanish jarayoni keng qamrovda yoritiladi. Maktabgacha ta’lim tashkilotlarida e’tiborni rivojlantirishga xizmat qiluvchi o‘yin texnologiyalari, sensor-motor mashqlar, konstruktiv faoliyat, muammoli vaziyatlar hamda pedagogik motivatsiya tizimining o‘rni ilmiy asosda tahlil qilinadi. Tadqiqot O‘zbekiston Respublikasining ta’lim sohasiga oid me’yoriy-huquqiy hujjatlar, davlat ta’lim standartlari va xalqaro psixologik nazariyalar asosida amalga oshirilgan. Mazkur yondashuvlar konsentrativ diqqatni mustahkamlash hamda bolalarning kognitiv rivojlanishini qo‘llab-quvvatlashda muhim omil sifatida baholanadi.
DOI: 10.17632/5nbbpc8y2b
title: Bird Observation Dataset, Yogyakarta 2025

description: The Bird Observation Dataset is a collection of original bird images acquired from a private data collection campaign conducted in Yogyakarta City, Indonesia, during November 2025. The dataset was developed to support research in bird detection, visual recognition, and object differentiation in outdoor environments, particularly for applications related to aerial surveillance and computer vision. All images were captured directly by the authors using DSLR cameras, ensuring high-quality visual data under natural imaging conditions. Image acquisition was carried out during three different time periods—daytime, late afternoon, and nighttime—to capture variations in illumination, background appearance, and visibility. This multitemporal acquisition strategy enables realistic representation of outdoor lighting transitions, including strong daylight, sunset conditions, and low-light nighttime scenes. The dataset includes diverse bird appearances with variations in orientation, scale, flight posture, and distance from the camera, as well as complex backgrounds such as urban structures, vegetation, and open sky typical of the Yogyakarta area. As the images were collected in real-world environments without artificial scene manipulation, the dataset reflects natural environmental complexity, including motion blur and background clutter. This dataset can be used independently or in combination with drone datasets to support research on false-positive reduction in drone detection systems, bird classification, and aerial object discrimination. The dataset is intended for academic and non-commercial research purposes.

The dataset is organized into image folders according to acquisition time periods and annotation availability:

1. Birds_01&gt;&gt;Images_Daytime/ — Bird images captured under daylight conditions
2. Birds_02&gt;&gt;Images_Afternoon/ — Bird images captured during late afternoon or sunset
3. Birds_03&gt;&gt;Images_Night/ — Bird images captured under nighttime illumination
4. Annotations/ — Annotation files (if applicable), provided in standard computer vision formats
5. Documentation/ — Dataset description and usage notes

All image files are provided in standard image formats (e.g., JPG or PNG) suitable for direct use in computer vision pipelines.
DOI: 10.17632/5nbbpc8y2b.1
title: Bird Observation Dataset, Yogyakarta 2025

description: The Bird Observation Dataset is a collection of original bird images acquired from a private data collection campaign conducted in Yogyakarta City, Indonesia, during November 2025. The dataset was developed to support research in bird detection, visual recognition, and object differentiation in outdoor environments, particularly for applications related to aerial surveillance and computer vision. All images were captured directly by the authors using DSLR cameras, ensuring high-quality visual data under natural imaging conditions. Image acquisition was carried out during three different time periods—daytime, late afternoon, and nighttime—to capture variations in illumination, background appearance, and visibility. This multitemporal acquisition strategy enables realistic representation of outdoor lighting transitions, including strong daylight, sunset conditions, and low-light nighttime scenes. The dataset includes diverse bird appearances with variations in orientation, scale, flight posture, and distance from the camera, as well as complex backgrounds such as urban structures, vegetation, and open sky typical of the Yogyakarta area. As the images were collected in real-world environments without artificial scene manipulation, the dataset reflects natural environmental complexity, including motion blur and background clutter. This dataset can be used independently or in combination with drone datasets to support research on false-positive reduction in drone detection systems, bird classification, and aerial object discrimination. The dataset is intended for academic and non-commercial research purposes.

The dataset is organized into image folders according to acquisition time periods and annotation availability:

1. Birds_01&gt;&gt;Images_Daytime/ — Bird images captured under daylight conditions
2. Birds_02&gt;&gt;Images_Afternoon/ — Bird images captured during late afternoon or sunset
3. Birds_03&gt;&gt;Images_Night/ — Bird images captured under nighttime illumination
4. Annotations/ — Annotation files (if applicable), provided in standard computer vision formats
5. Documentation/ — Dataset description and usage notes

All image files are provided in standard image formats (e.g., JPG or PNG) suitable for direct use in computer vision pipelines.
DOI: 10.17632/ypg9bbpdw3
title: YEBSAY DATA

description: SYSTEMATIC LITERATURE REVIEW MATRIX
DOI: 10.17632/ypg9bbpdw3.1
title: YEBSAY DATA

description: SYSTEMATIC LITERATURE REVIEW MATRIX
DOI: 10.17632/9vmmd328rh.2
title: Data Availability "A Novel Application of Pineapple Core Extract: In Vitro Antibacterial Activity Against Staphylococcus aureus and Streptococcus pyogenes from Impetigo Lesions "

description: This dataset contains the raw inhibition zone measurements and statistical analysis used in the study.
It includes:
Raw inhibition zone data for Staphylococcus aureus and Streptococcus pyogenes tested with pineapple core extract (50%, 70%, 100%), fusidic acid, and blank discs. Summary tables of inhibition results. Anonymized demographic data for study participants. Statistical analysis outputs used to generate the results in the manuscript. All data are fully anonymized and reported in English.
DOI: 10.17632/pn8zym54pz
title: Do Pseudosequences Matter in Neoantigen Prediction?

description: All pseudosequences and embeddings used in the paper "Do Pseudosequences Matter in Neoantigen Prediction?"

bioRxiv preprint: https://doi.org/10.64898/2025.12.09.693250

All code is freely available at https://github.com/KarchinLab/Do-Pseudoseqs-Matter

--------------------------------------------------------------------------------
FILE NAMES

The following are one-hot encoded pseudosequences, with each row representing an allele and each column representing the amino acid present at a given position. Each entry is either 0 or 1 to indicate whether that residue appears at that position.

Original_BigMHC_Pseudosequences.csv
Original_NetMHCpan-4.1_Pseudosequences.csv
Random_Pseudosequences.csv

BigMHC_Pseudosequences_Length_5.csv
BigMHC_Pseudosequences_Length_10.csv
BigMHC_Pseudosequences_Length_15.csv
BigMHC_Pseudosequences_Length_20.csv
BigMHC_Pseudosequences_Length_25.csv
BigMHC_Pseudosequences_Length_35.csv
BigMHC_Pseudosequences_Length_50.csv
BigMHC_Pseudosequences_Length_80.csv
BigMHC_Pseudosequences_Length_100.csv


The following are embeddings generated by ESM-2 and node2vec. Each row corresponds to an allele, and each column represents a dimension of the embedding space. The entries are floating-point values. 

BigMHC_ESM2_Embeddings.csv: ESM-2 embeddings of the original BigMHC pseudosequences
NetMHCpan-4.1_ESM2_Embeddings.csv: ESM-2 embeddings of the original NetMHCpan-4.1 pseudosequences
Annotation_Based_Embeddings.csv: graph-derived allele embeddings constructed using shared P-groups, supertypes, and allele nomenclature

A complete set of files is provided in a single ZIP file:
All_Pseudosequences_and_Embeddings.zip


The following are raw prediction scores described in the paper:
Supplementary_Information_1.xlsx: raw prediction scores for all models
Supplementary_Information_2.xlsx: raw prediction scores for all pseudosequence lengths
DOI: 10.17632/pn8zym54pz.2
title: Do Pseudosequences Matter in Neoantigen Prediction?

description: All pseudosequences and embeddings used in the paper "Do Pseudosequences Matter in Neoantigen Prediction?"

bioRxiv preprint: https://doi.org/10.64898/2025.12.09.693250

All code is freely available at https://github.com/KarchinLab/Do-Pseudoseqs-Matter

--------------------------------------------------------------------------------
FILE NAMES

The following are one-hot encoded pseudosequences, with each row representing an allele and each column representing the amino acid present at a given position. Each entry is either 0 or 1 to indicate whether that residue appears at that position.

Original_BigMHC_Pseudosequences.csv
Original_NetMHCpan-4.1_Pseudosequences.csv
Random_Pseudosequences.csv

BigMHC_Pseudosequences_Length_5.csv
BigMHC_Pseudosequences_Length_10.csv
BigMHC_Pseudosequences_Length_15.csv
BigMHC_Pseudosequences_Length_20.csv
BigMHC_Pseudosequences_Length_25.csv
BigMHC_Pseudosequences_Length_35.csv
BigMHC_Pseudosequences_Length_50.csv
BigMHC_Pseudosequences_Length_80.csv
BigMHC_Pseudosequences_Length_100.csv


The following are embeddings generated by ESM-2 and node2vec. Each row corresponds to an allele, and each column represents a dimension of the embedding space. The entries are floating-point values. 

BigMHC_ESM2_Embeddings.csv: ESM-2 embeddings of the original BigMHC pseudosequences
NetMHCpan-4.1_ESM2_Embeddings.csv: ESM-2 embeddings of the original NetMHCpan-4.1 pseudosequences
Annotation_Based_Embeddings.csv: graph-derived allele embeddings constructed using shared P-groups, supertypes, and allele nomenclature

A complete set of files is provided in a single ZIP file:
All_Pseudosequences_and_Embeddings.zip


The following are raw prediction scores described in the paper:
Supplementary_Information_1.xlsx: raw prediction scores for all models
Supplementary_Information_2.xlsx: raw prediction scores for all pseudosequence lengths
DOI: 10.17632/fsnph662h6.1
title: Bidirectional Mendelian Randomization and Genetic Insights Reveal Causal Relationships Between Cancer and Epilepsy Subtypes

description: The data that support the findings of this study are available in the IEU OpenGWAS database at https://gwas.mrcieu.ac.uk/. The reference numbers for all datasets are the GWAS IDs compiled in Supplementary Tables 1 and 10. These data were derived from the following resources available in the domain: the complete set of genome-wide association studies, as detailed with their respective consortium names and original publication links in the data sources section of the manuscript.
DOI: 10.15468/dl.bb6htw
title: Occurrence Download

description: A dataset containing 901 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Arctogadus glacialis"
 ]
}

 The dataset includes 901 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053712-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3rtnpyw2hs
title: Review of Treatment Outcomes in Fibrosing Alopecia in a Pattern Distribution (FAPD)

description: Supplementary Figure 1 and Table 1
DOI: 10.17632/3rtnpyw2hs.1
title: Review of Treatment Outcomes in Fibrosing Alopecia in a Pattern Distribution (FAPD)

description: Supplementary Figure 1 and Table 1
DOI: 10.15468/dl.2fuf23
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "DatasetKey is Lepidoptera collection of Hannu Saarenmaa"
 ]
}

 The dataset includes 51 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053713-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/39b6sxsvr4
title: Cloud Observation Dataset, Yogyakarta 2025

description: The Cloud Observation Dataset is a collection of original cloud images acquired from a private data collection campaign conducted in Yogyakarta City, Indonesia, during November 2025. The dataset was developed to support research in cloud detection, sky condition analysis, and environmental scene understanding, particularly for applications related to aerial surveillance, aviation safety, and computer vision. All images were captured directly by the authors using DSLR cameras, ensuring high-resolution visual data recorded under natural atmospheric conditions. Image acquisition was carried out during three different time periods—daytime, late afternoon, and nighttime—to capture variations in sky appearance, illumination intensity, and cloud visibility. This multitemporal approach enables the dataset to represent realistic atmospheric transitions, including clear daytime skies, sunset lighting conditions, and low-light nighttime cloud formations. The dataset includes diverse cloud types and formations, such as scattered clouds, dense cloud clusters, and partially overcast skies, captured against varied backgrounds typical of the Yogyakarta region. The images were collected in real-world outdoor environments without artificial manipulation, reflecting natural atmospheric complexity, brightness gradients, and background variation. This dataset can be used independently or in combination with drone and bird datasets to support research on sky segmentation, weather condition recognition, and false-positive reduction in aerial object detection systems. The Cloud Observation Dataset is suitable for training and evaluating computer vision models that analyze sky conditions, environmental context, and visual interference in airspace monitoring scenarios. The dataset is intended for academic and non-commercial use and provides a realistic visual resource for studies in computer vision, environmental monitoring, and intelligent airspace systems.

The dataset is organized in a clear hierarchical structure to support ease of access and direct use in computer vision workflows. Image files are grouped based on acquisition time periods, reflecting illumination variations during data collection. The dataset directory includes separate folders for images and annotations, as well as supporting documentation.

The main folders consist of Images_Daytime (Sky_01), Images_Afternoon (Sky_02), and Images_Night (Sky_03), which contain cloud images captured during daytime, late afternoon, and nighttime conditions, respectively. When annotations are provided, they are stored in a dedicated Annotations folder using standardized computer vision formats. Additional documentation describing dataset usage, metadata, and licensing information is included in a Documentation folder. All images are provided in common image formats such as JPG or PNG to ensure compatibility with standard image-processing libraries.
DOI: 10.17632/39b6sxsvr4.1
title: Cloud Observation Dataset, Yogyakarta 2025

description: The Cloud Observation Dataset is a collection of original cloud images acquired from a private data collection campaign conducted in Yogyakarta City, Indonesia, during November 2025. The dataset was developed to support research in cloud detection, sky condition analysis, and environmental scene understanding, particularly for applications related to aerial surveillance, aviation safety, and computer vision. All images were captured directly by the authors using DSLR cameras, ensuring high-resolution visual data recorded under natural atmospheric conditions. Image acquisition was carried out during three different time periods—daytime, late afternoon, and nighttime—to capture variations in sky appearance, illumination intensity, and cloud visibility. This multitemporal approach enables the dataset to represent realistic atmospheric transitions, including clear daytime skies, sunset lighting conditions, and low-light nighttime cloud formations. The dataset includes diverse cloud types and formations, such as scattered clouds, dense cloud clusters, and partially overcast skies, captured against varied backgrounds typical of the Yogyakarta region. The images were collected in real-world outdoor environments without artificial manipulation, reflecting natural atmospheric complexity, brightness gradients, and background variation. This dataset can be used independently or in combination with drone and bird datasets to support research on sky segmentation, weather condition recognition, and false-positive reduction in aerial object detection systems. The Cloud Observation Dataset is suitable for training and evaluating computer vision models that analyze sky conditions, environmental context, and visual interference in airspace monitoring scenarios. The dataset is intended for academic and non-commercial use and provides a realistic visual resource for studies in computer vision, environmental monitoring, and intelligent airspace systems.

The dataset is organized in a clear hierarchical structure to support ease of access and direct use in computer vision workflows. Image files are grouped based on acquisition time periods, reflecting illumination variations during data collection. The dataset directory includes separate folders for images and annotations, as well as supporting documentation.

The main folders consist of Images_Daytime (Sky_01), Images_Afternoon (Sky_02), and Images_Night (Sky_03), which contain cloud images captured during daytime, late afternoon, and nighttime conditions, respectively. When annotations are provided, they are stored in a dedicated Annotations folder using standardized computer vision formats. Additional documentation describing dataset usage, metadata, and licensing information is included in a Documentation folder. All images are provided in common image formats such as JPG or PNG to ensure compatibility with standard image-processing libraries.
DOI: 10.17632/ftr43f5g5w
title: Digital Development of Intangible Cultural Heritage

description: A total of 423 questionnaires were distributed, with 411 deemed valid for analysis. The effective questionnaire collection rate was approximately 97.2%.
DOI: 10.17632/ftr43f5g5w.1
title: Digital Development of Intangible Cultural Heritage

description: A total of 423 questionnaires were distributed, with 411 deemed valid for analysis. The effective questionnaire collection rate was approximately 97.2%.
DOI: 10.17632/4fhvcth8pz
title: Table S1-the cause of Tieling oxygenation

description: The raw data of Mid-Proterozoic transient oxygenation events linked to enhanced continental chemical weathering: Evidence from carbonate lithium isotopes
DOI: 10.17632/4fhvcth8pz.2
title: Table S1-the cause of Tieling oxygenation

description: The raw data of Mid-Proterozoic transient oxygenation events linked to enhanced continental chemical weathering: Evidence from carbonate lithium isotopes
DOI: 10.17632/77sztrg5ks.1
title: LCORE-D: A Benchmark Dataset for Core Network Analysis
DOI: 10.17632/hd9wm64yhx
title: 3RSALBP

description: 3RSALBP
DOI: 10.17632/hd9wm64yhx.1
title: 3RSALBP

description: 3RSALBP
DOI: 10.17632/42kw76b9n5
title: Supplemental Material for: Efficacy and Safety of Dupilumab in CSU

description: This dataset contains supplemental materials for a systematic review on dupilumab for chronic spontaneous urticaria (CSU). It includes the PRISMA flowchart.