cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/d4gsjmjprp
title: Experimental Data and Mixture-Model Outputs for a PET/ZnO-Reinforced Epoxy Corrosion-Resistant Coating for Roofing Applications

description: This dataset contains the modelling files and characterisation outputs supporting the study titled “Recycling PET Waste for Corrosion-Resistant Roof Coating: A Sustainable Approach to Enhance Protective Performance.” The data were generated to enable independent verification, replication, and further analysis of the formulation, performance evaluation, and optimisation of the corrosion-resistant composite coating (CRCC).
The dataset includes the Design-Expert® mixture simplex-lattice model file with the inputs being the raw measurements from mixture-designed coating formulations composed of epoxy resin (ER), recycled polyethylene terephthalate (PET) waste powder (PW), and zinc oxide (ZnO). Experimental data comprise average corrosion rate measurements obtained from immersion testing under alkaline exposure conditions, adhesive strength values determined via pull-off adhesion testing. From the Design-Expert® mixture simplex-lattice model file, the ANOVA tables, regression coefficients, optimisation constraints, predicted versus experimental values, and residual diagnostics used to evaluate model adequacy and predictive reliability can be obtained.
Fourier Transform Infrared (FTIR-ATR) spectra used to confirm component incorporation and epoxy cure progression are provided in both raw formats.
DOI: 10.17632/d4gsjmjprp.1
title: Experimental Data and Mixture-Model Outputs for a PET/ZnO-Reinforced Epoxy Corrosion-Resistant Coating for Roofing Applications

description: This dataset contains the modelling files and characterisation outputs supporting the study titled “Recycling PET Waste for Corrosion-Resistant Roof Coating: A Sustainable Approach to Enhance Protective Performance.” The data were generated to enable independent verification, replication, and further analysis of the formulation, performance evaluation, and optimisation of the corrosion-resistant composite coating (CRCC).
The dataset includes the Design-Expert® mixture simplex-lattice model file with the inputs being the raw measurements from mixture-designed coating formulations composed of epoxy resin (ER), recycled polyethylene terephthalate (PET) waste powder (PW), and zinc oxide (ZnO). Experimental data comprise average corrosion rate measurements obtained from immersion testing under alkaline exposure conditions, adhesive strength values determined via pull-off adhesion testing. From the Design-Expert® mixture simplex-lattice model file, the ANOVA tables, regression coefficients, optimisation constraints, predicted versus experimental values, and residual diagnostics used to evaluate model adequacy and predictive reliability can be obtained.
Fourier Transform Infrared (FTIR-ATR) spectra used to confirm component incorporation and epoxy cure progression are provided in both raw formats.
DOI: 10.17632/hkcyvhvmwp.2
title: Solar Energy Dataset

description: This dataset provides a monthly compilation of climate variables for an entire year crucial for evaluating the feasibility of solar power projects, including solar irradiance, surface albedo, cloud cover, and temperature—making it highly relevant for environmental and energy research communities.
The data can be used to train machine learning models that classify or rank geographic regions based on their suitability for solar energy harvesting, offering support to urban planners, energy developers, and policy-makers for data-driven decision-making.
Researchers working in climate science, renewable energy planning, or geospatial analytics can leverage this dataset to assess energy potential and analyze regional variability, helping optimize site selection strategies in different parts of the world.
The dataset facilitates the creation of interactive geospatial visualizations and maps, helping to democratize energy insights by making them accessible to a broader range of stakeholders including NGOs, startups, and governments focused on sustainable development.
This data can be used to benchmark and evaluate machine learning models aimed at site selection, climate prediction, or grid expansion, while also serving as a base for transfer learning in broader energy or environmental prediction tasks.
By offering publicly accessible, structured climate and solar energy data across a 1-year period(January to December 2022) [1], this collection supports longitudinal studies and helps compare trends in solar resource availability across seasons and regions—an essential step toward scaling clean energy initiatives globally
DOI: 10.17632/b5f9rp823c
title: Investments that Make our Homes Greener: The Role of Regulation

description: Replication package for: Investments that Make our Homes Greener: The Role of Regulation
DOI: 10.17632/b5f9rp823c.2
title: Investments that Make our Homes Greener: The Role of Regulation

description: Replication package for: Investments that Make our Homes Greener: The Role of Regulation
DOI: 10.17632/sh8zw92kyx
title: MalB_ASA

description: All obtained results were based on standard protocols which were presented in specific manners and details in our manuscript.
DOI: 10.17632/sh8zw92kyx.1
title: MalB_ASA

description: All obtained results were based on standard protocols which were presented in specific manners and details in our manuscript.
DOI: 10.5281/zenodo.18085697
title: GEMSTONE Research Data: EEG Recordings from Chemogenetic Applications on Mice

description: This file includes Labchart EEG images acquired from different mice strains with and without chemogenetic application, and accompanying summary tables, within the scope of the GEMSTONE Project.
DOI: 10.5281/zenodo.18085696
title: GEMSTONE Research Data: EEG Recordings from Chemogenetic Applications on Mice

description: This file includes Labchart EEG images acquired from different mice strains with and without chemogenetic application, and accompanying summary tables, within the scope of the GEMSTONE Project.
DOI: 10.15468/dl.rrhku8
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Rhabdosargus globiceps",
 "Year 2000-2025"
 ]
}

 The dataset includes 102 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068473-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/w4xdf8j8gz
title: ICI - Cutaneous toxicities

description: Supplementary material for the article describing the relative risk of cutaneous immune-related adverse events associated with immune checkpoint inhibitors compared with chemotherapy, and according to the underlying type of cancer.
DOI: 10.17632/w4xdf8j8gz.1
title: ICI - Cutaneous toxicities

description: Supplementary material for the article describing the relative risk of cutaneous immune-related adverse events associated with immune checkpoint inhibitors compared with chemotherapy, and according to the underlying type of cancer.
DOI: 10.17632/n5fwdyzwt8.1
title: Dataset about Vietnamese civil servants (Original data)

description: This dataset was constructed to measure the impact of digital skills on the output of local government officials in Vietnam to meet research needs. We employed the snowball sampling technique to collect online survey responses from civil servants currently working in local government agencies across all six socio-economic regions of Vietnam in the post-amalgamation period. Within 20 days (from November 15, 2025, to December 5, 2025), a total of 458 responses were collected, but some were not eligible for analysis such as extreme answers (all answers were 1 or 5) and arbitrary answers (trap questions). The remains of 380 samples were used for further analysis with the rate of 82.9%.
DOI: 10.58132/dvdcdd
title: First-generation quark couplings

description: Dataset contains data and codes related to the study of the possible use of radiative processes for precise measurement of first-generation quark couplings at future electron-positron collider running at the Z-pole. Results of the study were summarized in the publication "Determination of the first-generation quark couplings at the Z-pole" published in J. High Energ. Phys. 11 (2025) 050.This research was part of the project "Realistic simulations of exotic processes at future e+e- colliders" funded by National Science Centre, Poland, under grant agreement nr 2021/43/B/ST2/01778.MethodologyThe study was based on large event samples of quark pair-production events for electron-positron collider running at the Z-pole. Samples were generated with the WHIZARD program and processed with the DELPHES framework to model detector performance. Event selection efficiencies and acceptance of the cut used to select events with final state radiation extracted from the Monte Carlo simulation were then used in the semi-analytical framework to extract quark pair-production cross sections and quark couplings. We present a method to extract the values of the Z couplings to light quarks based on up- and down-quarks’ different contributions to radiative and non-radiative Z boson decays. We show that systematic uncertainty in the heavy-flavour tagging performance is the key factor in the analysis, and that reducing it to the sub-permille level is crucial to fully profit from the high luminosity of future e+e− machines.Files:example Jupyter notebook (Python scripts) for studying coupling measurement precision as a function of the integrated collider luminosity, for different assumptions concerning systematic uncertainties in flavour tagging (Fig. 7 in the paper),input file with photon cut acceptance for radiative and non-radiative events, for different quark flavours (required by the Jupyter notebook)CSV files with final analysis results: expected uncertainties in the quark coupling extraction as a function of the analysis cut and collider luminosity, as presented in Figs. 4 and 7 of the paper.PDF and PNG files of the figuresSoftware tools usedWHIZARD Event Generator web page: https://whizard.hepforge.org/DELPHES: https://github.com/delphes/delphes
DOI: 10.17632/knvtw3hg8p.2
title: Reprodution Folder"Kinship Intensity and Innovation Ambidexterity in Family Businesses: Venturing into the Unknown"

description: This dataset contains the replication materials for the article "Kinship Intensity and Innovation Ambidexterity in Family Businesses: Venturing into the Unknown" submitted in Economic Modelling.
The sample consists of 7,989 firm-year observations of Chinese A-share listed family businesses from 2012 to 2020. The data are primarily sourced from the China Stock Market & Accounting Research (CSMAR) database, with supplementary manual collection for kinship relationship information.
Key variables include:

Exploratory innovation (Expr): Measured by the number of exploratory patent applications (invention patents and design patents) plus one, followed by a natural logarithm transformation. Source: CSMAR patent database.
Exploitative innovation (Expi): Measured by the number of exploitative patent applications (utility model patents) plus one, followed by a natural logarithm transformation. Source: CSMAR patent database.
Kinship intensity (Kinship): The primary independent variable, calculated as the sum of kinship coefficients between the de facto controller and all family members on the board of directors. Kinship coefficients follow standard genealogical weighting (e.g., 0.5 for parent-child or spouses, 0.25 for siblings or grandchildren, etc.).
Control variables: Firm size, age, leverage, ROA, cash flow, board size, independent director ratio, ownership concentration, industry and year dummies, and other standard firm-level controls (full list available in the manuscript).
Instrumental variable: Per capita rice cultivation area in 1978 in the birthplace of the ultimate controller (used for IV estimation).

Important Privacy Notice:
The variables related to kinship relationships (e.g., Kinship, Kinship_core, Kinship_noncore, Kinship_blood, Kinship_inlaw, and family member identifiers) were manually collected from multiple sources including annual reports, prospectuses, listing announcements, executive supervision reports, news articles, and other public and semi-public channels. These data involve detailed familial relationships of entrepreneurs and controlling families, which are considered highly sensitive personal information.
Due to privacy and ethical considerations, researchers interested in accessing or replicating results using the kinship variables are kindly requested to contact the corresponding author or the first author directly for collaboration or controlled access under appropriate data use agreements.
The remaining variables (financial, patent, governance, and instrumental variable data) are derived from public sources (primarily CSMAR) and can be independently reconstructed by licensed users of the database using the variable definitions and sample selection criteria provided in the manuscript.
DOI: 10.17632/bbkd9f56v9
title: Comparative analysis of the technical–tactical characteristics and position-specific features of the top four teams and their opponents in the 2023 FIFA Women’s World Cup

description: Objectives: The aim of this study was to compare the technical–tactical and performance indicators of the top four teams and their opponents at the 2023 FIFA Women’s World Cup, with particular focus on positional differences. 
Methods: The analysis included 38 key variables related to passing, attacking, defending, and physical performance. Data processing was conducted using SPSS 29.0, applying a General Linear Model (GLM) and post hoc EMMeans analyses. The main effects and interactions of team (Top 4 vs. opponents) and position were examined. 
Results: The team had a significant effect on the number of attempts passes (p&lt;0.001; η²p=0.031), the number of completed passes (p&lt;0.001; η²p=0.043), the completions of passes (p&lt;0.001; η²p=0.042), completed of line breaks (p&lt;0.004; η²p=0.01), the completion of line breaks (p&lt;0.002; η²p=0.012), indirect pressure (p&lt;0.001; η²p =0.017), pushing on (p&lt;0.003; η²p=0.011), and pushing on into (p&lt;0.004; η²p=0.01).
The defenders and midfielders of the Top 4 teams showed higher passing activity, more completed and attempted line break, and better high-intensity running performance in Zone 4 than their opponents playing in these positions.
Conclusions: The findings confirm that the top four teams’ success was based on a structured, possession-oriented playing style characterized by high passing and attacking activity. Performance analysis provides valuable insights not only for scientific under-standing but also for practical football applications, including training design, tactical planning, and youth development. Identifying the indicators that distinguish elite teams helps to better understand the elements leading to success and to apply them effectively in practice.
DOI: 10.17632/bbkd9f56v9.1
title: Comparative analysis of the technical–tactical characteristics and position-specific features of the top four teams and their opponents in the 2023 FIFA Women’s World Cup

description: Objectives: The aim of this study was to compare the technical–tactical and performance indicators of the top four teams and their opponents at the 2023 FIFA Women’s World Cup, with particular focus on positional differences. 
Methods: The analysis included 38 key variables related to passing, attacking, defending, and physical performance. Data processing was conducted using SPSS 29.0, applying a General Linear Model (GLM) and post hoc EMMeans analyses. The main effects and interactions of team (Top 4 vs. opponents) and position were examined. 
Results: The team had a significant effect on the number of attempts passes (p&lt;0.001; η²p=0.031), the number of completed passes (p&lt;0.001; η²p=0.043), the completions of passes (p&lt;0.001; η²p=0.042), completed of line breaks (p&lt;0.004; η²p=0.01), the completion of line breaks (p&lt;0.002; η²p=0.012), indirect pressure (p&lt;0.001; η²p =0.017), pushing on (p&lt;0.003; η²p=0.011), and pushing on into (p&lt;0.004; η²p=0.01).
The defenders and midfielders of the Top 4 teams showed higher passing activity, more completed and attempted line break, and better high-intensity running performance in Zone 4 than their opponents playing in these positions.
Conclusions: The findings confirm that the top four teams’ success was based on a structured, possession-oriented playing style characterized by high passing and attacking activity. Performance analysis provides valuable insights not only for scientific under-standing but also for practical football applications, including training design, tactical planning, and youth development. Identifying the indicators that distinguish elite teams helps to better understand the elements leading to success and to apply them effectively in practice.
DOI: 10.5517/ccdc.csd.cc29hxnd
title: CCDC 2130464: Experimental Crystal Structure Determination

description: Related Article: Zhishan Luo, Yejing Liu, Yulian Liu, Chen Li, Yawen Li, Qian Li, Yi Wei, Liming Zhang, Bin Xu, Xiaoyong Chang, Zewei Quan|2023|Adv.Mater.|34|2200607|doi:10.1002/adma.202200607
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/k24k5j34fr.1
title: Fragile Firms, Fierce Shocks: The Asymmetric Firm-Level Transmission of Monetary Policy in India

description: Replication Code for the paper.
DOI: 10.5517/ccdc.csd.cc29xgk8
title: CCDC 2142520: Experimental Crystal Structure Determination

description: Related Article: Jeong-A Yang, Chae-Won Lee, Chaeyoon Kim, Michael Auer, In Cheol Yu, Jungkwon OH, Woojin Yoon, Hoseop Yun, Dongwook Kim, Mojca Jazbinsek, Fabian Rotermund, O-Pil Kwon|2024|Adv. Opt. Mater.|12|2400343|doi:10.1002/adom.202400343
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c94f8
title: CCDC 2183529: Experimental Crystal Structure Determination

description: Related Article: Muthumari Subramanian, Tamilthendral Veerappan, Clinton Savarimuthu Selvan, Nandeeswaran Chinnanatchimuthu, Sathiya Kamatchi Thangavel, Natarajan Karuppannan|2025|J.Org.Chem.|||doi:10.1021/acs.joc.5c01835
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2nxbnl
title: CCDC 2470100: Experimental Crystal Structure Determination

description: Related Article: Wen Lu, Miaomiao Liu, Wuhan Sang, Han Gao, Yanqin Wang, Shilong Yang, Mengyi Zhou, Caihua Zhang, Li Xu|2026|J.Mol.Struct.|1354||doi:10.1016/j.molstruc.2025.144921
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/pk437m5mx7
title: Good and Bad classification of Radish

description: Title: Good and Bad Classification of Radish: Techniques, Parameters, and Perspectives .Radish is an edible root vegetable belonging to the family Brassicaceae. It is widely grown and consumed fresh in salads, curries, and pickles. Botanical name Raphanus sativus Parts eaten Mainly the root, sometimes the leaves as leafy vegetables. Types White radish .
Red radish
Pink radish
Long and round varieties
Nutritional value (approx.)
High water content
Rich in vitamin C
Contains fiber, potassium, calcium
Very low in calories
Uses
Consumed raw (salads)
Quality characteristics
Firm texture
Smooth skin
Mild pungent taste
Free from cracks and discoloration
Radish (Raphanus sativus) – Nutritional Value
 Minerals
Calcium: 25 mg
Phosphorus: 20 mg
Potassium: 233 mg
Iron: 0.3 mg
Magnesium: 10 mg
Vitamins
Vitamin C: 14–15 mg
Standard Shelf Life of Radish (Mooli)
The shelf life of radish depends on storage condition and form:
At room temperature
1–3 days
Leaves wilt quickly, roots lose moisture
Refrigerated storage (0–4 °C, 90–95% RH)
2–3 weeks (without leaves)
7–10 days (with leaves)
Cold storage (commercial)
Temperature: 0 °C
Relative Humidity: 95–100%
Up to 3–4 weeks
Processed forms
Cut radish (refrigerated): 2–3 days
Pickled radish: Several months
Dehydrated radish: 6–12 months .
Sample Preparation of Radish
(Collected from Malda Rathabari )
 Objective 
To prepare a representative radish sample collected from Rathabari Market, Malda for quality or laboratory analysis.
Sample Collection
Fresh radishes were randomly collected from different vendors at Rathabari MaldaWest Bengal latitude and longitude.
Diseased, damaged, or over-mature samples were avoided.
Samples were placed in clean polyethylene bags and transported to the laboratory.
Washing of Radish (Sample Preparation Step)
 Objective
To remove soil, dust, pesticide residues, and microorganisms from radish before analysis or processing.
Procedure (Washing of Radish)
Pre-cleaning:
Remove adhering soil and mud by gentle shaking or brushing.
First washing:
Wash radish thoroughly under running potable water to remove visible dirt.
Final rinse:
Rinse again with clean potable or distilled water to remove chlorine residues.
Realme 7 – 
Camera
Rear Quad Camera System:
Main: 64 MP Sony sensor
Ultra wide: 8 MP, 119° field
B&W portrait lens
Macro lens
Camera features: Nightscape, Starry mode, Ultra wide/macros, portrait modes. 
Realme
Front Camera: 16 MP for selfies. 
Realme
 Battery & Charging
5000 mAh battery
Memory & Storage
Several options: 6 GB 
Storage: 64 GB 
For radish images:
Good radish images = 500
Bad radish images = 500
Total number of pictures = 1000 images
DOI: 10.5517/ccdc.csd.cc2nxbpm
title: CCDC 2470101: Experimental Crystal Structure Determination

description: Related Article: Wen Lu, Miaomiao Liu, Wuhan Sang, Han Gao, Yanqin Wang, Shilong Yang, Mengyi Zhou, Caihua Zhang, Li Xu|2026|J.Mol.Struct.|1354||doi:10.1016/j.molstruc.2025.144921
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2nxps2
title: CCDC 2470445: Experimental Crystal Structure Determination

description: Related Article: Wen Lu, Miaomiao Liu, Wuhan Sang, Han Gao, Yanqin Wang, Shilong Yang, Mengyi Zhou, Caihua Zhang, Li Xu|2026|J.Mol.Struct.|1354||doi:10.1016/j.molstruc.2025.144921
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.