cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/pdzpdgc497
title: Uterine disorders classification based on symptomic and behaviorial data

description: The Uterine Disorder Diagnostic Dataset (UDD-2025) consists of 223 anonymized patient records collected through hand-on interviews and clinical ultrasound reports from the Department of Gynecology, R.S. Unit, Shaheed Tajuddin Ahmad Medical College Hospital, Bangladesh. The data collection took place between May 5 – August 7, 2025, spanning a total of 95 days.

Each participant record includes validated diagnostic parameters extracted directly from ultrasound and medical reports, along with corresponding symptomatic and behavioral indicators gathered via structured in-person interviews. Each participant gave consent to interview willingly and shared their experience without any external influence. The dataset is stored in a single .xlsx file (which can be converted to a csv file), with each row representing one participant and each column corresponding to a clinical or behavioral variable.

The ethical clearance for this dataset was obtained from the Institutional Ethics Committee of Daffodil International University, and official permission for clinical data collection was granted by the Assistant Director of Shaheed Tajuddin Ahmad Medical College Hospital.

All diagnostic fields were verified and approved by experts at the same department before inclusion. The ultrasound-based diagnostic summaries were then manually entered and validated for consistency by the research team. All diagnosis summaries were input manually from the reports, maintaining the sequence of other data of each participant. No personally identifiable information is present in the final .xlsx file.

The ultrasound report summaries for each patient were collected immediately following their consultation with the attending physician. As approval had been obtained from the relevant institutional authorities, the procedure ensured the validity and reliability of the diagnostic information.

This dataset serves as a reliable resource for research in:
-Predictive modeling of uterine disorders
-Statistical correlation between menstrual behavior and clinical diagnosis
-Machine learning applications in reproductive health diagnostics
-Multimodal health data integration and analysis

By combining expert-validated ultrasound diagnostics with real-world behavioral data, the UDD-2025 dataset provides an authentic foundation for advancing AI-based women’s health research, reproductive disease prediction, and evidence-driven gynecological analytics.
DOI: 10.17632/pdzpdgc497.1
title: Uterine disorders classification based on symptomic and behaviorial data

description: The Uterine Disorder Diagnostic Dataset (UDD-2025) consists of 223 anonymized patient records collected through hand-on interviews and clinical ultrasound reports from the Department of Gynecology, R.S. Unit, Shaheed Tajuddin Ahmad Medical College Hospital, Bangladesh. The data collection took place between May 5 – August 7, 2025, spanning a total of 95 days.

Each participant record includes validated diagnostic parameters extracted directly from ultrasound and medical reports, along with corresponding symptomatic and behavioral indicators gathered via structured in-person interviews. Each participant gave consent to interview willingly and shared their experience without any external influence. The dataset is stored in a single .xlsx file (which can be converted to a csv file), with each row representing one participant and each column corresponding to a clinical or behavioral variable.

The ethical clearance for this dataset was obtained from the Institutional Ethics Committee of Daffodil International University, and official permission for clinical data collection was granted by the Assistant Director of Shaheed Tajuddin Ahmad Medical College Hospital.

All diagnostic fields were verified and approved by experts at the same department before inclusion. The ultrasound-based diagnostic summaries were then manually entered and validated for consistency by the research team. All diagnosis summaries were input manually from the reports, maintaining the sequence of other data of each participant. No personally identifiable information is present in the final .xlsx file.

The ultrasound report summaries for each patient were collected immediately following their consultation with the attending physician. As approval had been obtained from the relevant institutional authorities, the procedure ensured the validity and reliability of the diagnostic information.

This dataset serves as a reliable resource for research in:
-Predictive modeling of uterine disorders
-Statistical correlation between menstrual behavior and clinical diagnosis
-Machine learning applications in reproductive health diagnostics
-Multimodal health data integration and analysis

By combining expert-validated ultrasound diagnostics with real-world behavioral data, the UDD-2025 dataset provides an authentic foundation for advancing AI-based women’s health research, reproductive disease prediction, and evidence-driven gynecological analytics.
DOI: 10.17632/d7t99ykjkx
title: Fruit Detection and Multi-Variety Classification System

description: A diverse fruit dataset containing Apple, Mango, Banana, and Grape images supports classification and detection tasks. Each category includes varied samples with different sizes, colors, lighting conditions, and backgrounds. This dataset is suitable for training robust computer vision models, improving accuracy in fruit recognition, sorting, and real-time automation applications.
DOI: 10.17632/d7t99ykjkx.1
title: Fruit Detection and Multi-Variety Classification System

description: A diverse fruit dataset containing Apple, Mango, Banana, and Grape images supports classification and detection tasks. Each category includes varied samples with different sizes, colors, lighting conditions, and backgrounds. This dataset is suitable for training robust computer vision models, improving accuracy in fruit recognition, sorting, and real-time automation applications.
DOI: 10.5281/zenodo.17689372
title: Raster map of 1987 HELP soil codes for the Netherlands, based on the Bofek2020 raster map. Resolution 25x25m.

description: Download and create raster map in R:

r <- terra::rast("https://zenodo.org/api/records/17689373/files/HELP_map_NL2020.tif/content")
DOI: 10.5281/zenodo.17689373
title: Raster map of 1987 HELP soil codes for the Netherlands, based on the Bofek2020 raster map. Resolution 25x25m.

description: Download and create raster map in R:

r <- terra::rast("https://zenodo.org/api/records/17689373/files/HELP_map_NL2020.tif/content")
DOI: 10.17632/r28x53n69z
title: Beyond Institutions: Migration Networks, Trade, and the Limits of RTAs

description: This data is used in the econometric analysis for the research titled "Beyond Institutions: Migration Networks, Trade, and the Limits of Regional Trade Agreements."
DOI: 10.17632/r28x53n69z.1
title: Beyond Institutions: Migration Networks, Trade, and the Limits of RTAs

description: This data is used in the econometric analysis for the research titled "Beyond Institutions: Migration Networks, Trade, and the Limits of Regional Trade Agreements."
DOI: 10.17632/yjhybztwf4
title: A speech dataset of three ethnic languages of Bangladesh: Chakma, Marma and Garo.

description: This dataset provides a curated collection of speech recordings from three ethnic languages of Bangladesh: Chakma, Marma, and Garo. It contains a total of 2321 WAV audio recordings, each ranging from 1 to 7 seconds in duration. All recordings were collected from 11 native speakers aged 20–26 years, who read from a consistent set of 211 predefined Bengali sentences.

The audio samples were recorded using various smartphone devices across different, naturally occurring acoustic environments, providing diverse background conditions. The dataset includes audio files only, without any accompanying metadata file.

Speaker Distribution

The dataset includes:

Chakma: 5 speakers

Marma: 3 speakers

Garo: 3 speakers

This structured distribution ensures balanced representation across the three ethnic groups.

Key Features of the Dataset

Total Audio Files: 2321

Ethnic Languages: Chakma, Marma, Garo

Total Speakers: 11

Speaker Age Range: 20–26 years

Sentences Read: 211 predefined Bengali sentences

Audio Duration: 1–7 seconds per file

Audio Format: WAV

Recording Devices: Various smartphones

Metadata: No metadata file included; only audio files are provided

Data Organization

All recordings are supplied as individual WAV files. Depending on your folder structure, the files may be grouped by ethnic language or speaker (let me know if you want the folder structure documented in the description).

Potential Applications

This dataset can be used for:

Automatic Speech Recognition (ASR) for low-resource languages

Ethnic language identification (LID)

Speaker identification and verification

Acoustic and phonetic analysis

Training multilingual or cross-lingual speech models

Research on low-resource speech processing

Significance

Ethnic languages in Bangladesh are significantly underrepresented in speech technology research. This dataset offers an important resource for developing speech-based systems and contributing to language preservation, technology advancement, and computational linguistic studies.
DOI: 10.17632/yjhybztwf4.2
title: A speech dataset of three ethnic languages of Bangladesh: Chakma, Marma and Garo.

description: This dataset provides a curated collection of speech recordings from three ethnic languages of Bangladesh: Chakma, Marma, and Garo. It contains a total of 2321 WAV audio recordings, each ranging from 1 to 7 seconds in duration. All recordings were collected from 11 native speakers aged 20–26 years, who read from a consistent set of 211 predefined Bengali sentences.

The audio samples were recorded using various smartphone devices across different, naturally occurring acoustic environments, providing diverse background conditions. The dataset includes audio files only, without any accompanying metadata file.

Speaker Distribution

The dataset includes:

Chakma: 5 speakers

Marma: 3 speakers

Garo: 3 speakers

This structured distribution ensures balanced representation across the three ethnic groups.

Key Features of the Dataset

Total Audio Files: 2321

Ethnic Languages: Chakma, Marma, Garo

Total Speakers: 11

Speaker Age Range: 20–26 years

Sentences Read: 211 predefined Bengali sentences

Audio Duration: 1–7 seconds per file

Audio Format: WAV

Recording Devices: Various smartphones

Metadata: No metadata file included; only audio files are provided

Data Organization

All recordings are supplied as individual WAV files. Depending on your folder structure, the files may be grouped by ethnic language or speaker (let me know if you want the folder structure documented in the description).

Potential Applications

This dataset can be used for:

Automatic Speech Recognition (ASR) for low-resource languages

Ethnic language identification (LID)

Speaker identification and verification

Acoustic and phonetic analysis

Training multilingual or cross-lingual speech models

Research on low-resource speech processing

Significance

Ethnic languages in Bangladesh are significantly underrepresented in speech technology research. This dataset offers an important resource for developing speech-based systems and contributing to language preservation, technology advancement, and computational linguistic studies.
DOI: 10.17632/v5tzjrrz44
title: Supplementary Material for JAAD - KLRB1 and IL12RB1 are pretherapeutic predictors of time to relapse in patients of psoriasis treated with IL-17 blockade

description: Supplementary Material for "KLRB1 and IL12RB1 are pretherapeutic predictors of time to relapse in patients of psoriasis treated with IL-17 blockade" in Journal of the American Academy of Dermatology, including 9 Supplementary Figures in TIFF format and a document of 8 Supplementary Tables and 4 Supplementary Methods
DOI: 10.17632/v5tzjrrz44.1
title: Supplementary Material for JAAD - KLRB1 and IL12RB1 are pretherapeutic predictors of time to relapse in patients of psoriasis treated with IL-17 blockade

description: Supplementary Material for "KLRB1 and IL12RB1 are pretherapeutic predictors of time to relapse in patients of psoriasis treated with IL-17 blockade" in Journal of the American Academy of Dermatology, including 9 Supplementary Figures in TIFF format and a document of 8 Supplementary Tables and 4 Supplementary Methods
DOI: 10.17632/kzyzgdbzct
title: Analysis of intestinal flora composition and non-targeted metabolomics of PRTP in vitro fermentation

description: The composition of gut microbiota was explored through 16s rRNA sequencing, and non-targeted metabolomics was employed to detect the types of metabolites in the in vitro fermentation study of PRTP.
DOI: 10.17632/kzyzgdbzct.1
title: Analysis of intestinal flora composition and non-targeted metabolomics of PRTP in vitro fermentation

description: The composition of gut microbiota was explored through 16s rRNA sequencing, and non-targeted metabolomics was employed to detect the types of metabolites in the in vitro fermentation study of PRTP.
DOI: 10.5281/zenodo.17698625
title: Replication package for: "Why Don't People Lie More? Truth Is (Wrongly) Believed To Be More Persuasive"

description: Gneezy, Uri, and Serra-Garcia, Marta (2025). Why Don’t People Lie More? Truth Is (Wrongly) Believed To Be More Persuasive. Economic Journal.
DOI: 10.5281/zenodo.17698626
title: Replication package for: "Why Don't People Lie More? Truth Is (Wrongly) Believed To Be More Persuasive"

description: Gneezy, Uri, and Serra-Garcia, Marta (2025). Why Don’t People Lie More? Truth Is (Wrongly) Believed To Be More Persuasive. Economic Journal.
DOI: 10.15468/dl.9659ed
title: Occurrence Download

description: A dataset containing 1353 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Opuntia streptacantha"
 ]
}

 The dataset includes 1353 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005697-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.26cr7z
title: Occurrence Download

description: A dataset containing 5316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Opuntia tomentosa"
 ]
}

 The dataset includes 5316 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005699-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jr8gcyhsff
title: PRD-CLAY/6/3664

description: This dataset contains multi-source borehole records used in the study “A hybrid GPR–Transformer framework for uncertainty-aware stratigraphic profiling driven by multi-source geotechnical databases.” The data include depth-wise measurements of key geotechnical indices (natural water content w, liquid limit LL, plastic limit PL, and initial void ratio e₀) from 34 borehole sites in the Pearl River Delta region.
The dataset supports training, validation, and cross-regional benchmark experiments for stratigraphic sequence modeling. It can be used for geostatistical analysis, depth-wise soil inference, and machine learning model development in subsurface engineering applications.
All data are anonymized and provided exclusively for research and non-commercial use.
DOI: 10.17632/jr8gcyhsff.1
title: PRD-CLAY/6/3664

description: This dataset contains multi-source borehole records used in the study “A hybrid GPR–Transformer framework for uncertainty-aware stratigraphic profiling driven by multi-source geotechnical databases.” The data include depth-wise measurements of key geotechnical indices (natural water content w, liquid limit LL, plastic limit PL, and initial void ratio e₀) from 34 borehole sites in the Pearl River Delta region.
The dataset supports training, validation, and cross-regional benchmark experiments for stratigraphic sequence modeling. It can be used for geostatistical analysis, depth-wise soil inference, and machine learning model development in subsurface engineering applications.
All data are anonymized and provided exclusively for research and non-commercial use.
DOI: 10.17632/w996gmnkzy
title: Great Rebalancing

description: This dataset contains the cleaned corpus, topic‑modelling outputs, and theory‑mapping materials used in the study on AI and higher education from 2000–2025. It includes bibliographic metadata for 4,280 publications, BERTopic cluster assignments, the RCT–HFT theoretical lexicon, topic–theory similarity scores, and yearly efficiency/humanity indices used for temporal analysis. No full texts are included—only metadata and derived indicators—to support replication and further research on AI governance and human‑centred educational development.
DOI: 10.17632/w996gmnkzy.1
title: Great Rebalancing

description: This dataset contains the cleaned corpus, topic‑modelling outputs, and theory‑mapping materials used in the study on AI and higher education from 2000–2025. It includes bibliographic metadata for 4,280 publications, BERTopic cluster assignments, the RCT–HFT theoretical lexicon, topic–theory similarity scores, and yearly efficiency/humanity indices used for temporal analysis. No full texts are included—only metadata and derived indicators—to support replication and further research on AI governance and human‑centred educational development.
DOI: 10.17632/h2gndhfcfh
title: DOC and Soil

description: The data on soil physical and chemical properties collected and measured in the wetlands of the Qinghai-Tibet Plateau.
DOI: 10.17632/h2gndhfcfh.1
title: DOC and Soil

description: The data on soil physical and chemical properties collected and measured in the wetlands of the Qinghai-Tibet Plateau.
DOI: 10.17632/sv2dr3w529
title: Technical Limits in Prescriptive Building Cultures and Tectonic Approaches

description: The manuscript fully complies with research ethical standards, including approval from the research board, institutional permissions, and participant consent for the use of images and documents. All data collection and analysis processes in both research phases are clearly outlined, with complete protection of participant anonymity. These supporting documents are shared as PDF files via the MEDELEY DATA Repository (under the license CC BY-NC-ND 4.0). To clarify, this dataset is provided solely as supplementary material for the Journal’s Editorial Team to verify the methods and results described in the manuscript; it is not intended for publication, sharing, or any form of reuse.
DOI: 10.17632/sv2dr3w529.3
title: Technical Limits in Prescriptive Building Cultures and Tectonic Approaches

description: The manuscript fully complies with research ethical standards, including approval from the research board, institutional permissions, and participant consent for the use of images and documents. All data collection and analysis processes in both research phases are clearly outlined, with complete protection of participant anonymity. These supporting documents are shared as PDF files via the MEDELEY DATA Repository (under the license CC BY-NC-ND 4.0). To clarify, this dataset is provided solely as supplementary material for the Journal’s Editorial Team to verify the methods and results described in the manuscript; it is not intended for publication, sharing, or any form of reuse.
DOI: 10.15468/dl.gg9rtz
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Living Specimen)",
 "TaxonKey is Pelochelys cantorii"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005700-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2nmxmf55c4
title: The Impact of Artificial Intelligence on Financial and Investment Planning: Transforming Strategies for a Data-Driven Future

description: The advent of Artificial Intelligence (AI) is fundamentally reshaping the financial and investment 
landscape, ushering in an era of faster, smarter, and hyper-personalized decision-making. This study 
provides an analytical examination of AI's transformative influence on financial advisory models, risk 
analysis, and portfolio optimization. We explore how core AI-driven tools—specifically robo-advisors, 
predictive analytics, and machine learning algorithms—not only enhance financial forecasting accuracy 
and optimize investment diversification but also decisively mitigate human bias in strategic planning.
The analysis details the inherent shift from traditional human-centric advisory to algorithmic precision, 
critically assessing the opportunities and risks of this transformation. Maintaining trust and 
transparency in AI-integrated systems requires robust focus, thus ethical considerations, data 
governance, and regulatory preparedness are identified as paramount. The study proposes a strategic 
framework for responsible AI adoption, designed to effectively balance technological efficiency and 
innovation with crucial investor protection.
By integrating insights from financial technology, behavioural finance, and policy perspectives, this 
research makes a timely contribution to the ongoing dialogue on the digital evolution of wealth 
management. The findings are designed to guide financial institutions, policymakers, and investors 
toward sustainable and inclusive financial ecosystems powered by intelligent technologies.
DOI: 10.17632/2nmxmf55c4.1
title: The Impact of Artificial Intelligence on Financial and Investment Planning: Transforming Strategies for a Data-Driven Future

description: The advent of Artificial Intelligence (AI) is fundamentally reshaping the financial and investment 
landscape, ushering in an era of faster, smarter, and hyper-personalized decision-making. This study 
provides an analytical examination of AI's transformative influence on financial advisory models, risk 
analysis, and portfolio optimization. We explore how core AI-driven tools—specifically robo-advisors, 
predictive analytics, and machine learning algorithms—not only enhance financial forecasting accuracy 
and optimize investment diversification but also decisively mitigate human bias in strategic planning.
The analysis details the inherent shift from traditional human-centric advisory to algorithmic precision, 
critically assessing the opportunities and risks of this transformation. Maintaining trust and 
transparency in AI-integrated systems requires robust focus, thus ethical considerations, data 
governance, and regulatory preparedness are identified as paramount. The study proposes a strategic 
framework for responsible AI adoption, designed to effectively balance technological efficiency and 
innovation with crucial investor protection.
By integrating insights from financial technology, behavioural finance, and policy perspectives, this 
research makes a timely contribution to the ongoing dialogue on the digital evolution of wealth 
management. The findings are designed to guide financial institutions, policymakers, and investors 
toward sustainable and inclusive financial ecosystems powered by intelligent technologies.