cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e246569v1
title: Food Environment Atlas - Data Access and Documentation Downloads

description: Food environment factors—such as store and restaurant proximity, 
availability of local foods, food and nutrition assistance programs, and
 community characteristics—interact to influence food choices and diet 
quality.
The objectives of the Food Environment Atlas are:
to provide a spatial overview of a community's ability to access healthy food and its success in doing so, andto assemble statistics on food environment indicators to stimulate 
research on the determinants of food choices and diet quality.
The Atlas can be used to:
Create maps showing the variation in a single indicator across the 
United States; for example, number of farmers' markets or access to 
grocery stores across U.S. counties and county-equivalents;View all of the county-level indicators for a selected county or county equivalent;Zoom in to specific areas and export or print maps;Download the full dataset or selected data in Excel and machine-readable formats.The current version of the Food Environment Atlas has more than 300 
variables, including new indicators on all other general merchandise 
stores (including dollar stores) and deep poverty rates. All of the data
 included in the Atlas are aggregated into an Excel spreadsheet as well 
as a machine-readable CSV format for easy download. These data come from
 a variety of sources and cover varying years and geographic levels. 
Previous versions of the Food Environment Atlas are archived and 
available for download. The documentation for each version of the data 
provides complete information on definitions and data sources.
In the downloadable Excel/CSV spreadsheets:
State and county Federal information processing standards (FIPS) 
codes are provided. FIPS and GEOID are used interchangeably in the 
online mapping tool and data download and they represent the same value.The variable lookup file links the short field descriptions 
(indicator names) used in the data file with the longer indicator names 
used in the Atlas."No data" fields are referenced with N/A or with -9999. Counties 
that didn’t exist in a particular year are referenced with -8888.
The current version of the Food Environment Atlas data, along with 
seven previous versions of the data and documentation, are available.<br>
description: Downloaded 2026-02-24<br>
DOI: 10.34740/kaggle/dsv/14997324
title: Balochi Language to English Translation Dataset
DOI: 10.15468/dl.ueyv9v
title: Occurrence Download

description: A dataset containing 11399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Asio otus",
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2014",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 11399 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004625-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18817911
title: Common dolphin (Delphinus delphis) photoidentification dataset from the Bay of Algeciras_Gibraltar (Excel files)

description: This dataset contains three Excel spreadsheets related to the photoidentification study of common dolphins (Delphinus delphis) in the Bay of Algeciras–Gibraltar. The files include:



Catalogue_Ddelphis_BA_G_2017_20.xlsx – photographic identification of individual dolphins, including image quality and distinctiveness.

Number_Captures_Recaptures_Ddelphis_BA_G_2017_20.xlsx – the number of sightings and resightings per dolphin, with dates of first and last observation, as well as records of putative mothers photographed alongside a calf or neonate.

Captures_Recaptures_Ddelphis_BA_G_2017_20.xlsx – alphanumeric codes for all sightings and resightings, with representative photographs of each individual highlighted in bold.


The dataset was compiled as part of a research project on the ecology and conservation of D. delphis in the Bay of Algeciras–Gibraltar. It provides baseline information for population monitoring, photo-identification studies, and conservation planning in the western Mediterranean.
DOI: 10.5281/zenodo.18524338
title: Underpinning Data to Wort et al., 2026

description: This repository provides the underpinning data to:

Wort et al., Protocol for Preparing and Characterizing Samples for Combined Microsecond Freeze-Hyperquenching and Electron Paramagnetic Resonance Spectroscopy, StarProtocols, (2026) DOI: https://doi.org/10.1016/j.xpro.2026.104405.
DOI: 10.5281/zenodo.18524339
title: Underpinning Data to Wort et al., 2026

description: This repository provides the underpinning data to:

Wort et al., Protocol for Preparing and Characterizing Samples for Combined Microsecond Freeze-Hyperquenching and Electron Paramagnetic Resonance Spectroscopy, StarProtocols, (2026) DOI: https://doi.org/10.1016/j.xpro.2026.104405.
DOI: 10.5281/zenodo.17183965
title: Common dolphin (Delphinus delphis) photoidentification dataset from the Bay of Algeciras_Gibraltar (Excel files)

description: This dataset contains three Excel spreadsheets related to the photoidentification study of common dolphins (Delphinus delphis) in the Bay of Algeciras–Gibraltar. The files include:



Catalogue_Ddelphis_BA_G_2017_20.xlsx – photographic identification of individual dolphins, including image quality and distinctiveness.

Number_Captures_Recaptures_Ddelphis_BA_G_2017_20.xlsx – the number of sightings and resightings per dolphin, with dates of first and last observation, as well as records of putative mothers photographed alongside a calf or neonate.

Captures_Recaptures_Ddelphis_BA_G_2017_20.xlsx – alphanumeric codes for all sightings and resightings, with representative photographs of each individual highlighted in bold.


The dataset was compiled as part of a research project on the ecology and conservation of D. delphis in the Bay of Algeciras–Gibraltar. It provides baseline information for population monitoring, photo-identification studies, and conservation planning in the western Mediterranean.
DOI: 10.6084/m9.figshare.31438075
title: Meditation and Ownership Data

description: This work is original and has not been published, nor is it under review, in any other journal. All authors have read and approved the final version of the manuscript and agree with its submission to <i>Mindfulness</i>. We believe the article fits well within the journal’s scope, as it addresses self-related processes, emotion, and mindfulness from an experimental and theoretically grounded perspective.
DOI: 10.6084/m9.figshare.31438075.v2
title: Meditation and Ownership Data

description: This work is original and has not been published, nor is it under review, in any other journal. All authors have read and approved the final version of the manuscript and agree with its submission to <i>Mindfulness</i>. We believe the article fits well within the journal’s scope, as it addresses self-related processes, emotion, and mindfulness from an experimental and theoretically grounded perspective.
DOI: 10.6084/m9.figshare.31438075.v1
title: Meditation and Ownership Data 1

description: This work is original and has not been published, nor is it under review, in any other journal. All authors have read and approved the final version of the manuscript and agree with its submission to <i>Mindfulness</i>. We believe the article fits well within the journal’s scope, as it addresses self-related processes, emotion, and mindfulness from an experimental and theoretically grounded perspective.
DOI: 10.5281/zenodo.18500503
title: Dataset for "Oxygen limitation is not a major physiological mechanism restricting early life development in zebrafish"

description: This repository contains the raw and processed data underlying the study:

“Oxygen limitation is not a major physiological mechanism restricting early life development in zebrafish.”

The dataset includes morphometric, physiological, and survival measurements collected across two independent experiments (Experiment 1 and Experiment 2) designed to test the effects of oxygen availability (12.5, 25, 50, 100, and 200% air saturation) and temperature (15.7 to 39.3°C) on embryonic, larval, and juvenile zebrafish performance.

All files are provided in .csv format and can be opened with standard spreadsheet software or statistical programming environments (R, Python, MATLAB, etc.). Each row corresponds to an individual or experimental unit (holding beaker), and columns contain measured traits and associated metadata (e.g., Oxygen treatment, Temperature treatment, developmental stage in hours post fertilizaton).



File overview

Embryo traits





Yolk sac area – Experiment 1 (.csv)Quantified yolk sac area measurements from embryos (mm^2), used as a proxy for energy reserves and developmental progression.




Yolk sac area – Experiment 2 (.csv)Same measurements as above for the independent replicate experiment.




Heart rate – Experiment 2 (.csv)Embryonic heart rate measurements (Hertz, Hz), used as an indicator of metabolic and physiological performance.





Larval traits





Larval length – Experiment 1 (.csv)Standard body length measurements (mm) of larvae at the specified developmental stage.




Larval length – Experiment 2 (.csv)Replicate dataset of larval body length measurements.




Hatching rate and survival to first feeding – Experiment 1 (.csv)Hatching success and survival metrics quantified from embryo to first-feeding stage (e.g., see Larvae_count_cummulative, SurvivalToFeed).




Hatching rate and survival to first feeding – Experiment 2 (.csv)Replicate measurements of hatching success and early survival.





Juvenile traits





Body size and CTmax – Experiments 1 & 2 (.csv)Juvenile body size measurements and critical thermal maximum (CTmax) values (in °C), used to assess growth and upper thermal tolerance. See columns Beaker_ID and Experiment to track fish individuals rearing origin.





Experimental design notes





Data originate from two independent experiments (Exp 1 and Exp 2) performed under controlled oxygen treatments.




Traits span multiple life stages: embryo, larva, and juvenile.




Measurements include:





Morphology (yolk sac area, length, body size)




Physiology (heart rate, CTmax)




Performance and fitness proxies (hatching success, survival)







Data structure

Across files, columns typically include:





Beaker or clutch ID




Treatment (e.g., oxygen and temperature condition)




Photo or video ID




Developmental stage or time point




Measured trait value(s)



Exact variable names are specified within each file header.
DOI: 10.5281/zenodo.18500502
title: Dataset for "Oxygen limitation is not a major physiological mechanism restricting early life development in zebrafish"

description: This repository contains the raw and processed data underlying the study:

“Oxygen limitation is not a major physiological mechanism restricting early life development in zebrafish.”

The dataset includes morphometric, physiological, and survival measurements collected across two independent experiments (Experiment 1 and Experiment 2) designed to test the effects of oxygen availability (12.5, 25, 50, 100, and 200% air saturation) and temperature (15.7 to 39.3°C) on embryonic, larval, and juvenile zebrafish performance.

All files are provided in .csv format and can be opened with standard spreadsheet software or statistical programming environments (R, Python, MATLAB, etc.). Each row corresponds to an individual or experimental unit (holding beaker), and columns contain measured traits and associated metadata (e.g., Oxygen treatment, Temperature treatment, developmental stage in hours post fertilizaton).



File overview

Embryo traits





Yolk sac area – Experiment 1 (.csv)Quantified yolk sac area measurements from embryos (mm^2), used as a proxy for energy reserves and developmental progression.




Yolk sac area – Experiment 2 (.csv)Same measurements as above for the independent replicate experiment.




Heart rate – Experiment 2 (.csv)Embryonic heart rate measurements (Hertz, Hz), used as an indicator of metabolic and physiological performance.





Larval traits





Larval length – Experiment 1 (.csv)Standard body length measurements (mm) of larvae at the specified developmental stage.




Larval length – Experiment 2 (.csv)Replicate dataset of larval body length measurements.




Hatching rate and survival to first feeding – Experiment 1 (.csv)Hatching success and survival metrics quantified from embryo to first-feeding stage (e.g., see Larvae_count_cummulative, SurvivalToFeed).




Hatching rate and survival to first feeding – Experiment 2 (.csv)Replicate measurements of hatching success and early survival.





Juvenile traits





Body size and CTmax – Experiments 1 & 2 (.csv)Juvenile body size measurements and critical thermal maximum (CTmax) values (in °C), used to assess growth and upper thermal tolerance. See columns Beaker_ID and Experiment to track fish individuals rearing origin.





Experimental design notes





Data originate from two independent experiments (Exp 1 and Exp 2) performed under controlled oxygen treatments.




Traits span multiple life stages: embryo, larva, and juvenile.




Measurements include:





Morphology (yolk sac area, length, body size)




Physiology (heart rate, CTmax)




Performance and fitness proxies (hatching success, survival)







Data structure

Across files, columns typically include:





Beaker or clutch ID




Treatment (e.g., oxygen and temperature condition)




Photo or video ID




Developmental stage or time point




Measured trait value(s)



Exact variable names are specified within each file header.
DOI: 10.5281/zenodo.18817951
title: Replication Data and Coding Framework for Post-Qualification Internship Advertisements in India

description: This repository contains the replication data and coding materials for the article examining post-qualification internship advertisements in India using the U.S. Primary Beneficiary Test framework.

The dataset consists of internship advertisements collected in 2024 from a major online job platform and coded across multiple legal and economic variables reflecting wage structure, duration, supervision, training claims, and displacement risk.

The repository includes:(1) The cleaned and combined analytical dataset used for statistical analysis;(2) The original structured extraction dataset;(3) Intercoder reliability metrics (Cohen’s kappa) used to validate the coding framework; and(4) A codebook describing variable construction and operationalisation of the Primary Beneficiary Test factors.

All data have been anonymised and do not contain personal identifiers. The dataset enables replication of descriptive statistics, reliability calculations, and regression analyses reported in the article.




🔎 If You Want Slightly More Formal (For ABDC A Tone)



This repository provides the replication dataset and methodological documentation supporting the legal-empirical analysis of post-qualification internship advertisements in India. The study operationalises the Primary Beneficiary Test under U.S. labour jurisprudence to assess whether advertised internships function as substitute entry-level employment.

The materials include the final analytical dataset, coding framework, intercoder reliability outputs, and accompanying documentation necessary to reproduce the empirical findings.




⚠ Important

Do NOT:





Mention journal name




Mention submission status




Use argumentative language (“exploitative”, “illegal”, etc.)




Say “confidential data removed” unless necessary



Keep it professional and replicability-focused.



If you want, next I can draft:





Keywords




Version note (e.g., Version 1.0 – Pre-publication)




Data Availability Statement (journal-ready sentence)



Tell me which journal this is going to.
DOI: 10.5281/zenodo.18817952
title: Replication Data and Coding Framework for Post-Qualification Internship Advertisements in India

description: This repository contains the replication data and coding materials for the article examining post-qualification internship advertisements in India using the U.S. Primary Beneficiary Test framework.

The dataset consists of internship advertisements collected in 2024 from a major online job platform and coded across multiple legal and economic variables reflecting wage structure, duration, supervision, training claims, and displacement risk.

The repository includes:(1) The cleaned and combined analytical dataset used for statistical analysis;(2) The original structured extraction dataset;(3) Intercoder reliability metrics (Cohen’s kappa) used to validate the coding framework; and(4) A codebook describing variable construction and operationalisation of the Primary Beneficiary Test factors.

All data have been anonymised and do not contain personal identifiers. The dataset enables replication of descriptive statistics, reliability calculations, and regression analyses reported in the article.




🔎 If You Want Slightly More Formal (For ABDC A Tone)



This repository provides the replication dataset and methodological documentation supporting the legal-empirical analysis of post-qualification internship advertisements in India. The study operationalises the Primary Beneficiary Test under U.S. labour jurisprudence to assess whether advertised internships function as substitute entry-level employment.

The materials include the final analytical dataset, coding framework, intercoder reliability outputs, and accompanying documentation necessary to reproduce the empirical findings.




⚠ Important

Do NOT:





Mention journal name




Mention submission status




Use argumentative language (“exploitative”, “illegal”, etc.)




Say “confidential data removed” unless necessary



Keep it professional and replicability-focused.



If you want, next I can draft:





Keywords




Version note (e.g., Version 1.0 – Pre-publication)




Data Availability Statement (journal-ready sentence)



Tell me which journal this is going to.
DOI: 10.5281/zenodo.18817058
title: UAV Component Database

description: This project provides a validated component database for UAV design optimization using the HFRPP (Hybrid Fuzzy-Rough Physical Programming) framework. The database covers:



Batteries: LiPo battery specifications (capacity, voltage, weight, cost)

Motors: Brushless DC motors (KV, max current/power, weight)

ESC: Electronic Speed Controllers (current rating, efficiency, BEC)

Propellers: UIUC propeller database CT/CP coefficients

Airfoils: XFOIL-analyzed airfoil profiles
DOI: 10.5281/zenodo.18817057
title: UAV Component Database

description: This project provides a validated component database for UAV design optimization using the HFRPP (Hybrid Fuzzy-Rough Physical Programming) framework. The database covers:



Batteries: LiPo battery specifications (capacity, voltage, weight, cost)

Motors: Brushless DC motors (KV, max current/power, weight)

ESC: Electronic Speed Controllers (current rating, efficiency, BEC)

Propellers: UIUC propeller database CT/CP coefficients

Airfoils: XFOIL-analyzed airfoil profiles
DOI: 10.5281/zenodo.18818000
title: BTC_chemo RDS files

description: RDS file needed for github codeshttps://github.com/wjhlab/BTC_chemo/
DOI: 10.5281/zenodo.18817999
title: BTC_chemo RDS files

description: RDS file needed for github codeshttps://github.com/wjhlab/BTC_chemo/
DOI: 10.15468/dl.rxn3jf
title: Occurrence Download

description: A dataset containing 7231 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Panthera onca"
 ]
}

 The dataset includes 7231 records from 210 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004628-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18734846
title: Utilizing neural networks and finite element method to predict key fracture mechanic parameters under mixed-mode loading - NCN MINIATURA 8, Decision No. DEC-2024/08/X/ST8/00863

description: The dataset contains data collected during a project financed by National Centre Science (Poland) - Utilising neural networks and finite element method to predict key fracture mechanics parameters under mixed-mode loading (Miniatura 8, Decision No. DEC-2024/08/X/ST8/00863) 

The app and model files that are part of this dataset can also be obtained from https://github.com/oxygenum44/CTS_crack_path_generator 

The following files are part of the dataset:



Python files *.py - main file is "app" - it enables the user to open the GUI app that enables crack path generation or determining fracture parameters in CTS specimen

Machine learning models with the extension *.pkl (pickle objects) that can be loaded using a Python script and reused - they are also utilised by the GUI app

Image files *.jpg - used by the app

Datasets in the form of txt/csv files for fracture mechanics parameters (dataset 1) and strains (dataset 2) in CTS specimens containing information for diffrent location of the crack
DOI: 10.5281/zenodo.18734845
title: Utilizing neural networks and finite element method to predict key fracture mechanic parameters under mixed-mode loading - NCN MINIATURA 8, Decision No. DEC-2024/08/X/ST8/00863

description: The dataset contains data collected during a project financed by National Centre Science (Poland) - Utilising neural networks and finite element method to predict key fracture mechanics parameters under mixed-mode loading (Miniatura 8, Decision No. DEC-2024/08/X/ST8/00863) 

The app and model files that are part of this dataset can also be obtained from https://github.com/oxygenum44/CTS_crack_path_generator 

The following files are part of the dataset:



Python files *.py - main file is "app" - it enables the user to open the GUI app that enables crack path generation or determining fracture parameters in CTS specimen

Machine learning models with the extension *.pkl (pickle objects) that can be loaded using a Python script and reused - they are also utilised by the GUI app

Image files *.jpg - used by the app

Datasets in the form of txt/csv files for fracture mechanics parameters (dataset 1) and strains (dataset 2) in CTS specimens containing information for diffrent location of the crack
DOI: 10.5281/zenodo.18753732
title: Machine-Learning-Based Prediction of the Compressive Strength of Superabsorbent Polymer-Incorporated Cementitious Materials: Modeling and Experimental Insights into Polymer Characteristics

description: This repository provides the Python code used in the study "Machine-Learning-Based Prediction of the Compressive Strength of Superabsorbent Polymer-Incorporated Cementitious Materials: Modeling and Experimental Insights into Polymer Characteristics".

The code implements the M2 model for predicting the compressive strength of SAP-incorporated cementitious materials, and includes the CatBoost model (best-performing among the five ML methods evaluated) together with scripts to generate SHAP-based interpretability plots.