cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3zw47sr7r4.1
title: Iraqi-E-learning-Emotion-Dataset

description: This dataset presents an Iraqi E-Learning Emotion Dataset that captures the emotional experiences of students and academic staff toward e-learning in Iraqi universities. The data were collected in 2025 using a voluntary, structured online questionnaire distributed across public and private universities in Iraq. Participants were asked to describe their personal experiences, feelings, and challenges related to e-learning through open-ended free-text responses written in the Iraqi Arabic dialect.
In addition to the textual responses, participants selected one emotion label that best represented their expressed experience from a predefined set of six categories: Joy, Trust, Anticipation, Anger, Anxiety, and Neutral. This design enables the dataset to support supervised emotion classification tasks while preserving the authenticity of self-reported emotional expression.
The dataset consists of 1,182 annotated text entries stored in a UTF-8 encoded CSV file. A standardized preprocessing pipeline was applied to ensure data quality and consistency, including the removal of duplicate entries, URLs, non-text elements, emojis, foreign characters, and normalization of common Iraqi Arabic spelling variations. All preprocessing steps were implemented in Python and are fully documented to support reproducibility.
This dataset addresses a notable gap in Arabic Natural Language Processing resources, as most existing Arabic emotion datasets are derived from social media and do not adequately represent educational contexts. By focusing on e-learning environments in higher education and on a low-resource Arabic dialect, the dataset provides context-specific emotional data that can be reused in research on Arabic emotion analysis, affective computing, educational data mining, learner engagement, and the development of emotion-aware intelligent tutoring systems. The dataset is openly available for research and educational purposes.
DOI: 10.17632/pn56b34xf6
title: Investigator Global Assessment for PG (IGAPg).

description: This is an instrument to assess static disease severity for pyoderma gangrenosum. Summary of the content validity has been uploaded.
DOI: 10.17632/pn56b34xf6.1
title: Investigator Global Assessment for PG (IGAPg).

description: This is an instrument to assess static disease severity for pyoderma gangrenosum. Summary of the content validity has been uploaded.
DOI: 10.17632/cvbywssnk5
title: Genomic and phenotypic data of the manuscript "Divergence-with-gene-flow leads to decoupled phenotypic evolution in a passerine (Cranioleuca pyrrhophia, Furnariidae) along a dry forest gradient"

description: Aim: Most speciation in birds and mammals is assumed to require an allopatric phase. Yet, many studies challenge this idea and provide evidence that natural selection is the primary driver of diversification, regardless of the geographic setup (i.e., divergence-with-gene-flow). In South America, the Chaco-Andes dry forests belt spans a broad environmental gradient and provides opportunity for parapatric diversification. Our objective was to assess divergence-with-gene-flow in population evolution within a typical dry forest bird. 
We studied genomic and phenotypic divergence between subspecies of Cranioleuca pyrrhophia (Aves, Furnariidae), which are distributed along the Chaco-Andes dry forest belt (Central Andes Mountains and Chaco and Pampas regions of Bolivia, Uruguay, and Argentina), and that are mostly differentiated by plumage color. 
Methods: We 1) sequenced ddRAD loci of 24 samples and collected plumage and body size data of 180 specimens; 2) tested the association between genomic and phenotypic traits and elevation, geographic isolation, and climatic variation; and 3) to distinguish divergence in allopatry from divergence-with-gene-flow, compared phenotypic divergence with neutral genomic variation (S-test, PST–FST comparisons), estimated gene flow, and calculated relative (FST) and absolute (DXY) genomic divergence. 
Results: In average, strongly differentiated loci also had the highest DXY values. Geographic isolation and elevation predicted phenotypic and genomic variation, with a minor unique impact of climate. Yet, these metrics better predicted plumage than body size traits. Certain phenotypic traits exceeded neutral variation (e.g., tail color), with variable divergence patterns among phenotypic traits—plumage traits being the most variable between subspecies. 
Main conclusions: Phenotypic variation in C. pyrrhophia likely reflects local adaptation, shaped more by environmental factors than solely by drift. Phenotypic traits varied differently, with plumage diverging more rapidly or under stronger selection. Habitat gradients like the Chaco-Andes dry forests drive parapatric diversification, where divergence combined with local extinction of geographically intermediate populations, may lead to isolation and discontinuous variation typical of full species.

Check dataset usage in file Readme.txt
DOI: 10.17632/cvbywssnk5.2
title: Genomic and phenotypic data of the manuscript "Divergence-with-gene-flow leads to decoupled phenotypic evolution in a passerine (Cranioleuca pyrrhophia, Furnariidae) along a dry forest gradient"

description: Aim: Most speciation in birds and mammals is assumed to require an allopatric phase. Yet, many studies challenge this idea and provide evidence that natural selection is the primary driver of diversification, regardless of the geographic setup (i.e., divergence-with-gene-flow). In South America, the Chaco-Andes dry forests belt spans a broad environmental gradient and provides opportunity for parapatric diversification. Our objective was to assess divergence-with-gene-flow in population evolution within a typical dry forest bird. 
We studied genomic and phenotypic divergence between subspecies of Cranioleuca pyrrhophia (Aves, Furnariidae), which are distributed along the Chaco-Andes dry forest belt (Central Andes Mountains and Chaco and Pampas regions of Bolivia, Uruguay, and Argentina), and that are mostly differentiated by plumage color. 
Methods: We 1) sequenced ddRAD loci of 24 samples and collected plumage and body size data of 180 specimens; 2) tested the association between genomic and phenotypic traits and elevation, geographic isolation, and climatic variation; and 3) to distinguish divergence in allopatry from divergence-with-gene-flow, compared phenotypic divergence with neutral genomic variation (S-test, PST–FST comparisons), estimated gene flow, and calculated relative (FST) and absolute (DXY) genomic divergence. 
Results: In average, strongly differentiated loci also had the highest DXY values. Geographic isolation and elevation predicted phenotypic and genomic variation, with a minor unique impact of climate. Yet, these metrics better predicted plumage than body size traits. Certain phenotypic traits exceeded neutral variation (e.g., tail color), with variable divergence patterns among phenotypic traits—plumage traits being the most variable between subspecies. 
Main conclusions: Phenotypic variation in C. pyrrhophia likely reflects local adaptation, shaped more by environmental factors than solely by drift. Phenotypic traits varied differently, with plumage diverging more rapidly or under stronger selection. Habitat gradients like the Chaco-Andes dry forests drive parapatric diversification, where divergence combined with local extinction of geographically intermediate populations, may lead to isolation and discontinuous variation typical of full species.

Check dataset usage in file Readme.txt
DOI: 10.17632/4xjmf8f6y6
title: Large-Scale Synthetic Dataset for Deep Learning-Based TWDM-PON Parameter Optimization (1 Million Samples)

description: This dataset contains 1,000,000 synthetic samples representing the physical layer performance of a Time-and-Wavelength Division Multiplexed Passive Optical Network (TWDM-PON). The data was generated to support Deep Learning (DL) and Machine Learning (ML) research in optical network optimization, specifically for extending transmission reach and minimizing Bit Error Rate (BER).

Methodology: The dataset was created using a physics-based simulation environment that models the non-linear transfer functions of Single Mode Fiber (SMF). It incorporates both linear impairments (attenuation, chromatic dispersion) and non-linear impairments (Self-Phase Modulation/Kerr effect) based on standard optical link budget equations.

Data Structure: The dataset is provided as a CSV file (twdm_pon_synthetic_dataset.csv) with 16 columns:
- Input Features (11 Parameters):
- tx_power: Transmitter Power (8–15 dBm)
- distance: Link Distance (0–80 km)
- wavelength: Channel Wavelength (1534 nm / 1596 nm)
- fbg_reflectivity: Fiber Bragg Grating Reflectivity (0.85–0.99)
- laser_linewidth: Laser Linewidth (0.1–2.0 MHz)
- modulation_index: Modulation Index (0.5–0.95)
- fiber_dispersion: Chromatic Dispersion (15–18 ps/nm·km)
- amplifier_gain: Inline Amplifier Gain (0–6 dB)
- noise_figure: Amplifier Noise Figure (3–6 dB)
- extinction_ratio: Extinction Ratio (10–15 dB)
- apd_gain: Avalanche Photodiode Gain (5–15)

Target Metrics (5 Outputs):
- received_power: Optical Power at Receiver (dBm)
- q_factor: Signal Quality Factor (dB)
- ber: Bit Error Rate (calculated from Q-Factor)
- receiver_sensitivity: Required Sensitivity (dBm)
- power_budget: Total Link Power Budget (dB)

Potential Applications: This dataset is suitable for training regression models (DNN, CNN, Random Forest) to predict Quality of Transmission (QoT) or for reinforcement learning agents optimizing network configurations.
DOI: 10.17632/4xjmf8f6y6.1
title: Large-Scale Synthetic Dataset for Deep Learning-Based TWDM-PON Parameter Optimization (1 Million Samples)

description: This dataset contains 1,000,000 synthetic samples representing the physical layer performance of a Time-and-Wavelength Division Multiplexed Passive Optical Network (TWDM-PON). The data was generated to support Deep Learning (DL) and Machine Learning (ML) research in optical network optimization, specifically for extending transmission reach and minimizing Bit Error Rate (BER).

Methodology: The dataset was created using a physics-based simulation environment that models the non-linear transfer functions of Single Mode Fiber (SMF). It incorporates both linear impairments (attenuation, chromatic dispersion) and non-linear impairments (Self-Phase Modulation/Kerr effect) based on standard optical link budget equations.

Data Structure: The dataset is provided as a CSV file (twdm_pon_synthetic_dataset.csv) with 16 columns:
- Input Features (11 Parameters):
- tx_power: Transmitter Power (8–15 dBm)
- distance: Link Distance (0–80 km)
- wavelength: Channel Wavelength (1534 nm / 1596 nm)
- fbg_reflectivity: Fiber Bragg Grating Reflectivity (0.85–0.99)
- laser_linewidth: Laser Linewidth (0.1–2.0 MHz)
- modulation_index: Modulation Index (0.5–0.95)
- fiber_dispersion: Chromatic Dispersion (15–18 ps/nm·km)
- amplifier_gain: Inline Amplifier Gain (0–6 dB)
- noise_figure: Amplifier Noise Figure (3–6 dB)
- extinction_ratio: Extinction Ratio (10–15 dB)
- apd_gain: Avalanche Photodiode Gain (5–15)

Target Metrics (5 Outputs):
- received_power: Optical Power at Receiver (dBm)
- q_factor: Signal Quality Factor (dB)
- ber: Bit Error Rate (calculated from Q-Factor)
- receiver_sensitivity: Required Sensitivity (dBm)
- power_budget: Total Link Power Budget (dB)

Potential Applications: This dataset is suitable for training regression models (DNN, CNN, Random Forest) to predict Quality of Transmission (QoT) or for reinforcement learning agents optimizing network configurations.
DOI: 10.17632/d8r95wmzms
title: UniEload: University Electrical Load Dataset

description: This repository presents a dataset on electrical power collected from a university campus in Bangladesh. It is meant to help research on energy forecasting in university settings. The dataset has hourly measurements of system voltage, three-phase currents (R, Y, B), and power factor (pf). It was also combined with weather data to aid research on load forecasting that takes weather into account. The weather parameters include temperature, humidity, precipitation, wind speed, and solar radiation. The dataset spans from 1 August 2023 to 30 September 2025, covering all 24-hour periods from 9:00 AM to 8:00 AM daily.
DOI: 10.17632/d8r95wmzms.3
title: UniEload: University Electrical Load Dataset

description: This repository presents a dataset on electrical power collected from a university campus in Bangladesh. It is meant to help research on energy forecasting in university settings. The dataset has hourly measurements of system voltage, three-phase currents (R, Y, B), and power factor (pf). It was also combined with weather data to aid research on load forecasting that takes weather into account. The weather parameters include temperature, humidity, precipitation, wind speed, and solar radiation. The dataset spans from 1 August 2023 to 30 September 2025, covering all 24-hour periods from 9:00 AM to 8:00 AM daily.
DOI: 10.6084/m9.figshare.30945296
title: Online resource

description: Molecular Dynamics Simulation Results of an Al2O3 Platelet-Reinforced YSZ Composite
DOI: 10.6084/m9.figshare.30945296.v1
title: Online resource

description: Molecular Dynamics Simulation Results of an Al2O3 Platelet-Reinforced YSZ Composite
DOI: 10.17632/ct83zdsng5
title: Fish and Spatial_Bioclim

description: This database contains fish abundance data from the Cusiana River basin, as well as the bioclimatic and spatial data of the localities where the fish were collected.
DOI: 10.17632/ct83zdsng5.1
title: Fish and Spatial_Bioclim

description: This database contains fish abundance data from the Cusiana River basin, as well as the bioclimatic and spatial data of the localities where the fish were collected.
DOI: 10.17632/7dn5s6bv65
title: Physics of Retention - Operational and Experimental Framework (Core Paper)

description: Core theoretical paper defining the operational and experimental framework of the Physics of Retention.
This document formalizes concepts, definitions, measurable variables, and validation principles used for experimental replication and long-term coherence analysis.
This file represents the reference document for citation and conceptual alignment.
DOI: 10.17632/7dn5s6bv65.1
title: Physics of Retention - Operational and Experimental Framework (Core Paper)

description: Core theoretical paper defining the operational and experimental framework of the Physics of Retention.
This document formalizes concepts, definitions, measurable variables, and validation principles used for experimental replication and long-term coherence analysis.
This file represents the reference document for citation and conceptual alignment.
DOI: 10.17632/kc8cmw9p2w
title: Vacuum Information Density as the Fundamental Geometric Scalar: UIDT v3.5: A Proposed Theoretical Framework for the Yang–Mills Mass Gap and Gamma-Scaling Unification

description: The Unified Information-Density Theory (UIDT) version 3.6 presents the definitive,
non-perturbative framework for the unification of Quantum Field Theory (QFT) and General Relativity
via Information Geometry. This Complete Manuscript advances the theory from a mathematical construct to a phenomenologically constrained model anchored by the Three-Pillar Architecture and the formal
Covariant Scalar-Field (CSF) Synthesis.

Canonical parameters are derived self-consistently via the Extended Functional Renormalization Group (FRG) and the Banach Fixed-Point Theorem. The solution yields the unique stable vacuum state at Δ = 1.710 ± 0.015 GeV, κ = 0.500 ± 0.008, and γ ≈ 16.339. These values demonstrate numerical closure (residuals &lt; 10⁻⁴⁰) and
consistency with Lattice QCD continuum limits.

Version 3.6 resolves the Vacuum Energy Catastrophe by integrating a 99-step Hierarchical RG Suppression mechanism.
This geometric cascade, normalized by the holographic topology (π⁻²) and Standard Model dimension (D=12), reduces the 10¹²⁰ discrepancy to a residual factor compatible with observation [1]. Furthermore, the framework incorporates the
Barrow-Rényi-Kaniadakis entropy to connect information geometry with the dark energy equation of state.

Cosmological calibration via 2025 DESI DR2 and JWST CCHP fixes the holographic scale at λ = 0.66 nm.
A redshift-dependent gamma evolution, γ(z), provides a natural mechanism for dynamical dark energy (w(z) ≠ -1),
consistent with DESI’s preference for phantom-crossing models [2].

Scientific assessment classifies results strictly: Category A (Mathematically Robust Proofs),
Category B (Lattice QCD Alignment), and Category C/D (Cosmological Calibration & Experimental Prediction).
UIDT v3.6 stands as a specific, falsifiable system requiring rigorous independent testing.

Proven Mass Gap: 1.710035046742213182020771096611622363294044242291085581231747999663464376395570369445002815542192033041630851992293577578337148116022890290326969033792718321530044021016130813146135502941908808474427620022069439336733684080990670841868721862693239644 GeV*
*(Established analytic precision limit: O(10⁻²⁸⁰) use Core Verification: uidt_proof_core.py for reproduce)
CHANGELOG:


✨ UIDT v3.6.1 Canonical Update Highlights (Clean State)
📉 Refined Gamma-Evolution & DESI DR2: Quadratic redshift dependence γ(z) has been recalibrated to align theoretical dark energy density with 2025 DESI DR2 data, fixing the unified Hubble constant
at H0 = 70.4 km/s/Mpc

🤝 CSF-UIDT Synthesis & VEV Rectification: Formal integration with Covariant Scalar-Field theory.
This update explicitly corrects the Vacuum Expectation Value (VEV) to v = 47.7 MeV 
(Appendix O) to ensure analytical closure across all 14 simulation kernels, satisfying ∂μ Faμν = 0
DOI: 10.17632/kc8cmw9p2w.3
title: Vacuum Information Density as the Fundamental Geometric Scalar: UIDT v3.5: A Proposed Theoretical Framework for the Yang–Mills Mass Gap and Gamma-Scaling Unification

description: The Unified Information-Density Theory (UIDT) version 3.6 presents the definitive,
non-perturbative framework for the unification of Quantum Field Theory (QFT) and General Relativity
via Information Geometry. This Complete Manuscript advances the theory from a mathematical construct to a phenomenologically constrained model anchored by the Three-Pillar Architecture and the formal
Covariant Scalar-Field (CSF) Synthesis.

Canonical parameters are derived self-consistently via the Extended Functional Renormalization Group (FRG) and the Banach Fixed-Point Theorem. The solution yields the unique stable vacuum state at Δ = 1.710 ± 0.015 GeV, κ = 0.500 ± 0.008, and γ ≈ 16.339. These values demonstrate numerical closure (residuals &lt; 10⁻⁴⁰) and
consistency with Lattice QCD continuum limits.

Version 3.6 resolves the Vacuum Energy Catastrophe by integrating a 99-step Hierarchical RG Suppression mechanism.
This geometric cascade, normalized by the holographic topology (π⁻²) and Standard Model dimension (D=12), reduces the 10¹²⁰ discrepancy to a residual factor compatible with observation [1]. Furthermore, the framework incorporates the
Barrow-Rényi-Kaniadakis entropy to connect information geometry with the dark energy equation of state.

Cosmological calibration via 2025 DESI DR2 and JWST CCHP fixes the holographic scale at λ = 0.66 nm.
A redshift-dependent gamma evolution, γ(z), provides a natural mechanism for dynamical dark energy (w(z) ≠ -1),
consistent with DESI’s preference for phantom-crossing models [2].

Scientific assessment classifies results strictly: Category A (Mathematically Robust Proofs),
Category B (Lattice QCD Alignment), and Category C/D (Cosmological Calibration & Experimental Prediction).
UIDT v3.6 stands as a specific, falsifiable system requiring rigorous independent testing.

Proven Mass Gap: 1.710035046742213182020771096611622363294044242291085581231747999663464376395570369445002815542192033041630851992293577578337148116022890290326969033792718321530044021016130813146135502941908808474427620022069439336733684080990670841868721862693239644 GeV*
*(Established analytic precision limit: O(10⁻²⁸⁰) use Core Verification: uidt_proof_core.py for reproduce)
CHANGELOG:


✨ UIDT v3.6.1 Canonical Update Highlights (Clean State)
📉 Refined Gamma-Evolution & DESI DR2: Quadratic redshift dependence γ(z) has been recalibrated to align theoretical dark energy density with 2025 DESI DR2 data, fixing the unified Hubble constant
at H0 = 70.4 km/s/Mpc

🤝 CSF-UIDT Synthesis & VEV Rectification: Formal integration with Covariant Scalar-Field theory.
This update explicitly corrects the Vacuum Expectation Value (VEV) to v = 47.7 MeV 
(Appendix O) to ensure analytical closure across all 14 simulation kernels, satisfying ∂μ Faμν = 0
DOI: 10.17632/bmd7w8k378
title: Trends in Dual-Specialty Applications Among Dermatology Applicants: A Cross-Sectional

description: Supplemental Methods and Data
DOI: 10.17632/bmd7w8k378.1
title: Trends in Dual-Specialty Applications Among Dermatology Applicants: A Cross-Sectional

description: Supplemental Methods and Data
DOI: 10.17632/cdg7fcspbp
title: Original data of the article-Ecosystem carbon fluxes represent a key mechanism underlying the precipitation-dependent pattern of ecosystem multifunctionality

description: Original data of the article-Ecosystem carbon fluxes represent a key mechanism underlying the precipitation-dependent pattern of ecosystem multifunctionality
DOI: 10.17632/cdg7fcspbp.1
title: Original data of the article-Ecosystem carbon fluxes represent a key mechanism underlying the precipitation-dependent pattern of ecosystem multifunctionality

description: Original data of the article-Ecosystem carbon fluxes represent a key mechanism underlying the precipitation-dependent pattern of ecosystem multifunctionality
DOI: 10.17632/3y6zcnznpm
title: Uttarakhand@25: A Comprehensive Analysis of Aspirations, Developmental Contradictions, Governance Challenges, and Future Pathways

description: Uttarakhand, formed in 2000, marks 25 years of statehood at a crossroads of aspiration and fragility. This paper synthesizes economic, social, environmental, and governance evidence to explain why the state has grown unevenly — high service and tourism-driven growth alongside persistent rural out-migration, environmental vulnerabilities, and governance gaps. Combining secondary data analysis, literature synthesis, and policy review, we: (1) map economic structure and fiscal dynamics; (2) analyse migration and demographic shifts; (3) review environmental and disaster risk challenges; (4) assess governance performance and institutional bottlenecks; and (5) propose integrated policy pathways for sustainable, inclusive development that honours Himalayan ecologies and local communities. Key findings highlight the need to re-balance growth toward resilient rural economies, mainstream eco-sensitive infrastructure, strengthen sub-state governance and fiscal autonomy, and co-produce policy with local stakeholders. The paper concludes with actionable recommendations and a research agenda for the next decade.