cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14575859
title: A Novel Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks

description: Codes for the journal paper:



Title : A Novel Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks

Journal : Applied Sciences


The analysis of continuous events for any application involves the discretization of an event into sequences with potential historical dependencies. These sequences represent time stamps or samplings of a continuous process collectively forming a time series dataset utilized for training recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for pattern prediction. The challenge is to ensure that the estimates from the trained models are consistent in the same input domain for different discritizations of the same or similar continuous history dependent events. In other words, if different time stamps are used during the prediction phase after training, the model is still expected to give consistent predictions based on the knowledge it has learned. To address this, we present a novel RNN transition formula intended to produce consistent estimates in a wide range of engineering applications. The approach was validated with synthetically generated datasets in 1D, 2D, and 3D spaces, intentionally designed to exhibit high non-linearity and complexity. Furthermore,  we have verified our results with real-world datasets to ensure practical applicability and robustness. These assessments show the ability of the proposed method, which involves restructuring the mathematical structure and extending conventional RNN architectures, to provide reliable and consistent estimates for complex time series data.
DOI: 10.5281/zenodo.14575860
title: A Novel Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks

description: Codes for the journal paper:



Title : A Novel Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks

Journal : Applied Sciences


The analysis of continuous events for any application involves the discretization of an event into sequences with potential historical dependencies. These sequences represent time stamps or samplings of a continuous process collectively forming a time series dataset utilized for training recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for pattern prediction. The challenge is to ensure that the estimates from the trained models are consistent in the same input domain for different discritizations of the same or similar continuous history dependent events. In other words, if different time stamps are used during the prediction phase after training, the model is still expected to give consistent predictions based on the knowledge it has learned. To address this, we present a novel RNN transition formula intended to produce consistent estimates in a wide range of engineering applications. The approach was validated with synthetically generated datasets in 1D, 2D, and 3D spaces, intentionally designed to exhibit high non-linearity and complexity. Furthermore,  we have verified our results with real-world datasets to ensure practical applicability and robustness. These assessments show the ability of the proposed method, which involves restructuring the mathematical structure and extending conventional RNN architectures, to provide reliable and consistent estimates for complex time series data.
DOI: 10.15468/dl.v9nftb
title: Occurrence Download

description: A dataset containing 34258 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Animal Sound Archive"
 ]
}

 The dataset includes 34258 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051469-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hggbh5
title: Occurrence Download

description: A dataset containing 17552 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is one of (Malus Mill., Malus baccata var. baccata)"
 ]
}

 The dataset includes 17552 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051474-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/f5tzr98b5g.1
title: Central bank digital currency payment adoption dataset

description: Central bank digital currency payment adoption dataset
DOI: 10.15468/dl.zc2jhg
title: Occurrence Download

description: A dataset containing 80909 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum, Polygonum cuspidatum, Polygonum cuspidatum Willd., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Sieb. & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold & Zucc., Polygonum cuspidatum Siebold. & Zucc., Polygonum cuspidatum Siebold et Zucc., Polygonum cuspidatum Siebold & Zucc., 1846, Polygonum cuspidatum Willd. ex Spreng., Polygonum cuspidatum Willd. ex Spreng., Polygonum cuspidatum Willd. ex Soler., Polygonum cuspidatum Siebold & Zucc., 1846, Polygonum cuspidatum Siebold & Zucc., 1846, Polygonum cuspidatum Willd. ex Soler., Polygonum cuspidatum Willd. ex Spreng., 161226087, Polygonum cuspidatum Willd. ex Soler., Polygonum cuspidatum Willd. ex Spreng., 1825, Polygonum cuspidatum Willd. ex Soler., 1899, Polygonum cuspidatum subsp. compactum (Hook.f.) L.H.Bailey, Polygonum cuspidatum Siebold & Zuccarini nom. illeg., 161226085, Polygonum cuspidatum subsp. compactum (Hook.f.) Nakai, Polygonum cuspidatum Willd. ex Spreng., 1825, Polygonum cuspidatum P.F.Von Siebold & J.G.Zuccarini, 1846, Syritta pipiens (Linnaeus, 1758), Syritta pipiens (Linnaeus, 1758), Platycheirus angustatus (Zetterstedt, 1843), Meliscaeva cinctella (Zetterstedt, 1843), Didea fasciata Macquart, 1834, Leucozona lucorum (Linnaeus, 1758), Eupeodes luniger (Meigen, 1822), Eristalis intricaria (Linnaeus, 1758), Aphalara itadori (Shinji, 1938), Syritta pipiens (Linnaeus), Meliscaeva cinctella (Zetterstedt, 1843), Leucozona (Leucozona) lucorum (Linnaeus), Meliscaeva cinctella (Zetterstedt), Leucozona lucorum (Linnaeus, 1758), Pyrophaena granditarsis (Forster), Parabaliothrips betulaceae Masumoto & Okajima 2020, Fallopia japonica (Houtt.) Ronse Decr., Reynoutria japonica Houtt., Parabaliothrips betulaceae Masumoto & Okajima, 2020)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 80909 records from 527 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051475-241126133413365/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.28107053
title: Bases de datos rondas 1 y 2 Delphi

description: Bases de datos de las rondas 1 y 2 del Delphi contenido en el artículo de la Revista Española de Orientación y Psicopedagogía. https://revistas.uned.es/index.php/reop/article/view/36461
DOI: 10.6084/m9.figshare.28107053.v1
title: Bases de datos rondas 1 y 2 Delphi

description: Bases de datos de las rondas 1 y 2 del Delphi contenido en el artículo de la Revista Española de Orientación y Psicopedagogía. https://revistas.uned.es/index.php/reop/article/view/36461
DOI: 10.15468/dl.ubn9f9
title: Occurrence Download

description: A dataset containing 3170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "PublishingOrg is 75642970-f855-11dd-8235-b8a03c50a862",
 "RecordedBy is Barbara Ruthsatz"
 ]
}

 The dataset includes 3170 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051476-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/967pkrz4w6.1
title: Electrophysiology data for whsiking sound experiment

description: This dataset contains data from electrophysiology experiments used in the Ph.D thesis of B. Efron. on the project "Detection and Neural Encoding of Whisker-generated Sounds in Mice." The dataset contains representative data for the main awake experiment of the project, any additional data can be requested from ilan.lampl@weizmann.ac.il. The data files are objects of the class expWhisking. The function to define the class is provided with the data. For any questions regarding the data structure format, please contact ilan.lampl@weizamnn.ac.il.
The data relates to the following experiment: 
we examined if AC neurons encode the sound generated when mice whisk against different objects. Therefore, we recorded neuronal activity (from well-isolated units as well as MUA without discriminating based on spike waveform) from AC of head-fixed mice (n = 5) running on a rotating treadmill, allowing their whiskers to make contact with aluminum foil, attenuated foil, and no object. To diminish the potential confounding involvement of the somatosensory system in the activation of neurons in the AC upon whisking against objects, we severed the mouse's infraorbital nerve (ION), which conveys tactile sensations from the whisker pad and cheek. To discern if changes in firing rates were due to motor commands, we compared neuronal activity elicited by aluminum foil to that of attenuated foil, where the major difference between these two objects is the sound that is produced upon whisking. The motor switched between objects (aluminum, attenuated, and no object) at fixed intervals, moving them toward the whiskers, and whisking behavior was monitored with a high-speed camera.
DOI: 10.17632/6t6tjsk66d.1
title: Replication Files For The Paper Titled Welfare and Income Effects of Tuition Subsidies and Public Investment in Schooling (Economic Modelling)

description: The basic data and data for observables used in the paper along with Matlalb and Dynare files for replicating the results of the paper.
DOI: 10.15468/dl.wzpvfm
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tribulus alatus, Tribulus alatus, Tribulus alatus Delile, Tribulus alatus Delile, Tribulus alatus Drège, Tribulus alatus Delile, Tribulus alatus Delile, Tribulus alatus Delile, Tribulus alatus Delile, Tribulus alatus Delile, 1813, Tribulus alatus Delile, 1813, Tribulus alatus Drège ex C.Presl, Tribulus alatus Drège ex C.Presl, Tribulus alatus Drège ex C.Presl, Tribulus alatus Drège ex C.Presl, Tribulus alatus Drege ex C.Presl, 1845, 208123481, Tribulus alatus Drège ex C. Presl)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051478-241126133413365/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.28107059
title: Dataset - Bias Perceptions of Generative AI: A Comparison of Public Sentiment on Twitter and Workplace Perspectives.zip

description: Dataset from <b>Bias Perceptions of Generative AI: A Comparison of Public Sentiment on Twitter and Workplace Perspectives </b>research paper
DOI: 10.15468/dl.87cjz8
title: Occurrence Download

description: A dataset containing 181504 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is Africa",
 "TaxonKey is Euphorbiaceae"
 ]
}

 The dataset includes 181504 records from 275 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051467-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12577086
title: Behavioral Emotion Recognition Dataset (BERD)

description: The dataset has been updated to a new version (https://zenodo.org/records/14568959).

The expertise dataset has been added. 

 

Abstract: The Behavioral Emotion Recognition Dataset (BERD) was developed as part of the research study titled "Behavioral Research Methodologies for Bodily Emotion Recognition." This dataset comprises motion capture data collected from participants performing emotional body movements under various experimental conditions. It is designed to facilitate the development and evaluation of automatic emotion recognition (AER) systems using bodily movement data. The dataset offers insights into the effects of participant acting expertise, motion capture device types, and emotional stimuli on bodily emotion recognition accuracy.

Key Features:

1.    (Devices) Motion Capture Devices:



Marker-based motion capture (Optitrack system with 18 infrared cameras).

Pose estimation using RGB videos.

Kinect motion capture.

Mobile phone motion capture (iPhone 12 with ARKit).


2.    (Stimulus) Emotional Stimulus:



Word instructions (e.g., "happy," "sad").

Picture stimuli (Karolinska Directed Emotional Faces dataset).

Video stimuli (validated emotional film clips).


3.    Emotions Recorded:



Seven categories: happy, sad, surprised, angry, disgusted, fearful, and neutral.


4.    Data Format:



Skeletal data represented as 3D joint coordinates.

Sampling rate: 30 frames per second.

File format: CSV.


Potential Applications:



Developing deep learning models for bodily emotion recognition.

Studying the impact of data collection conditions on emotion recognition accuracy.


Citation: If you use this dataset in your research, please cite it as follows:



Cho, Y., Jung, M., Bae, J., & Kim, K. (2024). Behavioral Emotion Recognition Dataset (BERD). Zenodo. DOI: [https://zenodo.org/records/12577086]
DOI: 10.5281/zenodo.13774508
title: Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification

description: This dataset contains gravitational wave (GW) data used in our research work Abylkairov et al. (2024). The first 10,000 columns represent the gravitational wave strain D · h [cm] for the corresponding time values ranging from -993 ms to 6.9 ms, with a step size of 0.1 ms. The zero time refers to the time of core bounce. Each row within these first 10,000 columns corresponds to 864 different gravitational wave signals.

Columns 10,001 to 10,005 contain the following additional parameters:



T/|W|: The rotational parameter.

GR_or_GREP: Binary indicator for the signal type, where 0 denotes GR and 1 denotes GREP.

EOS: The equation of state (EOS) model, where 0 corresponds to SFHo, 1 to LS220, 2 to HSDD2, and 3 to GShenFSU2.1.

f_peak: The peak frequency [Hz].

D Delta h: D · ∆h [cm].


For each row (representing a single gravitational wave signal), these parameters provide information about the signal's rotational parameter, type (GR or GREP), EOS model, peak frequency, and D · ∆h.

Note: In the f_peak calculation procedure, we truncated the GW signal at 4.5 ms after the end of the core bounce (see Richers et al. (2017) for details).
DOI: 10.5281/zenodo.13774509
title: Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification

description: This dataset contains gravitational wave (GW) data used in our research work Abylkairov et al. (2024). The first 10,000 columns represent the gravitational wave strain D · h [cm] for the corresponding time values ranging from -993 ms to 6.9 ms, with a step size of 0.1 ms. The zero time refers to the time of core bounce. Each row within these first 10,000 columns corresponds to 864 different gravitational wave signals.

Columns 10,001 to 10,005 contain the following additional parameters:



T/|W|: The rotational parameter.

GR_or_GREP: Binary indicator for the signal type, where 0 denotes GR and 1 denotes GREP.

EOS: The equation of state (EOS) model, where 0 corresponds to SFHo, 1 to LS220, 2 to HSDD2, and 3 to GShenFSU2.1.

f_peak: The peak frequency [Hz].

D Delta h: D · ∆h [cm].


For each row (representing a single gravitational wave signal), these parameters provide information about the signal's rotational parameter, type (GR or GREP), EOS model, peak frequency, and D · ∆h.

Note: In the f_peak calculation procedure, we truncated the GW signal at 4.5 ms after the end of the core bounce (see Richers et al. (2017) for details).
DOI: 10.5281/zenodo.12577085
title: Behavioral Emotion Recognition Dataset (BERD)

description: Abstract: The Behavioral Emotion Recognition Dataset (BERD) was developed as part of the research study titled "Behavioral Research Methodologies for Bodily Emotion Recognition." This dataset comprises motion capture data collected from participants performing emotional body movements under various experimental conditions. It is designed to facilitate the development and evaluation of automatic emotion recognition (AER) systems using bodily movement data. The dataset offers insights into the effects of participant acting expertise, motion capture device types, and emotional stimuli on bodily emotion recognition accuracy.

Key Features:

1.    (Expertise) Participant Data:



Actors: Professional actors with at least three years of acting experience.

Non-actors: General participants with no formal acting training.

Test: General participants with no formal acting training for test


2.    (Devices) Motion Capture Devices:



Marker-based motion capture (Optitrack system with 18 infrared cameras).

Pose estimation using RGB videos.

Kinect motion capture.

Mobile phone motion capture (iPhone 12 with ARKit).


3.    (Stimulus) Emotional Stimulus:



Word instructions (e.g., "happy," "sad").

Picture stimuli (Karolinska Directed Emotional Faces dataset).

Video stimuli (validated emotional film clips).


4.    Emotions Recorded:



Seven categories: happy, sad, surprised, angry, disgusted, fearful, and neutral.


5.    Data Format:



Skeletal data represented as 3D joint coordinates.

Sampling rate: 30 frames per second.

File format: CSV.


Potential Applications:



Developing deep learning models for bodily emotion recognition.

Studying the impact of data collection conditions on emotion recognition accuracy.


Citation: If you use this dataset in your research, please cite it as follows:



Cho, Y., Jung, M., Bae, J., & Kim, K. (2024). Behavioral Emotion Recognition Dataset (BERD). Zenodo. DOI: [https://zenodo.org/records/14568959]
DOI: 10.5281/zenodo.14568959
title: Behavioral Emotion Recognition Dataset (BERD)

description: Abstract: The Behavioral Emotion Recognition Dataset (BERD) was developed as part of the research study titled "Behavioral Research Methodologies for Bodily Emotion Recognition." This dataset comprises motion capture data collected from participants performing emotional body movements under various experimental conditions. It is designed to facilitate the development and evaluation of automatic emotion recognition (AER) systems using bodily movement data. The dataset offers insights into the effects of participant acting expertise, motion capture device types, and emotional stimuli on bodily emotion recognition accuracy.

Key Features:

1.    (Expertise) Participant Data:



Actors: Professional actors with at least three years of acting experience.

Non-actors: General participants with no formal acting training.

Test: General participants with no formal acting training for test


2.    (Devices) Motion Capture Devices:



Marker-based motion capture (Optitrack system with 18 infrared cameras).

Pose estimation using RGB videos.

Kinect motion capture.

Mobile phone motion capture (iPhone 12 with ARKit).


3.    (Stimulus) Emotional Stimulus:



Word instructions (e.g., "happy," "sad").

Picture stimuli (Karolinska Directed Emotional Faces dataset).

Video stimuli (validated emotional film clips).


4.    Emotions Recorded:



Seven categories: happy, sad, surprised, angry, disgusted, fearful, and neutral.


5.    Data Format:



Skeletal data represented as 3D joint coordinates.

Sampling rate: 30 frames per second.

File format: CSV.


Potential Applications:



Developing deep learning models for bodily emotion recognition.

Studying the impact of data collection conditions on emotion recognition accuracy.


Citation: If you use this dataset in your research, please cite it as follows:



Cho, Y., Jung, M., Bae, J., & Kim, K. (2024). Behavioral Emotion Recognition Dataset (BERD). Zenodo. DOI: [https://zenodo.org/records/14568959]
DOI: 10.17632/7yy955mh7r.1
title: Supplemental Material - Treatment of pityriasis rubra pilaris with systemic and biological agents: A single-center retrospective cohort of 76 patients

description: The set of supplemental materials support the JAAD manuscript "Treatment of pityriasis rubra pilaris with systemic and biological agents: A single-center retrospective cohort of 76 patients", including one supplemental figure and one supplemental table.
Supplemental Figure. Flowchart of pityriasis rubra pilaris treatment in a cohort receiving systemic and biological agents.
Supplemental Table. Baseline demographics of 76 pityriasis rubra pilaris patients in the cohort.
DOI: 10.17632/mp9j46pgg2.1
title: Emission spectra from Brown and Yellow regions in peafowl tail feathers infused with R6g

description: Emission spectra from Brown and Yellow regions in peafowl tail feathers infused with rhodamine 6g in an 70% ethanol, 30% water solution
DOI: 10.15468/dl.jk29c8
title: Occurrence Download

description: A dataset containing 113052 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scorzonera humilis L."
 ]
}

 The dataset includes 113052 records from 1028 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051465-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc112050826
title: P.Oxy.VII 1051 Inventory of Property, P.Oxy.VII 1051

description: [Text Description:] A list of articles, chiefly of dress, belonging to a woman whose name is given at the end. ❧ [Text or Publication Number Note:] P.Oxy.VII 1051, P.Oxy. 1051, Spurlock 1914.21.0020 ❧ [Excavation Description:] Excavated by archaeologists, including Bernard Pyne Grenfell and Arthur Surridge Hunt, at an ancient rubbish dump near Oxyrhynchus in Egypt (28°32'N 30°40'E, modern el-Bahnasa). ❧ [Medium:] Papyrus ❧ [Script Note:] Greek ❧ [Physical Object Description:] Papyrus manuscript, 15.3 x 5.3 cm.
DOI: 10.15468/dl.gwywz8
title: Occurrence Download

description: A dataset containing 28553 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tachuris Lafresnaye, 1836"
 ]
}

 The dataset includes 28553 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034578-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.