cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/npk2zcm85h
title: Indoor Fire Dataset with Distributed Multi-Sensor Nodes (EN54 Test Room)

description: The dataset comprises 4 fire experiments (repeated 3 times) and 3 nuisance experiments (Ethanol: repeated 3 times, Deodorant: repeated 2 times, Hairspray: repeated 1 time), with various background sequences interspersed between the conducted experiments. All exeriments were caried out in random order to reduce the influence of prehistory. It consists of a total of 305,304 rows and 16 columns, structured as a continuous multivariate time series. Each row represents the sensor measurements (CO2, CO, H2, humidity, particulate matter of different sizes, air temperature, and UV) from a unique sensor node position in the EN54 test room at a specific timestamp. The columns correspond to the sensor measurements and include additional labels: a scenario-specific label ("scenario_label"), a binary label ("anomaly_label") distinguishing between "Normal" (background) and "Anomaly" (fire or nuisance scenario), a ternary label ("ternary_label") categorizing the data as "Nuisance," "Fire," or "Background," and a progress label ("progress_label") that allows for dividing the event sequences into sub-sequences based on ongoing physical sub-processes. The dataset comprises 82.98% background data points and 17.02% anomaly data points, which can be further divided into 12.50% fire anomaly data points and 4.52% nuisance anomaly data points. The "Sensor_ID" column can be utilized to access data from different sensor node positions.
DOI: 10.17632/npk2zcm85h.2
title: Indoor Fire Dataset with Distributed Multi-Sensor Nodes (EN54 Test Room)

description: The dataset comprises 4 fire experiments (repeated 3 times) and 3 nuisance experiments (Ethanol: repeated 3 times, Deodorant: repeated 2 times, Hairspray: repeated 1 time), with various background sequences interspersed between the conducted experiments. All exeriments were caried out in random order to reduce the influence of prehistory. It consists of a total of 305,304 rows and 16 columns, structured as a continuous multivariate time series. Each row represents the sensor measurements (CO2, CO, H2, humidity, particulate matter of different sizes, air temperature, and UV) from a unique sensor node position in the EN54 test room at a specific timestamp. The columns correspond to the sensor measurements and include additional labels: a scenario-specific label ("scenario_label"), a binary label ("anomaly_label") distinguishing between "Normal" (background) and "Anomaly" (fire or nuisance scenario), a ternary label ("ternary_label") categorizing the data as "Nuisance," "Fire," or "Background," and a progress label ("progress_label") that allows for dividing the event sequences into sub-sequences based on ongoing physical sub-processes. The dataset comprises 82.98% background data points and 17.02% anomaly data points, which can be further divided into 12.50% fire anomaly data points and 4.52% nuisance anomaly data points. The "Sensor_ID" column can be utilized to access data from different sensor node positions.
DOI: 10.17632/xfhj95zxcn
title: Research Data

description: Black Soil Horizon Thickness
DOI: 10.17632/xfhj95zxcn.1
title: Research Data

description: Black Soil Horizon Thickness
DOI: 10.34740/kaggle/dsv/12231490
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.34740/kaggle/dsv/12231492
title: Daftar Pengadilan Indonesia
DOI: 10.15468/dl.uwmsxz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Micrasterias C.Agardh ex Ralfs, 1848",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((18.2339 -32.5266,18.1411 -32.657,18.1007 -32.7021,18.0224 -32.6366,17.8923 -32.6419,17.754 -32.8255,17.7723 -32.9007,20.509 -32.9007,20.509 -28.5786,16.2782 -28.5786,16.4358 -28.7202,16.726 -29.1235,17.1957 -30.3752,17.6655 -31.1743,18.1322 -31.7679,18.2495 -32.2638,18.2339 -32.5266))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060632-250525065834625/datasets/export for details.
DOI: 10.17632/75hcchx647.1
title: Reported adverse events associated with finasteride use in androgenetic alopecia: Monitoring priorities from a 20-year FDA Adverse Event Reporting System database analysis

description: This dataset contains supplementary materials for the manuscript entitled "Reported adverse events associated with finasteride use in androgenetic alopecia: Monitoring priorities from a 20-year FDA Adverse Event Reporting System database analysis."

The files include:

Supplementary Figures illustrating the distribution of adverse event signals across organ systems and time-to-onset plots.

Supplementary Tables listing detailed results of PT-level and SOC-level signal detection, clinical priority scoring metrics, and onset time statistics.

A Supplementary Methods section describing advanced analytical procedures, including the multi-factor clinical priority scoring system.

A Supplementary Results section summarizing additional findings not included in the main text.

These materials provide extended support for the main analyses and are shared to promote transparency, reproducibility, and further pharmacovigilance research.
DOI: 10.17632/3sk43brs4p
title: A Reproducible Pipeline for Leveraging Operational Data through Machine Learning in Digitally Emerging Urban Bus Fleets

description: This dataset supports the study "A Reproducible Pipeline for Leveraging Operational Data through Machine Learning in Digitally Emerging Urban Bus Fleets" and contains operational data from a fleet of 164 hybrid diesel buses. The data were collected from the vehicles' onboard diagnostics (OBDII) and telematics systems, with a particular focus on variables related to the Diesel Particulate Filter (DPF) behavior. The goal is to develop and evaluate predictive models capable of estimating the duration a vehicle remains in critical soot accumulation zones (Zones 3 and 4), in order to support predictive maintenance decisions.

Two files are included:

First batch.xlsx – Contains the preprocessed data used to train and validate the initial regression models. It includes input features such as time, distance, and fuel since last regeneration, as well as the soot zone label and the target variable (duration in zone).

Second batch.xlsx – Contains a separate one-month batch of unseen data used to simulate model deployment and assess performance under real-world conditions.

All variables have been anonymized to preserve confidentiality while maintaining the structure required for temporal analysis.
DOI: 10.17632/3sk43brs4p.1
title: A Reproducible Pipeline for Leveraging Operational Data through Machine Learning in Digitally Emerging Urban Bus Fleets

description: This dataset supports the study "A Reproducible Pipeline for Leveraging Operational Data through Machine Learning in Digitally Emerging Urban Bus Fleets" and contains operational data from a fleet of 164 hybrid diesel buses. The data were collected from the vehicles' onboard diagnostics (OBDII) and telematics systems, with a particular focus on variables related to the Diesel Particulate Filter (DPF) behavior. The goal is to develop and evaluate predictive models capable of estimating the duration a vehicle remains in critical soot accumulation zones (Zones 3 and 4), in order to support predictive maintenance decisions.

Two files are included:

First batch.xlsx – Contains the preprocessed data used to train and validate the initial regression models. It includes input features such as time, distance, and fuel since last regeneration, as well as the soot zone label and the target variable (duration in zone).

Second batch.xlsx – Contains a separate one-month batch of unseen data used to simulate model deployment and assess performance under real-world conditions.

All variables have been anonymized to preserve confidentiality while maintaining the structure required for temporal analysis.
DOI: 10.17632/wpyp8nbs59
title: The study on Culicoides: the environment they live in and trypanosomatids they coexist

description: Raw data on Culicoides infection with trypanosomatids
DOI: 10.17632/wpyp8nbs59.1
title: The study on Culicoides: the environment they live in and trypanosomatids they coexist

description: Raw data on Culicoides infection with trypanosomatids
DOI: 10.17632/frwsdxf44s
title: IsoME: Streamlining high-precision Eliashberg calculations

description: This paper introduces the Julia package IsoME, an easy-to-use yet accurate and robust computational tool designed to calculate superconducting properties. Multiple levels of approximation are supported, ranging from the basic McMillan-Allen-Dynes formula and its machine learning-enhanced variant to Eliashberg theory, including static Coulomb interactions derived from GW calculations, offering a fully ab initio approach to determine superconducting properties, such as the critical superconducting temperature (T_C) and the superconducting gap function (Δ). We validate IsoME by benchmarking it against various materials, demonstrating its versatility and performance across different theoretical levels. The findings indicate that the previously held assumption that Eliashberg theory overestimates T_C is no longer valid when μ^⁎ is appropriately adjusted to account for the finite Matsubara frequency cutoff. Furthermore, we conclude that the constant density of states (DOS) approximation remains accurate in most cases. By unifying multiple approximation schemes within a single framework, IsoME combines first-principles precision with computational efficiency, enabling seamless integration into high-throughput workflows through its T_C search mode. This makes IsoME a powerful and reliable tool for advancing superconductivity research.
DOI: 10.17632/frwsdxf44s.1
title: IsoME: Streamlining high-precision Eliashberg calculations

description: This paper introduces the Julia package IsoME, an easy-to-use yet accurate and robust computational tool designed to calculate superconducting properties. Multiple levels of approximation are supported, ranging from the basic McMillan-Allen-Dynes formula and its machine learning-enhanced variant to Eliashberg theory, including static Coulomb interactions derived from GW calculations, offering a fully ab initio approach to determine superconducting properties, such as the critical superconducting temperature (T_C) and the superconducting gap function (Δ). We validate IsoME by benchmarking it against various materials, demonstrating its versatility and performance across different theoretical levels. The findings indicate that the previously held assumption that Eliashberg theory overestimates T_C is no longer valid when μ^⁎ is appropriately adjusted to account for the finite Matsubara frequency cutoff. Furthermore, we conclude that the constant density of states (DOS) approximation remains accurate in most cases. By unifying multiple approximation schemes within a single framework, IsoME combines first-principles precision with computational efficiency, enabling seamless integration into high-throughput workflows through its T_C search mode. This makes IsoME a powerful and reliable tool for advancing superconductivity research.
DOI: 10.17632/7pphhgrwfn
title: Understanding and Avoiding Plagiarism

description: Plagiarism remains a challenge for most academic and professional institutions. The paper identifies the forms, consequences and provide practical ways to avoid plagiarism.
DOI: 10.17632/7pphhgrwfn.1
title: Understanding and Avoiding Plagiarism

description: Plagiarism remains a challenge for most academic and professional institutions. The paper identifies the forms, consequences and provide practical ways to avoid plagiarism.
DOI: 10.15468/dl.8c7jvy
title: Occurrence Download

description: A dataset containing 842 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Chalcomitra fuliginosa (Bechstein, 1811)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 842 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060633-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hv5753xy22
title: Pollen and fungal spore counting data

description: This is counting of pollen and fungal spore of the soil analyzed samples.
DOI: 10.17632/hv5753xy22.1
title: Pollen and fungal spore counting data

description: This is counting of pollen and fungal spore of the soil analyzed samples.
DOI: 10.17632/ypn6yrzg2f
title: South Sumatra SDGs Based Tourism Dataset

description: This research aims to develop a dataset that can support the development of the tourism sector in South Sumatra in order to achieve the Sustainable Development Goals (SDGs) through a data and information architecture approach based on electronic government systems (SPBE). By identifying key variables in the tourism sector, this research examines the role of technology and infrastructure in supporting sustainable development, as well as its contribution to the local economy and poverty alleviation. The variables used include tourism indicators, SPBE technology use, tourism infrastructure quality, and local community participation. The data in this study was collected through a desk study, which included a review of relevant literature, official reports, and secondary data to understand the dynamics of tourism and SPBE implementation in South Sumatra. The results show that the implementation of SPBE in tourism sector management can improve the efficiency of data collection, accelerate decision-making, and support sustainable tourism development. The findings are expected to provide new insights into how data-driven technologies and policies can contribute to the achievement of SDGs in the tourism sector, particularly in the context of developing regions such as South Sumatra.
DOI: 10.17632/ypn6yrzg2f.2
title: South Sumatra SDGs Based Tourism Dataset

description: This research aims to develop a dataset that can support the development of the tourism sector in South Sumatra in order to achieve the Sustainable Development Goals (SDGs) through a data and information architecture approach based on electronic government systems (SPBE). By identifying key variables in the tourism sector, this research examines the role of technology and infrastructure in supporting sustainable development, as well as its contribution to the local economy and poverty alleviation. The variables used include tourism indicators, SPBE technology use, tourism infrastructure quality, and local community participation. The data in this study was collected through a desk study, which included a review of relevant literature, official reports, and secondary data to understand the dynamics of tourism and SPBE implementation in South Sumatra. The results show that the implementation of SPBE in tourism sector management can improve the efficiency of data collection, accelerate decision-making, and support sustainable tourism development. The findings are expected to provide new insights into how data-driven technologies and policies can contribute to the achievement of SDGs in the tourism sector, particularly in the context of developing regions such as South Sumatra.
DOI: 10.17632/dzp74m7swy.3
title: Data for "Sacchride Contents Change and the Role of PI3K-Akt Signal Transduction Pathway in Silkworm Eggs Treated with HCl to Prevent or Break Diapause"

description: Acid soaking with HCl is a common way to prevent or break diapause of silkworm eggs and has been widely used in production. However, its mechanism remains exploring. Few literature reported in metabolism of mono-, or disaccharides, and the differential expression patterns of genes and proteins related to the PI3K/Akt signaling pathway across the stages of silkworm diapause eggs, non-diapause eggs, instant acid soaking eggs, and red bean color acid-soaked eggs (Rbcase). Silkworm 932 strains eggs at different stages were used to explore the contents of several saccharides and analyze the expression profiles of 4 diapause-related genes (BmAkt, BmGsk-3, BmGs, and CaMKII) in the PI3K-Akt signaling pathway via qPCR. The results showed that in addition to monosaccharides such as glucose and fructose, the contents of polysaccharides, maltose, trehalose, and sorbitol varied significantly at different stages and states of the eggs. The expression profiles of the 4 genes were differently low but increased in instant acid-soaked eggs and Rbcase before hatching. Western blotting results showed two bands at 51 kDa and 61 kDa for BmAkt. The ratio of p-Akt/Akt in diapause eggs and Rbcase was equivalent to each other but significantly higher in Rbcase after activation of PI3K-Akt signaling pathway. These findings revealed that saccharides are basic energic sources, and PI3K-Akt signaling pathway play important roles in acid soaking to prevent or break diapause eggs.
DOI: 10.17632/7tjxrzkvjh
title: The efficacy of Traditional Chinese Exercise interventions in patients with chronic heart failure: An umbrella review and meta-analysis

description: This dataset compiles quantitative results from multiple randomized controlled trials (RCTs) investigating the effects of traditional Chinese exercise interventions—such as Baduanjin, Tai Chi, Yijinjing, Liuzijue, and Mixed Exercise—on various clinical outcomes in patient populations. The data includes both intervention (experimental) and control group statistics, covering sample sizes, means, and standard deviations for a range of measured outcomes, as well as details on intervention type and duration.
DOI: 10.17632/7tjxrzkvjh.1
title: The efficacy of Traditional Chinese Exercise interventions in patients with chronic heart failure: An umbrella review and meta-analysis

description: This dataset compiles quantitative results from multiple randomized controlled trials (RCTs) investigating the effects of traditional Chinese exercise interventions—such as Baduanjin, Tai Chi, Yijinjing, Liuzijue, and Mixed Exercise—on various clinical outcomes in patient populations. The data includes both intervention (experimental) and control group statistics, covering sample sizes, means, and standard deviations for a range of measured outcomes, as well as details on intervention type and duration.
DOI: 10.17632/vy6y3tjdr3
title: Tadah! a Swiss army knife for developing and deployment of machine learning interatomic potentials

description: The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a nuanced representation of system interactions, customized with unique cutoff functions and interaction distances. Tadah! supports Bayesian Linear Regression (BLR) and Kernel Ridge Regression (KRR) to enhance model accuracy and uncertainty management. A key feature is its hyperparameter optimization cycle, iteratively refining model architecture to improve transferability. This approach incorporates performance constraints, aligning predictions with experimental and theoretical data. Tadah! provides an interface for LAMMPS, enabling the deployment of MLIPs in molecular dynamics simulations. It is designed for broad accessibility, supporting parallel computations on desktop and HPC systems. Tadah! leverages a modular C++ codebase, utilizing both compile-time and runtime polymorphism for flexibility and efficiency. Neural network support and predefined bonding schemes are potential future developments, and Tadah! remains open to community-driven feature expansion. Comprehensive documentation and command-line tools further streamline the development and application of MLIPs.
DOI: 10.17632/vy6y3tjdr3.1
title: Tadah! a Swiss army knife for developing and deployment of machine learning interatomic potentials

description: The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a nuanced representation of system interactions, customized with unique cutoff functions and interaction distances. Tadah! supports Bayesian Linear Regression (BLR) and Kernel Ridge Regression (KRR) to enhance model accuracy and uncertainty management. A key feature is its hyperparameter optimization cycle, iteratively refining model architecture to improve transferability. This approach incorporates performance constraints, aligning predictions with experimental and theoretical data. Tadah! provides an interface for LAMMPS, enabling the deployment of MLIPs in molecular dynamics simulations. It is designed for broad accessibility, supporting parallel computations on desktop and HPC systems. Tadah! leverages a modular C++ codebase, utilizing both compile-time and runtime polymorphism for flexibility and efficiency. Neural network support and predefined bonding schemes are potential future developments, and Tadah! remains open to community-driven feature expansion. Comprehensive documentation and command-line tools further streamline the development and application of MLIPs.
DOI: 10.17632/dtfptcxxfk
title: Dataset of original research that evaluated the quality of generative artificial intelligence-synthesised advice on resuscitation and first aid

description: This dataset contains the results of an analysis of original studies published in English between 2017 and 2025 that reported quantitative data on the quality (accuracy, correctness, completeness, appropriateness) of generative artificial intelligence-synthesised advice for laypeople on cardiopulmonary resuscitation or first aid.
The search for eligible papers was conducted in May 2025 using PubMed, Scopus, and Google Scholar.
The dataset table collected the following data: article author(s) and year of publication, publication source link, publication type, study objectives, study design, target health condition(s), name(s) of tested generative artificial intelligence tool(s), a summary of research methods, evaluation metrics, number of evaluations, a summary of study results, conclusions and author recommendations related to the quality of synthesised information.
The dataset was utilised to perform the related scoping review.
DOI: 10.17632/dtfptcxxfk.1
title: Dataset of original research that evaluated the quality of generative artificial intelligence-synthesised advice on resuscitation and first aid

description: This dataset contains the results of an analysis of original studies published in English between 2017 and 2025 that reported quantitative data on the quality (accuracy, correctness, completeness, appropriateness) of generative artificial intelligence-synthesised advice for laypeople on cardiopulmonary resuscitation or first aid.
The search for eligible papers was conducted in May 2025 using PubMed, Scopus, and Google Scholar.
The dataset table collected the following data: article author(s) and year of publication, publication source link, publication type, study objectives, study design, target health condition(s), name(s) of tested generative artificial intelligence tool(s), a summary of research methods, evaluation metrics, number of evaluations, a summary of study results, conclusions and author recommendations related to the quality of synthesised information.
The dataset was utilised to perform the related scoping review.
DOI: 10.17632/7rks5vk5pp.2
title: Article : Routine Sarcopenia Evaluation: A Missing Piece in Crohn’s Disease Management?

description: Data Availability
DOI: 10.17632/4m5kchhfjx
title: 100 years-records of sediments from Lake Buir on the Mongolian Plateau

description: The data sets are the last 100-years records of sediments from Lake Buir on the Mongolian Plateau.