cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29987941
title: Modulating Aesthetic Furniture Preferences through Music: A Multimodal Neurophysiological Study Using Neural Ordinary Differential Equations Networks

description: Cross-modal perceptual integration plays a crucial role in aesthetic cognition, yet the neural regulatory mechanisms underlying music's influence on visual design preferences remain insufficiently elucidated. This study aims to explore how musical environments modulate aesthetic evaluation of traditional furniture through neural plasticity mechanisms and establish a preference prediction model based on multimodal neurophysiological signals. Four core aesthetic dimensions—"tactile density, lubricity, minimalist, and exquisite"—were extracted through sensory engineering methodology. We collected 32-channel electroencephalography (EEG), eye-tracking trajectories, and subjective aesthetic ratings from participants viewing 238 traditional square stool images under conditions with and without natural sound effects. A novel NeuralODE-Transformer multimodal fusion architecture was innovatively constructed: neural ordinary differential equations were employed for EEG temporal dynamics modeling, combined with Transformer attention mechanisms to achieve cross-modal feature fusion, with aesthetic preferences predicted through regularized regression.The findings revealed that musical intervention significantly modulated neural activity in specific frequency bands: prediction error for tactile density in the Alpha band decreased by 26.9%, while prediction accuracy for exquisiteness in the Beta band improved significantly. The NeuralODE-Transformer model demonstrated superior predictive performance across all four aesthetic dimensions compared to conventional methods. Design scheme validation through AHP-entropy weight method confirmed alignment with the proposed NeuralODE-Transformer prediction model. This study represents the first quantification of cross-sensory regulatory effects of music on furniture visual aesthetics through multimodal neurophysiological signals, establishing a complete chain from neural mechanisms to design applications. It provides novel technical insights for multimodal aesthetic cognition modeling.
DOI: 10.6084/m9.figshare.29987941.v1
title: Modulating Aesthetic Furniture Preferences through Music: A Multimodal Neurophysiological Study Using Neural Ordinary Differential Equations Networks

description: Cross-modal perceptual integration plays a crucial role in aesthetic cognition, yet the neural regulatory mechanisms underlying music's influence on visual design preferences remain insufficiently elucidated. This study aims to explore how musical environments modulate aesthetic evaluation of traditional furniture through neural plasticity mechanisms and establish a preference prediction model based on multimodal neurophysiological signals. Four core aesthetic dimensions—"tactile density, lubricity, minimalist, and exquisite"—were extracted through sensory engineering methodology. We collected 32-channel electroencephalography (EEG), eye-tracking trajectories, and subjective aesthetic ratings from participants viewing 238 traditional square stool images under conditions with and without natural sound effects. A novel NeuralODE-Transformer multimodal fusion architecture was innovatively constructed: neural ordinary differential equations were employed for EEG temporal dynamics modeling, combined with Transformer attention mechanisms to achieve cross-modal feature fusion, with aesthetic preferences predicted through regularized regression.The findings revealed that musical intervention significantly modulated neural activity in specific frequency bands: prediction error for tactile density in the Alpha band decreased by 26.9%, while prediction accuracy for exquisiteness in the Beta band improved significantly. The NeuralODE-Transformer model demonstrated superior predictive performance across all four aesthetic dimensions compared to conventional methods. Design scheme validation through AHP-entropy weight method confirmed alignment with the proposed NeuralODE-Transformer prediction model. This study represents the first quantification of cross-sensory regulatory effects of music on furniture visual aesthetics through multimodal neurophysiological signals, establishing a complete chain from neural mechanisms to design applications. It provides novel technical insights for multimodal aesthetic cognition modeling.
DOI: 10.15468/dl.cxrnge
title: Occurrence Download

description: A dataset containing 1973 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Syringa villosa"
 ]
}

 The dataset includes 1973 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046586-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9ksvef
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Alburnus macedonicus)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 73 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046587-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16949929
title: SEM Withered Plant (BSE, 2025-08-25) — Kikoh Matsuura (Art–Science Archive)

description: SEM Withered Plant (BSE, 2025-08-25) is a child dataset in the SEM Withered Plant (BSE Series) by Kikoh Matsuura.  It contains raw, uncompressed BMP images (without scale bars) and supplementary JPG previews (with scale bars), acquired with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.  

This dataset constitutes the primary observation data of the art–science work *Withered Plant*.  The images capture the hidden microstructures of a dried plant fragment, prepared with Au sputter coating and observed at 17 kV accelerating voltage with a magnification of 120×.  

By preserving both raw scientific evidence and its artistic context, this record stands as a unique trace at the boundary between art and science.  It links to the parent series record via *IsPartOf* and contributes to the long-term archival of observation as both scientific evidence and cultural record.
DOI: 10.15468/dl.cmvvc3
title: Occurrence Download

description: A dataset containing 6320 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Harpia harpyja"
 ]
}

 The dataset includes 6320 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046588-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25403/upresearchdata.14401178
title: Loughran McDonald-SA-2020 Sentiment Word List

description: The Loughran and McDonald Sentiment Word Lists were developed using corporate 10-K reports between 1994 and 2008 [14]. These reports are relevant to companies in the United States of America and required by the U.S. Securities and Exchange Commission (SEC)14.The motivation for building the LM-SA-2020 word list was based on an experiment using the above-mentioned original lists to detect sentiment-carrying words in South African financial article headlines. A corpus of 808 financial articles (relating to Sasol) were used and only 37% of headlines had words of which the sentiment matched that of the words in the Loughran and McDonald Sentiment Word Lists correctly according to ground truth labels. A gap was therefore identified in developing a method for predicting sentiment of financial articles in a South African context. Due to the size of data set, it was possible to manually examine the head-lines to identify sentiment-carrying words to be included in the original wordlists. Furthermore, synonyms were added for the existing words in the Loughran and McDonald Sentiment Word Lists using NLTK’s WordNet16 interface. The sentiment detection/prediction accuracy improved by 29% using the new word list. This sentiment word list can be further expanded/improved in future by increasing the size of the data set and/or including data from other companies. It highlights the need for not only domain-specific sentiment prediction tools but also region-specific corporate.
DOI: 10.25403/upresearchdata.14401178.v2
title: Loughran McDonald-SA-2020 Sentiment Word List

description: The Loughran and McDonald Sentiment Word Lists were developed using corporate 10-K reports between 1994 and 2008 [14]. These reports are relevant to companies in the United States of America and required by the U.S. Securities and Exchange Commission (SEC)14.The motivation for building the LM-SA-2020 word list was based on an experiment using the above-mentioned original lists to detect sentiment-carrying words in South African financial article headlines. A corpus of 808 financial articles (relating to Sasol) were used and only 37% of headlines had words of which the sentiment matched that of the words in the Loughran and McDonald Sentiment Word Lists correctly according to ground truth labels. A gap was therefore identified in developing a method for predicting sentiment of financial articles in a South African context. Due to the size of data set, it was possible to manually examine the head-lines to identify sentiment-carrying words to be included in the original wordlists. Furthermore, synonyms were added for the existing words in the Loughran and McDonald Sentiment Word Lists using NLTK’s WordNet16 interface. The sentiment detection/prediction accuracy improved by 29% using the new word list. This sentiment word list can be further expanded/improved in future by increasing the size of the data set and/or including data from other companies. It highlights the need for not only domain-specific sentiment prediction tools but also region-specific corporate.
DOI: 10.25403/upresearchdata.14401178.v1
title: Loughran McDonald-SA-2020 Sentiment Word List

description: The Loughran and McDonald Sentiment Word Lists were developed using corporate 10-K reports between 1994 and 2008 [14]. These reports are relevant to companies in the United States of America and required by the U.S. Securities and Exchange Commission (SEC)14.The motivation for building the LM-SA-2020 word list was based on an experiment using the above-mentioned original lists to detect sentiment-carrying words in South African financial article headlines. A corpus of 808 financial articles (relating to Sasol) were used and only 37% of headlines had words of which the sentiment matched that of the words in the Loughran and McDonald Sentiment Word Lists correctly according to ground truth labels. A gap was therefore identified in developing a method for predicting sentiment of financial articles in a South African context. Due to the size of data set, it was possible to manually examine the head-lines to identify sentiment-carrying words to be included in the original wordlists. Furthermore, synonyms were added for the existing words in the Loughran and McDonald Sentiment Word Lists using NLTK’s WordNet16 interface. The sentiment detection/prediction accuracy improved by 29% using the new word list. This sentiment word list can be further expanded/improved in future by increasing the size of the data set and/or including data from other companies. It highlights the need for not only domain-specific sentiment prediction tools but also region-specific corporate.
DOI: 10.15468/dl.w6zemu
title: Occurrence Download

description: A dataset containing 251 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Data assessment of the annual berries yields in the Prioksko-Terrasnyi Biosphere Reserve recorded in Chronicles of Nature"
 ]
}

 The dataset includes 251 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046591-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uzwc2v
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Citrus trifoliata",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 61 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046592-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z45wcj
title: Occurrence Download

description: A dataset containing 812 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eleutherococcus giraldii"
 ]
}

 The dataset includes 812 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046590-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.efnseh
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046593-250811113504898/datasets/export for details.
DOI: 10.15468/dl.rpjfxb
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Citrus trifoliata",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 18 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046594-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16927739
title: Arctic charr (Salvelinus alpinus) genome assembly and annotation

description: Arctic charr (Salvelinus alpinus) genome assembly plus structural and functional annotation delivered as part of the pilot project of the European Reference Genome Atlas (ERGA) initiative (Mc Cartney et al. 2024). Data generation, genome assembly and gene annotation were carried out at the Earlham Institute (United Kingdom), with the exception of Hi‐C library preparation (University of Antwerp, Belgium), Hi‐C sequencing (University of Florence, Italy) and genome curation post‐assembly (Wellcome Sanger Institute, United Kingdom).

Citation:

Whole Genome Sequencing Reveals How Plasticity and Genetic Differentiation Underlie Sympatric Morphs of Arctic Charr. bioRxiv doi:10.1101/2025.02.10.637319

 
description: # Arctic charr (Salvelinus alpinus) genome assembly plus structural and functional annotation

doi:10.1101/2025.02.10.637319

## File structure

Data_Package_fSalAlp1_assembly_20231024.tar.gz

Package content:

├── fSalAlp1.1.hap1.cur.20231016.fasta ├── fSalAlp1.1.hap1.stats ├── fSalAlp1.1.hap2.cur.20231016.fasta ├── fSalAlp1.1.hap2.stats ├── fSalAlp1.1.MT.20231016.fasta └── README.txt

   Data_Package_fSalAlp1_annotation_20240709.tar.gz

Package content:

Data_Package_fSalAlp1_annotation_20240221/├── By_biotype_confidence│   ├── Saalp_EIv1.0.release.protein_coding_HC.gff3│   ├── Saalp_EIv1.0.release.protein_coding_HC.gff3.cdna.fasta│   ├── Saalp_EIv1.0.release.protein_coding_HC.gff3.cds.fasta│   ├── Saalp_EIv1.0.release.protein_coding_HC.gff3.pep.fasta│   ├── Saalp_EIv1.0.release.protein_coding_HC-LC.gff3│   ├── Saalp_EIv1.0.release.protein_coding_HC-LC.gff3.cdna.fasta│   ├── Saalp_EIv1.0.release.protein_coding_HC-LC.gff3.cds.fasta│   └── Saalp_EIv1.0.release.protein_coding_HC-LC.gff3.pep.fasta├── config│   ├── minos_run.mikado_config.yaml│   ├── minos_run.run_config.yaml│   └── minos_run.scoring.yaml├── fSalAlp1.1.MT.20231016.faa├── fSalAlp1.1.MT.20231016.gb├── fSalAlp1.1.MT.20231016.gff├── functional_annotation│   ├── Saalp_EIv1.0.release.functional_annotation.gff3│   └── Saalp_EIv1.0.release.gff3.pep.fasta.functional_annotation.tsv├── README.txt├── Saalp_EIv1.0.release.busco_final_table.tsv├── Saalp_EIv1.0.release.gff3├── Saalp_EIv1.0.release.gff3.biotype_conf.summary├── Saalp_EIv1.0.release.gff3.cdna.fasta├── Saalp_EIv1.0.release.gff3.cds.fasta├── Saalp_EIv1.0.release.gff3.final_table.tsv├── Saalp_EIv1.0.release.gff3.mikado_stats.tsv├── Saalp_EIv1.0.release.gff3.mikado_stats.txt├── Saalp_EIv1.0.release.gff3.mikado_stats.txt.summary└── Saalp_EIv1.0.release.gff3.pep.fasta

## See the README.txt within the data packages for additional details
DOI: 10.5281/zenodo.16927738
title: Arctic charr (Salvelinus alpinus) genome assembly and annotation

description: Arctic charr (Salvelinus alpinus) genome assembly plus structural and functional annotation delivered as part of the pilot project of the European Reference Genome Atlas (ERGA) initiative (Mc Cartney et al. 2024). Data generation, genome assembly and gene annotation were carried out at the Earlham Institute (United Kingdom), with the exception of Hi‐C library preparation (University of Antwerp, Belgium), Hi‐C sequencing (University of Florence, Italy) and genome curation post‐assembly (Wellcome Sanger Institute, United Kingdom).

Citation:

Whole Genome Sequencing Reveals How Plasticity and Genetic Differentiation Underlie Sympatric Morphs of Arctic Charr. bioRxiv doi:10.1101/2025.02.10.637319

 
description: # Arctic charr (Salvelinus alpinus) genome assembly plus structural and functional annotation

doi:10.1101/2025.02.10.637319

## File structure

Data_Package_fSalAlp1_assembly_20231024.tar.gz

Package content:

├── fSalAlp1.1.hap1.cur.20231016.fasta ├── fSalAlp1.1.hap1.stats ├── fSalAlp1.1.hap2.cur.20231016.fasta ├── fSalAlp1.1.hap2.stats ├── fSalAlp1.1.MT.20231016.fasta └── README.txt

   Data_Package_fSalAlp1_annotation_20240709.tar.gz

Package content:

Data_Package_fSalAlp1_annotation_20240221/├── By_biotype_confidence│   ├── Saalp_EIv1.0.release.protein_coding_HC.gff3│   ├── Saalp_EIv1.0.release.protein_coding_HC.gff3.cdna.fasta│   ├── Saalp_EIv1.0.release.protein_coding_HC.gff3.cds.fasta│   ├── Saalp_EIv1.0.release.protein_coding_HC.gff3.pep.fasta│   ├── Saalp_EIv1.0.release.protein_coding_HC-LC.gff3│   ├── Saalp_EIv1.0.release.protein_coding_HC-LC.gff3.cdna.fasta│   ├── Saalp_EIv1.0.release.protein_coding_HC-LC.gff3.cds.fasta│   └── Saalp_EIv1.0.release.protein_coding_HC-LC.gff3.pep.fasta├── config│   ├── minos_run.mikado_config.yaml│   ├── minos_run.run_config.yaml│   └── minos_run.scoring.yaml├── fSalAlp1.1.MT.20231016.faa├── fSalAlp1.1.MT.20231016.gb├── fSalAlp1.1.MT.20231016.gff├── functional_annotation│   ├── Saalp_EIv1.0.release.functional_annotation.gff3│   └── Saalp_EIv1.0.release.gff3.pep.fasta.functional_annotation.tsv├── README.txt├── Saalp_EIv1.0.release.busco_final_table.tsv├── Saalp_EIv1.0.release.gff3├── Saalp_EIv1.0.release.gff3.biotype_conf.summary├── Saalp_EIv1.0.release.gff3.cdna.fasta├── Saalp_EIv1.0.release.gff3.cds.fasta├── Saalp_EIv1.0.release.gff3.final_table.tsv├── Saalp_EIv1.0.release.gff3.mikado_stats.tsv├── Saalp_EIv1.0.release.gff3.mikado_stats.txt├── Saalp_EIv1.0.release.gff3.mikado_stats.txt.summary└── Saalp_EIv1.0.release.gff3.pep.fasta

## See the README.txt within the data packages for additional details
DOI: 10.15468/dl.xsxb6b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hippopotamyrus aelsbroecki)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046595-250811113504898/datasets/export for details.
DOI: 10.5281/zenodo.16949466
title: SEM Withered Plant (BSE Series) — Kikoh Matsuura (Art–Science Archive)

description: Parent (series) record for *SEM Withered Plant (BSE)* by Kikoh Matsuura.  This record serves as the hub for all child datasets in the series.

**Files included:**- A series overview (TXT) documenting scope, naming rules, citation style, and file policy- A machine-readable index (CSV) that will be updated to list child datasets (DOIs, imaging parameters, etc.)

Each child dataset contains raw, uncompressed BMP images (≈13 per set, number varies) acquired with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.  Child records provide detailed READMEs with exact parameters (accelerating voltage, working distance, vacuum, pixel size, field of view, frame time, coating).

This parent provides a stable concept DOI for the entire *SEM Withered Plant (BSE)* series.  Child datasets link back via *IsPartOf*, and this parent enumerates them via *HasPart*.  

*This series visualizes the hidden order and chance in withered plants through SEM-BSE, leaving a unique trace at the boundary between art and science.*
DOI: 10.5281/zenodo.16572776
title: Multi-century global and regional sea-level rise commitments from cumulative greenhouse gas emissions in the coming decades

description: This dataset forms the basis of the sea-level commitment analysis of Nauels et al. (2025).

To process the data and reproduce the figures of the study, please clone the corresponding GitLab repository (https://gitlab.com/anauels/slr_commitment) and follow the instructions in its README file. This dataset should then be stored in the data/ directory of the local copy of the repository, while maintaining the directory structure.

Under the MAGICC/ subdirectory, the scenario forcing input to MAGICC, as well as the MAGICC model output for the RCPs and SSP-RCP-based commitment scenarios, is provided.

Under the P20/ subdirectory, the P20 comparison global mean sea-level rise (GMSLR) output is provided, as well as the input for and output of the regionalization.
DOI: 10.5281/zenodo.16572777
title: Multi-century global and regional sea-level rise commitments from cumulative greenhouse gas emissions in the coming decades

description: This dataset forms the basis of the sea-level commitment analysis of Nauels et al. (2025).

To process the data and reproduce the figures of the study, please clone the corresponding GitLab repository (https://gitlab.com/anauels/slr_commitment) and follow the instructions in its README file. This dataset should then be stored in the data/ directory of the local copy of the repository, while maintaining the directory structure.

Under the MAGICC/ subdirectory, the scenario forcing input to MAGICC, as well as the MAGICC model output for the RCPs and SSP-RCP-based commitment scenarios, is provided.

Under the P20/ subdirectory, the P20 comparison global mean sea-level rise (GMSLR) output is provided, as well as the input for and output of the regionalization.
DOI: 10.25574/23816
title: Chandra X-ray Observatory ObsId 23816

description: X-ray jets in hard X-ray selected Giant Radio Galaxies
DOI: 10.25574/23827
title: Chandra X-ray Observatory ObsId 23827

description: Chandra Observations of New Quadruply Lensed Quasars
DOI: 10.15468/dl.rz9327
title: Occurrence Download

description: A dataset containing 14972 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Trigona spinipes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 14972 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046596-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.