cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/t93xcwm75r.1
title: Fusion_dataset

description: Facial expression: 1152 x 2048 pixels and RGB tones were lost in the preprocessing of the collected facial expression images. "facial_expression.csv.zip" contains gray scale 48 x 48 pixel data.
Audio Signals.zip file: raw audio data
Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained.
DOI: 10.17632/t93xcwm75r.10
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, a short form of the Positive and Negative Affect Schedule (PANAS) and general stress test.

Facial expression: 

These facial expressions were converted into pixels of size 48x48 and in grayscale.
The labels for this data are assigned as: stress-free (0) and stressful (1).

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained. 

A "0" in the physiological signals file indicates stress-free conditions, while a "1" indicates the presence of stress. Additionally, the physiological signals file contains data files in three different window sizes: 15 seconds, 30 seconds, and 60 seconds of data windows.
DOI: 10.17632/t93xcwm75r.11
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, a short form of the Positive and Negative Affect Schedule (PANAS) and general stress test.

Facial expression: 

These facial expressions were converted into pixels of size 48x48 and in grayscale.
The labels for this data are assigned as: stress-free (0) and stressful (1).

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained. 

A "0" in the physiological signals file indicates stress-free conditions, while a "1" indicates the presence of stress. Additionally, the physiological signals file contains data files in three different window sizes: 15 seconds, 30 seconds, and 60 seconds of data windows.
DOI: 10.17632/t93xcwm75r.2
title: Stress analysis from physiological data under pressure: FUSION dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

Facial expression: 1152 x 2048 pixels and RGB tones were lost in the preprocessing of the collected facial expression images. "facial_expression.csv.zip" contains gray scale 48 x 48 pixel data.
Audio Signals.zip file: raw audio data
Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained.
DOI: 10.17632/t93xcwm75r.3
title: Stress analysis from physiological data under pressure: FUSION dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, panas scale and general stress test.

Facial expression: 1152 x 2048 pixels and RGB tones were lost in the preprocessing of the collected facial expression images. "facial_expression.csv.zip" contains gray scale 48 x 48 pixel data.
Audio Signals.zip file: raw audio data
Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained.
DOI: 10.17632/t93xcwm75r.4
title: Stress analysis from physiological data under pressure: FUSION dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, panas scale and general stress test.

Facial expression: 
The facial expressions in this file are classified according to 6 emotions. In addition, this dataset is categorized as binary for the stress state. 
The datasets in this file contain 48 x 48 pixel data in grayscale.

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained.
DOI: 10.17632/t93xcwm75r.5
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, panas scale and general stress test.

Facial expression: 
The facial expressions in this file are classified according to 6 emotions. In addition, this dataset is categorized as binary for the stress state. 
The datasets in this file contain 48 x 48 pixel data in grayscale.

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained.
DOI: 10.17632/t93xcwm75r.6
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, panas scale and general stress test.

Facial expression: 
The facial expressions in this file are classified according to 6 emotions. In addition, this dataset is categorized as binary for the stress state. 
The datasets in this file contain 48 x 48 pixel data in grayscale.

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained.
DOI: 10.17632/t93xcwm75r.7
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, panas scale and general stress test.

Facial expression: 
The facial expressions in this file are classified according to 6 emotions. In addition, this dataset is categorized as binary for the stress state. 
The datasets in this file contain 48 x 48 pixel data in grayscale.

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained.
DOI: 10.17632/t93xcwm75r.8
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress. The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, a short form of the Positive and Negative Affect Schedule (PANAS) and general stress test. Facial expression: While preparing facial expression documents, data from the fer2013 and Cohn-Kanade datasets were also utilized in addition to the facial expressions collected from the participants.. We transformed the facial expressions collected from the participants into 48x48 dimensions and converted them to grayscale pixels to match the format of these datasets. There are two different documents in the facial expressions file: facial_dataset_2_class.csv: In this data, the labels are assigned as stress-free (0) and stressed (1). When assigning these labels, positive emotions are defined as stress-free, while negative emotions are defined as stressed. facial_dataset_6_class.csv: In this data, the labels are assigned according to six different emotions, defined as follows: 0: Angry, 1: Disgust, 2: Fear, 3: Happy, 4: Sad, 5: Surprise, 6: Neutral. AudioData file: raw audio data Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained. A "0" in the physiological signals file indicates stress-free conditions, while a "1" indicates the presence of stress. Additionally, the physiological signals file contains data files in three different window sizes: 15 seconds, 30 seconds, and 60 seconds of data windows.
DOI: 10.17632/t93xcwm75r.9
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, a short form of the Positive and Negative Affect Schedule (PANAS) and general stress test.

Facial expression: 

While preparing facial expression documents, data from the fer2013 and Cohn-Kanade datasets were also utilized in addition to the facial expressions collected from the participants.. We transformed the facial expressions collected from the participants into 48x48 dimensions and converted them to grayscale pixels to match the format of these datasets. There are two different documents in the facial expressions file:

facial_dataset_2_class.csv: In this data, the labels are assigned as stress-free (0) and stressed (1). When assigning these labels, positive emotions are defined as stress-free, while negative emotions are defined as stressed.

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained. 

A "0" in the physiological signals file indicates stress-free conditions, while a "1" indicates the presence of stress. Additionally, the physiological signals file contains data files in three different window sizes: 15 seconds, 30 seconds, and 60 seconds of data windows.

All raw physiological signals recorded with the Empatica E4 armband are located in the EmpaticaSource.zip file.
DOI: 10.15468/dl.zj7xu5
title: Occurrence Download

description: A dataset containing 13604 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Gyrocarpus Jacq."
 ]
}

 The dataset includes 13604 records from 164 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001838-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/t93xcwm75r
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, a short form of the Positive and Negative Affect Schedule (PANAS) and general stress test.

Facial expression: 

These facial expressions were converted into pixels of size 48x48 and in grayscale.
The labels for this data are assigned as: stress-free (0) and stressful (1).

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained. 

A "0" in the physiological signals file indicates stress-free conditions, while a "1" indicates the presence of stress. Additionally, the physiological signals file contains data files in three different window sizes: 15 seconds, 30 seconds, and 60 seconds of data windows.

EmpeticaSource.zip : Contains raw real world data.
DOI: 10.17632/t93xcwm75r.12
title: Stress analysis from physiological data under pressure: WorkStress3D Dataset

description: This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, a short form of the Positive and Negative Affect Schedule (PANAS) and general stress test.

Facial expression: 

These facial expressions were converted into pixels of size 48x48 and in grayscale.
The labels for this data are assigned as: stress-free (0) and stressful (1).

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained. 

A "0" in the physiological signals file indicates stress-free conditions, while a "1" indicates the presence of stress. Additionally, the physiological signals file contains data files in three different window sizes: 15 seconds, 30 seconds, and 60 seconds of data windows.

EmpeticaSource.zip : Contains raw real world data.
DOI: 10.17632/2bk3wsghpp.1
title: Qiangjing Tablets Improve Spermatogenesis via Keap1/Nrf2 Pathway in Varicocele

description: Qiangjing Tablets Improve Spermatogenesis via Keap1/Nrf2 Pathway in Varicocele
DOI: 10.15468/dl.smd9hy
title: Occurrence Download

description: A dataset containing 1841779 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Batillaria cumingii (Crosse, 1862), Sebastes elongatus Ayres, 1859, Cyclopterus lumpus Linnaeus, 1758, Sebastes crameri (Jordan, 1897), Anarhichas minor Olafsen, 1772, Symphodus roissali (Risso, 1810), Lithophane leautieri Boisduval, 1829, Lithognathus mormyrus (Linnaeus, 1758), Daption capense (Linnaeus, 1758), Amphipyra perflua (Fabricius, 1787), Centropages chierchiae Giesbrecht, 1889, Temora stylifera (Dana, 1849), Sebastes flavidus (Ayres, 1862), Girella tricuspidata (Quoy & Gaimard, 1824), Pterostichus nigrita (Paykull, 1790), Bathytoshia brevicaudata (Hutton, 1875), Sterna forsteri Nuttall, 1834, Emiliania huxleyi (Lohmann) Hay & Mohler, 1967, Oligolophus tridens (Koch, 1836), Calanoides carinatus (Krøyer, 1849))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1841779 records from 1158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001825-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28340347.v1
title: Experimental validation and numerical modeling of interfacial effects in silanized hexagonal boron nitride (h-BN) reinforced epoxy composites by tailoring silane concentration

description: This study investigates the use of h-BN particles as fillers, focusing on tailoring surface chemistry to enhance the thermal conductivity of epoxy composites. By enriching the interface between h-BN particles with amino-silane groups and the epoxy matrix through controlled surface modification, thermal performance, and interfacial bonding were improved. To achieve a high degree of functionalization, h-BN particles were oxygenated to 13.6 atomic percent (at. %) through thermal treatment, followed by reduction using 3-amino-propyl-3-ethoxy-silane (APTES), which increased the amino content by 3.5% at. % under optimized reaction conditions. During composite manufacturing, 10 wt.% functionalized h-BN particles were reinforced into the epoxy matrix, increasing bulk thermal conductivity by 53%, from 0.2 W/mK to 0.34 W/mK. Heat flux simulations with ANSYS confirmed the interface interactions and thermal performance, with silanized h-BN achieving the highest heat flux of 70 W/mm<sup>2</sup>, aligning well with experimental results. While silanization improved thermal conductivity by strengthening interfacial bonding between h-BN and the epoxy matrix, it introduced brittleness, making the composites stiffer and more fragile. However, the silanized h-BN composite showed a 57.14% increase in toughness compared to neat h-BN, while the highest flexural modulus of 4126 MPa was achieved with neat h-BN.
DOI: 10.6084/m9.figshare.28340347
title: Experimental validation and numerical modeling of interfacial effects in silanized hexagonal boron nitride (h-BN) reinforced epoxy composites by tailoring silane concentration

description: This study investigates the use of h-BN particles as fillers, focusing on tailoring surface chemistry to enhance the thermal conductivity of epoxy composites. By enriching the interface between h-BN particles with amino-silane groups and the epoxy matrix through controlled surface modification, thermal performance, and interfacial bonding were improved. To achieve a high degree of functionalization, h-BN particles were oxygenated to 13.6 atomic percent (at. %) through thermal treatment, followed by reduction using 3-amino-propyl-3-ethoxy-silane (APTES), which increased the amino content by 3.5% at. % under optimized reaction conditions. During composite manufacturing, 10 wt.% functionalized h-BN particles were reinforced into the epoxy matrix, increasing bulk thermal conductivity by 53%, from 0.2 W/mK to 0.34 W/mK. Heat flux simulations with ANSYS confirmed the interface interactions and thermal performance, with silanized h-BN achieving the highest heat flux of 70 W/mm<sup>2</sup>, aligning well with experimental results. While silanization improved thermal conductivity by strengthening interfacial bonding between h-BN and the epoxy matrix, it introduced brittleness, making the composites stiffer and more fragile. However, the silanized h-BN composite showed a 57.14% increase in toughness compared to neat h-BN, while the highest flexural modulus of 4126 MPa was achieved with neat h-BN.
DOI: 10.15468/dl.cpse82
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acanthoscelides obtectus",
 "Country is Croatia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084619-250717081556266/datasets/export for details.