cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/h6p2w53my5.2
title: L-VAD (Low-Light Vehicle & Annotation Dataset)

description: Overview
This dataset is a specialized collection of annotated images designed for Nighttime Vehicle Detection. While most standard datasets focus on daylight conditions, this dataset addresses the specific challenges of low-light environments, such as glare from headlights, motion blur, and low contrast.
It provides high-quality images of three primary vehicle classes: Cars, Motorcycles, and Trucks. Every image has been meticulously annotated with bounding boxes, making it ready for supervised learning tasks in Computer Vision.
Key Features
• Environment: Exclusively nighttime and low-light conditions (urban streets, highways, and residential areas).
• Object Classes: 1. Cars (Sedans, SUVs, Vans) 2. Motorcycles (Scooters, Sportbikes) 3. Trucks (Freight trucks, Delivery trucks)
• Annotation Format: Provided in YOLO format.
• Diversity: Captured under various night conditions, including street-lit roads and total darkness with only vehicle headlights as light sources.
DOI: 10.17632/jd6dcghn8n
title: Simulation data for: Formation and Amplification of the Hypernetwork Effect

description: This dataset contains simulation-generated panel data used in the study
"Formation and Amplification of the Hypernetwork Effect: A Theoretical Model of Self-Reinforcing Growth in AI-Driven Networks."

The data were generated through computer simulations and used for panel regression analyses.
DOI: 10.17632/jd6dcghn8n.1
title: Simulation data for: Formation and Amplification of the Hypernetwork Effect

description: This dataset contains simulation-generated panel data used in the study
"Formation and Amplification of the Hypernetwork Effect: A Theoretical Model of Self-Reinforcing Growth in AI-Driven Networks."

The data were generated through computer simulations and used for panel regression analyses.
DOI: 10.3886/e244168
title: Provisional Death Counts for Influenza, Pneumonia, and COVID-19

description: Deaths counts for influenza, pneumonia, and COVID-19 reported to NCHS by week ending date, by state and HHS region, and age group.
description: (Downloaded 1/9/2026)
DOI: 10.3886/e244168v1
title: Provisional Death Counts for Influenza, Pneumonia, and COVID-19

description: Deaths counts for influenza, pneumonia, and COVID-19 reported to NCHS by week ending date, by state and HHS region, and age group.
description: (Downloaded 1/9/2026)
DOI: 10.5281/zenodo.18248073
title: Baccharis viminea DC. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.18248072
title: Baccharis viminea DC. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.17632/pykvcgs22y.1
title: Large-Scale Dataset of Porous Carbon Materials for Supercapacitors Extracted via Large Language Models

description: This dataset comprises a large-scale collection of experimental data on porous carbon supercapacitors, extracted from approximately 1,000 high-content research articles using an automated Large Language Model (LLM) mining framework.
The database covers over 5,000 distinct carbon samples and includes more than 10,000 specific capacitance data points under varying testing conditions. It offers a holistic view of the material properties, integrating synthesis parameters (preparation crafts), microstructural characteristics (pore size distribution, surface area), and surface elemental composition. Furthermore, it details key electrochemical performance metrics, including Specific Capacitance, Energy Density, Power Density, and Equivalent Series Resistance (ESR).
This dataset serves as a valuable resource for data-driven materials science, enabling quantitative analysis of structure-performance correlations and the inverse design of high-performance energy storage materials.

Note: For further modeling use, detailed data cleaning may be required for specific domains
DOI: 10.15468/dl.7crjwd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"IND.28.16_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009556-260108223611665/datasets/export for details.
DOI: 10.17632/f3mtsbnjs4
title: Supplementary Data (Appendices): Parental Involvement in Managing Senior Secondary Pre-class Tasks for Flipped Mathematics Instruction.

description: Supplementary Data (Appendices): Parental Involvement in Managing Senior Secondary Pre-class Tasks for Flipped Mathematics Instruction.
DOI: 10.15468/dl.fe4fzj
title: Occurrence Download

description: A dataset containing 6569 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tropaeolum majus, Watsonia meriana, Zantedeschia aethiopica)",
 "Country is Portugal",
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 6569 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009557-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/f3mtsbnjs4.1
title: Supplementary Data (Appendices): Parental Involvement in Managing Senior Secondary Pre-class Tasks for Flipped Mathematics Instruction.

description: Supplementary Data (Appendices): Parental Involvement in Managing Senior Secondary Pre-class Tasks for Flipped Mathematics Instruction.
DOI: 10.5281/zenodo.18248079
title: Baccharis vulneraria Baker. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:43
DOI: 10.5281/zenodo.18248078
title: Baccharis vulneraria Baker. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:43
DOI: 10.17632/vkjwwb8zsh
title: Binhai/BH-CPTU/10-44

description: The Binhai/BH-CPTU/10-44 database compiles 44 CPTU soundings, 10 borehole records, and associated laboratory test results from the Binhainan offshore wind farm in the Yellow Sea.
DOI: 10.17632/vkjwwb8zsh.2
title: Binhai/BH-CPTU/10-44

description: The Binhai/BH-CPTU/10-44 database compiles 44 CPTU soundings, 10 borehole records, and associated laboratory test results from the Binhainan offshore wind farm in the Yellow Sea.
DOI: 10.15468/dl.g4vgbp
title: Occurrence Download

description: A dataset containing 2541 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.37.14_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2541 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009558-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5hbrh7vrhn
title: Data for: The Environmental Cost of Rapid Urbanization: Residential Construction and Lake Depletion in Astana, Kazakhstan

description: RESEARCH HYPOTHESIS: Tests whether rapid urbanization in Astana, Kazakhstan contributed to Bolshoy Taldykol Lake depletion through groundwater extraction and land use changes. Hypothesis predicts increased construction activity correlates with decreased lake surface area when controlling for climatic variables.

WHAT THE DATA SHOWS: Dataset combines satellite-derived lake surface area, residential construction statistics, and weather data for Astana (2015-2024). Three sources: (1) Lake surface area from Landsat 8/9 imagery using Modified Normalized Difference Water Index (MNDWI); (2) Monthly residential construction data; (3) Weather indicators. Analysis reveals 70% lake reduction from 2017 peak (0.856 km²) to 2024 (0.260 km²), coinciding with construction boom.

DATA COLLECTION:

Lake Surface Area: Google Earth Engine with Landsat 8/9. Processing: filter cloud cover &lt;15%, create summer composites (June-September), calculate MNDWI=(Green-SWIR1)/(Green+SWIR1), apply threshold MNDWI&gt;0, export GeoTIFF 30m resolution for 2015, 2017, 2020, 2022, 2024. Collection 2 Level-2 surface reflectance, scaling: multiply 0.0000275, add -0.2. Region: lake plus 500m buffer. JavaScript code provided.

Surface Area Calculation: Python with rasterio loads water masks, sums water pixels (value=1), multiplies by pixel area (900 m²), converts to km². Visualization includes temporal grids, before/after comparisons, change detection. Python code provided (Google Colab compatible).

Weather Data (March 2001-December 2024): Weather Underground (wunderground.com) Astana/UACC station via Selenium scraper. Monthly: max/avg/min temperatures, dew point, precipitation (with sum), snow depth (with sum), wind, gust wind, sea level pressure. Python script with retry logic, checkpoints. 285 monthly observations.

Residential Construction (January 2015-December 2024): Bureau of National Statistics Kazakhstan (taldau.stat.gov.kz). "Total area of commissioned residential buildings" in m². Monthly completed construction. Manually extracted from "Investment and Construction Statistics / Commissioning of Housing / Residential Buildings Information". 120 monthly observations.

INTERPRETATION: Water masks processed using provided Python code (rasterio) or GIS software. Each pixel=900 m². Water detected where MNDWI&gt;0. Weather variables control for climate (precipitation inputs, evaporation via temperature/wind/dew point). Construction represents urbanization pressure on groundwater. Account for seasonal lake variations.

Limitations: (1) Summer satellite data only; (2) No direct groundwater measurements; (3) Completed buildings only; (4) Missing water policy data.

Applications: environmental impact assessment, urban planning, water management, climate adaptation, sustainable development.

Files: Water mask GeoTIFFs (5 years), RGB composites (5 years), weather CSV (285 months), construction CSV (120 months), JavaScript code (GEE), Python analysis code.
DOI: 10.17632/5hbrh7vrhn.1
title: Data for: The Environmental Cost of Rapid Urbanization: Residential Construction and Lake Depletion in Astana, Kazakhstan

description: RESEARCH HYPOTHESIS: Tests whether rapid urbanization in Astana, Kazakhstan contributed to Bolshoy Taldykol Lake depletion through groundwater extraction and land use changes. Hypothesis predicts increased construction activity correlates with decreased lake surface area when controlling for climatic variables.

WHAT THE DATA SHOWS: Dataset combines satellite-derived lake surface area, residential construction statistics, and weather data for Astana (2015-2024). Three sources: (1) Lake surface area from Landsat 8/9 imagery using Modified Normalized Difference Water Index (MNDWI); (2) Monthly residential construction data; (3) Weather indicators. Analysis reveals 70% lake reduction from 2017 peak (0.856 km²) to 2024 (0.260 km²), coinciding with construction boom.

DATA COLLECTION:

Lake Surface Area: Google Earth Engine with Landsat 8/9. Processing: filter cloud cover &lt;15%, create summer composites (June-September), calculate MNDWI=(Green-SWIR1)/(Green+SWIR1), apply threshold MNDWI&gt;0, export GeoTIFF 30m resolution for 2015, 2017, 2020, 2022, 2024. Collection 2 Level-2 surface reflectance, scaling: multiply 0.0000275, add -0.2. Region: lake plus 500m buffer. JavaScript code provided.

Surface Area Calculation: Python with rasterio loads water masks, sums water pixels (value=1), multiplies by pixel area (900 m²), converts to km². Visualization includes temporal grids, before/after comparisons, change detection. Python code provided (Google Colab compatible).

Weather Data (March 2001-December 2024): Weather Underground (wunderground.com) Astana/UACC station via Selenium scraper. Monthly: max/avg/min temperatures, dew point, precipitation (with sum), snow depth (with sum), wind, gust wind, sea level pressure. Python script with retry logic, checkpoints. 285 monthly observations.

Residential Construction (January 2015-December 2024): Bureau of National Statistics Kazakhstan (taldau.stat.gov.kz). "Total area of commissioned residential buildings" in m². Monthly completed construction. Manually extracted from "Investment and Construction Statistics / Commissioning of Housing / Residential Buildings Information". 120 monthly observations.

INTERPRETATION: Water masks processed using provided Python code (rasterio) or GIS software. Each pixel=900 m². Water detected where MNDWI&gt;0. Weather variables control for climate (precipitation inputs, evaporation via temperature/wind/dew point). Construction represents urbanization pressure on groundwater. Account for seasonal lake variations.

Limitations: (1) Summer satellite data only; (2) No direct groundwater measurements; (3) Completed buildings only; (4) Missing water policy data.

Applications: environmental impact assessment, urban planning, water management, climate adaptation, sustainable development.

Files: Water mask GeoTIFFs (5 years), RGB composites (5 years), weather CSV (285 months), construction CSV (120 months), JavaScript code (GEE), Python analysis code.
DOI: 10.6084/m9.figshare.28380443
title: <b>Raw and intermediate data for "An integrative chemical and genomic similarity approach linking fungal secondary </b><b>metabolites and biosynthetic gene clusters"</b>

description: <br>BGC_grouping_aS7_PROCESSED.csv: Manually corrected results of clustering antiSMASH BGC regions by gene content.BGC_grouping_aS7_unprocessed.csv: Initial results of clustering antiSMASH BGC regions by gene content.annot.anotations_for_BGCs.extended.txtanti.antiSMASH7_unscaffolded.prosmash_output.tsv: results after combining antiSMASH results using in-house script prosmash.py.mibig_to_DTO.AA.tsv: Combined results of diamond blast searches of MIBiG v.3.1 amino acid queries to the <i>A. fischeri</i> genomes.pan.Fischeri_pangenome_info.csv: Pangenome information, i.e. correspondence of genes/transcripts across the 16 strains. Orthologous genes are tracked by the same IDX.strain_names.csv: Throughout the project and before receiving official strain designations, the different collaborating labs used internal IDs which are provided here.mibig_BGCs_genes.tsv: List of all genes per BGC in MIBiG v3.1compounds_for_clustering.csv: SMILES and names of all compounds in MIBiG v3.1 and at the bottom, the experimental SMs found in <i>A. fischeri</i>.compounds_strain_temperature_count.csv: count of the number of replicates (0–3) in which a given SM was detected in a given strain at a given temperature.<br>
DOI: 10.6084/m9.figshare.28380443.v1
title: <b>Raw and intermediate data for "An integrative chemical and genomic similarity approach linking fungal secondary </b><b>metabolites and biosynthetic gene clusters"</b>

description: <br>BGC_grouping_aS7_PROCESSED.csv: Manually corrected results of clustering antiSMASH BGC regions by gene content.BGC_grouping_aS7_unprocessed.csv: Initial results of clustering antiSMASH BGC regions by gene content.annot.anotations_for_BGCs.extended.txtanti.antiSMASH7_unscaffolded.prosmash_output.tsv: results after combining antiSMASH results using in-house script prosmash.py.mibig_to_DTO.AA.tsv: Combined results of diamond blast searches of MIBiG v.3.1 amino acid queries to the <i>A. fischeri</i> genomes.pan.Fischeri_pangenome_info.csv: Pangenome information, i.e. correspondence of genes/transcripts across the 16 strains. Orthologous genes are tracked by the same IDX.strain_names.csv: Throughout the project and before receiving official strain designations, the different collaborating labs used internal IDs which are provided here.mibig_BGCs_genes.tsv: List of all genes per BGC in MIBiG v3.1compounds_for_clustering.csv: SMILES and names of all compounds in MIBiG v3.1 and at the bottom, the experimental SMs found in <i>A. fischeri</i>.compounds_strain_temperature_count.csv: count of the number of replicates (0–3) in which a given SM was detected in a given strain at a given temperature.<br>
DOI: 10.17632/43brttn9sr
title: LC MS Data of unfermented and fermented Cereal based food_VINEET SINGH

description: LC MS Dataset of fermented and unfermented probiotic cereal based food
DOI: 10.17632/43brttn9sr.1
title: LC MS Data of unfermented and fermented Cereal based food_VINEET SINGH

description: LC MS Dataset of fermented and unfermented probiotic cereal based food
DOI: 10.17632/274tyk9wy3
title: Fluorine-free transparent anti-fingerprint coating with superior wear resistance

description: 2.1 Materials
Tung oil anhydride (TOA) was purchased from Jining Hongming Chemical Reagent Co., Ltd. 1-hydroxycyclohexyl phenyl ketone (Irgacure 184), tetraethyl orthosilicate (TEOS), γ-methacryloxypropyltrimethoxysilane (KH-570), hexadecyltriemethoxysilane (HDTMS), hexamethyldisiloxane (HMDSO), glacial acetic acid (GAA), methyltrimethoxysilane (KH-131), 4-methoxyphenol (MEHQ, N, N-dimethylethanolamine, sodium hydroxide (NaOH), hydrochloric acid (HCl) were purchased from Adamas-beta. n-Butyl acetate (BAC, A.R.), 2,6-di-tert-butyl-p-cresol (BHT, A.R.), 2-Hydroxyethyl methacrylate (HEMA, A.R.), pentaerythritol triacrylate (PETA, AR),1,6-Hexanediol diacrylate (HDDA, A.R.).trimethylolpropane triacrylate (TMPTA, A.R.) were from Shanghai Titan Technology Corporation. Nano-silica (SiO2, particle size 20 nm) was provided by Tianxing New Materials Co. Deionized water was provided by Shanghai Huazhen Technology Co. Tea, cola, juice, milk and markers were purchased from local supermarkets. 
2.2 Preparation of MTQ
A series of silicone resins were prepared using different initial feed amounts of HMDSO, KH570, and TEOS. They were named according to their respective molar numbers. For example, the silicone resin obtained by the addition of 3 mol of HMDSO, 7 mol of KH570, and 5 mol of TEOS is named as MTQ-375. According to the definition, MTQ-370, MTQ-371, MTQ-373, MTQ-377. MTQ-379, MTQ-190 etc. were prepared. MTQ-375-6 can be obtained only by the addition 6 wt% HDTMS (monomer mass) into MTQ raw materials. Other steps are the same as the preparation of MTQ-375 .
2.3 Preparation of MTQ-375-6/T9/SiO2 coating
Mix MTQ-375-6/SiO2 or MTQ-375-6/131-SiO2 (preparation method see Supporting Information) and T9 (preparation method can be seen in Supporting Information) according to mass ratio of 9:1. A diluent (BAC: HDDA=1:1) was added to prepare a solution with a monomer concentration of 50%. Among them, photoinitiator 184 was added by 5 wt% of the total mass, and place the mixture in an ultrasonic cleaning machine for 15 minutes to mix evenly, obtaining SiO2 modified MTQ photocurable coating solution. Afterwards, evenly spray the above coating solution on the surface of the glass slide using a spray gun, with a nozzle diameter of 1.3 mm, a spraying air pressure of 0.29 MPa, and a coating spraying rate of 170 mL/min. During the spraying process, keep the spray gun perpendicular to the surface of the glass slide, with a distance of about 30 cm between two. Finally, place the coated glass slide into a UV curing machine. The UV wavelength is set at 365 nm, and the coating is 25 cm away from the UV lamp .
DOI: 10.17632/274tyk9wy3.1
title: Fluorine-free transparent anti-fingerprint coating with superior wear resistance

description: 2.1 Materials
Tung oil anhydride (TOA) was purchased from Jining Hongming Chemical Reagent Co., Ltd. 1-hydroxycyclohexyl phenyl ketone (Irgacure 184), tetraethyl orthosilicate (TEOS), γ-methacryloxypropyltrimethoxysilane (KH-570), hexadecyltriemethoxysilane (HDTMS), hexamethyldisiloxane (HMDSO), glacial acetic acid (GAA), methyltrimethoxysilane (KH-131), 4-methoxyphenol (MEHQ, N, N-dimethylethanolamine, sodium hydroxide (NaOH), hydrochloric acid (HCl) were purchased from Adamas-beta. n-Butyl acetate (BAC, A.R.), 2,6-di-tert-butyl-p-cresol (BHT, A.R.), 2-Hydroxyethyl methacrylate (HEMA, A.R.), pentaerythritol triacrylate (PETA, AR),1,6-Hexanediol diacrylate (HDDA, A.R.).trimethylolpropane triacrylate (TMPTA, A.R.) were from Shanghai Titan Technology Corporation. Nano-silica (SiO2, particle size 20 nm) was provided by Tianxing New Materials Co. Deionized water was provided by Shanghai Huazhen Technology Co. Tea, cola, juice, milk and markers were purchased from local supermarkets. 
2.2 Preparation of MTQ
A series of silicone resins were prepared using different initial feed amounts of HMDSO, KH570, and TEOS. They were named according to their respective molar numbers. For example, the silicone resin obtained by the addition of 3 mol of HMDSO, 7 mol of KH570, and 5 mol of TEOS is named as MTQ-375. According to the definition, MTQ-370, MTQ-371, MTQ-373, MTQ-377. MTQ-379, MTQ-190 etc. were prepared. MTQ-375-6 can be obtained only by the addition 6 wt% HDTMS (monomer mass) into MTQ raw materials. Other steps are the same as the preparation of MTQ-375 .
2.3 Preparation of MTQ-375-6/T9/SiO2 coating
Mix MTQ-375-6/SiO2 or MTQ-375-6/131-SiO2 (preparation method see Supporting Information) and T9 (preparation method can be seen in Supporting Information) according to mass ratio of 9:1. A diluent (BAC: HDDA=1:1) was added to prepare a solution with a monomer concentration of 50%. Among them, photoinitiator 184 was added by 5 wt% of the total mass, and place the mixture in an ultrasonic cleaning machine for 15 minutes to mix evenly, obtaining SiO2 modified MTQ photocurable coating solution. Afterwards, evenly spray the above coating solution on the surface of the glass slide using a spray gun, with a nozzle diameter of 1.3 mm, a spraying air pressure of 0.29 MPa, and a coating spraying rate of 170 mL/min. During the spraying process, keep the spray gun perpendicular to the surface of the glass slide, with a distance of about 30 cm between two. Finally, place the coated glass slide into a UV curing machine. The UV wavelength is set at 365 nm, and the coating is 25 cm away from the UV lamp .
DOI: 10.17632/gf33xf7h5r
title: Nitrogen Fate Data of Microencapsulated Inhibitor Fertilizer

description: Research Data on Microcapsule Inhibitors
DOI: 10.17632/gf33xf7h5r.1
title: Nitrogen Fate Data of Microencapsulated Inhibitor Fertilizer

description: Research Data on Microcapsule Inhibitors
DOI: 10.5281/zenodo.18248085
title: Baccharis weirii Baker. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:43