cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2sz69s
title: Occurrence Download

description: A dataset containing 538981 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus pinaster",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 538981 records from 797 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009648-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.prtph6
title: Occurrence Download

description: A dataset containing 233 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Blitum capitatum",
 "Country is Sweden",
 "HasCoordinate is true"
 ]
}

 The dataset includes 233 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009694-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zgd2b4fj2g
title: STROBE Statement—checklist of items that should be included in reports of observational studies for the Personalizing health call center services: results from an online survey on user experiences in Kuwait

description: This is a STROBE checklist for the cross-sectional study "Personalizing health call center services: results from an online survey on user experiences in Kuwait"
DOI: 10.17632/zgd2b4fj2g.1
title: STROBE Statement—checklist of items that should be included in reports of observational studies for the Personalizing health call center services: results from an online survey on user experiences in Kuwait

description: This is a STROBE checklist for the cross-sectional study "Personalizing health call center services: results from an online survey on user experiences in Kuwait"
DOI: 10.17632/vfscrxzz22
title: Data

description: Data
DOI: 10.17632/vfscrxzz22.1
title: Data

description: Data
DOI: 10.6084/m9.figshare.30191833
title: Supplementary Data for HT-PELSA

description: Systematic mapping of protein-ligand interactions is essential for understanding biological processes and drug mechanisms. Peptide-centric local stability assay (PELSA) is a powerful tool for detecting these interactions and identifying potential binding sites. However, its original workflow is limited in throughput, sample compatibility and accessible protein targets. Here, we introduce a high-throughput adaptation - HT-PELSA - that increases sample processing efficiency 100-fold while maintaining high sensitivity and reproducibility. HT-PELSA substantially extends the capabilities of the original method by enabling sensitive protein-ligand profiling in crude cell, tissue and bacterial lysates, allowing the identification of membrane protein targets in diverse biological systems. We demonstrate that HT-PELSA can precisely and accurately determine binding affinities of small molecule inhibitors, sensitively detect direct and allosteric ATP binding, and reveal off-target interactions of a marketed kinase inhibitor in heart tissue. By enhancing scalability, reducing costs, and enabling system-wide drug screening across a wide range of sample types, HT-PELSA - when combined with next-generation mass spectrometry - may offer a powerful platform poised to accelerate both drug discovery and basic biological research.<br>
DOI: 10.6084/m9.figshare.30191833.v1
title: Supplementary Data for HT-PELSA

description: Systematic mapping of protein-ligand interactions is essential for understanding biological processes and drug mechanisms. Peptide-centric local stability assay (PELSA) is a powerful tool for detecting these interactions and identifying potential binding sites. However, its original workflow is limited in throughput, sample compatibility and accessible protein targets. Here, we introduce a high-throughput adaptation - HT-PELSA - that increases sample processing efficiency 100-fold while maintaining high sensitivity and reproducibility. HT-PELSA substantially extends the capabilities of the original method by enabling sensitive protein-ligand profiling in crude cell, tissue and bacterial lysates, allowing the identification of membrane protein targets in diverse biological systems. We demonstrate that HT-PELSA can precisely and accurately determine binding affinities of small molecule inhibitors, sensitively detect direct and allosteric ATP binding, and reveal off-target interactions of a marketed kinase inhibitor in heart tissue. By enhancing scalability, reducing costs, and enabling system-wide drug screening across a wide range of sample types, HT-PELSA - when combined with next-generation mass spectrometry - may offer a powerful platform poised to accelerate both drug discovery and basic biological research.<br>
DOI: 10.17632/zkvb73tn79
title: Drying kinetics (MR) data and AI model scripts for ethanol-pretreated beetroot and ginger

description: Dataset supporting the study on drying kinetics of ethanol-pretreated beetroot cubes (10 mm) and ginger slices (2 mm) under forced-air convection at 60, 70, 80 °C, with and without 50% (v/v) ethanol pretreatment (15 min). Files include raw mass–time measurements (n=3 per condition), processed moisture ratio (MR) series, Page-model parameters (k, n), effective diffusivity (Fickian and anomalous), train/test splits, Python notebooks (TensorFlow/Scikit-learn/XGBoost), and trained model artifacts (DL/ML).
DOI: 10.17632/zkvb73tn79.1
title: Drying kinetics (MR) data and AI model scripts for ethanol-pretreated beetroot and ginger

description: Dataset supporting the study on drying kinetics of ethanol-pretreated beetroot cubes (10 mm) and ginger slices (2 mm) under forced-air convection at 60, 70, 80 °C, with and without 50% (v/v) ethanol pretreatment (15 min). Files include raw mass–time measurements (n=3 per condition), processed moisture ratio (MR) series, Page-model parameters (k, n), effective diffusivity (Fickian and anomalous), train/test splits, Python notebooks (TensorFlow/Scikit-learn/XGBoost), and trained model artifacts (DL/ML).
DOI: 10.17632/v6j3mffyr8.1
title: Análise dos polimorfismos da Interleucina 17A: rs3819025, rs2275913, rs3748067, e Interleucina 17F: rs763780 e rs9382084 em amostras de pacientes com doenças gástricas
DOI: 10.17632/k2ptxmfjhj
title: Strawberry Disease Detection Dataset with Hybrid Data Augmentation

description: This dataset provides annotated images for instance segmentation of strawberry diseases affecting both fruit and leaves. It extends two publicly available datasets:

[1] Afzaal, U., Bhattarai, B., Pandeya, Y.R., Lee, J., 2021. An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN. Sensors 21, 6565. https://doi.org/10.3390/s21196565.

[2] Pérez-Borrero, I., Marín-Santos, D., Gegúndez-Arias, M.E., Cortés-Ancos, E., 2020. A fast and accurate deep learning method for strawberry instance segmentation. Comput. Electron. Agric. 178, 105736. https://doi.org/10.1016/j.compag.2020.105736.

Both datasets are distributed under terms that allow reuse for academic and research purposes. This extended dataset (v1.0) applies systematic augmentation for class balance and increases the diversity of environmental conditions in the data.

The proposed dataset contains 5,610 annotated images across 8 classes, with approximately 700 images per class: Angular Leafspot, Anthracnose Fruit Rot, Blossom Blight, Gray Mold, Healthy Strawberry, Leaf Spot, Powdery Mildew Fruit, Powdery Mildew Leaf.

All images are provided in JPG format with a resolution of 640 × 640 pixels. Annotations are polygon-based instance masks provided in COCO segmentation format (.json).

The proposed dataset is organized into 80% training, 10% validation, and 10% testing, ensuring balanced representativeness of all classes.

Two directory structures are included at the root level:

/Strawberry-segmentation-hybrid-augmentation
 /train
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json
 /valid
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json
 /test
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json

/Strawberry-segmentation-traditional-augmentation
 /train
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json
 /valid
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json
 /test
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json

Traditional image augmentation includes geometric and photometric transformations, including random cropping, center cropping, rotation, horizontal flipping, vertical flipping, random brightness and contrast adjustments, and random hue and saturation value adjustments.

Hybrid image augmentation additionally incorporates synthetic images generated with a diffusion model (DALL·E 3). Synthetic images are included only in the training split.
DOI: 10.17632/k2ptxmfjhj.1
title: Strawberry Disease Detection Dataset with Hybrid Data Augmentation

description: This dataset provides annotated images for instance segmentation of strawberry diseases affecting both fruit and leaves. It extends two publicly available datasets:

[1] Afzaal, U., Bhattarai, B., Pandeya, Y.R., Lee, J., 2021. An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN. Sensors 21, 6565. https://doi.org/10.3390/s21196565.

[2] Pérez-Borrero, I., Marín-Santos, D., Gegúndez-Arias, M.E., Cortés-Ancos, E., 2020. A fast and accurate deep learning method for strawberry instance segmentation. Comput. Electron. Agric. 178, 105736. https://doi.org/10.1016/j.compag.2020.105736.

Both datasets are distributed under terms that allow reuse for academic and research purposes. This extended dataset (v1.0) applies systematic augmentation for class balance and increases the diversity of environmental conditions in the data.

The proposed dataset contains 5,610 annotated images across 8 classes, with approximately 700 images per class: Angular Leafspot, Anthracnose Fruit Rot, Blossom Blight, Gray Mold, Healthy Strawberry, Leaf Spot, Powdery Mildew Fruit, Powdery Mildew Leaf.

All images are provided in JPG format with a resolution of 640 × 640 pixels. Annotations are polygon-based instance masks provided in COCO segmentation format (.json).

The proposed dataset is organized into 80% training, 10% validation, and 10% testing, ensuring balanced representativeness of all classes.

Two directory structures are included at the root level:

/Strawberry-segmentation-hybrid-augmentation
 /train
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json
 /valid
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json
 /test
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json

/Strawberry-segmentation-traditional-augmentation
 /train
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json
 /valid
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json
 /test
 image_1.jpg
 image_2.jpg
 ...
 _annotations.coco.json

Traditional image augmentation includes geometric and photometric transformations, including random cropping, center cropping, rotation, horizontal flipping, vertical flipping, random brightness and contrast adjustments, and random hue and saturation value adjustments.

Hybrid image augmentation additionally incorporates synthetic images generated with a diffusion model (DALL·E 3). Synthetic images are included only in the training split.
DOI: 10.15468/dl.wfmfvn
title: Occurrence Download

description: A dataset containing 942 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Dryopteris decipiens)"
 ]
}

 The dataset includes 942 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009696-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jbe92q
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Blitum capitatum",
 "Country is Ukraine",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009695-250920141307145/datasets/export for details.
DOI: 10.21227/93j6-5q34
title: "Ontario Communities Energy Demand "

description: "Three region-specific datasets representing urban (Toronto), semi-urban (Kingston), and rural (Sarnia) Ontario communities are provided to perform the case studies. Each dataset captures the sectoral electricity demand (residential, commercial, and industrial), derived from the Independent Electricity System Operator (IESO) data and adjusted by Forward Sortation Area (FSA) codes. Hourly consumption patterns are scaled to reflect demographic and geographic conditions, while long-term demand growth projections are incorporated to 2050. These datasets provide realistic demand profiles for different community types, ensuring that the proposed formulation can be tested under diverse load conditions and regional characteristics. This design enables verification of the framework\u2019s efficiency in evaluating feasible energy transition pathways across contrasting system contexts."
DOI: 10.17632/235j3cfwkb
title: Corpus-Based Language Pedagogy Interview Transcripts

description: This data is semi-structured interview protocol focusing on students’ reading attitudes, challenges, instructional practices, and perspectives on corpus- and AI-supported pedagogy.
DOI: 10.17632/235j3cfwkb.1
title: Corpus-Based Language Pedagogy Interview Transcripts

description: This data is semi-structured interview protocol focusing on students’ reading attitudes, challenges, instructional practices, and perspectives on corpus- and AI-supported pedagogy.
DOI: 10.17632/kkfwkzyxnz
title: Culturally focused video education targeted towards Hispanic patients increases melanoma knowledge and sun-protective behaviors after 12 months - Supplemental Data

description: Culturally focused video education targeted towards Hispanic patients increases melanoma knowledge and sun-protective behaviors after 12 months - Supplemental Data
DOI: 10.17632/kkfwkzyxnz.1
title: Culturally focused video education targeted towards Hispanic patients increases melanoma knowledge and sun-protective behaviors after 12 months - Supplemental Data

description: Culturally focused video education targeted towards Hispanic patients increases melanoma knowledge and sun-protective behaviors after 12 months - Supplemental Data
DOI: 10.15468/dl.uknbnt
title: Occurrence Download

description: A dataset containing 57 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Dryopteris anthracinisquama)"
 ]
}

 The dataset includes 57 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009698-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sbvj5a
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Blitum nuttallianum",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009699-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7ehvy9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Blitum nuttallianum",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009700-250920141307145/datasets/export for details.
DOI: 10.15468/dl.3yse76
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Blitum nuttallianum",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009701-250920141307145/datasets/export for details.
DOI: 10.17632/k4v9znyxwc
title: matlab code

description: matlab code
DOI: 10.17632/k4v9znyxwc.1
title: matlab code

description: matlab code
DOI: 10.17632/czb7ww5pkz
title: Global Dataset of UHPC Mix Designs with Supplementary Cementitious Materials, Nano Additives, and Sustainable Fillers

description: This dataset comprises 2,188 ultra-high-performance concrete (UHPC) mix designs compiled from 168 publications representing 27 countries, with a strong focus on sustainable materials. The dataset features mixtures incorporating a wide range of supplementary cementitious materials (SCMs) along with nano particles and sustainable fillers (e.g., steel slag, recycled brick powder, silica flour, and natural pozzolana). The data are organized into five major categories: (i) Detailed mix constituents, including types and quantities of all materials; (ii) Curing method includes temperature, humidity, pressure and duration(for autoclave curing); (iii) Workability includes slump and slump flow, (iv) Mechanical properties such as compressive strength at multiple ages, tensile and flexural parameters, and elastic modulus, and (v) durability including shrinkage, rapid chloride permeability, freeze-thaw resistance, porosity, and surface resistivity. In addition, the dataset also documents the testing standards adopted in each study for measuring these properties and specifies specimen dimensions used in mechanical testing. This structured and diverse compilation offers a foundational resource for data-driven research and model development, supporting both forward prediction and inverse design of high-performance, eco-efficient UHPC mixtures.
DOI: 10.17632/czb7ww5pkz.2
title: Global Dataset of UHPC Mix Designs with Supplementary Cementitious Materials, Nano Additives, and Sustainable Fillers

description: This dataset comprises 2,188 ultra-high-performance concrete (UHPC) mix designs compiled from 168 publications representing 27 countries, with a strong focus on sustainable materials. The dataset features mixtures incorporating a wide range of supplementary cementitious materials (SCMs) along with nano particles and sustainable fillers (e.g., steel slag, recycled brick powder, silica flour, and natural pozzolana). The data are organized into five major categories: (i) Detailed mix constituents, including types and quantities of all materials; (ii) Curing method includes temperature, humidity, pressure and duration(for autoclave curing); (iii) Workability includes slump and slump flow, (iv) Mechanical properties such as compressive strength at multiple ages, tensile and flexural parameters, and elastic modulus, and (v) durability including shrinkage, rapid chloride permeability, freeze-thaw resistance, porosity, and surface resistivity. In addition, the dataset also documents the testing standards adopted in each study for measuring these properties and specifies specimen dimensions used in mechanical testing. This structured and diverse compilation offers a foundational resource for data-driven research and model development, supporting both forward prediction and inverse design of high-performance, eco-efficient UHPC mixtures.
DOI: 10.17632/4snkm43drx.1
title: Interconnectedness and Systemic Risk in Financial Networks: Fresh Evidence from India

description: It ahs the dataset used in the paper and codes for generating tables and figures
DOI: 10.17632/7m575xbxbn
title: The EL-POCO survey raw data

description: This dataset contains the raw data from the EL-POCO research (vascular E-Learning in the PO-st COvid-19 pandemic: the EL-POCO survey).
All records are anonymised with some sensitive information removed (e.g. ip address, timestamps, email addresses).
More information on this project is available at https://med-pie.com/the-el-poco-survey
DOI: 10.17632/7m575xbxbn.3
title: The EL-POCO survey raw data

description: This dataset contains the raw data from the EL-POCO research (vascular E-Learning in the PO-st COvid-19 pandemic: the EL-POCO survey).
All records are anonymised with some sensitive information removed (e.g. ip address, timestamps, email addresses).
More information on this project is available at https://med-pie.com/the-el-poco-survey
DOI: 10.17632/bk7wmz2bt5.1
title: Supplementary Material for microRNAs profile in colostrum and mature cow milk

description: The data shows the expression profiles of microRNAs from colostrum and mature cow milk
DOI: 10.17632/tp7f2z44wd
title: Effect of desulfurization gypsum on dissolution kinetics and phase evolution in the alkali-activated steel slag–gold tailings system

description: To address the issue of limited large-scale application of desulfurized gypsum (DG) due to the unclear mechanism of its action in alkali-activated systems, this study employed steel slag and gold tailings as precursors to investigate the influence of desulfurized gypsum on the dissolution kinetics, macroscopic properties, phase evolution, and reaction pathways of this alkali-activated system. The results demonstrate that an appropriate amount of desulfurized gypsum can enhance the 28-day compressive strength of the system to 22.4 MPa and shorten the initial and final setting times by more than 55 minutes. In an alkaline environment, desulfurized gypsum not only directly participates in the formation of ettringite but also maintains the Ca/Si ratio of C-(A)-S-H gel within the range of 0.8 - 1.3 and increases the Si/Al ratio to 2.0 - 3.1, thereby mitigating the mismatch in reaction rates between steel slag and gold tailings. When desulfurized gypsum is absent, the reaction pathway of the system is predominantly alkali-activated gelation, whereas with an appropriate amount of desulfurized gypsum, the reaction is primarily dominated by sulfate-activated ettringite crystallization. Furthermore, through hydration kinetics analysis, the exponential function model proposed in this study accurately describes the hydration heat release behavior of the system, reflecting the characteristic of reaction rate decay over time. This study provides a theoretical basis for the high-value utilization of desulfurized gypsum in solid waste-based cementitious materials.