cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/fcfkab
title: AgriLAC’s Guatemala Baseline datasets: Farmer and village level surveys.

description: This dataset compiles socioeconomic, productive, and community-level information from basic grain producers in the eastern and western regions of Guatemala. It is used to characterize their initial conditions, identify productive and social patterns, and enable territorial comparisons, generating evidence to inform interventions aimed at strengthening the sustainability of basic grain systems. The data correspond to the baseline of an impact evaluation designed under a Difference-in-Differences (DiD) approach, which will allow the analysis of changes over time between treatment and control groups. This baseline, collected in 2023 with reference to the 2022 agricultural cycle, serves as the starting point for measuring the effects of the intervention in future follow-up rounds (2026–2027 and 2029–2030).&lt;br&gt;&lt;br&gt; Methodology:The data were collected using a multistage sampling design within an impact evaluation framework based on a Difference-in-Differences (DiD) approach. First, treatment communities were identified through consultations with AgriLAC partners to map areas of operational influence, while control communities were selected within the same municipalities but sufficiently distant to avoid spillover effects. A total of 65 communities (35 treatment and 30 control) were selected. In the second stage, households within each community were chosen through simple random sampling, selecting approximately 16 households in treatment areas and 15 in control areas. Baseline data were collected in 2023, referencing the 2022 agricultural cycle, and will serve as the starting point for longitudinal comparisons in later survey rounds.
DOI: 10.5281/zenodo.17975304
title: EEG Measurement on Physical Phantom
title: Thickness of individual layers of the Rat Skull

description: The dataset contains EEG measurements on a physical phantom for validation of full-wave simulations in the paper "Forward Model of Rat Electroencephalogram: Influence of Inhomogeneous and Anisotropic Skull" (not yet published). Furthermore, there is a measurement of the thicknesses of individual layers of the rat skull (inner compacta, spongiform, outer compacta), which were used in the paper to calculate the electric conductivity of the cuboid-shaped model Level 2.
DOI: 10.5281/zenodo.17975303
title: EEG Measurement on Physical Phantom
title: Thickness of individual layers of the Rat Skull

description: The dataset contains EEG measurements on a physical phantom for validation of full-wave simulations in the paper "Forward Model of Rat Electroencephalogram: Influence of Inhomogeneous and Anisotropic Skull" (not yet published). Furthermore, there is a measurement of the thicknesses of individual layers of the rat skull (inner compacta, spongiform, outer compacta), which were used in the paper to calculate the electric conductivity of the cuboid-shaped model Level 2.
DOI: 10.5683/sp3/vrjmle
title: Pan-Arctic hydrogeology - model input and output files

description: The files in this dataset relate to the data presented in "Classifying pan-Arctic supra-permafrost aquifer function under present and projected conditions". See the "Read Me" file for more details.
DOI: 10.5281/zenodo.18010962
title: Internal Conjugate States of Matter: Energetic Activation, Degradation, Time Emergence, and Nuclear Processes

description: This work presents a speculative theoretical framework proposing that all material systems possess internal conjugate states coexisting inseparably with observable matter. These states are not identified strictly with standard antimatter, but are interpreted as a broader internal conjugate structure whose energetic activation governs material stability, irreversible degradation, aging, radioactive behavior, and nuclear processes such as fission and fusion.

Within this framework, energy is treated not as a creative agent but as an activator that induces phase transitions in the conjugate state. Time is interpreted as an emergent macroscopic parameter associated with irreversible conjugate activation. The Big Bang and black hole phenomena are reinterpreted as large-scale conjugate phase transitions rather than singular creation events.

The proposal is formulated to remain compatible with established physical principles and does not introduce new particles or contradict existing experimental results. While no direct experimental validation is claimed, the framework suggests indirect, testable implications involving material aging, nuclear stability, and energetic history effects.

This manuscript is intended as a conceptual and theoretical contribution aimed at stimulating discussion and further investigation across fundamental physics, nuclear science, and cosmology.

 This document proposes a conceptual framework for nuclear processes based on internal conjugate-state activation. It does not replace established nuclear physics models, nor does it claim direct experimental validation. Conceptual applications are provided for discussion and further investigation.

Creative Commons Attribution 4.0 International

“El autor solicita que cualquier aplicación médica o comercial derive en acuerdos de colaboración o reconocimiento explícito.”
DOI: 10.5281/zenodo.18010963
title: Internal Conjugate States of Matter: Energetic Activation, Degradation, Time Emergence, and Nuclear Processes

description: This work presents a speculative theoretical framework proposing that all material systems possess internal conjugate states coexisting inseparably with observable matter. These states are not identified strictly with standard antimatter, but are interpreted as a broader internal conjugate structure whose energetic activation governs material stability, irreversible degradation, aging, radioactive behavior, and nuclear processes such as fission and fusion.

Within this framework, energy is treated not as a creative agent but as an activator that induces phase transitions in the conjugate state. Time is interpreted as an emergent macroscopic parameter associated with irreversible conjugate activation. The Big Bang and black hole phenomena are reinterpreted as large-scale conjugate phase transitions rather than singular creation events.

The proposal is formulated to remain compatible with established physical principles and does not introduce new particles or contradict existing experimental results. While no direct experimental validation is claimed, the framework suggests indirect, testable implications involving material aging, nuclear stability, and energetic history effects.

This manuscript is intended as a conceptual and theoretical contribution aimed at stimulating discussion and further investigation across fundamental physics, nuclear science, and cosmology.

 This document proposes a conceptual framework for nuclear processes based on internal conjugate-state activation. It does not replace established nuclear physics models, nor does it claim direct experimental validation. Conceptual applications are provided for discussion and further investigation.

Creative Commons Attribution 4.0 International

“El autor solicita que cualquier aplicación médica o comercial derive en acuerdos de colaboración o reconocimiento explícito.”
DOI: 10.15468/dl.t4z373
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Acarospora aspera, Acarospora simplex)"
 ]
}

 The dataset includes 10 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063372-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30707714.v4
title: Transcriptome assembly used in the paper: "The endomicrobiome and weed invasiveness in Mediterranean ecosystems"

description: Mediterranean ecosystems, one of the most significant global hotspots of biodiversity, are threatened by invasive weeds. Although endomicrobiomes – the vast array of microbes colonising living plant tissues – are known to affect plant fitness, their contribution to weed invasiveness remains virtually unknown. Here we experimentally assessed the role of the<b> </b>endomicrobiome in the microevolution of <i>Taraxacum</i> <i>officinale</i> (common dandelion), a widespread invasive weed in Mediterranean ecosystems worldwide. In a culling experiment across five generations, we compared the microevolutionary rates of <i>T. officinale</i> from Mediterranean ecosystems on five continents grown with intact or reduced native seed-borne endomicrobiomes. Additionally, we conducted a competition experiment with F<sub>1</sub> and F<sub>5</sub> individuals to assess their impacts on native local Asteraceae species. We found <i>T. officinale</i> individuals growing with their intact endomicrobiomes to exhibit faster and more positive trait evolution than individuals growing with reduced endomicrobiomes. Enhanced competitiveness of endomicrobiome-colonized <i>T. officinale </i>plants<i> </i>with local Asteraceae species was apparently caused by increased synthesis of allelochemicals in shoots and rhizosphere soil, with gene expression analyses also showing the endomicrobiome to affect the expression by <i>T. officinale</i> of stress response and RNA-directed DNA methylation genes. Our findings provide novel insights into the mechanisms underlying weed invasiveness in Mediterranean ecosystems, highlighting the endomicrobiome as a potent driving force in the evolutionary processes associated with plant invasion success.
DOI: 10.3886/icpsr39471.v1
title: Semiparametric Causal Inference Methods for Adaptive Statistical Learning in Trauma Patient-Centered Outcomes Research [Methods Study], 2013-2018
title: Version 1

description: Electronic health records store a lot of data about a patient. These data often include age, health problems, current medicines, and lab results. Looking at these data may help doctors treating patients after a trauma predict how likely it is that they will respond well to a treatment and survive. This information can help doctors make better treatment decisions. But first, researchers need to figure out how to combine and analyze data to make accurate predictions. In this study, the research team created new statistical methods to combine data from patient records. They used these methods to predict patient health outcomes. Then the team used health record data collected from patients in hospital trauma centers to test their predictions.
 To access the methods and software, please visit the following GitHubs:

 origami 
 varimpact 
 opttx
description: To develop methods, using machine-learning platforms, for incorporating clinical data on many clinical variables to accurately and efficiently predict the effects of interventions on trauma patients. The study aims were the following:


Develop diagnostic (prediction) scores via SuperLearner for each of the trauma studies. Estimate the relative prediction accuracy of competing algorithms via cross-validation tools, such as cross-validated area under the curve.


Develop and evaluate variable importance estimators, which can potentially quantify the differentiable impact of treatments within covariate groups.


Develop and evaluate algorithms and software for deriving optimal patient rules using machine learning methods.
description: This study developed and evaluated methods to improve automated assessments of prognosis and predictions of treatment effects for acute trauma care patients to support individualized recommendations for patient care.
Researchers used a machine-learning framework to apply multiple statistical methods for predicting the effects of interventions. The researchers used:


SuperLearner, an analytic approach that generates optimal predictions of clinical outcomes and treatment effects using a library of prediction algorithms
Causal inference models to identify optimal predictor variables
Targeted maximum likelihood estimation quantifying variable importance related to clinical outcomes and treatment effects

Researchers developed and tested machine-learning methods using both simulated data and data from four patient-outcome studies in trauma units from major hospitals across the United States.

The data sets included information on vital signs, lab results, and trauma severity. To test applications of the methods, researchers constructed both baseline and dynamic prediction algorithms for patient outcomes, including hemostasis, or blood clotting, and death.
description: Datasets: 
DS1: Semiparametric Causal Inference Methods for Adaptive Statistical Learning in Trauma Patient-Centered Outcomes Research [Methods Study], 2013-2018
DOI: 10.3886/icpsr39471
title: Semiparametric Causal Inference Methods for Adaptive Statistical Learning in Trauma Patient-Centered Outcomes Research [Methods Study], 2013-2018
title: Archival Version

description: Electronic health records store a lot of data about a patient. These data often include age, health problems, current medicines, and lab results. Looking at these data may help doctors treating patients after a trauma predict how likely it is that they will respond well to a treatment and survive. This information can help doctors make better treatment decisions. But first, researchers need to figure out how to combine and analyze data to make accurate predictions. In this study, the research team created new statistical methods to combine data from patient records. They used these methods to predict patient health outcomes. Then the team used health record data collected from patients in hospital trauma centers to test their predictions.
 To access the methods and software, please visit the following GitHubs:

 origami 
 varimpact 
 opttx
description: To develop methods, using machine-learning platforms, for incorporating clinical data on many clinical variables to accurately and efficiently predict the effects of interventions on trauma patients. The study aims were the following:


Develop diagnostic (prediction) scores via SuperLearner for each of the trauma studies. Estimate the relative prediction accuracy of competing algorithms via cross-validation tools, such as cross-validated area under the curve.


Develop and evaluate variable importance estimators, which can potentially quantify the differentiable impact of treatments within covariate groups.


Develop and evaluate algorithms and software for deriving optimal patient rules using machine learning methods.
description: This study developed and evaluated methods to improve automated assessments of prognosis and predictions of treatment effects for acute trauma care patients to support individualized recommendations for patient care.
Researchers used a machine-learning framework to apply multiple statistical methods for predicting the effects of interventions. The researchers used:


SuperLearner, an analytic approach that generates optimal predictions of clinical outcomes and treatment effects using a library of prediction algorithms
Causal inference models to identify optimal predictor variables
Targeted maximum likelihood estimation quantifying variable importance related to clinical outcomes and treatment effects

Researchers developed and tested machine-learning methods using both simulated data and data from four patient-outcome studies in trauma units from major hospitals across the United States.

The data sets included information on vital signs, lab results, and trauma severity. To test applications of the methods, researchers constructed both baseline and dynamic prediction algorithms for patient outcomes, including hemostasis, or blood clotting, and death.
description: Datasets: 
DS1: Semiparametric Causal Inference Methods for Adaptive Statistical Learning in Trauma Patient-Centered Outcomes Research [Methods Study], 2013-2018
DOI: 10.15468/dl.vrz2y7
title: Occurrence Download

description: A dataset containing 539 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Hongos formadores de micorrizas en la Amazonia colombiana"
 ]
}

 The dataset includes 539 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063374-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dsht7g
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Porites tubulata)"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063375-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2fksgs
title: Occurrence Download

description: A dataset containing 5586919 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mergus merganser",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))"
 ]
}

 The dataset includes 5586919 records from 850 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063361-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/icpsr39472.v1
title: Development of Practical Outcome Measures to Account for Individual Differences and Temporal Changes in Quality of Life Appraisal [Methods Study], New York, 2013-2019
title: Version 1

description: Many research studies seek to learn how treatments affect patients' quality of life. Quality of life includes mood and energy. It also includes how people view their roles in their families or communities and whether they can perform those roles. Researchers use surveys to ask about patients' quality of life. But patients may answer the same question differently depending on different characteristics, such as their age or where they live. Some patients may think about their work roles while others may think about their families or social lives. How patients think about quality of life can affect what researchers learn about the effects of treatment.
In this study, the research team tested two surveys they created to measure differences in how patients think about their quality of life. The first, long survey had 74 questions, and the second, short survey had 23 questions.
description: To develop practical measures of patients' cognitive appraisal of QOL, the study aims were to (1) leverage existing data to identify important aspects of appraisal; (2) derive
concise appraisal measures in conjunction with stakeholders who use QOL assessments for research, practice, or policy evaluation; and (3) test these measures in a sample of patients with bladder cancer.
description: Researchers tested the QOL Appraisal Profile Version 2 (QOLAPv2) and Brief Appraisal Inventory (BAI) in a prospective cohort study that included 110 patients treated for bladder cancer. Of these, 72% were white, 17% were black, and 16% were multiracial. The average age was 71, and 70% were male. Patients filled out the instruments at the beginning of the study and again three months later. They also completed other surveys that measured mood and life experiences.
To test construct validity, researchers examined relationships between the quality of life (QOL) assessment items and patients' demographics, using correlations and regressions.
Researchers conducted a redundancy analysis to test whether the BAI could serve in place of the longer QOLAPv2, using correlations, principal component analysis, and regression models to compare the two instruments.
To examine response shift, researchers used regression models to assess whether the instruments accounted for changes in QOL due to changes in mood and life experiences.
description: Datasets: 
DS1: Development of Practical Outcome Measures to Account for Individual Differences and Temporal Changes in Quality of Life Appraisal [Methods Study], New York, 2013-2019
description: Bladder cancer survivors recruited from 3 sites: Montefiore Medical Center in the Bronx, New York (Montefiore); Memorial Sloan Kettering Cancer Center on the Upper East Side of Manhattan (MSKCC); and White Plains Hospital in suburban Westchester County immediately north of the Bronx..
DOI: 10.3886/icpsr39472
title: Development of Practical Outcome Measures to Account for Individual Differences and Temporal Changes in Quality of Life Appraisal [Methods Study], New York, 2013-2019
title: Archival Version

description: Many research studies seek to learn how treatments affect patients' quality of life. Quality of life includes mood and energy. It also includes how people view their roles in their families or communities and whether they can perform those roles. Researchers use surveys to ask about patients' quality of life. But patients may answer the same question differently depending on different characteristics, such as their age or where they live. Some patients may think about their work roles while others may think about their families or social lives. How patients think about quality of life can affect what researchers learn about the effects of treatment.
In this study, the research team tested two surveys they created to measure differences in how patients think about their quality of life. The first, long survey had 74 questions, and the second, short survey had 23 questions.
description: To develop practical measures of patients' cognitive appraisal of QOL, the study aims were to (1) leverage existing data to identify important aspects of appraisal; (2) derive
concise appraisal measures in conjunction with stakeholders who use QOL assessments for research, practice, or policy evaluation; and (3) test these measures in a sample of patients with bladder cancer.
description: Researchers tested the QOL Appraisal Profile Version 2 (QOLAPv2) and Brief Appraisal Inventory (BAI) in a prospective cohort study that included 110 patients treated for bladder cancer. Of these, 72% were white, 17% were black, and 16% were multiracial. The average age was 71, and 70% were male. Patients filled out the instruments at the beginning of the study and again three months later. They also completed other surveys that measured mood and life experiences.
To test construct validity, researchers examined relationships between the quality of life (QOL) assessment items and patients' demographics, using correlations and regressions.
Researchers conducted a redundancy analysis to test whether the BAI could serve in place of the longer QOLAPv2, using correlations, principal component analysis, and regression models to compare the two instruments.
To examine response shift, researchers used regression models to assess whether the instruments accounted for changes in QOL due to changes in mood and life experiences.
description: Datasets: 
DS1: Development of Practical Outcome Measures to Account for Individual Differences and Temporal Changes in Quality of Life Appraisal [Methods Study], New York, 2013-2019
description: Bladder cancer survivors recruited from 3 sites: Montefiore Medical Center in the Bronx, New York (Montefiore); Memorial Sloan Kettering Cancer Center on the Upper East Side of Manhattan (MSKCC); and White Plains Hospital in suburban Westchester County immediately north of the Bronx..