cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mbwxy48223.1
title: DigitalExposome: A Dataset for Wellbeing Classification using Environmental Air Quality and Human Physiological data

description: The DigitalExposome dataset is a comprehensive collection of multi-sensor data designed to explore the relationship between urban environmental factors and human wellbeing. A total of 42, 437 samples were collected from 40 participants who undertook the experiment. 


Key features of the DigitalExposome dataset include:

Environmental Data: This includes measurements of air pollutants (e.g., Particulate Matter (1.0, 2.5 & 10), Carbon Monoxide, Ammonia, Nitrogen Dioxide and noise levels. These environmental factors are crucial for assessing the impact of pollution and other urban stressors on health.

Physiological Data: The dataset captures a range of physiological responses using an E4 Empatica, including Electrodermal Activity (EDA), Heart Rate (HR), Heart Rate Variability (HRV), and other indicators such as Blood Volume Pulse (BVP). These signals provide insights into the body’s responses to environmental stressors.

Perceived Wellbeing Data: The dataset comprises self-reported responses from participants regarding their overall wellbeing, capturing their emotional and mental states in response to various environmental exposures. Wellbeing was assessed using a 5-point Likert scale, where participants rated their feelings from 1 (very negative) to 5 (very positive). This scale provides a structured way to quantify subjective experiences, allowing researchers to analyse trends in emotional valence (e.g., positive or negative feelings) across different conditions. 

The dataset was developed as part of the DigitalExposome framework, which integrates data from multiple sources, including environmental sensors, physiological sensors, and self-reported wellbeing responses. This framework aims to provide a deeper understanding of how urban exposures—such as air pollution, noise, and other environmental stressors can impact individual health and wellbeing. 

The three data collection devices operated at different sampling rates, which has been accounted for in the processing stage. After data collection and integration, the combined dataset from 40 users has been normalised and prepared for analysis. The physiological data recorded by the E4 Empatica included Heart Rate (HR) at 1Hz, ElectroDermal Activity (EDA) at 4Hz, and Blood Volume Pulse (BVP) at 64Hz. Heart Rate Variability (HRV), instead of being recorded at a fixed rate, was provided as a sequence of time intervals between detected heartbeats. Environmental air quality data was sampled at 0.2Hz. To ensure uniformity across all data sources, physiological data from the Empatica device was downsampled to 1Hz to align with HR measurements. Similarly, environmental data was upsampled to match the physiological data rate of 1Hz. Additionally, self-reported mental wellbeing data collected via smartphone was extracted and upsampled to 1Hz to maintain consistency with both the environmental and physiological datasets.
DOI: 10.17632/mbwxy48223.2
title: DigitalExposome: A Dataset for Wellbeing Classification using Environmental Air Quality and Human Physiological data

description: The DigitalExposome dataset is a comprehensive collection of multi-sensor data designed to explore the relationship between urban environmental factors and human wellbeing. A total of 42, 437 samples were collected from 40 participants who undertook the experiment. 


Key features of the DigitalExposome dataset include:

Environmental Data: This includes measurements of air pollutants (e.g., Particulate Matter (1.0, 2.5 & 10), Carbon Monoxide, Ammonia, Nitrogen Dioxide and noise levels. These environmental factors are crucial for assessing the impact of pollution and other urban stressors on health.

Physiological Data: The dataset captures a range of physiological responses using an E4 Empatica, including Electrodermal Activity (EDA), Heart Rate (HR), Heart Rate Variability (HRV), and other indicators such as Blood Volume Pulse (BVP). These signals provide insights into the body’s responses to environmental stressors.

Perceived Wellbeing Data: The dataset comprises self-reported responses from participants regarding their overall wellbeing, capturing their emotional and mental states in response to various environmental exposures. Wellbeing was assessed using a 5-point Likert scale, where participants rated their feelings from 1 (very negative) to 5 (very positive). This scale provides a structured way to quantify subjective experiences, allowing researchers to analyse trends in emotional valence (e.g., positive or negative feelings) across different conditions. 

The dataset was developed as part of the DigitalExposome framework, which integrates data from multiple sources, including environmental sensors, physiological sensors, and self-reported wellbeing responses. This framework aims to provide a deeper understanding of how urban exposures—such as air pollution, noise, and other environmental stressors can impact individual health and wellbeing. 

The three data collection devices operated at different sampling rates, which has been accounted for in the processing stage. After data collection and integration, the combined dataset from 40 users has been normalised and prepared for analysis. The physiological data recorded by the E4 Empatica included Heart Rate (HR) at 1Hz, ElectroDermal Activity (EDA) at 4Hz, and Blood Volume Pulse (BVP) at 64Hz. Heart Rate Variability (HRV), instead of being recorded at a fixed rate, was provided as a sequence of time intervals between detected heartbeats. Environmental air quality data was sampled at 0.2Hz. To ensure uniformity across all data sources, physiological data from the Empatica device was downsampled to 1Hz to align with HR measurements. Similarly, environmental data was upsampled to match the physiological data rate of 1Hz. Additionally, self-reported mental wellbeing data collected via smartphone was extracted and upsampled to 1Hz to maintain consistency with both the environmental and physiological datasets.
DOI: 10.15468/dl.x44n4y
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Merulaxis stresemanni Sick, 1960"
 ]
}

 The dataset includes 70 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023854-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a87jhk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetId is one of (5a659248-1f70-11e3-b2c5-00145eb45e9a)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023859-250127130748423/datasets/export for details.
DOI: 10.15468/dl.gnkqgr
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Atherinella balsana (Meek, 1902)"
 ]
}

 The dataset includes 83 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023860-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fedcky
title: Occurrence Download

description: A dataset containing 13030 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Paraguay",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 13030 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023865-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b62qum
title: Occurrence Download

description: A dataset containing 13056 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen)",
 "Country is Paraguay",
 "OccurrenceStatus is Present",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 13056 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023861-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v3cqs5
title: Occurrence Download

description: A dataset containing 276 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gastrolobium linearifolium G.Chandler & Crisp"
 ]
}

 The dataset includes 276 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023862-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.saf5ec
title: Occurrence Download

description: A dataset containing 4129 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations)",
 "Geometry POLYGON((-94.47086 37.11966,-94.43568 37.11966,-94.43486 37.17223,-94.47031 37.17278,-94.47086 37.11966))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4129 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023855-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mm9cvx
title: Occurrence Download

description: A dataset containing 592 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Triprion spinosus (Steindachner, 1864)"
 ]
}

 The dataset includes 592 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023852-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2fmxbhmxv8.1
title: Vitiligo: Prevalence Among Patients Attending the Skin Clinic at Mbarara Regional Referral Hospital, Southwestern Uganda: A Retrospective Review.

description: These are appendices of documents obtained before collecting data for this study and they include; the data collection form, the Mbarara University of Science and Technology Research ethics committee approval letter, the letter requesting waiver to access patient records, the administrative clearance letter from the hospital director at Mbarara Regional Referral Hospital and a Responsible Conduct of Research certificate
DOI: 10.17632/j66cgrc25y.1
title: LINF_060011500

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/mf2cgph9cy.1
title: Multimodal Pain Dataset

description: Pain is considered subjective since it is based on personal experience; however, it may be possible to objectively analyze pain through a self-reporting system supported by bio-signals. Based on this hypothesis, a multimodal dataset is created, combining EEG and wristband signals (including EDA, BVP, temperature, accelerometer data) along with participants' responses to a survey, including the McGill Pain Questionnaire. This dataset, collected from 99 participants, allows for the analysis of three different types of pain: headache, back pain, and menstrual pain. E4 Empatica wristband and Mindwave Mobile 2 EEG device are used to collect the data. Both raw and processed data of the devices with survey answers are stored in the dataset by participants' unique IDs.
DOI: 10.17632/mf2cgph9cy.2
title: Multimodal Pain Dataset

description: Pain is considered subjective since it is based on personal experience; however, it may be possible to objectively analyze pain through a self-reporting system supported by bio-signals. Based on this hypothesis, a multimodal dataset is created, combining EEG and wristband signals (including EDA, BVP, temperature, accelerometer data) along with participants' responses to a survey, including the McGill Pain Questionnaire. This dataset, collected from 99 participants, allows for the analysis of three different types of pain: headache, back pain, and menstrual pain. E4 Empatica wristband and Mindwave Mobile 2 EEG device are used to collect the data. Both raw and processed data of the devices with survey answers are stored in the dataset by participants' unique IDs.
DOI: 10.17632/mf2cgph9cy.3
title: PhysioPain Dataset

description: Pain is considered subjective since it is based on personal experience; however, it may be possible to objectively analyze pain through a self-reporting system supported by bio-signals. Based on this hypothesis, a multimodal dataset is created, combining EEG and wristband signals (including EDA, BVP, temperature, accelerometer data) along with participants' responses to a survey, including the McGill Pain Questionnaire. This dataset, collected from 99 participants, allows for the analysis of three different types of pain: headache, back pain, and menstrual pain. E4 Empatica wristband and Mindwave Mobile 2 EEG device are used to collect the data. Both raw and processed data of the devices with survey answers are stored in the dataset by participants' unique IDs.
DOI: 10.17632/mf2cgph9cy.4
title: PhysioPain Dataset

description: Pain is considered subjective since it is based on personal experience; however, it may be possible to objectively analyze pain through a self-reporting system supported by bio-signals. Based on this hypothesis, a multimodal dataset is created, combining EEG and wristband signals (including EDA, BVP, temperature, accelerometer data) along with participants' responses to a survey, including the McGill Pain Questionnaire. This dataset, collected from 99 participants, allows for the analysis of three different types of pain: headache, back pain, and menstrual pain. E4 Empatica wristband and Mindwave Mobile 2 EEG device are used to collect the data. Both raw and processed data of the devices with survey answers are stored in the dataset by participants' unique IDs.
DOI: 10.17632/44ksrbzmd7.1
title: Impact of chlorantraniliprole exposure on nutritional and physiological responses of lab-selected susceptible and resistant populations of Spodoptera litura (Fabricius)

description: Supported raw data for tables and figures of Impact of chlorantraniliprole exposure on nutritional and physiological responses of lab-selected susceptible and resistant populations of Spodoptera litura (Fabricius)
DOI: 10.17632/97mfx5ysws.1
title: LINF_060010400

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/gg57cfkzy3.1
title: CBT for CUD: Meta-analysis data

description: The data is stored in an MS Excel file and has been processed in SPSS v28.0, with further analysis conducted using STATA 18 for ease. The dataset includes study-level variables such as study ID, country, and setting, along with participant demographics like sample size, age, and gender distribution. It also details the intervention, including the number of CBT sessions and the type of CBT used, and compares it against other interventions such as psychosocial treatments, contingency management, and treatment as usual. The primary outcome measure is cannabis use frequency, reported as mean percentages over past days at short-, medium-, and long-term follow-ups. The dataset includes sample sizes, mean frequencies, and standard deviations for both the CBT and comparison groups at each time point. This data can be interpreted for individual studies or used for meta-analysis, making it a valuable resource for assessing the effectiveness of CBT in reducing cannabis use over time.
DOI: 10.17632/y7cgz69654.1
title: A model for developing self-management skills among left-behind children in rural areas: a design case study based on weak schools

description: Data from the daily record sheet, calculated the average time spent by students in each grade on each activity (total time divided by the number of participants), and obtained the average time distribution for Grades 5 and 6.Shown in "Statistics of the Average Duration of Each Activity _ Grade 5 and Statistics of the Average Duration of Each Activity _ Grade 6".
By calculating the average planning rate for each activity (the total number of plans for each activity divided by 60 days), obtained the distribution of average planning rates for students in both grades, as shown in "The average planned rate of each activity _ Grade 5 and The average planned rate of each activity _ Grade 6".
DOI: 10.17632/33f6zy3trz.1
title: Supplementary material

description: Supplementary material to "VLE-ML: Prediction of vapor-liquid phase equilibrium based on molecular descriptors with No Code Machine Learning"
DOI: 10.17632/33f6zy3trz.2
title: Supplementary material

description: Supplementary material to "VLE-ML: Prediction of vapor-liquid phase equilibrium based on molecular descriptors with No Code Machine Learning"
DOI: 10.17632/4vy22fjwfv.1
title: Clusterin inspires mesothelial endocytosis of fibers to revert liver fibrosis

description: Original data of Clusterin inspires mesothelial endocytosis of fibers to revert liver fibrosis
DOI: 10.17632/4vy22fjwfv.2
title: Clusterin induces mesothelial endocytosis of fibers to reverse liver fibrosis

description: Original data of Clusterin induces mesothelial endocytosis of fibers to reverse liver fibrosis
DOI: 10.17632/4vy22fjwfv.3
title: CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis

description: Original data of CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis
DOI: 10.17632/4vy22fjwfv.4
title: CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis

description: Original data of CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis
DOI: 10.17632/4vy22fjwfv.5
title: CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis

description: Original data of CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis
DOI: 10.17632/4vy22fjwfv.6
title: CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis

description: Original data of CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis
DOI: 10.17632/4vy22fjwfv.7
title: CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis

description: Original data of CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis
DOI: 10.17632/4vy22fjwfv.8
title: CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis

description: Original data of CLUtide mimics clusterin to induce mesothelial endocytosis of fibers and reverse liver fibrosis
DOI: 10.24396/ordar-170
title: Supplementary material: data availability compiling the database and our analytical results

description: Files used in this study:

(i) "IDP_Neve_Concordia_CRPG_MNHN_GFénisseEtAl_EPSL.xlsx", a snow mass compilation from clean snow at the CONCORDIA station (Dome C, Antarctica);
(ii) "Calibration_of_He_concentration_inFurnace_GFénisseEtAl_EPSL.xlsx", the furnace calibration for He measurements;
(iii) "HeResults_IDPsMMs_GFénisseEtAl_EPSL.xlsx", the He isotopic compositions and total masses of cosmic dust, categorized by size range, from the CONCORDIA snow;
(iv) "ETMassFluxSynthesis_GFénisseEtAl_EPSL.xlsx", ET mass comparisons from collection, He method and CABMOD-ZoDy modeling.
(v) "Database_IDPs&MMs_He&Ne_GFénisseEtAl_EPSL.xlsx", an exhaustive database containing all published light noble gas data (³He, ⁴He, and ²⁰Ne) measured in IDPs and MMs collected in the stratosphere and Antarctica;
(vi) "ReferencesTable_3HeETFlux_GFénisseEtAl_EPSL.xlsx", a database of light noble gas measurements in Antarctica MMs. This dataset of He vs. MM mass is used here to convert ³He concentrations into dust abundances.
DOI: 10.6084/m9.figshare.28395005
title: AI-AS Responds.docx

description: Artificial Intelligence- Ankylosing Spondylitis
DOI: 10.6084/m9.figshare.28395005.v1
title: AI-AS Responds.docx

description: Artificial Intelligence- Ankylosing Spondylitis
DOI: 10.21227/fa73-3y07
title: Validation of electromagnetic scattering from a 2D slot backed by a semielliptical cavity: Part II

description: We have developed exact analytical solutions of new canonical electromagnetic scattering problems. These new analytical solutions provide benchmarks for the validation of computational electromagnetic software.The associated article considers the electromagnetic penetration through an aperture into a cavity. The structure of interest is a semielliptical channel flush-mounted under a metal plane and slotted along the interfocal distance of its cross-section. The channel is filled with a material isorefractive to the medium that occupies the half-space above the metal plane. Three independent integral equations are developed to compute the currents induced on the structure of interest by plane wave and line source excitations. Numerical results from the integral equation methods are compared with the evaluation of the analytical expressions, derived in a previous paper, that involve the summation of Mathieu functions. Data are presented for two polarizations, various values of intrinsic impedances and ratio between aperture width and incident radiation wavelength. Further data are presented for the bistatic radar cross-section of the structure of interest. All data obtained from the integral equation methods and the evaluations of the analytical formulas are in excellent agreement.
DOI: 10.17632/cwxkhgjhht.1
title: soil detachment

description: soil detachment
DOI: 10.15468/dl.2994r9
title: Occurrence Download

description: A dataset listing the 215 species recorded in GBIF matching the query:
{
 "and" : [
 "Locality is Pinal De Amoles",
 "StateProvince is Queretaro"
 ]
}

 The dataset's 215 records were derived from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023813-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tdvpft8m5c.1
title: Data for Accessibility method for the WordPress Gutenberg block editor - 2025

description: Usability report on Gutenberg Accessibility Checker plugin in 2025 for publication titled "Accessibility method for the WordPress Gutenberg block editor".
DOI: 10.6084/m9.figshare.28394987
title: DIPY DEEPN4 default weights

description: The data is for the DeepN4 model in dipy
DOI: 10.17632/hz6jhm7m2j.1
title: Blockchain and Food Supply Chain

description: Data related to Blockchain and Food Operations
DOI: 10.17632/n3jm24htg8.1
title: Data for "Sound velocity and Grüneisen parameter in shock-melted silica at deep Earth pressures"

description: This dataset includes the wave profiles discussed in the associated manuscript, "Sound velocity and Grüneisen parameter in shock-melted silica at deep Earth pressures."
DOI: 10.17632/s74k4jkr77.1
title: Code for Conklin, Gerardi, Lambie-Hanson Home Equity Racial Disparities

description: This directory contains all of the Stata code used to produce the exhibits in "Can Everyone Tap into the Housing Piggy Bank? Racial Disparities in Access to Home Equity" by Conklin, Gerardi, and Lambie-Hanson, published in the Journal of Financial Economics. The data used in the paper is not included in this directory because it is confidential HMDA data that is not allowed to be posted on a public website.