cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/vr2wb38r6t.1
title: Raw DNA Methylation Data; Møller et al., 2025

description: Raw de-identifying DNA methylation data associated with 117 non-functioning pituitary adenoma (NFPA) tumor tissue samples included in the full cohort study are provided in the folder “Deidentified_IDATs”. These samples were analyzed using the Illumina Infinium MethylationEPIC BeadChip (850K) array. A corresponding sample annotation file (“SampleSheet.xlsx”) containing Sentrix_ID and Sample_ID is included to enable proper linkage between IDAT files and the analyzed cohort.
This repository supports the publication “DNA methylation profiling identifies prognostic subgroups in SF1-lineage non-functioning pituitary adenomas” and enables reproducibility of the analyses presented therein.
DOI: 10.17632/gn75f35ybc.1
title: Duy et al., 2011. Raw data for Figure 2b and Supplement Figure 21c-d

description: Corrected Figure 2b and Supplemental Figure 21c-d
DOI: 10.15468/dl.cgnw7m
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sommeromys macrorhinos",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037363-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jpzg4r
title: Occurrence Download

description: A dataset containing 971 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is one of (Adenocalymma adenophorum (Sandwith) L.G.Lohmann, Adenocalymma albiflorum (Salzm. ex DC.) B.M.Gomes & L.H.Fonseca, Adenocalymma allamandiflorum (Bureau ex K.Schum.) L.G.Lohmann, Adenocalymma apurense (Kunth) Sandwith, Adenocalymma biternatum (A.Samp.) L.G.Lohmann, Adenocalymma bracteolatum DC., Adenocalymma bracteosum (DC.) L.G.Lohmann, Adenocalymma calcareum Udulutsch & P.Dias, Adenocalymma cidii (A.H.Gentry ex Hauk) L.G.Lohmann, Adenocalymma cladotrichum (Sandwith) L.G.Lohmann)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 971 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037362-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pgy8bhp4zb
title: Dataset with parameters of offshore wind farm localizations in the Polish Baltic Sea zones

description: The dataset comprises information gathered for seven proposed offshore wind farms located within the Polish Exclusive Economic Zone of the Baltic Sea. These projects fall within the zones outlined for offshore development near the Słupsk Bank, Central Bank, and Odrzana Bank. The data was compiled internally by the company during the analysis of the Offshore Wind Farm Development Program, focusing on seven alternative locations identified in the plan based on https://www.gov.pl/web/morska-energetyka-wiatrowa/program-rozwoju-morskich-farm-wiatrowych.

The dataset is related to the research paper:
Karczmarczyk, A., Wątróbski, J., Bączkiewicz, A., Mróz-Malik, O., & Drożdż, W. (2025). New robust multi-criteria decision-making method for wind farm location problems. Applied Energy, 398, 126401.
DOI: 10.17632/pgy8bhp4zb.1
title: Dataset with parameters of offshore wind farm localizations in the Polish Baltic Sea zones

description: The dataset comprises information gathered for seven proposed offshore wind farms located within the Polish Exclusive Economic Zone of the Baltic Sea. These projects fall within the zones outlined for offshore development near the Słupsk Bank, Central Bank, and Odrzana Bank. The data was compiled internally by the company during the analysis of the Offshore Wind Farm Development Program, focusing on seven alternative locations identified in the plan based on https://www.gov.pl/web/morska-energetyka-wiatrowa/program-rozwoju-morskich-farm-wiatrowych.

The dataset is related to the research paper:
Karczmarczyk, A., Wątróbski, J., Bączkiewicz, A., Mróz-Malik, O., & Drożdż, W. (2025). New robust multi-criteria decision-making method for wind farm location problems. Applied Energy, 398, 126401.
DOI: 10.17632/2jvknhv465
title: A Spatiotemporal, Congestion-Context-Aware Vehicular Communication Dataset for Malicious Flooding Attack Analysis

description: This dataset presents a comprehensive spatiotemporal representation of vehicular communication under benign and malicious flooding conditions, simulated across both highway and metropolitan mobility environments. It contains twelve flow-level datasets, each representing one of three calibrated malicious flooding attack intensities (LOW, MEDIUM, HIGH) under the two mobility scenarios. The dataset is intended to support research on spatiotemporal anomaly detection, congestion-context-aware security, and machine-learning-based intrusion detection in Vehicular Communication Networks (VCNs).
 
Each data instance corresponds to a single unidirectional communication flow between a transmitting and receiving vehicle. The dataset includes rich temporal features (start time, end time, duration, temporal mid-point), spatial features (positions, mid-points, distances), mobility features (radial displacement and velocity), and communication-layer metrics such as packet and byte counts, throughput, delay, jitter, hop count, delivery ratio, and loss indicators. These combined descriptors capture how movement, network load, and communication dynamics evolve jointly over time.

Malicious flooding scenarios were simulated within the communication environment, with attackers transmitting elevated packet or byte volumes while remaining fully consistent with their underlying mobility behavior. This ensures that malicious flows do not exhibit unrealistic spatial artefacts and cannot be trivially separated from benign activity based solely on positional features. The resulting communication patterns provide challenging and realistic attack conditions that require context-aware and temporally informed detection methods. Full details of the flooding attack design and simulation methodology are provided in the associated research publication.

This release contains only the flow-level datasets. To enable higher-level analysis, we additionally provide Jupyter notebooks demonstrating how to transform flows into (i) session-level representations (aggregating all flows exchanged between a source–destination pair) and (ii) receiver-window representations (aggregating flows received by each node within a cluster). These notebooks also illustrate how to derive congestion-aware contextual features used in our associated research (see citation in documentation).

By combining realistic vehicle mobility, detailed communication metrics, calibrated attack intensities, and extensible multi-level processing scripts, this dataset offers a comprehensive foundation for research on spatiotemporal security, anomaly modelling, congestion-aware intrusion detection, and machine-learning analysis in VCNs.
DOI: 10.17632/2jvknhv465.1
title: A Spatiotemporal, Congestion-Context-Aware Vehicular Communication Dataset for Malicious Flooding Attack Analysis

description: This dataset presents a comprehensive spatiotemporal representation of vehicular communication under benign and malicious flooding conditions, simulated across both highway and metropolitan mobility environments. It contains twelve flow-level datasets, each representing one of three calibrated malicious flooding attack intensities (LOW, MEDIUM, HIGH) under the two mobility scenarios. The dataset is intended to support research on spatiotemporal anomaly detection, congestion-context-aware security, and machine-learning-based intrusion detection in Vehicular Communication Networks (VCNs).
 
Each data instance corresponds to a single unidirectional communication flow between a transmitting and receiving vehicle. The dataset includes rich temporal features (start time, end time, duration, temporal mid-point), spatial features (positions, mid-points, distances), mobility features (radial displacement and velocity), and communication-layer metrics such as packet and byte counts, throughput, delay, jitter, hop count, delivery ratio, and loss indicators. These combined descriptors capture how movement, network load, and communication dynamics evolve jointly over time.

Malicious flooding scenarios were simulated within the communication environment, with attackers transmitting elevated packet or byte volumes while remaining fully consistent with their underlying mobility behavior. This ensures that malicious flows do not exhibit unrealistic spatial artefacts and cannot be trivially separated from benign activity based solely on positional features. The resulting communication patterns provide challenging and realistic attack conditions that require context-aware and temporally informed detection methods. Full details of the flooding attack design and simulation methodology are provided in the associated research publication.

This release contains only the flow-level datasets. To enable higher-level analysis, we additionally provide Jupyter notebooks demonstrating how to transform flows into (i) session-level representations (aggregating all flows exchanged between a source–destination pair) and (ii) receiver-window representations (aggregating flows received by each node within a cluster). These notebooks also illustrate how to derive congestion-aware contextual features used in our associated research (see citation in documentation).

By combining realistic vehicle mobility, detailed communication metrics, calibrated attack intensities, and extensible multi-level processing scripts, this dataset offers a comprehensive foundation for research on spatiotemporal security, anomaly modelling, congestion-aware intrusion detection, and machine-learning analysis in VCNs.
DOI: 10.17632/tgfcdtss3d
title: Air Quality Index Prediction for Dhaka City

description: This dataset contains atmospheric pollutant concentration values for Dhaka City, collected using Google Earth Engine (GEE) from satellite-based environmental monitoring products. The dataset includes daily to periodic measurements of major air pollutants such as Aerosol Optical Depth (AOD), Nitrogen Dioxide (NO₂), Methane (CH₄), Sulfur Dioxide (SO₂), Ozone (O₃), Formaldehyde (HCHO), and Carbon Monoxide (CO).

For each pollutant, the dataset includes:

Raw concentration values

Interquartile Range (IQR)

25th percentile (p25) values

Acquisition timestamp (system:time_start)

These air quality indicators are essential for understanding pollutant trends, seasonal behavior, and long-term environmental changes in Dhaka. The dataset can be used for:

Air Quality Index (AQI) prediction

Environmental modeling

Health impact studies

Time-series forecasting

Machine learning research

Source:
Data extracted from Google Earth Engine using satellite products such as Sentinel-5P, MODIS, and related atmospheric datasets.

Format:
Microsoft Excel (.xlsx), containing 22 columns and pollutant measurements for multiple years.

Geographical Location:
Dhaka City, Bangladesh.

Temporal Coverage:
2018–2024 (depending on pollutant availability).
DOI: 10.17632/tgfcdtss3d.1
title: Air Quality Index Prediction for Dhaka City

description: This dataset contains atmospheric pollutant concentration values for Dhaka City, collected using Google Earth Engine (GEE) from satellite-based environmental monitoring products. The dataset includes daily to periodic measurements of major air pollutants such as Aerosol Optical Depth (AOD), Nitrogen Dioxide (NO₂), Methane (CH₄), Sulfur Dioxide (SO₂), Ozone (O₃), Formaldehyde (HCHO), and Carbon Monoxide (CO).

For each pollutant, the dataset includes:

Raw concentration values

Interquartile Range (IQR)

25th percentile (p25) values

Acquisition timestamp (system:time_start)

These air quality indicators are essential for understanding pollutant trends, seasonal behavior, and long-term environmental changes in Dhaka. The dataset can be used for:

Air Quality Index (AQI) prediction

Environmental modeling

Health impact studies

Time-series forecasting

Machine learning research

Source:
Data extracted from Google Earth Engine using satellite products such as Sentinel-5P, MODIS, and related atmospheric datasets.

Format:
Microsoft Excel (.xlsx), containing 22 columns and pollutant measurements for multiple years.

Geographical Location:
Dhaka City, Bangladesh.

Temporal Coverage:
2018–2024 (depending on pollutant availability).
DOI: 10.17632/n6wdvz5n3d.1
title: PromptShield: A Human-Annotated Dataset for Prompt Injection Safety Classification

description: PromptShield is a human-annotated dataset designed to support research on prompt injection detection, adversarial prompting behavior, and safety classification in Large Language Models (LLMs). The dataset contains 3,000 cleaned prompt instances, collected from an original pool of 4,000 samples and refined through manual screening to remove duplicates, sensitive content, and incomplete entries. Each prompt is labeled independently by three trained annotators using a standardized guideline, resulting in four label fields: three annotator labels and one majority-vote final label.
The dataset includes three risk categories:
Benign – normal user intent
Suspicious – ambiguous or potentially harmful intent
Malicious – explicit attempts to bypass safety mechanisms or manipulate system behavior
Each instance also contains an attack_type field, identifying adversarial strategies such as meta-querying, instruction manipulation, role exploitation, or system-prompt inversion. Additionally, every entry includes a short rationale that explains the annotator’s decision, supporting research in explainable AI and human–model alignment.

The dataset is stored in a single CSV file with structured columns covering prompt text, annotation labels, metadata, annotator rationales, and reviewer identifiers. This format enables broad utility in supervised classification, adversarial robustness analysis, annotation reliability studies, and the development of Human-in-the-Loop (HITL) safety systems.
DOI: 10.17632/hhhx4ksg7b
title: QTNano: Computational Investigation of the Size Evolution of (La2B2O7)n Nanoclusters (B = Ce, Ti, Zr) - ACS Omega 2025, 10, 41, 48829–48843

description: The following directories contain the data related to the DFT calculations used in the analysis described in the research article "Computational Investigation of the Size Evolution of (La2B2O7)n Nanoclusters (B = Ce, Ti, Zr) ", available on: ACS Omega 2025, 10, 41, 48829–48843. https://doi.org/10.1021/acsomega.5c06927
DOI: 10.17632/hhhx4ksg7b.1
title: QTNano: Computational Investigation of the Size Evolution of (La2B2O7)n Nanoclusters (B = Ce, Ti, Zr) - ACS Omega 2025, 10, 41, 48829–48843

description: The following directories contain the data related to the DFT calculations used in the analysis described in the research article "Computational Investigation of the Size Evolution of (La2B2O7)n Nanoclusters (B = Ce, Ti, Zr) ", available on: ACS Omega 2025, 10, 41, 48829–48843. https://doi.org/10.1021/acsomega.5c06927
DOI: 10.17632/tkp7pktgsn
title: Supplement-Data

description: This is molecular-level chemical composition data for ambient organic aerosols from Indo-Gangetic Plain measured using High resolution mass spectrometer.
DOI: 10.17632/tkp7pktgsn.1
title: Supplement-Data

description: This is molecular-level chemical composition data for ambient organic aerosols from Indo-Gangetic Plain measured using High resolution mass spectrometer.
DOI: 10.15468/dl.rqytq4
title: Occurrence Download

description: A dataset containing 30743 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is Vespa velutina",
 "Year is one of (2024, 2025)",
 "HasCoordinate is true",
 "Geometry POLYGON((3.5 43.5, 12.5 43.5, 12.5 50.5, 3.5 50.5, 3.5 43.5))"
 ]
}

 The dataset includes 30743 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037364-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pj47xxk7yk
title: QTNano - Ab Initio Investigation of CH4 Dehydrogenation on a (CeO2)10 Cluster - J. Phys. Chem. C 126, 11937−11948 (2022)

description: The following directories contain the data related to the DFT calculations used in the analysis described in the research article "QTNano – Ab Initio Investigation of CH₄ Dehydrogenation on a (CeO₂)₁₀ Cluster," published in The Journal of Physical Chemistry C and accessible at https://doi.org/10.1021/acs.jpcc.2c02038.
DOI: 10.17632/pj47xxk7yk.2
title: QTNano - Ab Initio Investigation of CH4 Dehydrogenation on a (CeO2)10 Cluster - J. Phys. Chem. C 126, 11937−11948 (2022)

description: The following directories contain the data related to the DFT calculations used in the analysis described in the research article "QTNano – Ab Initio Investigation of CH₄ Dehydrogenation on a (CeO₂)₁₀ Cluster," published in The Journal of Physical Chemistry C and accessible at https://doi.org/10.1021/acs.jpcc.2c02038.
DOI: 10.17632/ppd2c7sz8j
title: Federated Knowledge-Driven Biomedical Dataset for Heterogeneous Modeling of Neonatal Drug-Adverse Reaction Associations

description: The Federated Knowledge-Driven Biomedical Dataset for Heterogeneous Modeling of Neonatal Drug-Adverse Reaction Associations comprises a meticulously curated and enriched dataset intended for advanced analysis and predictive modeling of neonatal Adverse Drug Reactions (ADRs). This dataset has been derived from the FDA Adverse Event Reporting System (FAERS), with a targeted focus on neonatal cases across 140+ high-risk health conditions.

Key Statistics:
Total Instances (Rows): 203616
Total Unique Instances: 118512
Number of Unique Drugs: 1,976
Number of Unique Adverse Reactions: 4,993
Total Drug-ADR Associations: 203,616
Unique Drug-ADR Pairs: 118,512

The dataset is well-suited for federated learning frameworks, enabling distributed predictive analytics from multiple biomedical sources while ensuring patient privacy. Each instance is anonymized and structured in a format conducive for training machine learning models across nodes without centralizing sensitive data. Given the structured, multi-relational nature of drug–ADR associations, the dataset is well-suited for heterogeneous graph modeling. It supports the following;

Knowledge graph construction
Link prediction
Multi-hop reasoning
Embedding-based learning
Subgraph extraction for focused ADR clusters

The dataset package includes:
The full dataset (CSV/XLS formats)
Python scripts for data parsing, preprocessing, exploratory data analysis, and graph generation
DOI: 10.17632/ppd2c7sz8j.3
title: Federated Knowledge-Driven Biomedical Dataset for Heterogeneous Modeling of Neonatal Drug-Adverse Reaction Associations

description: The Federated Knowledge-Driven Biomedical Dataset for Heterogeneous Modeling of Neonatal Drug-Adverse Reaction Associations comprises a meticulously curated and enriched dataset intended for advanced analysis and predictive modeling of neonatal Adverse Drug Reactions (ADRs). This dataset has been derived from the FDA Adverse Event Reporting System (FAERS), with a targeted focus on neonatal cases across 140+ high-risk health conditions.

Key Statistics:
Total Instances (Rows): 203616
Total Unique Instances: 118512
Number of Unique Drugs: 1,976
Number of Unique Adverse Reactions: 4,993
Total Drug-ADR Associations: 203,616
Unique Drug-ADR Pairs: 118,512

The dataset is well-suited for federated learning frameworks, enabling distributed predictive analytics from multiple biomedical sources while ensuring patient privacy. Each instance is anonymized and structured in a format conducive for training machine learning models across nodes without centralizing sensitive data. Given the structured, multi-relational nature of drug–ADR associations, the dataset is well-suited for heterogeneous graph modeling. It supports the following;

Knowledge graph construction
Link prediction
Multi-hop reasoning
Embedding-based learning
Subgraph extraction for focused ADR clusters

The dataset package includes:
The full dataset (CSV/XLS formats)
Python scripts for data parsing, preprocessing, exploratory data analysis, and graph generation
DOI: 10.15468/dl.33yadq
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nyctibatrachus manalari"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037365-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jdhtn9nwxh.2
title: Do Aggregate Dynamics in Developing Economies Differ after a Rise in Home and Foreign Productivity?

description: The dataset stored here contains all the files for reproducing the results in the manuscript titled``Do Aggregate Dynamics in Developing Economies Differ after a Rise in Home and Foreign Productivity?'', submitted for publication to Economic Modelling. The readme document below explains how to reproduce the tables and figures in both the manuscript and its online supplement as well as in the online excel tables accompanying the manuscript..
DOI: 10.17632/9gdxkc5z6w
title: Rs_data

description: This dataset was generated to test the overarching hypothesis that different patterns of drought (varying in intensity, duration, and frequency) and increased nitrogen (N) deposition would differentially affect the total soil respiration (Rs) and its heterotrophic (Rh) and autotrophic (Ra) fractions. 

The data were gathered through a manipulative field experiment in a typical temperate steppe in northern China.

Experimental Design: A fully factorial design was established with four main treatments: 1) Control (ambient rainfall,CK), 2) Intense Early-Season Drought (ID), 3) Chronic Drought (CD), and 4) Reduced Rainfall Frequency (RF). Each drought treatment was combined with two subplots: with or without Nitrogen addition (simulating increased N deposition).

Data File Structure & Interpretation

The primary dataset is provided in a structured CSV file.

Rows: Each row represents a single measurement event for one experimental subplot at a specific date.

Key Columns & How to Interpret Them:
Year:Measurement year (e.g., "Y2019")
Date:Measurement date (e.g., "2019/6/17")
Plot: Record each measurement as a plot.
Block: Five repetitions.
Treat: The treatment combination (e.g., "NRF").
Treat_N: Drought treatment (e.g., "RF").
Treat_D: Nitrogen treatment (e.g., "N").
Rs, Rh, Ra: Flux rates in units of μmol CO₂ m⁻² s⁻¹. Interpretation: Positive values represent CO₂ efflux from soil to atmosphere.
ST (°C), SM (%): soil temperature and soil moisture. Key drivers of respiration.
Soil microbial community (nmol g dry-1 ) : Total PLFAs: total soil microbial phospholipid fatty acids, F: fungi, B: bacteria, AC: actinomycetes, AMF: arbuscular mycorrhizal fungi, F:B: fungi-to-bacteria ratio, GP: gram-positive bacteria, GN: gram-negative bacteria, GP:GN: gram-positive-to-gram-negative bacteria ratio
Soil enzyme activity (mg kg-1 h-1): ACP: acid phosphatase activity, BG: β-1,4-glucosidase activity.
Plant aboveground biomass (g m-2) : AGB: total aboveground biomass, GB: grass biomass, FB: forb biomass, GB:FB: grass biomass-to-forb biomass ratio
Root biomass of species (g m-2): LC_RB: root biomass of Leymus chinensis; CS_RB: root biomass of Cleistogenes squarrosa; SK_RB: root biomass of Stipa krylovii; CK_RB: root biomass of Carex korshinskyi; TS_RB: root biomass of Thalictrum squarrosum; SC_RB: root biomass of Salsola collina; TA_RB: root biomass of Teloxys aristata
Soil Biogeochemistry: TC (soil Carbon), DOC (Dissolved Organic Carbon), DON (Dissolved Organic Nitrogen), IN (NH₄⁺-N and NO₃⁻-N).