cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6075/j0h9965b
title: Secret Valentine. In Art of Science

description: Caption: First (ever) glimpse of a genetically novel sea slug, with appendages resembling bouquets of roses.
Participant category: Graduate student
Department: Scripps Institution of Oceanography

The researcher discovered this gem while skimming through heaps of gray barnacles. Its radiant hues were unlike any they had seen. Lo and behold, it was genetically unique from other known species! This goes to show that nature's beauty shines in unexpected places. With curiosity and a keen eye, these treasures reveal themselves to you.
DOI: 10.17632/hwp4wsb549.1
title: Personality Traits and Academic Motivation as Predictors of Attitudes Toward Distance Education

description: This dataset contains the raw data from two independent samples collected for the study titled "Personality Traits and Academic Motivation as Predictors of Attitudes Toward Distance Education: A Multi-Sample Study."
Sample 1 — Motivation Cohort (n = 412): Undergraduate students enrolled at various Turkish universities during the 2019–2020 academic year (54.9% female; mean age = 20.60, SD = 1.50). Participants completed the Attitude Scale toward Distance Learning (ASTDL; Çelik & Uzunboylu, 2022) and the Academic Motivation Scale (AMS; Vallerand et al., 1992; Turkish adaptation: Ünal-Karagüven, 2012). The dataset includes scores for four ASTDL subscales (Perceived Usefulness, Social Presence, Preference for Distance Learning, Preference for Face-to-Face Learning) and seven AMS subscales (intrinsic motivation to know, to accomplish, to experience stimulation; external regulation, introjected regulation, identified regulation; amotivation).
Sample 2 — Personality Cohort (n = 653): Undergraduate students enrolled at various Turkish universities during the 2019–2020 academic year (60.8% female; mean age = 21.30, SD = 2.12). Participants completed the ASTDL and the Big Five Inventory (BFI; John & Srivastava, 1999). The dataset includes scores for four ASTDL subscales and five BFI personality dimensions (Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness to Experience).
Data were collected anonymously via Google Forms with informed consent. No personally identifiable information is included. All participants were volunteers. Ethical approval was granted by the Near East University Scientific Research Ethics Committee (Approval No: YDÜ/EB/2021/663, Date: 07 May 2021). The study was conducted in accordance with ethical research principles.
DOI: 10.25549/pcra-ouc11399ai9e
title: Embassy Suites, letter, 1994, to Bell; East Memphis Hilton contract, 1994

description: Embassy Suites, 1022 South Shady Grove Road, Memphis, Tennessee, letter, 1994 April 9, to H. Jenkins Bell (Church of God in Christ), P.O. Box 25169, Nashville, Tennessee. East Memphis Hilton contract with church of God in Christ Women's International Conference, 1994 March 25.
DOI: 10.6075/j0n29xw8
title: Mitochondrial and endoplasmic reticulum in-vivo. In Art of Science

description: Caption: A snapshot of the mitochondria and endoplasmic reticulum as they move across time.
Participant category: Postdoctoral researcher/fellow
Department: Neurobiology

The mitochondria and endoplasmic reticulum along with other organelles are typically illustrated as siloed organelles with defined morphology. However, the reality is that these organelles have a diverse morphology and are dynamic structures that can form intimate contacts to maintain a functional cell.
DOI: 10.17632/5343z8zfxg
title: Fragmentation of CVSS Scores in the NVD: A Quantitative Analysis of Inconsistency Across Vulnerability Scoring Standards

description: This dataset contains vulnerability records from the National Vulnerability Database (NVD), retrieved via the NVD API 2.0 in October 2025. It accompanies the paper "Fragmentation of CVSS Scores in the NVD: A Quantitative Analysis of Inconsistency Across Vulnerability Scoring Standards." 

The dataset comprises 24 yearly JSON files (nvdcve-2.0-{year}.json, years 2002–2025) plus two supplementary feeds (modified, recent), covering over 270,000 CVE entries. Each record includes the CVE identifier, textual descriptions, references, and, where available, CVSS metrics across multiple scoring standard versions: CVSS v2.0, v3.0, v3.1, and v4.0. The data preserves the original NVD API 2.0 JSON schema, with no modifications to the source records. 
 
In addition to the raw JSON files, the dataset includes pre-extracted tabular summaries in TSV format: 
- scores_cvss20.tsv, scores_cvss30.tsv, scores_cvss31.tsv, scores_cvss40.tsv - base scores per CVE for each CVSS version (~190k, ~54k, ~179k, and ~14k records, respectively);
- severity_all.tsv - aggregated severity-level counts (None/Low/Medium/High/Critical) across all four CVSS versions;
- severity_changes_detailed.tsv - per-CVE severity category comparison across CVSS versions, with change pattern annotations;
- severity_changes_summary.txt and severity_changes_by_pattern.txt - summary statistics of severity migration patterns between CVSS versions;
- cvss_30_31_vector_differences.txt - detailed comparison of CVSS v3.0 vs. v3.1 vector strings for CVEs scored under both versions (11,114 CVEs; 52.6% with differing vectors);
- year_counts.tsv - annual CVE publication counts (1988–2025).

The dataset enables reproducibility of all quantitative analyses presented in the paper, including cross-version score distribution comparisons, severity category migration analysis, and CVSS vector-level discrepancy assessment.
DOI: 10.17632/5343z8zfxg.1
title: Fragmentation of CVSS Scores in the NVD: A Quantitative Analysis of Inconsistency Across Vulnerability Scoring Standards

description: This dataset contains vulnerability records from the National Vulnerability Database (NVD), retrieved via the NVD API 2.0 in October 2025. It accompanies the paper "Fragmentation of CVSS Scores in the NVD: A Quantitative Analysis of Inconsistency Across Vulnerability Scoring Standards." 

The dataset comprises 24 yearly JSON files (nvdcve-2.0-{year}.json, years 2002–2025) plus two supplementary feeds (modified, recent), covering over 270,000 CVE entries. Each record includes the CVE identifier, textual descriptions, references, and, where available, CVSS metrics across multiple scoring standard versions: CVSS v2.0, v3.0, v3.1, and v4.0. The data preserves the original NVD API 2.0 JSON schema, with no modifications to the source records. 
 
In addition to the raw JSON files, the dataset includes pre-extracted tabular summaries in TSV format: 
- scores_cvss20.tsv, scores_cvss30.tsv, scores_cvss31.tsv, scores_cvss40.tsv - base scores per CVE for each CVSS version (~190k, ~54k, ~179k, and ~14k records, respectively);
- severity_all.tsv - aggregated severity-level counts (None/Low/Medium/High/Critical) across all four CVSS versions;
- severity_changes_detailed.tsv - per-CVE severity category comparison across CVSS versions, with change pattern annotations;
- severity_changes_summary.txt and severity_changes_by_pattern.txt - summary statistics of severity migration patterns between CVSS versions;
- cvss_30_31_vector_differences.txt - detailed comparison of CVSS v3.0 vs. v3.1 vector strings for CVEs scored under both versions (11,114 CVEs; 52.6% with differing vectors);
- year_counts.tsv - annual CVE publication counts (1988–2025).

The dataset enables reproducibility of all quantitative analyses presented in the paper, including cross-version score distribution comparisons, severity category migration analysis, and CVSS vector-level discrepancy assessment.
DOI: 10.6075/j0rv0pnp
title: A Living Meshwork. In Art of Science

description: Caption: Cellular membranes fluorescently labelled reveal a complex biological architecture.
Participant category: Faculty
Department: Cell and Developmental Biology

Biology is incredibly complex. Within every cell exists an intricate network of interconnected parts, working together to ensure survival of biological life. Pictured is a human cancer cell in which I've fluorescently labeled some of its interior compartments (organelles). The endoplasmic reticulum (ER) is colored orange, and the mitochondria are colored blue.
DOI: 10.17632/rd757jmm2n
title: NeuroAI Repositories: Biological and Artificial Neural Networks, Deep Learning and Brain Models

description: This dataset provides a curated and structured inventory of the main repositories, databases, simulation frameworks, toolkits, and research projects at the intersection of computational neuroscience and artificial intelligence (Neuro-AI). It covers resources that relate biological neural networks with artificial neural networks, deep learning architectures, brain models, and neural representations.

The dataset includes 18 entries compiled from Perplexity AI (February 2026), organized into the following categories: model repositories, evaluation benchmarks, neuromorphic datasets, brain atlases, data portals, simulation frameworks, analysis toolkits, and large-scale research projects.

Each record contains: Name, URL, Description, Main Topics, Type, Category, Year Active, and Open Access status.

Thematic areas covered: neural circuit modeling, brain-inspired AI, spiking neural networks, neuromorphic computing, connectomics, whole-brain simulation, behavioral neuroscience, representational similarity analysis, fMRI analysis, pose estimation, and cognitive modeling.

Key resources included: Open Source Brain, Brain-Score, UNDR, Allen Brain Atlas, EBRAINS, Blue Brain Project, Human Brain Project, NEST Simulator, Nengo, PyNN, NetPyNE, BrainPy, NeuroGym, NeuralPredictions, BrainIAK, DeepLabCut, The Virtual Brain, and Blue Brain Nexus.

Intended for researchers in computational neuroscience, AI, cognitive science, and related fields seeking to navigate the landscape of Neuro-AI resources.
DOI: 10.25549/pcra-ouc11399aiav
title: Ephriam, letter (or journal notes), [s.d.]

description: Mablean Ephriam (Fox TV Divorce Court judge), letter (or journal notes), [s.d.].
DOI: 10.6075/j0wm1fcd
title: Negative Spaces--2025 Judges' Award Winner, Postdoctoral Participant Category. In Art of Science

description: Caption: A heterogeneous structure called the extracellular matrix surrounds cells throughout the brain.

Participant category: Postdoctoral researcher
Department: Neurosciences

The extracellular matrix (ECM) in the mammalian brain plays an essential role in normal brain function, though its structure and composition remain poorly understood. This image is a creative, hand-painted representation of the negatively charged ECM that surrounds neurons. It is inspired by research that visualizes individual ECM molecules in the mouse brain and was created using gouache on watercolor paper.
DOI: 10.17632/rd757jmm2n.1
title: NeuroAI Repositories: Biological and Artificial Neural Networks, Deep Learning and Brain Models

description: This dataset provides a curated and structured inventory of the main repositories, databases, simulation frameworks, toolkits, and research projects at the intersection of computational neuroscience and artificial intelligence (Neuro-AI). It covers resources that relate biological neural networks with artificial neural networks, deep learning architectures, brain models, and neural representations.

The dataset includes 18 entries compiled from Perplexity AI (February 2026), organized into the following categories: model repositories, evaluation benchmarks, neuromorphic datasets, brain atlases, data portals, simulation frameworks, analysis toolkits, and large-scale research projects.

Each record contains: Name, URL, Description, Main Topics, Type, Category, Year Active, and Open Access status.

Thematic areas covered: neural circuit modeling, brain-inspired AI, spiking neural networks, neuromorphic computing, connectomics, whole-brain simulation, behavioral neuroscience, representational similarity analysis, fMRI analysis, pose estimation, and cognitive modeling.

Key resources included: Open Source Brain, Brain-Score, UNDR, Allen Brain Atlas, EBRAINS, Blue Brain Project, Human Brain Project, NEST Simulator, Nengo, PyNN, NetPyNE, BrainPy, NeuroGym, NeuralPredictions, BrainIAK, DeepLabCut, The Virtual Brain, and Blue Brain Nexus.

Intended for researchers in computational neuroscience, AI, cognitive science, and related fields seeking to navigate the landscape of Neuro-AI resources.
DOI: 10.17632/hpn6sd3j9s
title: Corruption and Corporate Cross-regional Supply Chain: Evidence from the Anti-corruption Campaign in China

description: README: Replication Package
Paper Title
Corruption and Corporate Cross-regional Supply Chain: Evidence from the Anti-corruption Campaign in China
Authors:
Prof. Xianhang Qian (Shandong University)
Kunzhu Zhao (Renmin University of China)
Date: February 26, 2026

1.Overview
This replication package provides all the publicly available data, code, and instructions required to reproduce the main empirical results of the paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.
DOI: 10.25549/pcra-ouc11399a693
title: "The Holy Spirit frees us from sin", notes for a sermon, [s.d.]

description: "The Holy Spirit frees us from sin", notes for a sermon or lecture, [s.d.].
DOI: 10.17632/65ns9j829j
title: Novel alternative splicing associated with feed efficiency in dairy cattle under distinct diet compositions

description: Transcriptomic data were generated to investigate alternative splicing and transcript-level expression variation associated with feed efficiency in Holstein and Jersey cattle fed control and high-concentrate diets. This study explored potential relationships between feed efficiency phenotypes, breed, diet composition, and transcript-level regulatory patterns. RNA-Seq data were processed using CLC Genomics Workbench to obtain transcript quantification and differential expression results. The dataset includes raw transcript read count matrices prior to normalization, differential expression analysis tables, and RNA-Seq alignment statistics.
DOI: 10.17632/hpn6sd3j9s.1
title: Corruption and Corporate Cross-regional Supply Chain: Evidence from the Anti-corruption Campaign in China

description: README: Replication Package
Paper Title
Corruption and Corporate Cross-regional Supply Chain: Evidence from the Anti-corruption Campaign in China
Authors:
Prof. Xianhang Qian (Shandong University)
Kunzhu Zhao (Renmin University of China)
Date: February 26, 2026

1.Overview
This replication package provides all the publicly available data, code, and instructions required to reproduce the main empirical results of the paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.
DOI: 10.25549/pcra-ouc11399amwy
title: The Ethnohistory of Saint's Home (COGIC) Los Angeles, Calif., 1972 [excerpt]

description: Rose Marie McDuff. The Ethnohistory of Saint's Home (COGIC) Los Angeles, Calif. California State University, Sacramento (Sacramento State College): Thesis (Masters of Arts in Anthropology), 1972. Pages 16-25 only.
DOI: 10.6075/j0pv6mcx
title: A Colorful Array of Nanoparticles. In Art of Science

description: Caption: Beyond the colors the eyes perceive, lie hidden structures hard to believe.
Participant category: Graduate student
Department: Chemical and Nano Engineering

Plasmonic nanomaterials absorb, reflect, and scatter light in unique ways depending on their size and shape, creating a dazzling variety of colors. This collection of transmission electron microscopy (TEM) images highlights the wide range of shapes and colors these particles can take, illuminating how their structure and assembly can alter their behavior with light at the nanoscale.
DOI: 10.17632/65ns9j829j.2
title: Novel alternative splicing associated with feed efficiency in dairy cattle under distinct diet compositions

description: Transcriptomic data were generated to investigate alternative splicing and transcript-level expression variation associated with feed efficiency in Holstein and Jersey cattle fed control and high-concentrate diets. This study explored potential relationships between feed efficiency phenotypes, breed, diet composition, and transcript-level regulatory patterns. RNA-Seq data were processed using CLC Genomics Workbench to obtain transcript quantification and differential expression results. The dataset includes raw transcript read count matrices prior to normalization, differential expression analysis tables, and RNA-Seq alignment statistics.
DOI: 10.17632/7gcttj8wz7
title: Shelf life determination of Hypophthalmicthys molitrix ( Silver Carp )

description: This project, titled “Shelf Life Determination of Silver Carp (Hypophthalmichthys molitrix) Using Image Classification,” is designed to develop an image classification system that distinguishes between healthy (good) and unhealthy (bad) Silver Carp fish. The dataset consists of 2000 images, evenly distributed between good and bad samples. All images were captured using a Realme 5i mobile camera, providing high-resolution visual data suitable for machine learning applications.
The fish were photographed against a black background using natural sunlight and LED lighting to ensure uniform illumination, minimize shadows, and improve image clarity. 
Dataset Composition
Good Samples (Healthy Fish)
The dataset contains approximately 1000 images of healthy Silver Carp fish. These images show fish with the following characteristics:
Bright and shiny scales
Clear, transparent eyes
Firm body texture
Natural coloration
No visible physical damage, deformity, or spoilage
These images represent the fresh and acceptable condition of fish and are used as positive samples for training the classification model.
Bad Samples (Unhealthy or Spoiled Fish)
The dataset also includes approximately 1000 images of unhealthy or spoiled Silver Carp fish. These fish may show:
Dull or faded scales
Cloudy or sunken eyes
Discoloration
Physical damage or deformities
Signs of spoilage and reduced freshness
These images represent the deteriorated condition of fish and serve as negative samples for training the model.
Data Collection Setup
Images were captured using the Realme 5i smartphone camera, which provides high-quality imaging suitable for detailed visual analysis.
Camera Specifications (Realme 5i)
Main Camera: 12 MP primary sensor with f/1.8 aperture, 5P lens, and Phase Detection Autofocus (PDAF)
Ultra-Wide Lens: 8 MP sensor with 119° field of view and f/2.25 aperture
Macro Lens: 2 MP sensor with 4 cm close-up shooting distance
Portrait Lens: 2 MP depth sensor for background blur
Zoom Capability: Up to 10× digital zoom
Battery: A large 5,000mAh battery .
Charging: Includes a standard 10W (5V/2A) charger.
These camera features allow clear capture of fish texture, color, and structural details essential for classification.
Lighting and Background Conditions
To ensure consistency and high image quality, the following setup was used:
Black background to improve contrast and isolate fish features
Natural sunlight to capture true color and natural appearance
360-degree LED lighting to provide uniform illumination and eliminate shadows
This setup improves feature extraction and enhances model performance.
Image Characteristics
The dataset includes variations in:
Fish size
Body shape
Color intensity
Freshness level
Physical condition
This diversity helps the machine learning model learn robust features and improves its ability to generalize to new data.
Data Annotation
Each image is carefully labeled into one of two categories:
Good (Fresh / Healthy)
Bad (Spoiled / Unhealthy)
DOI: 10.6075/j0tm7c3n
title: The Creation of a Cell--2025 Judges' Award Winner, Honorable Mention. In Art of Science

description: Caption: Inspired by Michelangelo's <i>The Creation of Adam</i>, one colorful cell reaches toward another.
Participant category: Graduate student
Department: Neurosciences 

This image shows two groups of cells with distinct genetics. In the upper right, a genetically modified cell symbolizes science and knowledge; in the lower left, a cluster of unaltered cells represents humanity. The modified cell reaches toward the others, evoking science’s ongoing effort to connect with and illuminate the human experience. Though a gap remains, this interaction inspires a personal, ongoing pursuit of understanding and discovery.
DOI: 10.25549/pcra-ouc11399aigm
title: Historical Seminar, State Supervisors and Missionaries, minutes, 1994

description: Historical Seminar, State Supervisors and Missionaries, Florida?, minutes, 1994 September 8.
DOI: 10.17632/7gcttj8wz7.1
title: Shelf life determination of Hypophthalmicthys molitrix ( Silver Carp )

description: This project, titled “Shelf Life Determination of Silver Carp (Hypophthalmichthys molitrix) Using Image Classification,” is designed to develop an image classification system that distinguishes between healthy (good) and unhealthy (bad) Silver Carp fish. The dataset consists of 2000 images, evenly distributed between good and bad samples. All images were captured using a Realme 5i mobile camera, providing high-resolution visual data suitable for machine learning applications.
The fish were photographed against a black background using natural sunlight and LED lighting to ensure uniform illumination, minimize shadows, and improve image clarity. 
Dataset Composition
Good Samples (Healthy Fish)
The dataset contains approximately 1000 images of healthy Silver Carp fish. These images show fish with the following characteristics:
Bright and shiny scales
Clear, transparent eyes
Firm body texture
Natural coloration
No visible physical damage, deformity, or spoilage
These images represent the fresh and acceptable condition of fish and are used as positive samples for training the classification model.
Bad Samples (Unhealthy or Spoiled Fish)
The dataset also includes approximately 1000 images of unhealthy or spoiled Silver Carp fish. These fish may show:
Dull or faded scales
Cloudy or sunken eyes
Discoloration
Physical damage or deformities
Signs of spoilage and reduced freshness
These images represent the deteriorated condition of fish and serve as negative samples for training the model.
Data Collection Setup
Images were captured using the Realme 5i smartphone camera, which provides high-quality imaging suitable for detailed visual analysis.
Camera Specifications (Realme 5i)
Main Camera: 12 MP primary sensor with f/1.8 aperture, 5P lens, and Phase Detection Autofocus (PDAF)
Ultra-Wide Lens: 8 MP sensor with 119° field of view and f/2.25 aperture
Macro Lens: 2 MP sensor with 4 cm close-up shooting distance
Portrait Lens: 2 MP depth sensor for background blur
Zoom Capability: Up to 10× digital zoom
Battery: A large 5,000mAh battery .
Charging: Includes a standard 10W (5V/2A) charger.
These camera features allow clear capture of fish texture, color, and structural details essential for classification.
Lighting and Background Conditions
To ensure consistency and high image quality, the following setup was used:
Black background to improve contrast and isolate fish features
Natural sunlight to capture true color and natural appearance
360-degree LED lighting to provide uniform illumination and eliminate shadows
This setup improves feature extraction and enhances model performance.
Image Characteristics
The dataset includes variations in:
Fish size
Body shape
Color intensity
Freshness level
Physical condition
This diversity helps the machine learning model learn robust features and improves its ability to generalize to new data.
Data Annotation
Each image is carefully labeled into one of two categories:
Good (Fresh / Healthy)
Bad (Spoiled / Unhealthy)