cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ys4t55m57x
title: Synthetic and Realistic Laser–Tissue Interaction Data for Machine-Learning Applications

description: This dataset provides two complementary CSV files containing simulated laser–tissue interaction measurements, designed to support machine-learning research in biomedical optics and computational modeling:

1- **laser_tissue_data.csv**

- Samples: 1,000,000

Features:

- wavelength (nm)
- pulse_duration (ns)
- energy_density (J/cm²)
- absorption_coeff (cm⁻¹)
- thermal_conductivity (W/(m·K))
- fluence (J/cm²/ns) – derived
- beam_profile – simulated optical absorption
-- success (0/1) – binary target indicating whether the laser pulse meets thermal diffusion and energy thresholds

These data were generated via uniform, log-normal, and normal distributions to represent idealized measurements of laser–tissue interactions under controlled conditions.


2- **realistic_laser_tissue_data.csv**

- Samples: 1,000,000

Same feature set as above, with two added realism factors:

- Measurement noise (configurable noise_level = 0.1) applied to each physical variable
- Label noise (flip_prob = 0.05) randomly inverting 5% of the success outcomes

This version simulates sensor variance and occasional mislabeling, making it suitable for testing robustness of classification and regression models.

-------------------------------------------------

Use Cases & Applications

- Train and benchmark classification models (e.g., logistic regression, random forests, neural networks) on binary outcome prediction.
- Explore feature-engineering strategies (e.g., interaction terms, normalization) for optical parameters.
- Evaluate the impact of measurement and label noise on model performance and generalizability.
- Develop synthetic-data augmentation pipelines for scarce or ethically constrained biomedical datasets.
DOI: 10.17632/ys4t55m57x.1
title: Synthetic and Realistic Laser–Tissue Interaction Data for Machine-Learning Applications

description: This dataset provides two complementary CSV files containing simulated laser–tissue interaction measurements, designed to support machine-learning research in biomedical optics and computational modeling:

1- **laser_tissue_data.csv**

- Samples: 1,000,000

Features:

- wavelength (nm)
- pulse_duration (ns)
- energy_density (J/cm²)
- absorption_coeff (cm⁻¹)
- thermal_conductivity (W/(m·K))
- fluence (J/cm²/ns) – derived
- beam_profile – simulated optical absorption
-- success (0/1) – binary target indicating whether the laser pulse meets thermal diffusion and energy thresholds

These data were generated via uniform, log-normal, and normal distributions to represent idealized measurements of laser–tissue interactions under controlled conditions.


2- **realistic_laser_tissue_data.csv**

- Samples: 1,000,000

Same feature set as above, with two added realism factors:

- Measurement noise (configurable noise_level = 0.1) applied to each physical variable
- Label noise (flip_prob = 0.05) randomly inverting 5% of the success outcomes

This version simulates sensor variance and occasional mislabeling, making it suitable for testing robustness of classification and regression models.

-------------------------------------------------

Use Cases & Applications

- Train and benchmark classification models (e.g., logistic regression, random forests, neural networks) on binary outcome prediction.
- Explore feature-engineering strategies (e.g., interaction terms, normalization) for optical parameters.
- Evaluate the impact of measurement and label noise on model performance and generalizability.
- Develop synthetic-data augmentation pipelines for scarce or ethically constrained biomedical datasets.
DOI: 10.15468/dl.yqu8j6
title: Occurrence Download

description: A dataset containing 148 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Melanothamnus savatieri (Hariot) Díaz-Tapia & Maggs, 2017"
 ]
}

 The dataset includes 148 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033623-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hszzkjy5m7
title: Eletromechanical device for photogrammetry photo-acquisition

description: This repository accompanies the article "Photogrammetry as a low-cost method to obtain facial photorealistic 3D meshes: is it precise enough for virtual surgical planning in orthognathic procedures? "

Contents:
Arduino Code Folder:
 - Contains the Arduino Code for the device. This code was made to run in a Arduino Mega. The first part of the text corresponds to information about the code and hardware that is necessary to build a similar device for non commercial porpuses.

Frame Project folder:
 - A PDF file (Frame - parts and instructions.pdf) with detailed descriptions and illustrations of the frame's components.
 - A Blender 3D file (frame_project.blend) presenting the frame's parts in an animated format, allowing viewers to explore the role of each moving component. Tags in the Blender file correspond to the descriptions provided in the PDF. All the 3D printable parts can be exported from this Blender 3D file

Keypad Instructions Folder
 - Contains PDF files (Portuguese and English) explaining the function of each key, allowing the operation of the device.

Stickers for Matrix Keypad Folder
 - Contains printable PDF files (in both Portuguese and English). It is advisable to print them on adhesive paper, allowing the stickers to be placed directly over the 4x4 Arduino matrix keypad.

Video folder:
 - A video demonstrating the photo-capture process, as described in the Materials and Methods section of the article.
DOI: 10.17632/hszzkjy5m7.1
title: Eletromechanical device for photogrammetry photo-acquisition

description: This repository accompanies the article "Photogrammetry as a low-cost method to obtain facial photorealistic 3D meshes: is it precise enough for virtual surgical planning in orthognathic procedures? "

Contents:
Arduino Code Folder:
 - Contains the Arduino Code for the device. This code was made to run in a Arduino Mega. The first part of the text corresponds to information about the code and hardware that is necessary to build a similar device for non commercial porpuses.

Frame Project folder:
 - A PDF file (Frame - parts and instructions.pdf) with detailed descriptions and illustrations of the frame's components.
 - A Blender 3D file (frame_project.blend) presenting the frame's parts in an animated format, allowing viewers to explore the role of each moving component. Tags in the Blender file correspond to the descriptions provided in the PDF. All the 3D printable parts can be exported from this Blender 3D file

Keypad Instructions Folder
 - Contains PDF files (Portuguese and English) explaining the function of each key, allowing the operation of the device.

Stickers for Matrix Keypad Folder
 - Contains printable PDF files (in both Portuguese and English). It is advisable to print them on adhesive paper, allowing the stickers to be placed directly over the 4x4 Arduino matrix keypad.

Video folder:
 - A video demonstrating the photo-capture process, as described in the Materials and Methods section of the article.
DOI: 10.15468/dl.z5xk2j
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Lophuromys chrysopus Osgood, 1936",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033625-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tr5jze
title: Occurrence Download

description: A dataset containing 13451 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Salmo salar Linnaeus, 1758, Anguilla rostrata (Lesueur, 1817), Anguilla anguilla (Linnaeus, 1758), Urobatis halleri (Cooper, 1863), Raja clavata Linnaeus, 1758, Dipturus laevis (Mitchill, 1818), Sphyrna lewini (Griffith & Smith, 1834), Prionace glauca (Linnaeus, 1758), Carcharhinus plumbeus (Nardo, 1827), Isurus oxyrinchus Rafinesque, 1810, Carcharodon carcharias (Linnaeus, 1758))"
 ]
}

 The dataset includes 13451 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033626-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rhvkpmh4cb
title: How Do Dental Professionals and Students Assess Orthodontic Case Complexity?

description: This study aims to compare the perception of orthodontic case complexity among dental professionals. A total of 35 cases (30 unique and 5 repeated for consistency analysis) were selected based on their ABO DI scores (10 mild, 10 moderate and 10 complex cases). Participants were asked to rate the perceived complexity of each case on a scale from 1 (simple) to 10 (complex).
The accompanying data files include all participant responses, along with the corresponding ABO DI score for each case included in the study.
DOI: 10.17632/rhvkpmh4cb.1
title: How Do Dental Professionals and Students Assess Orthodontic Case Complexity?

description: This study aims to compare the perception of orthodontic case complexity among dental professionals. A total of 35 cases (30 unique and 5 repeated for consistency analysis) were selected based on their ABO DI scores (10 mild, 10 moderate and 10 complex cases). Participants were asked to rate the perceived complexity of each case on a scale from 1 (simple) to 10 (complex).
The accompanying data files include all participant responses, along with the corresponding ABO DI score for each case included in the study.
DOI: 10.17632/56r53mc3pz
title: Data paper Exploring binder–ionic liquid electrolyte systems in silicon oxycarbide negative electrodes for lithium-ion batteries

description: Enhancing the safety of lithium-ion batteries (LIB) by replacing flammable electrolytes is a key challenge. This study investigates the electrochemical behavior of two newly synthesized ionic liquids (ILs) comparing them to conventional alkyl carbonate-based electrolytes. Nitrogen-doped carbon silicon oxycarbide (NC-SiOC) as the active material in negative electrodes was combined with two polymeric binders: poly(acrylic acid) (PAA) and poly(acrylonitrile) (PAN). NC-SiOC/PAN electrodes exhibited a significantly higher initial charge capacity—approximately 25–30% greater than their PAA-based counter-parts in the first cycle at 0.1 A g⁻¹ (850–990 mAh g⁻¹ vs. 600–700 mAh g⁻¹), and demon-strated an improved initial Coulombic efficiency (67% vs. 62%). Long-term cycling sta-bility over 1000 cycles at 1.6 A g⁻¹ retained 75–80% of the initial 0.1 A g⁻¹ capacity. This outstanding performance is attributed to the synergistic effects of nitrogen-rich carbo-naceous phases within the NC-SiOC material and the cyclized-PAN binder, which fa-cilitate structural stability by accommodating volumetric changes and enhancing solid electrolyte interphase (SEI) stability. Notably, despite the lower ionic transport properties of the IL electrolytes, their incorporation did not compromise performance, supporting their feasibility as safer electrolyte alternatives. These findings offer one of the most promising electrochemical performances reported for SiOC materials to date.
DOI: 10.17632/56r53mc3pz.1
title: Data paper Exploring binder–ionic liquid electrolyte systems in silicon oxycarbide negative electrodes for lithium-ion batteries

description: Enhancing the safety of lithium-ion batteries (LIB) by replacing flammable electrolytes is a key challenge. This study investigates the electrochemical behavior of two newly synthesized ionic liquids (ILs) comparing them to conventional alkyl carbonate-based electrolytes. Nitrogen-doped carbon silicon oxycarbide (NC-SiOC) as the active material in negative electrodes was combined with two polymeric binders: poly(acrylic acid) (PAA) and poly(acrylonitrile) (PAN). NC-SiOC/PAN electrodes exhibited a significantly higher initial charge capacity—approximately 25–30% greater than their PAA-based counter-parts in the first cycle at 0.1 A g⁻¹ (850–990 mAh g⁻¹ vs. 600–700 mAh g⁻¹), and demon-strated an improved initial Coulombic efficiency (67% vs. 62%). Long-term cycling sta-bility over 1000 cycles at 1.6 A g⁻¹ retained 75–80% of the initial 0.1 A g⁻¹ capacity. This outstanding performance is attributed to the synergistic effects of nitrogen-rich carbo-naceous phases within the NC-SiOC material and the cyclized-PAN binder, which fa-cilitate structural stability by accommodating volumetric changes and enhancing solid electrolyte interphase (SEI) stability. Notably, despite the lower ionic transport properties of the IL electrolytes, their incorporation did not compromise performance, supporting their feasibility as safer electrolyte alternatives. These findings offer one of the most promising electrochemical performances reported for SiOC materials to date.
DOI: 10.17632/b6bybwnpxh.1
title: KenLumachiQuAD - A QA dataset for Kenyan Luhya Lumarachi dialect

description: KenLumachiQuAD is a result of a project that annotated a total of 820 QA pairs based on 137 texts of Kenyan language of Luhya, the Lumarachi dialect. These source texts are from the text data collected by the Kenyan languages corpus, Kencorpus project (https://kencorpus.maseno.ac.ke/corpus-datasets/) [1]. The total Luhya Lumarachi texts available in the Kencorpus project were 483 texts. We annotated each of the selected 137 texts with at least 5 QA pairs. The KenLumachiQuAD QA dataset is available for download as one single CSV file. Each row on the CSV file shows the reference number of the source text and the associated QA pair for that text. 

The columns are on the CSV file are: ‘Story_ID’ to represent the source text from the Kencorpus project, where the QA pairs are derived. The column labeled ‘Q’ contains the question text, while the column labeled ‘A’ contains the answer text. This QA dataset is a gold standard dataset annotated by human annotators who are natives of the language. It was formulated using the same modalities and quality assurance checks of a similar project that was done for the low resource language of Kiswahili [2].

This QA dataset is useful for testing machine learning QA systems for the low-resource language of Luhya, specifically the Lumarachi dialect that is predominantly spoken in Western Kenya. A semantic network approach to the QA task as applied to the Kiswahili language [2] is currently being tested on this dataset to confirm if such approach can be applicable, in such cases where there is little training data (source texts) to otherwise train deep learning systems.

[1] Wanjawa, B., Wanzare, L., Indede, F., McOnyango, O., Ombui, E., & Muchemi, L. (2023). Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks. Journal for Language Technology and Computational Linguistics, 36(2), 1–27.
[2] Wanjawa, B. W., Wanzare, L. D. A., Indede, F., McOnyango, O., Muchemi, L., & Ombui, E. (2023). KenSwQuAD—A Question Answering Dataset for Swahili Low-resource Language. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(4), 1–20.
DOI: 10.15468/dl.x5c8vz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius horstockii (Schlegel, 1837)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((31.8862 -21.3285,30.5701 -20.3186,30.5701 -24.0715,35.5909 -24.0715,35.6007 -24.0157,35.6007 -23.8125,35.4969 -23.7035,35.6007 -23.165,35.6007 -22.9702,35.227 -22.2482,34.6862 -21.6944,34.013 -21.3095,33.5 -21.1593,32.9675 -21.1092,32.4352 -21.1612,31.8862 -21.3285))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033627-250525065834625/datasets/export for details.
DOI: 10.17632/b6bybwnpxh
title: KenLumachiQuAD - A QA dataset for Kenyan Luhya Lumarachi dialect

description: KenLumachiQuAD is a result of a project that annotated a total of 1,000 QA pairs based on 137 texts of Kenyan language of Luhya, the Lumarachi dialect. These source texts are from the text data collected by the Kenyan languages corpus, Kencorpus project (https://kencorpus.maseno.ac.ke/corpus-datasets/) [1]. The total Luhya Lumarachi texts available in the Kencorpus project were 483 texts. We annotated each of the selected 137 texts with at least 5 QA pairs. The KenLumachiQuAD QA dataset is available for download as one single CSV file. Each row on the CSV file shows the reference number of the source text and the associated QA pair for that text. The updated version has converted all texts into lowercase for ease of processing. 

The columns are on the CSV file are: ‘Story_ID’ to represent the source text from the Kencorpus project, where the QA pairs are derived. The column labeled ‘Q’ contains the question text, while the column labeled ‘A’ contains the answer text. This QA dataset is a gold standard dataset annotated by human annotators who are natives of the language. It was formulated using the same modalities and quality assurance checks of a similar project that was done for the low resource language of Kiswahili [2].

This QA dataset is useful for testing machine learning QA systems for the low-resource language of Luhya, specifically the Lumarachi dialect that is predominantly spoken in Western Kenya. A semantic network approach to the QA task as applied to the Kiswahili language [2] is currently being tested on this dataset to confirm if such approach can be applicable, in such cases where there is little training data (source texts) to otherwise train deep learning systems.

[1] Wanjawa, B., Wanzare, L., Indede, F., McOnyango, O., Ombui, E., & Muchemi, L. (2023). Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks. Journal for Language Technology and Computational Linguistics, 36(2), 1–27.
[2] Wanjawa, B. W., Wanzare, L. D. A., Indede, F., McOnyango, O., Muchemi, L., & Ombui, E. (2023). KenSwQuAD—A Question Answering Dataset for Swahili Low-resource Language. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(4), 1–20.
DOI: 10.17632/b6bybwnpxh.2
title: KenLumachiQuAD - A QA dataset for Kenyan Luhya Lumarachi dialect

description: KenLumachiQuAD is a result of a project that annotated a total of 1,000 QA pairs based on 137 texts of Kenyan language of Luhya, the Lumarachi dialect. These source texts are from the text data collected by the Kenyan languages corpus, Kencorpus project (https://kencorpus.maseno.ac.ke/corpus-datasets/) [1]. The total Luhya Lumarachi texts available in the Kencorpus project were 483 texts. We annotated each of the selected 137 texts with at least 5 QA pairs. The KenLumachiQuAD QA dataset is available for download as one single CSV file. Each row on the CSV file shows the reference number of the source text and the associated QA pair for that text. The updated version has converted all texts into lowercase for ease of processing. 

The columns are on the CSV file are: ‘Story_ID’ to represent the source text from the Kencorpus project, where the QA pairs are derived. The column labeled ‘Q’ contains the question text, while the column labeled ‘A’ contains the answer text. This QA dataset is a gold standard dataset annotated by human annotators who are natives of the language. It was formulated using the same modalities and quality assurance checks of a similar project that was done for the low resource language of Kiswahili [2].

This QA dataset is useful for testing machine learning QA systems for the low-resource language of Luhya, specifically the Lumarachi dialect that is predominantly spoken in Western Kenya. A semantic network approach to the QA task as applied to the Kiswahili language [2] is currently being tested on this dataset to confirm if such approach can be applicable, in such cases where there is little training data (source texts) to otherwise train deep learning systems.

[1] Wanjawa, B., Wanzare, L., Indede, F., McOnyango, O., Ombui, E., & Muchemi, L. (2023). Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks. Journal for Language Technology and Computational Linguistics, 36(2), 1–27.
[2] Wanjawa, B. W., Wanzare, L. D. A., Indede, F., McOnyango, O., Muchemi, L., & Ombui, E. (2023). KenSwQuAD—A Question Answering Dataset for Swahili Low-resource Language. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(4), 1–20.
DOI: 10.15468/dl.r6wr8x
title: Occurrence Download

description: A dataset containing 736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Arctocephalus galapagoensis Heller, 1904"
 ]
}

 The dataset includes 736 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033628-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15632843
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS250610o Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS250610o Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.15632844
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS250610o Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS250610o Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.17632/4kwh3zzrrz.1
title: Replication Package for "Climbing and Falling Off the Ladder: Asset Pricing Implications of Labor Market Event Risk"

description: Repository includes all source data and codes required to reproduce tables and figures from "Climbing and Falling Off the Ladder: Asset Pricing Implications of Labor Market Event Risk", which is forthcoming in the Journal of Financial Economics. 

Paper Abstract: Administrative earnings data reveal that households are exposed to large, countercyclical idiosyncratic tail risks in labor earnings. I illustrate how these risks affect asset prices within an asset pricing framework with recursive preferences, heterogeneous agents and incomplete markets. Quantitatively, a model in which agents face a time-varying probability of experiencing a rare, idiosyncratic disaster, with parameters disciplined by data, matches the level and dynamics of the equity premium. Stock returns are highly informative about labor market event risk, and, consistent with model predictions, initial claims for unemployment, a proxy for labor market uncertainty, is a highly robust predictor of returns.