cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xskzrswnzw.1
title: Personal thesis dataset-High-precision trajectory tracking control for multiple unmanned surface vessels

description: This dataset is designed to study unmanned surface vehicle (USV) formation control strategies based on monotonic boundary performance functions. It includes simulation results and is used to analyze and demonstrate that, compared to formation control strategies based on exponential performance functions, this control scheme has significant advantages in response speed, anti-disturbance capability, and input smoothness, and also has good potential for engineering application.
DOI: 10.13018/bmr11081
title: Solution structure of the human DDEF1 SH3 domain
DOI: 10.13018/bmr11082
title: Solution structure of the complex between the DDEF1 SH3 domain and the APC SAMP1 motif
DOI: 10.13018/bmr11083
title: Solution structure of the Leucine Rich Repeat of human Acidic leucine-rich nuclear phosphoprotein 32 family member B
DOI: 10.13018/bmr11084
title: Solution structure of the CH domain of human calponin-2
DOI: 10.13018/bmr11085
title: Solution structure of the CH domain of human NEDD9 interacting protein with calponin homology and LIM domains
DOI: 10.17632/sr99ryf4ns
title: BengVoice: A Stratified Dataset of Code-Mixed Bengali-English Voice Commands for Intent Classification in Conversational AI Systems

description: This dataset presents a meticulously curated benchmark collection of 1,200 Bengali voice assistant utterances for intent classification research in conversational AI systems. BengVoice addresses the critical gap in Natural Language Understanding resources for Bengali, one of the world's most widely spoken languages with over 230 million speakers, yet significantly underrepresented in publicly available language technology datasets.

The dataset comprises utterances across 10 fundamental voice assistant intent categories: weather queries, time queries, alarm setting, news requests, music playback, phone calls, messaging, translation, calculations, and general knowledge questions. Each intent category contains exactly 120 samples, ensuring perfect class balance. All 1,200 utterances are unique with zero duplicates.

A distinguishing feature is authentic code-mixing behaviour—natural integration of English words within Bengali speech. Analysis reveals 290 samples (24.2%) contain code-mixed content, with patterns reflecting genuine usage: technical domains like alarm setting show 71.7% code-mixing, while traditional domains show minimal mixing (0.8%). This reflects natural speech patterns of urban Bengali speakers in Bangladesh.

The dataset incorporates cultural authenticity through references to Bangladeshi locations (Dhaka, Chittagong, Sylhet), local media (Prothom Alo, Kaler Kantho), and cultural elements specific to Bangladesh, ensuring real-world usage scenarios for Bengali-speaking populations.

For robust evaluation, the dataset provides stratified 5-fold cross-validation splits. Each fold contains exactly 240 samples with 24 per intent, maintaining perfect balance. This stratification enables fair model comparison and supports multiple evaluation methodologies including traditional machine learning, deep learning, retrieval-augmented generation (RAG), and few-shot prompting.

Baseline validation experiments using TF-IDF vectorization with character-level n-grams and Logistic Regression achieved mean accuracy of 93.92% (±0.50%) across 5-fold cross-validation, with fold accuracies from 93.33% to 94.58%. Per-intent performance ranged from 81.67% (news requests) to 100% (translation), establishing clear benchmarks and validating dataset quality.

The dataset is provided in multiple formats: complete datasets in JSON and CSV (with and without fold labels), individual fold files for pre-separated evaluation. No proprietary software required.

This resource enables Bengali voice assistant development, intent classification benchmarking, code-mixing investigation, cross-lingual transfer learning, multilingual NLU systems, and low-resource language processing. Released under Creative Commons Attribution 4.0 International (CC BY 4.0) license for maximum research impact.
DOI: 10.13018/bmr11086
title: Solution structure of the CH domain of human microtubule-associated protein RP/EB family member 3
DOI: 10.17632/sr99ryf4ns.1
title: BengVoice: A Stratified Dataset of Code-Mixed Bengali-English Voice Commands for Intent Classification in Conversational AI Systems

description: This dataset presents a meticulously curated benchmark collection of 1,200 Bengali voice assistant utterances for intent classification research in conversational AI systems. BengVoice addresses the critical gap in Natural Language Understanding resources for Bengali, one of the world's most widely spoken languages with over 230 million speakers, yet significantly underrepresented in publicly available language technology datasets.

The dataset comprises utterances across 10 fundamental voice assistant intent categories: weather queries, time queries, alarm setting, news requests, music playback, phone calls, messaging, translation, calculations, and general knowledge questions. Each intent category contains exactly 120 samples, ensuring perfect class balance. All 1,200 utterances are unique with zero duplicates.

A distinguishing feature is authentic code-mixing behaviour—natural integration of English words within Bengali speech. Analysis reveals 290 samples (24.2%) contain code-mixed content, with patterns reflecting genuine usage: technical domains like alarm setting show 71.7% code-mixing, while traditional domains show minimal mixing (0.8%). This reflects natural speech patterns of urban Bengali speakers in Bangladesh.

The dataset incorporates cultural authenticity through references to Bangladeshi locations (Dhaka, Chittagong, Sylhet), local media (Prothom Alo, Kaler Kantho), and cultural elements specific to Bangladesh, ensuring real-world usage scenarios for Bengali-speaking populations.

For robust evaluation, the dataset provides stratified 5-fold cross-validation splits. Each fold contains exactly 240 samples with 24 per intent, maintaining perfect balance. This stratification enables fair model comparison and supports multiple evaluation methodologies including traditional machine learning, deep learning, retrieval-augmented generation (RAG), and few-shot prompting.

Baseline validation experiments using TF-IDF vectorization with character-level n-grams and Logistic Regression achieved mean accuracy of 93.92% (±0.50%) across 5-fold cross-validation, with fold accuracies from 93.33% to 94.58%. Per-intent performance ranged from 81.67% (news requests) to 100% (translation), establishing clear benchmarks and validating dataset quality.

The dataset is provided in multiple formats: complete datasets in JSON and CSV (with and without fold labels), individual fold files for pre-separated evaluation. No proprietary software required.

This resource enables Bengali voice assistant development, intent classification benchmarking, code-mixing investigation, cross-lingual transfer learning, multilingual NLU systems, and low-resource language processing. Released under Creative Commons Attribution 4.0 International (CC BY 4.0) license for maximum research impact.
DOI: 10.13018/bmr11087
title: Solution structure of the PH domain of Kindlin-3 from human
DOI: 10.13018/bmr11088
title: Solution structure of the PH domain of Dynamin-2 from human
DOI: 10.13018/bmr11089
title: Solution structure of the PH domain of Pleckstrin homology domain-containing family A member 6 from human
DOI: 10.13018/bmr11090
title: Solution structure of the N-terminal PapD-like domain of HYDIN protein fromhuman
DOI: 10.13018/bmr11091
title: Solution structure of the C-terminal PapD-like domain from human HYDIN protein
DOI: 10.13018/bmr11092
title: Solution structure of the chromo domain of Mortality factor 4-like protein 1 from human
DOI: 10.17632/c8nypf2g9x
title: Upper Ewaso Ng'iro North River Basin LULC data

description: This data consists of raw LULC maps for the years 1990,1995,2000,2005,2010,2015, and 2018 obtained from the Directorate of Resource Surveys and Remote Sensing (DRSRS), classified LULC map for 2025 and simulated maps for 2025, 2034 and 2050.
DOI: 10.13018/bmr11093
title: Solution structure of the BTK motif of tyrosine-protein kinase ITK from human
DOI: 10.17632/c8nypf2g9x.1
title: Upper Ewaso Ng'iro North River Basin LULC data

description: This data consists of raw LULC maps for the years 1990,1995,2000,2005,2010,2015, and 2018 obtained from the Directorate of Resource Surveys and Remote Sensing (DRSRS), classified LULC map for 2025 and simulated maps for 2025, 2034 and 2050.
DOI: 10.13018/bmr11094
title: Solution structure of the complex of the PTB domain of SNT-2 and 19-residue peptide (aa 1571-1589) of hALK
DOI: 10.13018/bmr11095
title: Solution structure of the chimera of the PTB domain of SNT-2 and 19-residue peptide (aa 1571-1589) of hALK
DOI: 10.13018/bmr11096
title: Solution structure of the first fibronectin type III domain of human Netrin receptor DCC
DOI: 10.13018/bmr11097
title: Solution structure of the fifth fibronectin type III domain of human Netrin receptor DCC
DOI: 10.13018/bmr11098
title: Solution structure of the sixth fibronectin type III domain of human Netrin receptor DCC
DOI: 10.18758/h2v1uo6x
title: CCI+P HCHO tropospheric column L3 data from OMI, v1

description: OMI level-3 gridded tropospheric HCHO column, version 1.

Monthly grids at spatial resolution of 0.125°x0.125°.
Daily grids at spatial resolution of 0.25°x0.25°.

Based on the QA4ECV OMI Level 2 Formaldehyde Total Column product. Version 1.2. https://doi.org/10.18758/71021031

In addition to the main product results, such as HCHO slant column, vertical column and air mass factor, the level 3 data files contain several additional parameters and diagnostic information such as uncertainties, a priori profiles and averaging kernels.

Created for the ESA CCI ozone and aerosol precursors project.
DOI: 10.13018/bmr11099
title: Solution structure of the fourth fibronectin type III domain of human Netrin receptor DCC
DOI: 10.13018/bmr11100
title: The solution structure of the first thioredoxin domain of mouse Protein disulfide-isomerase A6
DOI: 10.17632/gcy6t4ftfy
title: Data for 'Ethanol depletion dynamics in non-recirculating system breath alcohol simulators'

description: This is the breath-alcohol simulator ethanol concentration depletion data used in the technical note “Ethanol depletion dynamics in non-recirculating system breath alcohol simulators.” This data set includes ethanol depletion data from all trials, instrument stability test data, flow rate data, temperature data, statistical analysis results, figures used in the manuscript, and an equation that can predict ethanol concentration in the solution after repeated testing. 

Excerpt from the technical note abstract: “Breath alcohol simulators are devices that use a heated ethanol/water solution to simulate human breath. Vapor created from heating the solution is commonly used to calibrate alcohol breath testing instruments. When the vapor of the same solution is repeatedly sampled, it causes the ethanol concentration to deplete at a currently unknown rate. The goal of this study was to quantify and better understand the ethanol depletion in non-recirculating system breath alcohol simulators… It was found that when observing different flow rates, the depletion of ethanol concentration was directly correlated to the total liters of air that passed through the system (r &gt; −0.95). Altering initial concentrations showed that as the solution concentration increased, the rate of depletion of ethanol also increased. Our findings indicate that the total ethanol depletion in a non-recirculating system breath alcohol simulator can be described as a percentage of the original concentration being lost when a specific volume of air is passed through the system. We then calculated an equation that can predict ethanol concentration in the solution after repeated testing.”
DOI: 10.13018/bmr11101
title: The solution structure of the first thioredoxin domain of mouse Protein disulfide-isomerase A4
DOI: 10.17632/gcy6t4ftfy.1
title: Data for 'Ethanol depletion dynamics in non-recirculating system breath alcohol simulators'

description: This is the breath-alcohol simulator ethanol concentration depletion data used in the technical note “Ethanol depletion dynamics in non-recirculating system breath alcohol simulators.” This data set includes ethanol depletion data from all trials, instrument stability test data, flow rate data, temperature data, statistical analysis results, figures used in the manuscript, and an equation that can predict ethanol concentration in the solution after repeated testing. 

Excerpt from the technical note abstract: “Breath alcohol simulators are devices that use a heated ethanol/water solution to simulate human breath. Vapor created from heating the solution is commonly used to calibrate alcohol breath testing instruments. When the vapor of the same solution is repeatedly sampled, it causes the ethanol concentration to deplete at a currently unknown rate. The goal of this study was to quantify and better understand the ethanol depletion in non-recirculating system breath alcohol simulators… It was found that when observing different flow rates, the depletion of ethanol concentration was directly correlated to the total liters of air that passed through the system (r &gt; −0.95). Altering initial concentrations showed that as the solution concentration increased, the rate of depletion of ethanol also increased. Our findings indicate that the total ethanol depletion in a non-recirculating system breath alcohol simulator can be described as a percentage of the original concentration being lost when a specific volume of air is passed through the system. We then calculated an equation that can predict ethanol concentration in the solution after repeated testing.”
DOI: 10.13018/bmr11102
title: The solution structure of the second thioredoxin domain of human Protein disulfide-isomerase A3
DOI: 10.13018/bmr11103
title: Solution structure of the second fibronectin type III domain of human Netrin receptor DCC
DOI: 10.13018/bmr11104
title: The solution structure of the second thioredoxin domain of mouse Protein disulfide-isomerase A4
DOI: 10.13018/bmr11105
title: The solution structure of the thioredoxin domain of human Thioredoxin-related transmembrane protein 2
DOI: 10.13018/bmr11106
title: The solution structure of the FN3 domain of human Midline 2 protein
DOI: 10.13018/bmr11107
title: The solution structure of the third thioredoxin domain of human Thioredoxin domain-containing protein 5
DOI: 10.13018/bmr11108
title: The solution structure of the thioredoxin domain of human Thioredoxin-like protein 2
DOI: 10.13018/bmr11109
title: The solution structure of the third thioredoxin domain of mouse Protein disulfide-isomerase A4
DOI: 10.13018/bmr11110
title: The solution structure of the 33rd fibronectin type III domain of human Tenascin-X
DOI: 10.13018/bmr11111
title: The solution structure of the sixth fibronectin type III domain of human Neogenin
DOI: 10.15468/dl.vuxy5u
title: Occurrence Download

description: A dataset containing 436 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.29_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Prunella fulvescens"
 ]
}

 The dataset includes 436 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003032-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13018/bmr11112
title: The solution structure of the fourth fibronectin type III domain of human Neogenin
DOI: 10.13018/bmr11113
title: The solution structure of the thioredoxin-like domain of human Thioredoxin-related transmembrane protein
DOI: 10.13018/bmr11114
title: The solution structure of the second thioredoxin-like domain of human Protein disulfide-isomerase
DOI: 10.13018/bmr11115
title: Complex structure of the zf-CW domain and the H3K4me3 peptide
DOI: 10.17632/y64393bjj2
title: Good and Bad classification of cooked pasta

description: Good and Bad Classification of Cooked Pasta
This project, titled “Good and Bad Classification of Cooked Pasta”, aims to develop an image classification system that distinguishes between properly cooked (good) and improperly cooked (bad) pasta. The dataset consists of approximately 2000 images, equally divided into two classes: 1000 good samples and 1000 bad samples. All images were captured using a Realme C25Y smartphone camera, providing high-resolution images suitable for machine learning applications. The photographs were taken under controlled lighting conditions to maintain clarity, consistency, and accurate feature representation.
Dataset Composition
Good Samples (Properly Cooked Pasta):
The dataset includes around 1000 images of properly cooked pasta. These images display pasta with uniform texture, smooth surface, consistent color, and well-maintained shape and structure. There are no burnt edges, undercooked portions, or discoloration. These samples represent the positive class and help the model learn the characteristics of well-cooked pasta.
Bad Samples (Improperly Cooked Pasta):
The remaining 1000 images represent improperly cooked pasta. These samples may show overcooked or burnt edges, raw or undercooked areas, uneven texture, sogginess, and discoloration. These images form the negative class and enable the model to identify defects and poor cooking conditions accurately.
Data Collection Setup
All images were captured using the Realme C25Y smartphone, equipped with a 50 MP rear camera system. The high-resolution camera ensured detailed image capture, making important visual features such as texture, color variation, and structural consistency clearly visible.
A simple, neutral background was used intentionally during image capture to enhance contrast between the pasta and the background and reduce distractions. Consistent lighting conditions were maintained throughout the data collection process to ensure accurate color representation and minimize environmental variations.
Image Characteristics
The dataset includes variations in pasta shape, size, and cooking style, such as boiled or baked preparations. It also captures different cooking defects, including burnt portions, raw areas, sogginess, and uneven consistency. This variation increases dataset diversity and improves the robustness of the classification model.
Data Annotation and Timeline
Each image is carefully labeled as either “Good” (Properly Cooked) or “Bad” (Improperly Cooked). These labels serve as ground truth for supervised learning. The images were collected over a period of seven days, during which pasta samples gradually transitioned from good to bad conditions. This approach helps the model learn realistic deterioration patterns, making it more reliable for practical applications.
DOI: 10.13018/bmr11116
title: The solution structure of the second thioredoxin-like domain of human Protein disulfide-isomerase A6
DOI: 10.17632/y64393bjj2.1
title: Good and Bad classification of cooked pasta

description: Good and Bad Classification of Cooked Pasta
This project, titled “Good and Bad Classification of Cooked Pasta”, aims to develop an image classification system that distinguishes between properly cooked (good) and improperly cooked (bad) pasta. The dataset consists of approximately 2000 images, equally divided into two classes: 1000 good samples and 1000 bad samples. All images were captured using a Realme C25Y smartphone camera, providing high-resolution images suitable for machine learning applications. The photographs were taken under controlled lighting conditions to maintain clarity, consistency, and accurate feature representation.
Dataset Composition
Good Samples (Properly Cooked Pasta):
The dataset includes around 1000 images of properly cooked pasta. These images display pasta with uniform texture, smooth surface, consistent color, and well-maintained shape and structure. There are no burnt edges, undercooked portions, or discoloration. These samples represent the positive class and help the model learn the characteristics of well-cooked pasta.
Bad Samples (Improperly Cooked Pasta):
The remaining 1000 images represent improperly cooked pasta. These samples may show overcooked or burnt edges, raw or undercooked areas, uneven texture, sogginess, and discoloration. These images form the negative class and enable the model to identify defects and poor cooking conditions accurately.
Data Collection Setup
All images were captured using the Realme C25Y smartphone, equipped with a 50 MP rear camera system. The high-resolution camera ensured detailed image capture, making important visual features such as texture, color variation, and structural consistency clearly visible.
A simple, neutral background was used intentionally during image capture to enhance contrast between the pasta and the background and reduce distractions. Consistent lighting conditions were maintained throughout the data collection process to ensure accurate color representation and minimize environmental variations.
Image Characteristics
The dataset includes variations in pasta shape, size, and cooking style, such as boiled or baked preparations. It also captures different cooking defects, including burnt portions, raw areas, sogginess, and uneven consistency. This variation increases dataset diversity and improves the robustness of the classification model.
Data Annotation and Timeline
Each image is carefully labeled as either “Good” (Properly Cooked) or “Bad” (Improperly Cooked). These labels serve as ground truth for supervised learning. The images were collected over a period of seven days, during which pasta samples gradually transitioned from good to bad conditions. This approach helps the model learn realistic deterioration patterns, making it more reliable for practical applications.
DOI: 10.13018/bmr11117
title: The solution structure of the second fibronectin type III domain of mouse Ephrintype-A receptor 1
DOI: 10.13018/bmr11118
title: The solution structure of the fifth fibronectin type III domain of human Neogenin
DOI: 10.13018/bmr11119
title: The solution structure of the first fibronectin type III domain of human Neogenin
DOI: 10.13018/bmr11120
title: The solution structure of the third fibronectin type III domain of human Neogenin
DOI: 10.13018/bmr11121
title: The solution structure of the second fibronectin type III domain of humanNeogenin
DOI: 10.17632/2yzxdgvb85
title: Maltol Induces Diabetic Fragility Fractures by Disrupting the Balance of Bone Remodeling

description: All data generated or analyzed during "Maltol Induces Diabetic Fragility Fractures by Disrupting the Balance of Bone Remodeling" are included in the source data file.
DOI: 10.13018/bmr11122
title: The solution structure of the tetratrico peptide repeat of human Smooth muscle cell associated protein-1, isoform 2
DOI: 10.17632/2yzxdgvb85.7
title: Maltol Induces Diabetic Fragility Fractures by Disrupting the Balance of Bone Remodeling

description: All data generated or analyzed during "Maltol Induces Diabetic Fragility Fractures by Disrupting the Balance of Bone Remodeling" are included in the source data file.
DOI: 10.13018/bmr11123
title: Solution structure of the SANT domain of human SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily C member 1
DOI: 10.13018/bmr11124
title: Solution structure of the CH domain of human Calponin 1
DOI: 10.13018/bmr11125
title: Solution structure of the 6th fibronectin type III domain from human fibronectintype III domain containing protein 3
DOI: 10.13018/bmr11126
title: Solution structure of the second CH domain of human spectrin beta chain, brain 2
DOI: 10.13018/bmr11127
title: Solution structure of the 2nd fibronectin type III domain from mouse biregionalcell adhesion molecule-related/down-regulated oncogenes (Cdon) binding protein