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

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "Geometry POLYGON((-96.69666 -10.58488,-61.30334 -10.58488,-61.30334 3.58488,-96.69666 3.58488,-96.69666 -10.58488))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anolis podocarpus Ayala-Varela & Torres-Carvajal, 2010"
 ]
}

 The dataset includes 68 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001186-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d7ez8z
title: Occurrence Download

description: A dataset containing 1697 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Spizaetus isidori (Des Murs, 1845)",
 "Year 2010-2025"
 ]
}

 The dataset includes 1697 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001188-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cbf45a
title: Occurrence Download

description: A dataset containing 349546 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mesostigmata",
 "Year is greater than or equal to 2000",
 "Continent is Europe"
 ]
}

 The dataset includes 349546 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001179-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wytrv4
title: Occurrence Download

description: A dataset containing 582 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xiphydria prolongata (Geoffroy, 1785)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 582 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001189-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5445hq8h
title: MassIVE MSV000097280 - Calicioid lichens and fungi metabolome
title: Lichens are chemically unique symbiotic organisms formed by the association of a fungus with one or more photosynthetic partners and a diverse microbiota. We investigated the microbiota and metabolome composition and diversity in calicioid lichens and fungi, a polyphyletic yet morphologically similar group which shares specialized microhabitats associated with old growth forests. We also studied the microbiota and metabolome of putative endolichenic Penicillium spp. isolated from calicioid apothecia. Solirubrobacterales (Actinobacteria) was the most abundant bacterial order in Chaenotheca samples, while Acetobacterales (Proteobacteria) dominated Mycocalicium and Penicillium samples. Metabolites and fungal richness were significantly higher in lichenized and non-lichenized fungi than in axenic cultures. Bacterial alpha diversity had no effect on metabolome diversity, but fungal alpha diversity explained 27.5% of the observed variability in metabolome diversity. Bacterial and fungal community composition were positively correlated with metabolome composition. We show that molecules considered exclusive to lichens such as lecanoric, protocetraric, and pulvinic acids can also be biosynthesized by putative endolichenic fungi in a non lichenized state. We conclude that microbiota influences metabolome composition and diversity in lichens and non lichenized fungi, and we argue that organisms associated with intact forest ecosystems are a promising source for the discovery of novel molecules.
DOI: 10.15468/dl.e8tgzr
title: Occurrence Download

description: A dataset containing 160 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Pudella Thomas, 1913, Pudu mephistophiles (de Winton, 1896))"
 ]
}

 The dataset includes 160 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001191-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pzqj37
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "Geometry POLYGON((-96.69666 -8.08488,-61.30334 -8.08488,-61.30334 6.08488,-96.69666 6.08488,-96.69666 -8.08488))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anolis otongae Ayala-Varela & Velasco, 2010"
 ]
}

 The dataset includes 48 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001190-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hkmcyd3s8h.1
title: Efficacy of Baricitinib in Moderate Alopecia Areata, Severe Alopecia Areata, Alopecia Totalis, and Alopecia Universalis: A Real-world Comparative Study.

description: This dataset supports the findings in the manuscript "Efficacy of Baricitinib in Moderate Alopecia Areata, Severe Alopecia Areata, Alopecia Totalis, and Alopecia Universalis: A Real-world Comparative Study." The dataset includes:Demographic and baseline characteristics of 95 patients with Alopecia Areata (AA), Alopecia Totalis (AT) and Alopecia Universalis (AU), including age, gender, disease duration, age of onset, number of initial onsets, number of recurrences, and presence of nail lesions.Dermoscopic data including terminal hair density at baseline, 4, 8, 16, and 24 weeks following treatment with baricitinib, for moderate AA, severe AA, AT and AU patient groups.Adverse events data including type and incidence of mild adverse reactions (upper respiratory tract infection, folliculitis, acne, elevated aminotransferase and creatine phosphokinase levels, and local scalp pruritus) during baricitinib treatment.Statistical analysis comparing the effectiveness of baricitinib across the four groups, including p-values for comparisons of terminal hair density.This data provides valuable insights into the real-world effectiveness of baricitinib in treating different subtypes of alopecia areata and highlights the role of dermoscopy in monitoring treatment response. The dataset can be used to validate our findings and for further research on alopecia areata and the use of JAK inhibitors.
DOI: 10.17632/kkzjk253cy.1
title: PHQ-9 Student Depression Dataset

description: The PHQ-9 Student Depression Dataset contains responses from 250 students to the PHQ-9 questionnaire, a well-established tool for diagnosing depression. This dataset is designed to support the development of machine learning models aimed at automated depression detection by analyzing text responses to common depression-related questions.

The PHQ-9 questionnaire includes 9 questions that assess symptoms of depression over the past two weeks, covering areas like mood, energy levels, sleep, appetite, and thoughts of self-harm. The responses are scored on a scale from 0 (Not at all) to 3 (Nearly every day), with the total score ranging from 0 to 27. Based on this score, the depression severity is classified into one of the following categories:
Minimal (0-4)
Mild (5-9)
Moderate (10-14)
Moderately Severe (15-19)
Severe (20-27)

This dataset is primarily designed for building models that can assist in automated depression detection. Some potential use cases include:
Sentiment Analysis: Analyzing emotional tones in text responses to assess depression.
Text Classification: Classifying responses into different depression severity levels.
Predictive Modeling: Predicting depression severity based on textual responses.
Feature Engineering: Extracting linguistic features (e.g., sentiment, keywords) to predict depression.
The dataset is diverse, with synthetic responses across different levels of depression, providing a versatile foundation for machine learning applications. While the dataset does not contain personally identifiable information (PII), real-world applications should follow ethical guidelines regarding privacy, consent, and mental health resources.
When working with real data or applying this dataset in clinical research, it is essential to adhere to ethical standards, including:

Data Privacy: Anonymizing personal information.
Informed Consent: Ensuring participants give consent before data collection.
Support Resources: Providing support for individuals who may exhibit serious mental health concerns.

Applications:
Clinical Research: This dataset is valuable for studying depression detection using natural language processing and machine learning techniques.
AI in Healthcare: It can be used in the development of tools for automated mental health assessment.
Education: Training students or professionals in recognizing depression symptoms and analyzing responses.
DOI: 10.17632/kkzjk253cy.2
title: PHQ-9 Student Depression Dataset

description: The PHQ-9 Student Depression Dataset contains responses from 500 students to the PHQ-9 questionnaire, a well-established tool for diagnosing depression. This dataset is designed to support the development of machine learning models aimed at automated depression detection by analyzing text responses to common depression-related questions.

The PHQ-9 questionnaire includes 9 questions that assess symptoms of depression over the past two weeks, covering areas like mood, energy levels, sleep, appetite, and thoughts of self-harm. The responses are scored on a scale from 0 (Not at all) to 3 (Nearly every day), with the total score ranging from 0 to 27. Based on this score, the depression severity is classified into one of the following categories:
Minimal (0-4)
Mild (5-9)
Moderate (10-14)
Moderately Severe (15-19)
Severe (20-27)

This dataset is primarily designed for building models that can assist in automated depression detection. Some potential use cases include:
Sentiment Analysis: Analyzing emotional tones in text responses to assess depression.
Text Classification: Classifying responses into different depression severity levels.
Predictive Modeling: Predicting depression severity based on textual responses.
Feature Engineering: Extracting linguistic features (e.g., sentiment, keywords) to predict depression.
The dataset is diverse, with synthetic responses across different levels of depression, providing a versatile foundation for machine learning applications. While the dataset does not contain personally identifiable information (PII), real-world applications should follow ethical guidelines regarding privacy, consent, and mental health resources.
When working with real data or applying this dataset in clinical research, it is essential to adhere to ethical standards, including:

Data Privacy: Anonymizing personal information.
Informed Consent: Ensuring participants give consent before data collection.
Support Resources: Providing support for individuals who may exhibit serious mental health concerns.

Applications:
Clinical Research: This dataset is valuable for studying depression detection using natural language processing and machine learning techniques.
AI in Healthcare: It can be used in the development of tools for automated mental health assessment.
Education: Training students or professionals in recognizing depression symptoms and analyzing responses.
DOI: 10.17632/kkzjk253cy.3
title: PHQ-9 Student Depression Dataset

description: The PHQ-9 Student Depression Dataset contains responses from 500 students to the PHQ-9 questionnaire, a well-established tool for diagnosing depression. This dataset is designed to support the development of machine learning models aimed at automated depression detection by analyzing text responses to common depression-related questions.

The PHQ-9 questionnaire includes 9 questions that assess symptoms of depression over the past two weeks, covering areas like mood, energy levels, sleep, appetite, and thoughts of self-harm. The responses are scored on a scale from 0 (Not at all) to 3 (Nearly every day), with the total score ranging from 0 to 27. Based on this score, the depression severity is classified into one of the following categories:
Minimal (0-4)
Mild (5-9)
Moderate (10-14)
Moderately Severe (15-19)
Severe (20-27)

This dataset is primarily designed for building models that can assist in automated depression detection. Some potential use cases include:
Sentiment Analysis: Analyzing emotional tones in text responses to assess depression.
Text Classification: Classifying responses into different depression severity levels.
Predictive Modeling: Predicting depression severity based on textual responses.
Feature Engineering: Extracting linguistic features (e.g., sentiment, keywords) to predict depression.
The dataset is diverse, with synthetic responses across different levels of depression, providing a versatile foundation for machine learning applications. While the dataset does not contain personally identifiable information (PII), real-world applications should follow ethical guidelines regarding privacy, consent, and mental health resources.
When working with real data or applying this dataset in clinical research, it is essential to adhere to ethical standards, including:

Data Privacy: Anonymizing personal information.
Informed Consent: Ensuring participants give consent before data collection.
Support Resources: Providing support for individuals who may exhibit serious mental health concerns.

Applications:
Clinical Research: This dataset is valuable for studying depression detection using natural language processing and machine learning techniques.
AI in Healthcare: It can be used in the development of tools for automated mental health assessment.
Education: Training students or professionals in recognizing depression symptoms and analyzing responses.
DOI: 10.5281/zenodo.14990367
title: miniGiraffe NA19239 + 1000Genome GBZ

description: Repository to host data to run miniGiraffe proxy application
DOI: 10.5281/zenodo.14990368
title: miniGiraffe NA19239 + 1000Genome GBZ

description: Repository to host data to run miniGiraffe proxy application
DOI: 10.5281/zenodo.15002374
title: Hydrodynamic Data
DOI: 10.5281/zenodo.15002375
title: Hydrodynamic Data
DOI: 10.5281/zenodo.15002800
title: Bilayer 5

description: Modifications to the LJ parameters of P and O. Base FF is Sage 2.2.0
DOI: 10.5281/zenodo.15002801
title: Bilayer 5

description: Modifications to the LJ parameters of P and O. Base FF is Sage 2.2.0
DOI: 10.5281/zenodo.15002663
title: Dataset for manuscript "Quantifying the impact of atmospheric circulation and soil preconditioning with large ensembles of simulation under constrained circulation: a case study of the 2021 Pacific Northwest heatwave"

description: Archived are the datasets used in the "Quantifying the impact of atmospheric circulation and soil preconditioning with large ensembles of simulation under constrained circulation: a case study of the 2021 Pacific Northwest heatwave" submitted to the Earth's Future.

1. Simulation data from the four sets of model experiment:



Data_PNWheatNudgeSimu_i0615_100mem_LargePNWChunk.nc

Data_PNWheatNudgeSimu_i0605_100mem_LargePNWChunk.nc

Data_PNWheatNudgeSimu_New2090_i0615_100mem_LargePNWChunk.nc

Data_PNWheatNudgeSimu_New2090_i0605_100mem_LargePNWChunk.nc


2. CESM GOGA simulation (used as a general climatology of CESM2, original data accessible at https://www.cesm.ucar.edu/working-groups/climate/simulations/cam6-prescribed-sst):



CESM_goga_mem01_cli1980-2009_LargePNWChunk.nc

CESM_goga_mem01_cli1980-2009_LargePNWChunk_var2.nc


3. ERA5 2021



ERA5_2021_LargePNWChunk.nc


4. ERA5 climatology



ERA5_cli1980-2009_LargePNWChunk.nc


5. The detected critical soil moisture based on CESM GOGA



CurveFit_PNWLargeChunk_WarmSeason_GogaMem01_3ModelTypes_EFSHLH.nc


6. The detected critical soil moisture based on ERA5



CurveFit_PNWLargeChunk_WarmSeason_ERA5SM10cm_RHSfix0_EFSHLH.nc
DOI: 10.5281/zenodo.15002490
title: LC-MS/MS raw data_Nespawershefyt&Comparison samples

description: LC-MS/MS raw data from the coffin set of Nespawershefyt and comparison samples, for Novor Cloud, MaxQuant and Unipept data processing
DOI: 10.5281/zenodo.15002491
title: LC-MS/MS raw data_Nespawershefyt&Comparison samples

description: LC-MS/MS raw data from the coffin set of Nespawershefyt and comparison samples, for Novor Cloud, MaxQuant and Unipept data processing
DOI: 10.5281/zenodo.15002662
title: Dataset for manuscript "Quantifying the impact of atmospheric circulation and soil preconditioning with large ensembles of simulation under constrained circulation: a case study of the 2021 Pacific Northwest heatwave"

description: Archived are the datasets used in the "Quantifying the impact of atmospheric circulation and soil preconditioning with large ensembles of simulation under constrained circulation: a case study of the 2021 Pacific Northwest heatwave" submitted to the Earth's Future.

1. Simulation data from the four sets of model experiment:



Data_PNWheatNudgeSimu_i0615_100mem_LargePNWChunk.nc

Data_PNWheatNudgeSimu_i0605_100mem_LargePNWChunk.nc

Data_PNWheatNudgeSimu_New2090_i0615_100mem_LargePNWChunk.nc

Data_PNWheatNudgeSimu_New2090_i0605_100mem_LargePNWChunk.nc


2. CESM GOGA simulation (used as a general climatology of CESM2, original data accessible at https://www.cesm.ucar.edu/working-groups/climate/simulations/cam6-prescribed-sst):



CESM_goga_mem01_cli1980-2009_LargePNWChunk.nc

CESM_goga_mem01_cli1980-2009_LargePNWChunk_var2.nc


3. ERA5 2021



ERA5_2021_LargePNWChunk.nc


4. ERA5 climatology



ERA5_cli1980-2009_LargePNWChunk.nc


5. The detected critical soil moisture based on CESM GOGA



CurveFit_PNWLargeChunk_WarmSeason_GogaMem01_3ModelTypes_EFSHLH.nc


6. The detected critical soil moisture based on ERA5



CurveFit_PNWLargeChunk_WarmSeason_ERA5SM10cm_RHSfix0_EFSHLH.nc
DOI: 10.71796/wits-figshare.28570223
title: Layered intrusions in the Tugela segment

description: Dataset about layered intrusions in the Tugela segment. The data is presented in Excel spreadsheets and further data is shown in colour pictures. The pictures exhibit the central dunite unit in the Tugela Rand complex viewed to the west side. Where the Tugela River cuts through the Mambula Complex, shows the anorthosite and magnetitite layers as well as sample sites.
DOI: 10.71796/wits-figshare.28570223.v1
title: Layered intrusions in the Tugela segment

description: Dataset about layered intrusions in the Tugela segment. The data is presented in Excel spreadsheets and further data is shown in colour pictures. The pictures exhibit the central dunite unit in the Tugela Rand complex viewed to the west side. Where the Tugela River cuts through the Mambula Complex, shows the anorthosite and magnetitite layers as well as sample sites.
DOI: 10.5281/zenodo.15002817
title: Integration of mental health support in the routine work of community-based NGOs involved in HIV prevention in Ukraine: Dataset

description: Background: Mental health issues can adversely affect retention in care and treatment adherence among people living with HIV. However, little is known about the extent to which HIV-focused non-governmental organisations (NGOs) in Ukraine integrate mental health support into their routine work, especially amid the ongoing humanitarian challenges.

Objectives: To assess (1) current practices of mental health service delivery by Ukrainian NGOs involved in HIV prevention, (2) perceptions and attitudes of NGO administrators regarding the need for integrated HIV and mental health services, and (3) policy recommendations to strengthen such integration.

Design: This observational, cross-sectional study surveyed NGOs that (a) have been active in HIV prevention over the last 5 years and (b) participated in the “Community Action for HIV Control” project.

Setting and Participants: 17 eligible NGOs from 14 oblasts of Ukraine were invited; 15 completed the survey (88% response rate). The survey collected data on organisational size, mental health service delivery (including types of services offered and personnel involved), collaboration with external mental health providers, and administrative perspectives on expanding mental health support.

Methods: Descriptive statistics (frequencies, medians with interquartile ranges) were used to characterise nominal and non-normally distributed variables. Spearman’s rho correlation was employed to explore associations between organisational size, service types, and motivation to expand mental health support.
DOI: 10.5281/zenodo.15002818
title: Integration of mental health support in the routine work of community-based NGOs involved in HIV prevention in Ukraine: Dataset

description: Background: Mental health issues can adversely affect retention in care and treatment adherence among people living with HIV. However, little is known about the extent to which HIV-focused non-governmental organisations (NGOs) in Ukraine integrate mental health support into their routine work, especially amid the ongoing humanitarian challenges.

Objectives: To assess (1) current practices of mental health service delivery by Ukrainian NGOs involved in HIV prevention, (2) perceptions and attitudes of NGO administrators regarding the need for integrated HIV and mental health services, and (3) policy recommendations to strengthen such integration.

Design: This observational, cross-sectional study surveyed NGOs that (a) have been active in HIV prevention over the last 5 years and (b) participated in the “Community Action for HIV Control” project.

Setting and Participants: 17 eligible NGOs from 14 oblasts of Ukraine were invited; 15 completed the survey (88% response rate). The survey collected data on organisational size, mental health service delivery (including types of services offered and personnel involved), collaboration with external mental health providers, and administrative perspectives on expanding mental health support.

Methods: Descriptive statistics (frequencies, medians with interquartile ranges) were used to characterise nominal and non-normally distributed variables. Spearman’s rho correlation was employed to explore associations between organisational size, service types, and motivation to expand mental health support.
DOI: 10.17632/ryct9y97rb.1
title: Data for the analysis of the impact of sustainability ratings on the corporate value of Chinese food companies

description: This dataset includes data from 75 Chinese A-share listed food companies spanning from 2006 to 2022. It encompasses key financial indicators such as profitability, growth, and leverage, as well as ESG (Environmental, Social, and Governance) ratings. The dataset has been cleaned and processed to support research on the relationship between sustainability ratings and corporate value. The data has been analyzed using Excel and STATA 18.0.
DOI: 10.17632/9sp99gt3r4.1
title: Central Bank Liquidity Reallocation and Bank Lending: Evidence from the Tiering System

description: Replication package for “Central Bank Liquidity Reallocation and Bank Lending: Evidence from the Tiering System” to fulfill the data and code disclosure requirement for accepted manuscripts in Journal of Financial Economics. This document provides a roadmap to the data and code used in the above-referenced paper
DOI: 10.17632/5j892sjvns.1
title: The Impact of Capital Structure on Financial Performance of Russian Public Oil and Gas Companies

description: Master's thesis on the topic:
"The Impact of Capital Structure on Financial Performance of Russian Public Oil and Gas Companies"