cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8g4pnzy6fc.1
title: Good and bad characteristics of molina (Triticum durum)

description: Data Description for Good and Bad Classification of Triticum durum

The dataset used in this project focuses on the classification of Triticum durum (commonly known as durum wheat) into two categories: good and bad quality samples. The goal of the project is to build a reliable model that can accurately differentiate between high-quality and low-quality grains based on selected features.

The dataset comprises a total of 2000 samples, with an equal distribution of 1000 good and 1000 bad samples. Each sample represents a batch or unit of Triticum durum and is labeled according to quality assessment standards used in agricultural or industrial evaluation.

1. Data Collection

The samples were gathered from different harvests and suppliers to ensure variability in grain quality. Good quality samples represent grains suitable for milling and pasta production, showing desirable traits such as uniform size, color, low impurity content, and good hardness. Bad quality samples may include grains that are damaged, discolored, infested, or otherwise not meeting quality standards.

2. Features

Each sample in the dataset is characterized by several features (depending on available sensors or measurement tools), which may include:

Physical Attributes: kernel size, shape, and weight

Color Features: RGB or HSV color values from grain images

Texture Features: surface roughness or uniformity derived from image processing

Chemical/Quality Parameters (if available): protein content, moisture, and hardness index

Image-based Features: edge sharpness, contrast, or other pattern-based metrics (if machine vision is used)


These features may be numerical, categorical, or derived from digital image processing techniques.

3. Data Labeling

Each sample is labeled either as:

Good (Label = 1)

Bad (Label = 0)


Labeling was performed based on expert evaluation or using existing standards in grain quality assessment (e.g., ISO, USDA grading systems).

4. Data Format

The dataset is stored in a structured format such as:

CSV file: with rows representing individual samples and columns for each feature plus the label

Image folder (optional): if visual classification is performed, images are stored in labeled folders (e.g., /good/, /bad/) and may be linked to metadata in a CSV file


5. Purpose and Application

This dataset is designed to train and test machine learning models for the purpose of automating quality control in grain processing and agriculture. Accurate classification of Triticum durum quality can enhance supply chain efficiency, reduce waste, and improve food product quality.
DOI: 10.34740/kaggle/dsv/11787351
title: Balochi Language to English Translation Dataset
DOI: 10.17632/fzbrd8rxzk
title: Dataset for "Representations of human–elephant interactions on YouTube: Analyzing content and engagement to inform sustainable practices for wildlife tourism"

description: These data represent metadata and content present in a sample of YouTube videos depicting human-elephant interactions in Sri Lanka. Data are in an Excel workbook (.xlsx); the first sheet of the workbook ('Data') is the complete dataset, and the second sheet ('Variables') provides a description of each column in the first sheet.
DOI: 10.17632/fzbrd8rxzk.1
title: Dataset for "Representations of human–elephant interactions on YouTube: Analyzing content and engagement to inform sustainable practices for wildlife tourism"

description: These data represent metadata and content present in a sample of YouTube videos depicting human-elephant interactions in Sri Lanka. Data are in an Excel workbook (.xlsx); the first sheet of the workbook ('Data') is the complete dataset, and the second sheet ('Variables') provides a description of each column in the first sheet.
DOI: 10.15468/dl.qczpza
title: Occurrence Download

description: A dataset containing 927 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-69.31465 -32.72523,-69.00455 -32.72523,-69.00455 -32.45119,-69.31465 -32.45119,-69.31465 -32.72523))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 927 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026908-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/d37ybrw4mh.1
title: Acoustic absorption moss - impedance tube testing

description: Dataset used for the publication: "Evaluating Mosses on Bioreceptive Concrete: Effective Sound Absorbers?"
DOI: 10.15468/dl.kmz2cg
title: Occurrence Download

description: A dataset listing the 896 species recorded in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Digitalización y Sistematización de las Colecciones Biológicas Nacionales del Instituto de Biología, UNAM (vertebrados)",
 "OccurrenceStatus is Present",
 "PublishingOrg is ff90b050-c256-11db-b71b-b8a03c50a862",
 "TaxonKey is Squamata"
 ]
}

 The dataset's 896 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026899-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.26txkr
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Brazil",
 "OccurrenceStatus is Present",
 "TaxonKey is Amanita craseoderma Bas"
 ]
}

 The dataset includes 34 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026910-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cgx6dk
title: Occurrence Download

description: A dataset containing 12987 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-68.37769 -33.15749,-66.73354 -33.15749,-66.73354 -32.09864,-68.37769 -32.09864,-68.37769 -33.15749))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 12987 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026909-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/shh54xbkrz
title: Research Data Aulia et al. (2025)

description: This data related to research regarding budgetary control, green innovation, and digital payment services on the performance of micro, small, and medium enterprises (SMEs)
DOI: 10.17632/shh54xbkrz.1
title: Research Data Aulia et al. (2025)

description: This data related to research regarding budgetary control, green innovation, and digital payment services on the performance of micro, small, and medium enterprises (SMEs)
DOI: 10.15468/dl.5gvkhk
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Nepal",
 "TaxonKey is Tagetes minuta L."
 ]
}

 The dataset includes 22 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008983-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26060628
title: Additive manufacturing technology adoption for supply chain agility: a systematic search and review

description: Motivated by the dynamic capability view of the firm toward technology adoption, in this study, a systematic search and critical review of the literature is performed to explain <i>how</i> additive manufacturing (AM), more commonly known as 3D printing, affects different dimensions of supply chain agility (SCA), subsequently influencing the macro-level Supply Chain Operations Reference (SCOR) model processes, namely, <i>Plan</i>, <i>Source</i>, <i>Make</i>, <i>Deliver</i>, and <i>Enable</i>. Based on the findings, a comprehensive framework is developed outlining the adoption features and barriers of AM technology that affect different SCA dimensions and macro-level SCOR processes. Hence, this macro-level framework promotes strategy analysis for improving SCA and top-level supply chain management (SCM) processes through AM technology adoption. Moreover, propositions are put forward that can be tested empirically and refined contextually by future research. Finally, directions for future research are identified to further investigate the subject matter.
DOI: 10.6084/m9.figshare.26060628.v1
title: Additive manufacturing technology adoption for supply chain agility: a systematic search and review

description: Motivated by the dynamic capability view of the firm toward technology adoption, in this study, a systematic search and critical review of the literature is performed to explain <i>how</i> additive manufacturing (AM), more commonly known as 3D printing, affects different dimensions of supply chain agility (SCA), subsequently influencing the macro-level Supply Chain Operations Reference (SCOR) model processes, namely, <i>Plan</i>, <i>Source</i>, <i>Make</i>, <i>Deliver</i>, and <i>Enable</i>. Based on the findings, a comprehensive framework is developed outlining the adoption features and barriers of AM technology that affect different SCA dimensions and macro-level SCOR processes. Hence, this macro-level framework promotes strategy analysis for improving SCA and top-level supply chain management (SCM) processes through AM technology adoption. Moreover, propositions are put forward that can be tested empirically and refined contextually by future research. Finally, directions for future research are identified to further investigate the subject matter.
DOI: 10.15468/dl.phbumb
title: Occurrence Download

description: A dataset containing 2355184 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present",
 "Year 2018-2018"
 ]
}

 The dataset includes 2355184 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0145812-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6puzaq
title: Occurrence Download

description: A dataset containing 331 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Espeletia barclayana Cuatrec."
 ]
}

 The dataset includes 331 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004668-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hytuz3
title: Occurrence Download

description: A dataset containing 331 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Espeletia barclayana Cuatrec."
 ]
}

 The dataset includes 331 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004652-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6acwbw
title: Occurrence Download

description: A dataset containing 50252 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Espeletia Mutis ex Humb. & Bonpl."
 ]
}

 The dataset includes 50252 records from 186 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005282-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qxfckz
title: Occurrence Download

description: A dataset containing 53615 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Espeletia Mutis ex Humb. & Bonpl."
 ]
}

 The dataset includes 53615 records from 228 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004684-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4yrqx9
title: Occurrence Download

description: A dataset containing 25158 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-99.51650 -6.10660,-46.48350 -6.10660,-46.48350 15.10660,-99.51650 15.10660,-99.51650 -6.10660))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Espeletia Mutis ex Humb. & Bonpl."
 ]
}

 The dataset includes 25158 records from 155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073932-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fvbjmc
title: Occurrence Download

description: A dataset containing 813301 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-03-17",
 "EventDate is less than or equal to 2025-04-16",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 813301 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026902-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vjqqrk
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Comanthera elegans (Bong.) L.R.Parra & Giul."
 ]
}

 The dataset includes 73 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077868-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jrtwfg274g.1
title: The Supplemental information of the manuscript "Association of β-Blockers with Methotrexate Efficacy in Psoriasis Patients with Hypertension: A Retrospective Cohort Study"

description: This supplemental document supports the study titled "Association of β-Blockers with Methotrexate Efficacy in Psoriasis Patients with Hypertension: A Retrospective Cohort Study". It includes comprehensive tables and statistical analyses: Table S1. Baseline characteristics of patients; Table S2. Results of sensitivity analysis
DOI: 10.15468/dl.33w6zf
title: Occurrence Download

description: A dataset containing 371 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.54059 -5.62991,-78.55017 -5.63453,-78.55663 -5.63499,-78.56124 -5.64647,-78.55478 -5.65519,-78.54832 -5.65473,-78.54463 -5.6607,-78.53264 -5.66483,-78.52803 -5.66896,-78.51512 -5.68869,-78.50589 -5.69466,-78.5022 -5.70521,-78.51004 -5.71623,-78.50082 -5.71898,-78.4939 -5.71439,-78.48663 -5.71482,-78.44235 -5.75612,-78.4299 -5.75795,-78.42344 -5.74465,-78.42852 -5.73776,-78.43912 -5.7318,-78.46726 -5.70885,-78.47233 -5.70196,-78.48156 -5.6859,-78.48709 -5.67305,-78.4917 -5.66158,-78.5203 -5.63817,-78.52999 -5.64322,-78.5346 -5.63404,-78.54059 -5.62991))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 371 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026911-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3ggxwf2vpr.1
title: Indoor Object Detection Dataset

description: The indoor object detection dataset is divided into three parts: the training set (94%), validation set (4%), and test set (2%), with 12,012 images for training, 490 for validation, and 245 for testing. The dataset consists of seven classes, which are chair, door, human, table, bin, fire extinguisher, and shelf. These include 1,985 instances of chairs, 1,735 instances of doors, 1,011 instances of humans, 809 instances of tables, 696 instances of bins, 534 instances of fire extinguishers, and 288 instances of shelves. Moreover, the training set of the dataset contains 1,299 instances of chairs, 1,150 instances of doors, 673 instances of humans, 533 instances of tables, 469 instances of bins, 364 instances of fire extinguishers, and 188 instances of shelves. Additionally, the validation set of the dataset contains 429 instances of chairs, 305 instances of doors, 216 instances of humans, 161 instances of tables, 135 instances of bins, 97 instances of fire extinguishers, and 48 instances of shelves. Furthermore, the test set of the dataset contains several classes of everyday objects, with the following distribution: 175 instances of doors, 168 instances of chairs, 83 instances of humans, 67 instances of tables, 65 instances of bins, 49 instances of fire extinguishers, and 20 instances of shelves. In addition, preprocessing steps included auto-orientation and resizing all images to 640×640 pixels to maintain uniformity. To improve generalization for real-world applications, we applied data augmentation techniques, including horizontal and vertical flipping, 90-degree rotations (clockwise, counterclockwise, and upside down), random rotations within -15° to +15°, shearing within ±10° horizontally and vertically, and brightness adjustments between -15% and +15%. Additionally, this annotated, preprocessed, and augmented dataset enhances object detection accuracy in indoor scenes.
DOI: 10.17632/3ggxwf2vpr.2
title: Indoor Object Detection Dataset

description: The indoor object detection dataset is divided into three parts: the training set (94%), validation set (4%), and test set (2%), with 12,012 images for training, 490 for validation, and 245 for testing. The dataset contains a total of 12,747 images. The dataset is organized into seven classes, which are fire extinguisher, shelf, door, table, human, chair, and bin. The dataset class distribution for 7 classes: 2,693 fire extinguishers, 1,384 shelves, 8,530 doors, 3,959 tables, 5,010 humans, 9,687 chairs, and 3,475 bins. Moreover, in the training split, the chair class is most prevalent with 9,090 instances, followed by doors (8,050), humans (4,711), tables (3,731), bins (3,275), fire‑extinguishers (2,547) and shelves (1,316). The validation set shows a similar hierarchy—chairs (429) leading, then doors (305), humans (216), tables (161), bins (135), fire‑extinguishers (97) and shelves (48). In the test split, doors edge ahead with 175 instances, while chairs follow closely at 168, then humans (83), tables (67), bins (65), fire‑extinguishers (49) and shelves (20). In addition, preprocessing steps included auto-orientation and resizing all images to 640×640. To improve generalization for real-world applications, we applied data augmentation techniques, including horizontal and vertical flipping, 90-degree rotations (clockwise, counterclockwise, and upside down), random rotations within -15° to +15°, shearing within ±10° horizontally and vertically, and brightness adjustments between -15% and +15%. Additionally, this annotated, preprocessed, and augmented dataset enhances object detection accuracy in indoor scenes.
DOI: 10.17632/ffzx5fcwdn.1
title: Structured Dataset of Egyptian Drug-Related Criminal Cases

description: This dataset contains structured legal data extracted from multiple Egyptian legal references, primarily focused on drug-related criminal cases. The sources include the books "مجموعة الأحكام الصادرة من الهيئة العامة للمواد الجنائية ومن الدوائر الجنائية" (Collection of Judgments Issued by the General Authority for Criminal Matters and Criminal Circuits) and "الموسوعة الذهبية في قضايا المخدرات" (The Golden Encyclopedia of Drug Cases), both of which are publicly available and government-authorized.

The dataset includes approximately 200 entries and 9 columns, covering key fields such as charges, case facts, legal reasoning, applicable laws, judgment outcomes, prison terms, fines, and more. Data was extracted using a combination of manual transcription and OCR techniques, and then formatted into a structured CSV file.

This resource is designed for researchers and NLP engineers working in Arabic legal text processing. It supports various tasks including classification, prediction (e.g., law prediction based on facts), legal reasoning analysis, and fine-tuning Arabic legal NLP models.

Use Restrictions:
This dataset is intended strictly for academic and research purposes. It must not be redistributed for commercial use or re-published under another name. Any use must acknowledge the original source and contributors.
DOI: 10.17632/ffzx5fcwdn.2
title: Structured Dataset of Egyptian Drug-Related Criminal Cases

description: This dataset contains structured legal data extracted from multiple Egyptian legal references, primarily focused on drug-related criminal cases. The sources include the books "مجموعة الأحكام الصادرة من الهيئة العامة للمواد الجنائية ومن الدوائر الجنائية" (Collection of Judgments Issued by the General Authority for Criminal Matters and Criminal Circuits) and "الموسوعة الذهبية في قضايا المخدرات" (The Golden Encyclopedia of Drug Cases), both of which are publicly available and government-authorized.

The dataset includes approximately 200 entries and 9 columns, covering key fields such as charges, case facts, legal reasoning, applicable laws, judgment outcomes, prison terms, fines, and more. Data was extracted using a combination of manual transcription and OCR techniques, and then formatted into a structured CSV file.

This resource is designed for researchers and NLP engineers working in Arabic legal text processing. It supports various tasks including classification, prediction (e.g., law prediction based on facts), legal reasoning analysis, and fine-tuning Arabic legal NLP models.

Use Restrictions:
This dataset is intended strictly for academic and research purposes. It must not be redistributed for commercial use or re-published under another name. Any use must acknowledge the original source and contributors.