cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3z7c48bdjd.1
title: Preliminary Qualitative Analysis of Plant-based Industrial Food Processing Waste in Anand City, Gujarat, India

description: This dataset forms the bedrock for a preliminary qualitative analysis centered on industrial food processing waste, specifically focusing on sweet corn and tomato by-products. These waste materials were meticulously collected directly from hot processing lines within various food industries situated in and around Anand City, Gujarat, India. This collection method ensures that the samples represent the waste generated under actual industrial operational conditions, providing a realistic perspective on their composition immediately post-processing. Upon collection, the raw waste samples underwent a crucial pre-treatment step: they were thoroughly dried to remove excess moisture and then carefully stored. This preparation was essential to prevent degradation and maintain sample integrity, ensuring their suitability for subsequent comprehensive analyses. A key aspect of this dataset involves the qualitative phytochemical screening of these dried waste materials, a methodology adapted from Shaikh et al. (2020). The study specifically looked for the qualitative detection of alkaloids, carbohydrates, phenolic compounds, sugars, and proteins. Beyond the organic components, the dataset also encompasses a detailed screening of essential elemental nutrients. This includes carbon (C), hydrogen (H), nitrogen (N), phosphorus (P), and potassium (K), collectively referred to as NPK, which are vital for agricultural applications and biochemical conversions. The elemental analysis for carbon, hydrogen, and nitrogen was meticulously performed using a high-precision Element Analyzer Instrument (Model: Elemetar Analysensysteme GmbH, Germany, Make: Unicube), a technique consistent with the methods described by Firdaus et al. (2018). For phosphorus and potassium, the analysis was conducted using Wavelength Dispersive X-Ray Fluorescence (WD-XRF) (Model: AxiosMAX, Make: PANalytical, Netherland). These advanced analytical instruments, housed at the Sophisticated Instrumentation Centre for Applied Research and Testing (SICART) in Vallabh Vidyanagar, ensured accurate and reliable elemental quantification, building upon established protocols like those referenced by Horf et al. (2021). The fundamental information derived from this study, encompassing both phytochemical and elemental composition, offers crucial insights into the chemical profile of industrial food processing wastes in the Anand region. This comprehensive understanding is vital for moving beyond simple waste disposal towards innovative waste utilization strategies. Ultimately, the outcomes of this preliminary qualitative analysis provide a foundational dataset that can significantly aid in estimating and enhancing the overall quality and utility of plant-based industrial food processing waste, not just at a regional level within Gujarat but also on a broader national and global scale, fostering a more sustainable and circular economy within the food industry.
DOI: 10.48577/jpl.lvjqbd
title: PRIMA Mission Overview

description: PRIMA Mission Overview
DOI: 10.17632/ptz6fzgm7y
title: Godfrey et al_Phosphoglycerate mutase regulates Treg differentiation through control of serine synthesis and one-carbon metabolism

description: Data files associated with paper
DOI: 10.17632/ptz6fzgm7y.1
title: Godfrey et al_Phosphoglycerate mutase regulates Treg differentiation through control of serine synthesis and one-carbon metabolism

description: Data files associated with paper
DOI: 10.17632/346dr5698v
title: Inefficient burial of organic carbon from eutrophication in large shallow lakes: A case of Lake Taihu and beyond

description: Raw data for all figures and tables in the manuscript titled “Inefficient burial of organic carbon from eutrophication in large shallow lakes: A case of Lake Taihu and beyond” by Xu YH, Pang JL, Zhu GW, He YX, Wu JL, and Sun YG
DOI: 10.17632/346dr5698v.2
title: Inefficient burial of organic carbon from eutrophication in large shallow lakes: A case of Lake Taihu and beyond

description: Raw data for all figures and tables in the manuscript titled “Inefficient burial of organic carbon from eutrophication in large shallow lakes: A case of Lake Taihu and beyond” by Xu YH, Pang JL, Zhu GW, He YX, Wu JL, and Sun YG
DOI: 10.17632/kxr7d9gvsv
title: Inefficient sinking and burial of phytoplankton-derived organic carbon in a large shallow eutrophic lake: A case of Lake Taihu and beyond

description: Raw data for all figures and tables in the manuscript titled “Inefficient sinking and burial of phytoplankton-derived organic carbon in a large shallow eutrophic lake: A case of Lake Taihu and beyond ” by Xu YH, Pang JL, Zhu GW, He YX, Wu JL, and Sun YG
DOI: 10.17632/kxr7d9gvsv.1
title: Inefficient sinking and burial of phytoplankton-derived organic carbon in a large shallow eutrophic lake: A case of Lake Taihu and beyond

description: Raw data for all figures and tables in the manuscript titled “Inefficient sinking and burial of phytoplankton-derived organic carbon in a large shallow eutrophic lake: A case of Lake Taihu and beyond ” by Xu YH, Pang JL, Zhu GW, He YX, Wu JL, and Sun YG
DOI: 10.17632/b3y99c7yh7
title: Dataset Core Values of Universities (QS Ranking 2025)

description: This is a structured dataset providing details about universities, organized by their QS Rank, along with their geographical location, Core Values (keywords), and supporting references.
• QS Ranking 2025: This column contains the ranking of universities for the year 2025.
• University: This column lists the names of universities.
• Country: This column indicates the country where each university is located.
• Region: This column specifies the broader geographical region of the university.
• Words: This column indicates the count of words in a university's core values statement or description. 
• Values (Keyword): This column reflects the core values of the universities.
• Reference: This column will contain references or sources for the information provided, possibly links to official university websites
DOI: 10.17632/b3y99c7yh7.1
title: Dataset Core Values of Universities (QS Ranking 2025)

description: This is a structured dataset providing details about universities, organized by their QS Rank, along with their geographical location, Core Values (keywords), and supporting references.
• QS Ranking 2025: This column contains the ranking of universities for the year 2025.
• University: This column lists the names of universities.
• Country: This column indicates the country where each university is located.
• Region: This column specifies the broader geographical region of the university.
• Words: This column indicates the count of words in a university's core values statement or description. 
• Values (Keyword): This column reflects the core values of the universities.
• Reference: This column will contain references or sources for the information provided, possibly links to official university websites
DOI: 10.17632/kkzjk253cy.4
title: PHQ-9 Student Depression Dataset

description: The PHQ-9 Student Depression Dataset contains responses from 400 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.15602166
title: A Framework for Synthetic ICH to Assess AI Generalizability

description: Synthetic intracranial hemorrhage (ICH) datasets and artificial intelligence model files to recreate figures found in Knowledge-based synthetic intracranial hemorrhage CT datasets for device evaluations and generalizability assessments (under submission) using the Jupyter Notebooks found in the manuscript repository.

This repository contains the dataset used to reproduce the results presented in the paper: Knowledge-based synthetic intracranial hemorrhage CT datasets for device evaluations and generalizability assessments (preprint in Files).

Deep learning models for Computer-Assisted Detection (CAD) of intracranial hemorrhage (ICH) often struggle with generalizability when encountering CT data with characteristics underrepresented in their training sets (e.g., variations in patient demographics, hemorrhage types, or image acquisition parameters).

This project introduces an open-source framework to:



Generate synthetic ICH CT data by inserting realistic, modeled hemorrhages (epidural, subdural, intraparenchymal) into a digital head phantom.

Simulate mass effect and control hemorrhage volume and attenuation based on real data distributions.

Create datasets with varied CT acquisition parameters (mAs, kVp) to robustly evaluate the generalizability of ICH detection models.


Our work validates this approach by demonstrating comparable performance of an ICH detection model on our synthetic dataset (AUC 0.877) versus an independent real dataset (AUC 0.919). This framework enables more comprehensive testing and evaluation of CAD devices for ICH.
DOI: 10.5281/zenodo.15750437
title: A Framework for Synthetic ICH to Assess AI Generalizability

description: Synthetic intracranial hemorrhage (ICH) datasets and artificial intelligence model files to recreate figures found in Knowledge-based synthetic intracranial hemorrhage CT datasets for device evaluations and generalizability assessments (under submission) using the Jupyter Notebooks found in the manuscript repository.

This repository contains the dataset used to reproduce the results presented in the paper: Knowledge-based synthetic intracranial hemorrhage CT datasets for device evaluations and generalizability assessments (preprint in Files).

Deep learning models for Computer-Assisted Detection (CAD) of intracranial hemorrhage (ICH) often struggle with generalizability when encountering CT data with characteristics underrepresented in their training sets (e.g., variations in patient demographics, hemorrhage types, or image acquisition parameters).

This project introduces an open-source framework to:



Generate synthetic ICH CT data by inserting realistic, modeled hemorrhages (epidural, subdural, intraparenchymal) into a digital head phantom.

Simulate mass effect and control hemorrhage volume and attenuation based on real data distributions.

Create datasets with varied CT acquisition parameters (mAs, kVp) to robustly evaluate the generalizability of ICH detection models.


Our work validates this approach by demonstrating comparable performance of an ICH detection model on our synthetic dataset (AUC 0.877) versus an independent real dataset (AUC 0.919). This framework enables more comprehensive testing and evaluation of CAD devices for ICH.
DOI: 10.15468/dl.tpch8c
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Mentocrex beankaensis Goodman, Raherilalao & Block, 2011",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 27 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081663-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ttnjvtkgbf
title: Supporting Data for Satellite-Based Observations and Wind Response of the Skjálfandafljót and Jökulsá á Fjöllum River Plumes on Iceland’s Arctic Coast

description: This dataset contains the supporting data for figures in “Satellite-based observations and wind response of the Skjálfandafljót and Jökulsá á Fjöllum river plumes on Iceland’s Arctic coast,” a manuscript for Estuarine, Coastal and Shelf Science by Michael M. Whitney, Luke C. Glass, and Brittney N. Collins (affiliated with the University of Connecticut). This study analyzes the Skjálfandafljót river plume in Skjálfandi and the Jökulsá á Fjöllum plume in Öxarfjörður, on Iceland’s Arctic coast. These freshwaters influence ecosystems that support cetaceans, ecotourism, and fisheries. Objectives are 1) determining plume distributions with satellite imagery, 2) comparing results based on True Color and Total Suspended Matter (TSM) images, 3) describing low-wind plume characteristics, and 4) characterizing wind response. The approach is applicable to river plumes around the world and findings can help inform new studies, monitoring, and marine protection efforts. The dataset includes MATLAB data files that contain all the output data presented in the corresponding figures within the manuscript. Details about variables and units are described within the figure captions and manuscript text. Zipped files of the Sentinel-2 True Color and Sentinel-3 TSM satellite images (downloaded from the online Copernicus Browser and analyzed) also are included. The manuscript contains complete descriptions of methods, analysis, and interpretation.

List of MATLAB data files:
Figure01_data.mat
Figure02_data.mat 
Figure03_data.mat 
Figure04_data.mat 
Figure05_data.mat 
Figure06_data.mat 
Figure07_data.mat 
Figure08_data.mat 
Figure09_data.mat
Figure10_data.mat
FigureS1_data.mat
FigureS2_data.mat
FigureS3_data.mat

List of zipped satellite image files:
Sentinel-2_L2A_True_color.zip
Sentinel-3_OLCI_L2_Water_TSM.zip
DOI: 10.17632/ttnjvtkgbf.2
title: Supporting Data for Satellite-Based Observations and Wind Response of the Skjálfandafljót and Jökulsá á Fjöllum River Plumes on Iceland’s Arctic Coast

description: This dataset contains the supporting data for figures in “Satellite-based observations and wind response of the Skjálfandafljót and Jökulsá á Fjöllum river plumes on Iceland’s Arctic coast,” a manuscript for Estuarine, Coastal and Shelf Science by Michael M. Whitney, Luke C. Glass, and Brittney N. Collins (affiliated with the University of Connecticut). This study analyzes the Skjálfandafljót river plume in Skjálfandi and the Jökulsá á Fjöllum plume in Öxarfjörður, on Iceland’s Arctic coast. These freshwaters influence ecosystems that support cetaceans, ecotourism, and fisheries. Objectives are 1) determining plume distributions with satellite imagery, 2) comparing results based on True Color and Total Suspended Matter (TSM) images, 3) describing low-wind plume characteristics, and 4) characterizing wind response. The approach is applicable to river plumes around the world and findings can help inform new studies, monitoring, and marine protection efforts. The dataset includes MATLAB data files that contain all the output data presented in the corresponding figures within the manuscript. Details about variables and units are described within the figure captions and manuscript text. Zipped files of the Sentinel-2 True Color and Sentinel-3 TSM satellite images (downloaded from the online Copernicus Browser and analyzed) also are included. The manuscript contains complete descriptions of methods, analysis, and interpretation.

List of MATLAB data files:
Figure01_data.mat
Figure02_data.mat 
Figure03_data.mat 
Figure04_data.mat 
Figure05_data.mat 
Figure06_data.mat 
Figure07_data.mat 
Figure08_data.mat 
Figure09_data.mat
Figure10_data.mat
FigureS1_data.mat
FigureS2_data.mat
FigureS3_data.mat

List of zipped satellite image files:
Sentinel-2_L2A_True_color.zip
Sentinel-3_OLCI_L2_Water_TSM.zip
DOI: 10.17632/dmr2m4rtnj
title: Fan et al., 2025.

description: Original immunoblot data.
DOI: 10.17632/dmr2m4rtnj.1
title: Fan et al., 2025.

description: Original immunoblot data.
DOI: 10.17632/xy38c9nmg6
title: Development and Validation of the Karma Yoga Scale using Mixed Method Approach

description: The shared dataset include: a) Dataset for conducting reliability analysis and EFA. b) Dataset for conducting CFA. c) Raw and modified model of Karma Yoga scale along with model fit scores. d) Dataset for calculating the correlation between Karma Yoga (KY), Spiritual Fortitude (SF) and Satisfaction with life scale (LS).
DOI: 10.17632/xy38c9nmg6.1
title: Development and Validation of the Karma Yoga Scale using Mixed Method Approach

description: The shared dataset include: a) Dataset for conducting reliability analysis and EFA. b) Dataset for conducting CFA. c) Raw and modified model of Karma Yoga scale along with model fit scores. d) Dataset for calculating the correlation between Karma Yoga (KY), Spiritual Fortitude (SF) and Satisfaction with life scale (LS).
DOI: 10.17632/8ttv9cz282
title: Exploratory Factor Analysis for DISS

description: It is a 15 items scale measuring digital intelligence among higher secondary school and under graduation students
DOI: 10.17632/8ttv9cz282.1
title: Exploratory Factor Analysis for DISS

description: It is a 15 items scale measuring digital intelligence among higher secondary school and under graduation students
DOI: 10.15468/dl.ahww8a
title: Occurrence Download

description: A dataset containing 1557 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia L.",
 "Country is Canada",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1557 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081667-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9vd5xp8syv
title: A Cobot Bared Its Soul—Should I Even Respond? How cobots' intimate self-disclosure affects human knowledge-sharing behavior？

description: Research Data
DOI: 10.17632/9vd5xp8syv.1
title: A Cobot Bared Its Soul—Should I Even Respond? How cobots' intimate self-disclosure affects human knowledge-sharing behavior？

description: Research Data
DOI: 10.15468/dl.p96sgv
title: Occurrence Download

description: A dataset containing 92 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia L.",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 92 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081664-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/d3cw4gyz85.1
title: CNN-GNN-Text

description: This source code and data are part of a research project to develop an efficient text representation architecture for web apps. The current version is suited for text classification tasks.
DOI: 10.15468/dl.hjn2gu
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brachyteles hypoxanthus (Kuhl, 1820)"
 ]
}

 The dataset includes 83 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081665-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2mgf2c
title: Occurrence Download

description: A dataset containing 139 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia L.",
 "Country is Bulgaria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 139 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081666-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/r9c5ns4jft.2
title: Lipid-induced granule assembled by DDX49 in hepatocytes restrains liver fibrosis

description: Original raw data for“Lipid-induced granule assembled by DDX49 in hepatocytes restrains liver fibrosis”
DOI: 10.15468/dl.tyjqmt
title: Occurrence Download

description: A dataset containing 16241 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Material citation, Specimen, Occurrence evidence)",
 "Continent is one of (North America, South America)",
 "TaxonKey is Blenniidae"
 ]
}

 The dataset includes 16241 records from 139 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081668-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8tdr8m62xk
title: CIPS Raman

description: experimental raman data
DOI: 10.17632/8tdr8m62xk.1
title: CIPS Raman

description: experimental raman data
DOI: 10.15468/dl.x6jr84
title: Occurrence Download

description: A dataset containing 1436 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Mentocrex kioloides (Pucheran, 1845)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1436 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081670-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/cwf2xmz3fh
title: FT-MS data for SDOM study

description: This dataset comprises the Fourier Transform Mass Spectrometry (FT-MS) data generated for the study "Dissolved organic matter in surface sediments along a river-to-ocean continuum: Molecular characteristics and sediment–water exchange dynamics".
DOI: 10.17632/cwf2xmz3fh.1
title: FT-MS data for SDOM study

description: This dataset comprises the Fourier Transform Mass Spectrometry (FT-MS) data generated for the study "Dissolved organic matter in surface sediments along a river-to-ocean continuum: Molecular characteristics and sediment–water exchange dynamics".