cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wc3eez
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Tejidos de árboles del bosque seco tropical de Colombia que cuentan con muestra de semillas, iNaturalist Research-grade Observations, Herbario de la Universidad Industrial de Santander, Herbario Nacional Colombiano (COL), Naturalis Biodiversity Center (NL) - Botany, ICN - Universidad Nacional de Colombia, Herbario CDMB - Jardín Botánico Eloy Valenzuela, Semillas de árboles del bosque seco tropical de Colombia, Colección Herbario Federico Medem Bogotá - FMB)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Zamia encephalartoides"
 ]
}

 The dataset includes 35 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043253-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16943719
title: Integrated Dataset Linking Macroalgal Traits with Protein Domain Annotations

description: This dataset integrates macroalgal trait information with protein domain annotations, providing a large-scale resource for connecting ecological and functional traits with genomic signatures. Trait data were collected from WoRMS and AlgaeTraits, while protein domain annotations were derived from genomic and transcriptomic datasets using HMMscan and MMseqs2. Species names were used as key identifiers to merge the datasets, resulting in an integrated table of approximately 1.7 million rows and 42 columns. Columns include Pfam domain identifiers, query names, E-values, domain descriptions, and 21 standardized macroalgal traits. This resource enables trait–domain association studies and supports machine learning and deep learning analyses to uncover biologically meaningful links between macroalgal traits and genomic features.
DOI: 10.17632/bpns8vzwrj.1
title: Behavioral Data-Driven Prediction of Suicide Risk Using Machine Learning Approaches

description: The Suicide Risk Prediction Dataset comprises 1,128 structured records collected from multiple medical institutions and open sources in Bangladesh. This dataset has been curated to support machine learning and statistical modeling for suicide risk assessment and prediction. Each record captures critical demographic, behavioral, and psychological features, enabling effective feature analysis and risk factor identification for suicidal behavior.

Data Collection Details

Collected From:

1.Dhaka Medical College & Hospital – 38 records
2.Bangladesh Medical University (BMU) – 19 records
3.Enam Medical College & Hospital – 27 records
4.Shaheed Tajuddin Ahmed Medical College – 36 records
5.Online Survey (Google Form) – 358 records
6.Open-source data (GitHub) – 650 records


Collection Period: February 2025 – July 2025

Collection Methods:

Hospital records supervised by mental health professionals

Online survey conducted through Google Forms

External open-source datasets carefully selected from GitHub

Dataset Statistics

Total Records: 1,128

Features per Record: 16 structured features + 1 dependent variable

Features Include:

1.Age

2.Gender

3.Religion

4.Occupation

5.Civil Status

6.Level of Education

7.Psychological Session

8.Past Attempt

9.Disorder

10.Illness

11.Alcohol

12.Anger

13.Sleep Problem

14.Isolation

15.Humiliation

16.Sad/Weary

Dependent Variable:

Attempted? (Binary indicator: history of suicide attempt or not)

Ethical Considerations

All data were collected under ethical approval and professional supervision. Mental health experts from BMU, Dhaka Medical College, and other institutions were directly involved to ensure accuracy, patient confidentiality, and clinical relevance.

Key Applications

Risk Factor Analysis: Identifying major behavioral and psychological triggers behind suicidal tendencies.

Predictive Modeling: Training machine learning algorithms for suicide risk prediction.

Healthcare Decision Support: Assisting clinicians in early detection of high-risk patients.

Public Health Policy: Supporting targeted interventions and preventive strategies.

Academic Research: Providing a benchmark dataset for behavioral health and suicide prevention studies.
DOI: 10.5281/zenodo.16943718
title: Integrated Dataset Linking Macroalgal Traits with Protein Domain Annotations

description: This dataset integrates macroalgal trait information with protein domain annotations, providing a large-scale resource for connecting ecological and functional traits with genomic signatures. Trait data were collected from WoRMS and AlgaeTraits, while protein domain annotations were derived from genomic and transcriptomic datasets using HMMscan and MMseqs2. Species names were used as key identifiers to merge the datasets, resulting in an integrated table of approximately 1.7 million rows and 42 columns. Columns include Pfam domain identifiers, query names, E-values, domain descriptions, and 21 standardized macroalgal traits. This resource enables trait–domain association studies and supports machine learning and deep learning analyses to uncover biologically meaningful links between macroalgal traits and genomic features.
DOI: 10.17632/vjv8gg3tkx
title: Data Set for Academic Writing Skills

description: This study evaluated changes in academic writing skills and overall academic performance before and after the training. Analysis using correlation techniques and descriptive statistics demonstrated notable improvements in writing abilities, particularly in enhancing students’ confidence in composing scientific papers, essays, dissertations, and citations. Additionally, the training positively impacted time management skills, indicating that targeted academic writing instruction can significantly enhance the scientific writing capabilities of postgraduate students.
DOI: 10.17632/vjv8gg3tkx.1
title: Data Set for Academic Writing Skills

description: This study evaluated changes in academic writing skills and overall academic performance before and after the training. Analysis using correlation techniques and descriptive statistics demonstrated notable improvements in writing abilities, particularly in enhancing students’ confidence in composing scientific papers, essays, dissertations, and citations. Additionally, the training positively impacted time management skills, indicating that targeted academic writing instruction can significantly enhance the scientific writing capabilities of postgraduate students.
DOI: 10.17632/tdtbdsty9h
title: Moss Biocrusts Enhance Hydrological Functionality on Engineered Slopes

description: This dataset comprises comprehensive experimental data from a study investigating the hydrological functions of moss-dominated biological soil crusts (BSCs) on engineered slopes in a humid subtropical ecosystem in Hubei Province, China. It includes in-situ and laboratory measurements comparing moss crust-covered soil with adjacent bare soil across three slope positions: bench top (BF), bench surface (BS), and bench toe (BT). Key data encompass soil physicochemical properties (texture, bulk density, porosity, soil organic matter, macroaggregate content), hydrological parameters (soil water retention curves fitted with van Genuchten model parameters, field capacity, wilting point, plant-available water), and permeability metrics (saturated and unsaturated hydraulic conductivity, sorptivity, water repellency index). Crucially, the dataset features high-resolution X-ray μCT 3D pore network analyses, providing quantitative metrics on macroporosity, pore connectivity, pore-throat characteristics, and coordination numbers. Additional data include moss crust thickness and water contact angles. This rich, multi-faceted dataset is valuable for understanding how BSCs modify soil structure and water dynamics, supporting research in ecohydrology, soil science, slope stability, and ecological restoration.

Keywords: Biological soil crusts, Moss, Engineered slopes, Soil hydrology, Water retention, Hydraulic conductivity, Pore structure, X-ray micro-CT, Soil water characteristic curve, van Genuchten model, Subtropical ecosystem.
DOI: 10.17632/tdtbdsty9h.1
title: Moss Biocrusts Enhance Hydrological Functionality on Engineered Slopes

description: This dataset comprises comprehensive experimental data from a study investigating the hydrological functions of moss-dominated biological soil crusts (BSCs) on engineered slopes in a humid subtropical ecosystem in Hubei Province, China. It includes in-situ and laboratory measurements comparing moss crust-covered soil with adjacent bare soil across three slope positions: bench top (BF), bench surface (BS), and bench toe (BT). Key data encompass soil physicochemical properties (texture, bulk density, porosity, soil organic matter, macroaggregate content), hydrological parameters (soil water retention curves fitted with van Genuchten model parameters, field capacity, wilting point, plant-available water), and permeability metrics (saturated and unsaturated hydraulic conductivity, sorptivity, water repellency index). Crucially, the dataset features high-resolution X-ray μCT 3D pore network analyses, providing quantitative metrics on macroporosity, pore connectivity, pore-throat characteristics, and coordination numbers. Additional data include moss crust thickness and water contact angles. This rich, multi-faceted dataset is valuable for understanding how BSCs modify soil structure and water dynamics, supporting research in ecohydrology, soil science, slope stability, and ecological restoration.

Keywords: Biological soil crusts, Moss, Engineered slopes, Soil hydrology, Water retention, Hydraulic conductivity, Pore structure, X-ray micro-CT, Soil water characteristic curve, van Genuchten model, Subtropical ecosystem.
DOI: 10.17632/dyvfhycpg5
title: Supplementary Data to: Green signals? Assessing price dynamics in the European renewable energy certificate market*

description: Supplementary raw data on EUA prices, electricity prices and electricity consumption for EU countries.
DOI: 10.17632/dyvfhycpg5.2
title: Supplementary Data to: Green signals? Assessing price dynamics in the European renewable energy certificate market*

description: Supplementary raw data on EUA prices, electricity prices and electricity consumption for EU countries.
DOI: 10.17632/3bhvnsc8bg
title: A ketogenic diet mitigates hippocampal astrogliosis in epileptic brain

description: The ketogenic diet (KD) is an established treatment for patients with medically intractable epilepsy and is chiefly characterized by high fat/low-carbohydrate intake and the production of ketone bodies (KB) such as β-hydroxybutyrate (BHB). However, after more than a century of clinical use, the mechanisms underlying its efficacy remain unclear. While prior investigations have examined the effects of the KD and its metabolic substrates on synaptic transmission, few studies have explored a potential connection between astrocytic ion channels and seizure genesis. One essential function of astrocytes is spatial potassium buffering which influences passive potassium conductance (PPC), and when impaired, can result in neuronal hyperexcitability. In the present study, we demonstrate that the KD can mitigate hippocampal astrogliosis in the Kcna1-null (KO) mouse model of developmental epilepsy. Specifically, we observed a significant increase in GFAP expression in KO mice fed a control diet compared to wild-type (WT) mice, and that the KD prevented this change. Furthermore, we noted a reduction in hippocampal astrocytic PPC in epileptic mice, whereas KD-treated KO animals exhibited nearly normal passive conductance levels. In this regard, we found that while Kir4.1, TREK-1 and TWIK-1 RNA expression levels were not significantly altered by KD treatment in either WT or KO mice, BHB appeared to only minimally affect Kir4.1-mediated currents in transfected HEK cells. Furthermore, bulk RNA-seq analysis of the various treatment groups revealed KD-induced down-regulation of factors linked to hippocampal astrogliosis. Our findings indicate that the KD protects against epilepsy-associated astrogliosis and astrocytic PPC changes, underscoring a novel mechanism of action, and implicate extracellular potassium in its anti-seizure effects.
DOI: 10.17632/3bhvnsc8bg.3
title: A ketogenic diet mitigates hippocampal astrogliosis in epileptic brain

description: The ketogenic diet (KD) is an established treatment for patients with medically intractable epilepsy and is chiefly characterized by high fat/low-carbohydrate intake and the production of ketone bodies (KB) such as β-hydroxybutyrate (BHB). However, after more than a century of clinical use, the mechanisms underlying its efficacy remain unclear. While prior investigations have examined the effects of the KD and its metabolic substrates on synaptic transmission, few studies have explored a potential connection between astrocytic ion channels and seizure genesis. One essential function of astrocytes is spatial potassium buffering which influences passive potassium conductance (PPC), and when impaired, can result in neuronal hyperexcitability. In the present study, we demonstrate that the KD can mitigate hippocampal astrogliosis in the Kcna1-null (KO) mouse model of developmental epilepsy. Specifically, we observed a significant increase in GFAP expression in KO mice fed a control diet compared to wild-type (WT) mice, and that the KD prevented this change. Furthermore, we noted a reduction in hippocampal astrocytic PPC in epileptic mice, whereas KD-treated KO animals exhibited nearly normal passive conductance levels. In this regard, we found that while Kir4.1, TREK-1 and TWIK-1 RNA expression levels were not significantly altered by KD treatment in either WT or KO mice, BHB appeared to only minimally affect Kir4.1-mediated currents in transfected HEK cells. Furthermore, bulk RNA-seq analysis of the various treatment groups revealed KD-induced down-regulation of factors linked to hippocampal astrogliosis. Our findings indicate that the KD protects against epilepsy-associated astrogliosis and astrocytic PPC changes, underscoring a novel mechanism of action, and implicate extracellular potassium in its anti-seizure effects.
DOI: 10.17632/yff7r65jzz.2
title: Iron sources and cycling during diagenetic transformations of Negev phosphorites (Israel)

description: The dataset comprises geochemical and mineralogical data generated in the course of this study. Table 1 summarizes iron (Fe) speciation and the concentrations of Fe, phosphorus (P), aluminum (Al), and total organic carbon (TOC) in the analyzed samples. Table 2 provides estimates of the impact of pyrite and Fe(II) oxidation on iron speciation. Table 3 reports mineral phase abundances (in wt%, normalized to 100%) of ten pristine phosphorite samples based on XRD analysis.
DOI: 10.6084/m9.figshare.23972205
title: <b>Temporal Cytokine Profiling of Acute Dengue, Zika, Chikungunya, and Mayaro Virus Infections in Northern Peru</b>

description: <b>Background</b>: Arboviruses, including Dengue virus (DENV), Chikungunya virus (CHIKV), Zika virus (ZIKV), and Mayaro virus (MAYV) are important causes of acute febrile illness (AFI) in high-risk regions in Peru. Differential diagnosis based on clinical manifestations is challenging due to overlapping symptoms. We conducted a pioneering study to compare cytokine profiles among these arboviruses, aiming to elucidate distinct immunological features for diagnostic and therapeutic insights.<b>Methods:</b> We conducted a cross-sectional study in patients with acute febrile illness from Cajamarca, Peru. Blood samples were collected for the diagnosis of arboviruses and the quantification of cytokines. Diagnosis of Dengue, Zika, Chikungunya, and Mayaro virus infections relied on specific IgM antibody detection via enzyme-linked immunosorbent assays (ELISA). Cytokine quantification was carried out by ELISA assays for IL-2, IL-6, IL-10, TNF-α, and IFN-γ. Comparative analysis of cytokine levels was performed between all viruses and controls.<b>Results</b>: A total of 20 patients were recruited for each arbovirus and control group. Our study revealed similarities in clinical symptoms across arboviral infections, with headaches, myalgias, and arthralgias as the predominant symptoms. IL-2 levels were similar across the arboviruses; however, DENV and ZIKV patients had higher levels compared to controls. Elevated IL-6 levels were associated with CHIKV and ZIKV, while IL-10 levels were highest in DENV as compared to other arboviruses. TNF-α and IFN-γ were significantly elevated in DENV and ZIKV, and they were significantly higher in DENV compared to MAYV.<b>Conclusion</b>: Our findings provide valuable insights into the unique cytokine profiles of arboviral infections. We found similar levels of IL-2 across the arboviral groups, IL-6 was predominantly elevated in CHIKV and ZIKV, while IL-10 showed the highest levels in DENV. TNF-α and IFN-γ showed a similar pattern, with DENV and ZIKV patients expressing the highest levels. Understanding the distinct immunological signatures of these arboviruses is crucial for effective diagnostic and therapeutic approaches in high-risk regions.
DOI: 10.6084/m9.figshare.23972205.v2
title: <b>Temporal Cytokine Profiling of Acute Dengue, Zika, Chikungunya, and Mayaro Virus Infections in Northern Peru</b>

description: <b>Background</b>: Arboviruses, including Dengue virus (DENV), Chikungunya virus (CHIKV), Zika virus (ZIKV), and Mayaro virus (MAYV) are important causes of acute febrile illness (AFI) in high-risk regions in Peru. Differential diagnosis based on clinical manifestations is challenging due to overlapping symptoms. We conducted a pioneering study to compare cytokine profiles among these arboviruses, aiming to elucidate distinct immunological features for diagnostic and therapeutic insights.<b>Methods:</b> We conducted a cross-sectional study in patients with acute febrile illness from Cajamarca, Peru. Blood samples were collected for the diagnosis of arboviruses and the quantification of cytokines. Diagnosis of Dengue, Zika, Chikungunya, and Mayaro virus infections relied on specific IgM antibody detection via enzyme-linked immunosorbent assays (ELISA). Cytokine quantification was carried out by ELISA assays for IL-2, IL-6, IL-10, TNF-α, and IFN-γ. Comparative analysis of cytokine levels was performed between all viruses and controls.<b>Results</b>: A total of 20 patients were recruited for each arbovirus and control group. Our study revealed similarities in clinical symptoms across arboviral infections, with headaches, myalgias, and arthralgias as the predominant symptoms. IL-2 levels were similar across the arboviruses; however, DENV and ZIKV patients had higher levels compared to controls. Elevated IL-6 levels were associated with CHIKV and ZIKV, while IL-10 levels were highest in DENV as compared to other arboviruses. TNF-α and IFN-γ were significantly elevated in DENV and ZIKV, and they were significantly higher in DENV compared to MAYV.<b>Conclusion</b>: Our findings provide valuable insights into the unique cytokine profiles of arboviral infections. We found similar levels of IL-2 across the arboviral groups, IL-6 was predominantly elevated in CHIKV and ZIKV, while IL-10 showed the highest levels in DENV. TNF-α and IFN-γ showed a similar pattern, with DENV and ZIKV patients expressing the highest levels. Understanding the distinct immunological signatures of these arboviruses is crucial for effective diagnostic and therapeutic approaches in high-risk regions.
DOI: 10.6084/m9.figshare.23972205.v1
title: <b>Dataset Citoquinas Febriles Jaen [61]</b>

description: <b>ABSTRACT</b><b>Background</b>: Arboviruses, including Dengue virus (DENV), Chikungunya virus (CHIKV), Zika virus (ZIKV), and Mayaro virus (MAYV) are important causes of acute febrile illness (AFI) in high-risk regions in Peru. Differential diagnosis based on clinical manifestations is challenging due to overlapping symptoms. We conducted a pioneering study to compare cytokine profiles among these arboviruses, aiming to elucidate distinct immunological features for diagnostic and therapeutic insights.<b>Methods:</b> We conducted a cross-sectional study in patients with acute febrile illness from Cajamarca, Peru. Blood samples were collected for the diagnosis of arboviruses and the quantification of cytokines. Diagnosis of Dengue, Zika, Chikungunya, and Mayaro virus infections relied on specific IgM antibody detection via enzyme-linked immunosorbent assays (ELISA). Cytokine quantification was carried out by ELISA assays for IL-2, IL-6, IL-10, TNF-α, and IFN-γ. Comparative analysis of cytokine levels was performed between all viruses and controls.<b>Results</b>: A total of 20 patients were recruited for each arbovirus and control group. Our study revealed similarities in clinical symptoms across arboviral infections, with headaches, myalgias, and arthralgias as the predominant symptoms. IL-2 levels were similar across the arboviruses; however, DENV and ZIKV patients had higher levels compared to controls. Elevated IL-6 levels were associated with CHIKV and ZIKV, while IL-10 levels were highest in DENV as compared to other arboviruses. TNF-α and IFN-γ were significantly elevated in DENV and ZIKV, and they were significantly higher in DENV compared to MAYV.<b>Conclusion</b>: Our findings provide valuable insights into the unique cytokine profiles of arboviral infections. We found similar levels of IL-2 across the arboviral groups, IL-6 was predominantly elevated in CHIKV and ZIKV, while IL-10 showed the highest levels in DENV. TNF-α and IFN-γ showed a similar pattern, with DENV and ZIKV patients expressing the highest levels. Understanding the distinct immunological signatures of these arboviruses is crucial for effective diagnostic and therapeutic approaches in high-risk regions.
DOI: 10.17632/pgr766jbhp
title: Morphologically Annotated Orani-Arbaic Dialect Dataset (MADOran)

description: We introduce a new morphologically annotated dataset for the Orani Arabic dialect (ORN), comprising 33,000 words gathered from diverse genres, including written sources (11.83%) which cover topics such as college life, culture, history, humor, politics, and traditions, as well as spoken language (88.17%) spanning storytelling, music, and daily conversation. Each word is manually annotated with a fine-grained tagset that provides part-of-speech, root, pattern, and English and French translations of the glosses. The morphological annotation was performed using the Dialectal Word Annotation Tool for Arabic (DIWAN) and it followed guidelines established for The Dynamic Arabella Corpus (Arabella), with adaptations to fit the dialectal context.
DOI: 10.17632/pgr766jbhp.2
title: Morphologically Annotated Orani-Arbaic Dialect Dataset (MADOran)

description: We introduce a new morphologically annotated dataset for the Orani Arabic dialect (ORN), comprising 33,000 words gathered from diverse genres, including written sources (11.83%) which cover topics such as college life, culture, history, humor, politics, and traditions, as well as spoken language (88.17%) spanning storytelling, music, and daily conversation. Each word is manually annotated with a fine-grained tagset that provides part-of-speech, root, pattern, and English and French translations of the glosses. The morphological annotation was performed using the Dialectal Word Annotation Tool for Arabic (DIWAN) and it followed guidelines established for The Dynamic Arabella Corpus (Arabella), with adaptations to fit the dialectal context.