cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.sb4q2r
title: Occurrence Download

description: A dataset containing 49632416 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hemixos castanonotus, Hemixos flavala, Heterophasia capistrata, Hippolais olivetorum, Hypsipetes leucocephalus, Icterus galbula, Icterus graceannae, Icterus icterus, Icterus pectoralis, Iduna rama, Junco hyemalis, Lagonosticta rhodopareia, Lagonosticta senegala, Lamprotornis chalybaeus, Lamprotornis nitens, Lamprotornis purpureus, Lamprotornis regius, Lamprotornis splendidus, Lamprotornis superbus, Lanius meridionalis, Leiothrix argentauris, Leiothrix lutea, Leucopsar rothschildi, Linaria cannabina, Linaria flavirostris, Lonchura atricapilla, Lonchura castaneothorax, Lonchura cucullata, Lonchura ferruginosa, Lonchura fringilloides, Lonchura hunsteini, Lonchura maja, Lonchura malacca, Lonchura oryzivora, Lonchura punctulata, Lonchura striata, Manorina melanocephala, Megalurus punctatus, Melanocorypha mongolica, Melanospiza bicolor, Melospiza melodia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 49632416 records from 5385 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002673-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17611700
title: Quantifying Knowledge Production Efficiency with Thermodynamics: A Data Driven Study of Scientific Concepts
DOI: 10.5281/zenodo.17611701
title: Quantifying Knowledge Production Efficiency with Thermodynamics: A Data Driven Study of Scientific Concepts
DOI: 10.5281/zenodo.17611119
title: Energy Equilibrium Manifold for Qubit Coherence Stabilization in Gaussian-Coupled Field Models

description: This package presents a complete formulation of the Energy Equilibrium Manifold applied to qubit coherence stabilization in Gaussian-coupled scalar field models.The work introduces an analytical balance condition

ETt=E∇ϕ,

which defines a self-consistent manifold where the system remains dynamically stable. Deviations from this balance are quantified through the energetic ratio

Δlog⁡=log⁡10(ETtE∇ϕ),

yielding a robust geometric dephasing law of the form

T2(Δlog)/T2(0)=1/1+κ(Δlog/Δ0)^2.

This mechanism produces a sharp coherence peak on the manifold and a controlled suppression off-manifold, suggesting a new strategy for qubit protection based on energetic self-consistency rather than dynamical error correction.

The deposit includes:





Technical report: full derivation of the energy-equilibrium law and its application to qubit coherence.




Figures (SVG): block diagram of the physical model and coherence law.




Python code: scripts for reproducing the diagrams and numerical coherence curves.




Checksums file: integrity verification (SHA-256).



The model is directly testable in experimental platforms such as superconducting qubits, cavity QED, trapped ions, and bosonic modes, offering a falsifiable prediction for geometric dephasing suppression at equilibrium.
DOI: 10.48366/r1563767
title: Teaching Cognitive Biases and Psychology in Kerala's Schools to Improve Civic Sense and Mental Health

description: This paper proposes adding cognitive biases, basic brain anatomy, psychology, and behavioral economics to Kerala’s school curriculum to enhance students’ civic sense, decision-making, and mental health. Grounded in interdisciplinary research, the hypothesis posits that early education on biases can promote critical thinking, curb impulsive choices, and strengthen societal well-being. A 12-week pilot program for grades 6–8 is outlined, using interactive methods to teach key biases and psychological concepts. Testable predictions include higher decision-making scores and greater bias awareness. This approach could reshape education in Kerala, a state with high literacy but rising mental health challenges [1]. The paper explores implications for theory, practice, and future research.
DOI: 10.17632/5h9g98cttx
title: Code for paper titled "Impact of Global Geopolitical Risk on Equity Market Returns in South Asia: Evidence from a Structural VAR Analysis"

description: This is the code for the paper titled "Impact of Global Geopolitical Risk on Equity Market Returns in South Asia: Evidence from a Structural VAR Analysis"
DOI: 10.17632/5h9g98cttx.2
title: Code for paper titled "Impact of Global Geopolitical Risk on Equity Market Returns in South Asia: Evidence from a Structural VAR Analysis"

description: This is the code for the paper titled "Impact of Global Geopolitical Risk on Equity Market Returns in South Asia: Evidence from a Structural VAR Analysis"
DOI: 10.17632/whcrgr5p96
title: Dataset for Manuscript: Flower strips promote natural enemy density and species richness in sugar beet fields, but show mixed effects on polyphagous pests

description: Data for manuscript "Flower strips promote natural enemy density and species richness in sugar beet fields, but show mixed effects on polyphagous pests"
authors: Nika Jachowicz, Stine Kramer Jacobsen, Lene Sigsgaard
Corresponding author: Nika Jachowicz, nija@plen.ku.dk

Dataset consist of: 
visual_observations.csv - containing the findings of visual observations of arthropods, including date, location, treatment and distance
pitfalls.csv - containing the total numbers of carabids and spiders found in pitfall traps, including period, location, treatment and distance
pitfalls_diversity.csv - containing species classification of carabids found in pitfalls including period, location, treatment and distance
yield.csv - containing netto and brutto beet weight, total sugar, and sugar per ha and location, treatment and distance
botanical.csv - containing the realised botanical composition of each flower strip per location and treatment
DOI: 10.15468/dl.hgs2yf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Coregonus wartmanni",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033845-251025141854904/datasets/export for details.
DOI: 10.17632/whcrgr5p96.1
title: Dataset for Manuscript: Flower strips promote natural enemy density and species richness in sugar beet fields, but show mixed effects on polyphagous pests

description: Data for manuscript "Flower strips promote natural enemy density and species richness in sugar beet fields, but show mixed effects on polyphagous pests"
authors: Nika Jachowicz, Stine Kramer Jacobsen, Lene Sigsgaard
Corresponding author: Nika Jachowicz, nija@plen.ku.dk

Dataset consist of: 
visual_observations.csv - containing the findings of visual observations of arthropods, including date, location, treatment and distance
pitfalls.csv - containing the total numbers of carabids and spiders found in pitfall traps, including period, location, treatment and distance
pitfalls_diversity.csv - containing species classification of carabids found in pitfalls including period, location, treatment and distance
yield.csv - containing netto and brutto beet weight, total sugar, and sugar per ha and location, treatment and distance
botanical.csv - containing the realised botanical composition of each flower strip per location and treatment
DOI: 10.17632/xyrv7k83f6
title: Interviews - Exploring Demands and Resources for Mothers in Leadership Roles

description: Interview transcripts for the article "Exploring Demands and Resources for Mothers in Leadership Roles", published by BAR - Brazilian Administration Review
DOI: 10.17632/xyrv7k83f6.1
title: Interviews - Exploring Demands and Resources for Mothers in Leadership Roles

description: Interview transcripts for the article "Exploring Demands and Resources for Mothers in Leadership Roles", published by BAR - Brazilian Administration Review
DOI: 10.6084/m9.figshare.30621936
title: Integrating terahertz time-domain spectroscopy with XGBoost for rapid and interpretable species-level wood identification of <i>Pterocarpus</i>

description: Overexploitation of global forest resources has led to severe survival pressure on endangered <i>Pterocarpus</i> species, and the illegal trade of their wood poses a serious threat to biodiversity and market order. To achieve accurate identification of <i>Pterocarpus</i> wood, this study integrated terahertz time-domain spectroscopy (THz-TDS) with gradient boosting algorithms to develop classification models for seven <i>Pterocarpus</i> wood species. The results showed that the XGBoost model performed best, achieving 100% accuracy in binary classification (<i>P</i>. <i>santalinus</i> and <i>P</i>. <i>tinctorius</i>) and 98.63% in three-class classification (<i>P</i>. <i>indicus</i>, <i>P</i>. <i>macrocarpus</i>, and <i>P</i>. <i>soyauxii</i>). After screening the THz frequency bands and performing feature selection on THz refractive indices using the Uninformative Variable Elimination (UVE) method, the seven-class classification accuracy of the constructed UVE-XGBoost model was improved to 88.64%, confirming that the 0.1-0.3 THz band is the most important frequency range for <i>Pterocarpus</i> wood classification models. SHAP interpretability analysis further revealed that 0.106 and 0.107 THz are the key characteristic frequencies for identifying the seven <i>Pterocarpus</i> wood species. This study demonstrates that THz-TDS combined with the XGBoost algorithm can achieve rapid and accurate identification of <i>Pterocarpus</i> wood, providing effective technical support for endangered wood protection and market supervision.
DOI: 10.6084/m9.figshare.30621936.v1
title: Integrating terahertz time-domain spectroscopy with XGBoost for rapid and interpretable species-level wood identification of <i>Pterocarpus</i>

description: Overexploitation of global forest resources has led to severe survival pressure on endangered <i>Pterocarpus</i> species, and the illegal trade of their wood poses a serious threat to biodiversity and market order. To achieve accurate identification of <i>Pterocarpus</i> wood, this study integrated terahertz time-domain spectroscopy (THz-TDS) with gradient boosting algorithms to develop classification models for seven <i>Pterocarpus</i> wood species. The results showed that the XGBoost model performed best, achieving 100% accuracy in binary classification (<i>P</i>. <i>santalinus</i> and <i>P</i>. <i>tinctorius</i>) and 98.63% in three-class classification (<i>P</i>. <i>indicus</i>, <i>P</i>. <i>macrocarpus</i>, and <i>P</i>. <i>soyauxii</i>). After screening the THz frequency bands and performing feature selection on THz refractive indices using the Uninformative Variable Elimination (UVE) method, the seven-class classification accuracy of the constructed UVE-XGBoost model was improved to 88.64%, confirming that the 0.1-0.3 THz band is the most important frequency range for <i>Pterocarpus</i> wood classification models. SHAP interpretability analysis further revealed that 0.106 and 0.107 THz are the key characteristic frequencies for identifying the seven <i>Pterocarpus</i> wood species. This study demonstrates that THz-TDS combined with the XGBoost algorithm can achieve rapid and accurate identification of <i>Pterocarpus</i> wood, providing effective technical support for endangered wood protection and market supervision.
DOI: 10.17632/d8r95wmzms.1
title: UniEload: University Electrical Load Dataset

description: This paper 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.
DOI: 10.17632/mzsxv67czt
title: Data cleaning and simulation program code files for "Productivity-Adjusted Life Year Loss among the Late Middle-aged Adults in China"

description: Data cleaning and simulation program code files for "Productivity-Adjusted Life Year Loss among the Late Middle-aged Adults in China"
DOI: 10.17632/mzsxv67czt.1
title: Data cleaning and simulation program code files for "Productivity-Adjusted Life Year Loss among the Late Middle-aged Adults in China"

description: Data cleaning and simulation program code files for "Productivity-Adjusted Life Year Loss among the Late Middle-aged Adults in China"
DOI: 10.17632/hs7xfrnnrv
title: Thrombo JAK supplemental data

description: Supplemental data for the thrombo JAK study
DOI: 10.17632/hs7xfrnnrv.1
title: Thrombo JAK supplemental data

description: Supplemental data for the thrombo JAK study
DOI: 10.15468/dl.wsjgfs
title: Occurrence Download

description: A dataset containing 19466 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (USA.41_1, USA.34_1, USA.11_1, USA.10_1, USA.1_1, USA.43_1)",
 "TaxonKey is Terrapene carolina carolina"
 ]
}

 The dataset includes 19466 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033846-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/g6fndymznk
title: Loss of Hepatocyte PI3K𝛼 Reduces Hepatocellular Carcinoma in Association with Altered Lipid Metabolism Gene Expression

description: Immunoblots
DOI: 10.17632/g6fndymznk.1
title: Loss of Hepatocyte PI3K𝛼 Reduces Hepatocellular Carcinoma in Association with Altered Lipid Metabolism Gene Expression

description: Immunoblots
DOI: 10.5281/zenodo.17612135
title: Data and Rscript
DOI: 10.5281/zenodo.17612134
title: Data and Rscript
DOI: 10.17632/j9yyyjhrrr
title: Supplementary Dataset for High-Impact Practice Profiles and Academic Achievement

description: This dataset contains anonymized supplementary materials supporting the article “Privilege or pathway? Unequal academic returns from high-impact practices in higher education.” It includes robustness checks, methodological validation procedures, and statistical outputs associated with the latent profile analysis and the relationship between High-Impact Practice (HIP) participation and academic achievement among 1,689 final-year undergraduate students. The dataset provides regression diagnostics, bootstrap replications, sensitivity analyses, heteroscedasticity tests, influential case assessments, and cross-validation metrics. These materials ensure transparency, replicability, and methodological rigor, offering researchers additional evidence on the stability and reliability of the study's findings within a stratified Global South higher education context.
DOI: 10.17632/j9yyyjhrrr.1
title: Supplementary Dataset for High-Impact Practice Profiles and Academic Achievement

description: This dataset contains anonymized supplementary materials supporting the article “Privilege or pathway? Unequal academic returns from high-impact practices in higher education.” It includes robustness checks, methodological validation procedures, and statistical outputs associated with the latent profile analysis and the relationship between High-Impact Practice (HIP) participation and academic achievement among 1,689 final-year undergraduate students. The dataset provides regression diagnostics, bootstrap replications, sensitivity analyses, heteroscedasticity tests, influential case assessments, and cross-validation metrics. These materials ensure transparency, replicability, and methodological rigor, offering researchers additional evidence on the stability and reliability of the study's findings within a stratified Global South higher education context.
DOI: 10.17632/rftt3ft2pp
title: Proteomics data of NPTN-binding proteins

description: Proteomics data of NPTN-binding proteins
DOI: 10.17632/rftt3ft2pp.1
title: Proteomics data of NPTN-binding proteins

description: Proteomics data of NPTN-binding proteins
DOI: 10.6084/m9.figshare.30623585
title: Dvorski et al. dataset

description: Due to conventional measures like V̇O<sub>2</sub>max not fully capturing endurance performance, this study investigated if a 2.4 km Cooper test elicits measurable biomarker changes (decorin, hypoxanthine, BDNF, NT-proBNP) and if these markers improve performance prediction in a heterogeneous sample of runners. Thirty-three participants completed the 2.4 km Cooper test, with venous blood samples collected at baseline and post-test. Regression models were developed to predict 10 km race time and ranking, validated in a subsample of 24 participants who completed a 10 km race two weeks later. Post-test decorin (<i>ρ</i> = –0.44) and hypoxanthine (<i>ρ</i> = –0.37) negatively correlated with Cooper test time. For 10 km race time, a model using the Riegel-predicted time explained performance with <i>R</i>² = 0.927 and <i>MAE</i> = 1.76 min; an extended model including BMI, post-test decorin, and relative hypoxanthine change from baseline improved prediction (<i>R</i>² = 0.956, <i>MAE</i> = 1.34 min). For ranking, estimated V̇O<sub>2</sub>max alone yielded <i>MAE</i> = 1.92 ranks (<i>ρ</i> = 0.93), while including post-test decorin improved prediction to <i>MAE</i> = 1.46 ranks (<i>ρ</i> = 0.96). The Cooper test elicited measurable responses in decorin, hypoxanthine, and BDNF. Both decorin and hypoxanthine provided corrective value, improving competitive performance prediction alongside conventional measures.
DOI: 10.6084/m9.figshare.30623585.v1
title: Dvorski et al. dataset

description: Due to conventional measures like V̇O<sub>2</sub>max not fully capturing endurance performance, this study investigated if a 2.4 km Cooper test elicits measurable biomarker changes (decorin, hypoxanthine, BDNF, NT-proBNP) and if these markers improve performance prediction in a heterogeneous sample of runners. Thirty-three participants completed the 2.4 km Cooper test, with venous blood samples collected at baseline and post-test. Regression models were developed to predict 10 km race time and ranking, validated in a subsample of 24 participants who completed a 10 km race two weeks later. Post-test decorin (<i>ρ</i> = –0.44) and hypoxanthine (<i>ρ</i> = –0.37) negatively correlated with Cooper test time. For 10 km race time, a model using the Riegel-predicted time explained performance with <i>R</i>² = 0.927 and <i>MAE</i> = 1.76 min; an extended model including BMI, post-test decorin, and relative hypoxanthine change from baseline improved prediction (<i>R</i>² = 0.956, <i>MAE</i> = 1.34 min). For ranking, estimated V̇O<sub>2</sub>max alone yielded <i>MAE</i> = 1.92 ranks (<i>ρ</i> = 0.93), while including post-test decorin improved prediction to <i>MAE</i> = 1.46 ranks (<i>ρ</i> = 0.96). The Cooper test elicited measurable responses in decorin, hypoxanthine, and BDNF. Both decorin and hypoxanthine provided corrective value, improving competitive performance prediction alongside conventional measures.
DOI: 10.17632/nmpjzrky3r
title: SWIR hyperspectral data cubes for plastics detection in the environment

description: The dataset contains 9 hyperspectral cubes related to paper "Attention‑Gated U‑Net for Robust Cross‑Domain Plastic Waste Segmentation using UAV Based Hyperspectral SWIR Sensor" by S. Bouchelaghem, M. Balsi, M. Moroni, submitted in November 2025 to Remote Sensing Applications: Society and Environment. Corresponding author: soufyane.bouchelaghem@uniroma1.it
Data were acquired using a push-broom SWIR camer mounted on a drone, in controlled natural environments, where sorted plastics objects were placed on the ground.
The files contain hyperspectral cubes organized as 81 layers corresponding to wavelengths from 900 to 1700 nm, sampled every 10nm in the folder "drone_cube". Layers from 900 to 930 are dummy (filled with zeros) because they were not actually acquired. For each cube, manually-drawn masks are provided, for labelling according to plastics polymer or other material in the folder "masks". Additional folder "Training_dataset2" include the training used for the deep learning model.
DOI: 10.17632/nmpjzrky3r.1
title: SWIR hyperspectral data cubes for plastics detection in the environment

description: The dataset contains 9 hyperspectral cubes related to paper "Attention‑Gated U‑Net for Robust Cross‑Domain Plastic Waste Segmentation using UAV Based Hyperspectral SWIR Sensor" by S. Bouchelaghem, M. Balsi, M. Moroni, submitted in November 2025 to Remote Sensing Applications: Society and Environment. Corresponding author: soufyane.bouchelaghem@uniroma1.it
Data were acquired using a push-broom SWIR camer mounted on a drone, in controlled natural environments, where sorted plastics objects were placed on the ground.
The files contain hyperspectral cubes organized as 81 layers corresponding to wavelengths from 900 to 1700 nm, sampled every 10nm in the folder "drone_cube". Layers from 900 to 930 are dummy (filled with zeros) because they were not actually acquired. For each cube, manually-drawn masks are provided, for labelling according to plastics polymer or other material in the folder "masks". Additional folder "Training_dataset2" include the training used for the deep learning model.
DOI: 10.17632/4xrk8g6458
title: Machine Learning Insights for Fire Impacts on Snow Disappearance Predictability in Northern California

description: These data were used to support findings reported in "Machine Learning Insights for Fire Impacts on Snow Disappearance Predictability in Northern California" authored by Mohanty et al. 

Paper Abstract
Snowpacks are a critical water resource in the western United States (WUS), where effective water management depends on accurate monitoring and forecasting. Wildfires cause lasting perturbations to atmosphere–vegetation–snow interactions that are increasingly altering snow dynamics and affecting snowpack predictability. This study examines the influence of fires on snow disappearance predictability using a suite of machine learning (ML) experiments. Models trained solely on no/low-burn areas to predict the day of snow disappearance (DSD), using hydrometeorological predictors without explicit fire information, perform significantly worse when applied to moderate-to-high burn areas. Specifically, across no/low-burn areas, random forest models achieve a bias standard deviation of 12.0 days and R2 of 0.86, compared to 14.0–15.3 days and R2 of 0.72–0.79 in more heavily burned categories, with qualitatively consistent results across other machine learning models with variable complexity (multiple linear regression, long short-term memory (LSTM), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost)). The systematic overestimation of DSD across very high burn areas likely indicates that post-fire-enhanced snow ablation is not captured by the models. Including samples from burned areas in model training—implicitly incorporating fire effects—improves DSD predictions in burned landscapes without degrading performance in unburned regions. The largest accuracy improvements occur in pixels with &gt;75% burn fraction, where DSD overestimation is reduced by over 50% (mean bias from 4.0 to 1.6 days). Nevertheless, model performance in moderate-to-high burn areas remains lower than in no/low-burn regions (R2 lower by 0.05–0.06; bias standard deviation higher by 1.8–2.3 days). Adding explicit fire-related predictors, namely burn fraction and vegetation greenness, does not yield further accuracy gains. These results demonstrate that wildfires significantly impact snowpack dynamics and that including post-fire landscapes in training data is essential to improving ML-based snow predictions in burned areas. Fully capturing fire impacts, however, may require more detailed or process-specific fire information to account for complex post-fire snow dynamics.