cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.82199/oqei-cnl1-data-5rtr-je98
title: Jeu de données relatif à la première campagne nationale de mesures de la qualité de l'air intérieur dans les logements français (CNL1)

description: La campagne nationale dans les logements conduite par l'Observatoire de la Qualité de l'Air Intérieur (OQAI) sur la période allant de septembre 2003 à décembre 2005 autorise à dresser un premier état de la qualité de l’air intérieur représentatif de la situation des 24 millions de résidences principales en France métropolitaine continentale.
Les paramètres ont été choisis en fonction de leur impact sur la qualité de l'air ou sur le confort, de leur dangerosité et de leur fréquence d'apparition : monoxyde de carbone, composés organiques volatils, particules, radon, allergènes de chiens, de chats, d'acariens, rayonnement gamma, dioxyde de carbone, température, humidité relative (Mosqueron et Nedellec, 2002, <https://www.oqai.fr/fr/media/rapports/hierarchisation-sanitaire-des-parametres-mesures-dans-les-batiments-par-l-observatoire-de-la-qualite-de-l-air-interieur>). Pour la plupart, ce sont des paramètres différents de ceux retenus habituellement pour caractériser la qualité de l'air extérieur car ils sont le reflet de la présence de multiples sources potentielles de pollution intérieure : matériaux, équipements, mobilier, produits ménagers, activité humaine, environnement extérieur, etc.
Des informations détaillées ont également été collectées sur les caractéristiques techniques des logements et leur environnement ainsi que sur les ménages, leurs activités et le temps passé au contact de la pollution. Les données ont été recueillies dans 567 résidences principales (1 612 individus enquêtés) réparties sur 50 départements et 74 communes de la France continentale métropolitaine, sur une durée d'une semaine, à l'intérieur des logements, dans les garages communiquant avec le logement (lorsqu'ils existaient) et à l'extérieur.
Il existe une spécificité de la qualité de l’air à l’intérieur des logements par rapport à l’extérieur qui s'exprime en particulier par la présence de certaines substances non observées à l'extérieur ou par des concentrations nettement plus importantes à l'intérieur. Les polluants visés sont présents à des niveaux quantifiables dans la majorité des logements du parc. La répartition de la pollution chimique organique n'est pas homogène dans le parc. Seule une minorité de logement (9%) présente des concentrations très élevées pour plusieurs polluants simultanément ; à l'inverse 45% des logements présentent des niveaux de concentrations très faibles pour l'ensemble des polluants mesurés. Polluant par polluant, de 5 à 30% des logements présentent des valeurs nettement plus élevées que les concentrations trouvées en moyenne dans le parc.
Le détail des enquêtes, la procédure d’échantillonnage des logements, les éléments d’assurance qualité et les résultats détaillés relatifs à cette campagne sont consultables dans le rapport mis en ligne par l’OQAI (Kirchner et al., 2007, <https://www.oqai.fr/fr/media/rapports/1-log-cnl1-etat-qai> ).
DOI: 10.3886/e242434v1
title: College of Nursing Sciences, Ituk-Mbang, Akwa Ibom 2026/2027 Admission Form Is On Sale.Call 07076040542 To Apply and further admission assistance. Call Admin +2347076040542

description: College of Nursing Sciences, Ituk-Mbang, Akwa Ibom 2026/2027 Admission Form Is On Sale.Call 07076040542 To Apply and further admission assistance. Call Admin +2347076040542
DOI: 10.7910/dvn/thcrb2
title: A Theoretical Model of Quantum Coherence in Periodontal Low-Level Laser Therapy Using a Biological Hamiltonian

description: This repository contains simulation data and code supporting the theoretical model presented in the manuscript "A Theoretical Model of Quantum Coherence in Periodontal Low-Level Laser Therapy Using a Biological Hamiltonian." The study investigates quantum effects in low-level laser therapy (LLLT) applied to periodontal tissues using a two-level system (TLS) framework, rooted in open quantum systems theory. The model simulates laser-driven excitations and decoherence, incorporating clinically relevant LLLT parameters (e.g., wavelengths 600-1000 nm, fluences 1-10 J/cm², Rabi frequencies Ω = [0.05, 0.1, 0.2, 0.5] MHz, decoherence rates γ = [0.05, 0.1, 0.2] MHz) to predict coherence timescales and energy transfer efficiencies. Data includes steady-state populations, coherence decay fits, and time-series outputs, while code (Jupyter notebooks and Python scripts) enables replication using QuTiP, NumPy, Pandas, and Matplotlib.

The uploaded files comprise raw data (e.g., TLS_HAMILTONIAN_SIMULATION_DATA.xlsx, fig1_population_evolution_data.xlsx), derived datasets (e.g., tls_steady_state_populations.xlsx), and analysis scripts (e.g., STEADY-STATE_POPULATIONS_COHERENCE_DECAY_FITTING_TIME-SERIES_ANALYSIS.ipynb, fig1_population_evolution.py), accompanied by a README.md for usage instructions. This work bridges quantum physics and clinical periodontology, offering a foundation for future experimental validation.
DOI: 10.15468/dl.knu6an
title: Occurrence Download

description: A dataset containing 9499 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Minois dryas",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1970",
 "Country is one of (Italy, Switzerland, Germany, Austria, Slovenia, France)"
 ]
}

 The dataset includes 9499 records from 147 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073681-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qhpphc
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "Geometry POLYGON((5.87205 52.03423,5.89508 52.03764,5.90118 52.05049,5.8567 52.04434,5.87267 52.07933,5.88645 52.06998,5.89779 52.06292,5.93533 52.06859,5.93533 52.06859,5.94651 52.05584,5.92682 52.01873,5.95627 52.00977,5.99146 52.00311,6.02575 52.01153,6.09031 52.03706,6.08986 52.04455,6.06913 52.08467,6.03889 52.10396,6.01113 52.11244,6.00559 52.11371,5.91812 52.11669,5.90032 52.11771,5.88405 52.11791,5.88402 52.11623,5.87383 52.1152,5.86917 52.12378,5.84265 52.12618,5.79604 52.12004,5.78021 52.11063,5.7742 52.09537,5.81645 52.03434,5.87205 52.03423))",
 "OccurrenceStatus is Present",
 "TaxonKey is Accipiter nisus",
 "Year 2014-2014"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073682-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e242433v1
title: College of Nursing Sciences, Anua-Uyo, Akwa Ibom 2026/2027 Admission Form Is On Sale.Call 07076040542 To Apply and further admission assistance. Call Admin +2347076040542

description: College of Nursing Sciences, Anua-Uyo, Akwa Ibom 2026/2027 Admission Form Is On Sale.Call 07076040542 To Apply and further admission assistance. Call Admin +2347076040542
DOI: 10.15468/dl.bnkng2
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Linum littorale)",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 64 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073683-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d5k75t
title: Occurrence Download

description: A dataset containing 144640 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Mercurialis annua)",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 144640 records from 1167 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073667-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kg7ar6
title: Occurrence Download

description: A dataset containing 351 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Stewartia malacodendron L."
 ]
}

 The dataset includes 351 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006436-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.97yfcu
title: Occurrence Download

description: A dataset containing 128816 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phoenicopterus ruber Linnaeus, 1758"
 ]
}

 The dataset includes 128816 records from 232 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006424-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.spbygk
title: Occurrence Download

description: A dataset containing 9406 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "TaxonKey is Python bivittatus Kuhl, 1820"
 ]
}

 The dataset includes 9406 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006437-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2rf5nt
title: Occurrence Download

description: A dataset containing 1688 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Thunbergia fragrans Roxb."
 ]
}

 The dataset includes 1688 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006438-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60503/d3/rkx4dr
title: MATERIAL Somali-English Language Pack

description: MATERIAL Somali-English Language Pack was developed by Appen for the IARPA (Intelligence Advanced Research Projects Activity) MATERIAL (Machine Translation for English Retrieval of Information in Any Language) program. It contains approximately 80 hours of Somali conversational telephone speech, transcripts, English translations, annotations and queries. The MATERIAL program focused on underserved languages with the ultimate goal to build cross language information retrieval systems to find speech and text content using English search queries.
DOI: 10.15468/dl.7wrs9e
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhododendron minus var. chapmanii (Alph.Wood) Gandhi & Zarucchi"
 ]
}

 The dataset includes 80 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006439-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q6aem6
title: Occurrence Download

description: A dataset containing 1397 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Neoraputia Emmerich ex Kallunki"
 ]
}

 The dataset includes 1397 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006436-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xbe2gf
title: Occurrence Download

description: A dataset containing 363380 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-70.61822 -33.60148,-70.10646 -33.60148,-70.10646 -33.20316,-70.61822 -33.20316,-70.61822 -33.60148))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 2015-2025"
 ]
}

 The dataset includes 363380 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006423-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/4bgt2p5hrd.1
title: Generative AI-Assisted Title and Abstract Screening

description: This dataset supports the study titled "Do it Faster with PICOS: Generative AI-Assisted Systematic Review Screening." The research explores the impact of using an open-source Large Language Model (LLM), Mistral-Nemo-Instruct-2407, to generate structured PICOS (Population, Intervention/Exposure, Comparison, Outcome, Study Design) summaries for title and abstract screening in systematic reviews. The dataset includes article metadata, manually labeled inclusion/exclusion decisions by human reviewers, and LLM-generated structured PICOS summaries.

This dataset can be useful for researchers working on AI-assisted evidence synthesis, systematic review automation, and human-machine collaboration in biomedical literature screening.

The dataset consists of the following columns, capturing key metadata and screening decisions for systematic review articles:

Title – The title of the research article.
Authors – List of authors for the article.
Journal – Name of the journal where the article was published.
Year – Year of publication.
Volume – Journal volume number.
Issue – Journal issue number.
DOI – Digital Object Identifier (DOI) for the article.
Abstract – The abstract of the research article.
PICOS Summary – Structured summary generated by the LLM
Gold_std dataset – Ground truth classification (Include/Exclude) based on expert review.
A1_reviewer – Screening decision by Reviewer A1 (LLM-assisted, less experienced).
A2_reviewer – Screening decision by Reviewer A2 (Traditional, less experienced).
B1_reviewer – Screening decision by Reviewer B1 (LLM-assisted, more experienced).
B2_reviewer – Screening decision by Reviewer B2 (Traditional, more experienced).
DOI: 10.17632/33khhwbhwn.1
title: BRADS: A Multipurpose Audio Dataset For Bangla Regional Word Detection

description: Research Hypothesis
This dataset tests the hypothesis that Bangla ASR performance is affected by regional dialects and pronunciation variations. Despite Bangla’s widespread use, speech recognition models struggle with dialect diversity. This dataset enables the development of more accurate and inclusive ASR models.

DATA SUMMARY
The dataset contains 298 Bangla words (233 regional, 65 chaste Bangla) recorded by 85 native speakers from eight divisions, resulting in 2,439 high-quality audio samples.

Data formats include .wav (audio files) and .xlsx (text data). Words were recorded using recommended apps, verified manually, and include background noise for real-world ASR training.

NOTABLE FINDINGS
Pronunciation varies significantly across regions. For example, the word "আমি" (Ami) [I] differs:

Chittagong: "আই" (Ayi)
Barisal: "মুই" (Mui)
Rajshahi: "আমাক" (Amak)
Rangpur: "হামি" (Hami)

Chittagong has the most dialectal variance, while Rangpur is closest to chaste Bangla. Most contributions came from ages 23-27, indicating generational trends.

HOW TO USE THE DATA
The dataset can be used for ASR training in CNN, RNN, and Transformer models. It also improves NLP applications, chatbots, and speech-to-text systems. Linguists can study phonetic variations, and it enhances Bangla-English machine translation.

VALUE OF THE DATA
This dataset fills a gap in Bangla ASR research, supporting inclusive AI development and linguistic diversity preservation. It serves as a benchmark for Bangla speech-based AI, making voice technology more accessible
DOI: 10.17632/33khhwbhwn.2
title: BRADS: A Multipurpose Audio Dataset For Bangla Regional Word Detection

description: Research Hypothesis
This dataset tests the hypothesis that Bangla ASR performance is affected by regional dialects and pronunciation variations. Despite Bangla’s widespread use, speech recognition models struggle with dialect diversity. This dataset enables the development of more accurate and inclusive ASR models.

DATA SUMMARY
The dataset contains 298 Bangla words (233 regional, 65 chaste Bangla) recorded by 85 native speakers from eight divisions, resulting in 2,439 high-quality audio samples.

Data formats include .wav (audio files) and .xlsx (text data). Words were recorded using recommended apps, verified manually, and include background noise for real-world ASR training.

NOTABLE FINDINGS
Pronunciation varies significantly across regions. For example, the word "আমি" (Ami) [I] differs:

Chittagong: "আই" (Ayi)
Barisal: "মুই" (Mui)
Rajshahi: "আমাক" (Amak)
Rangpur: "হামি" (Hami)

Chittagong has the most dialectal variance, while Rangpur is closest to chaste Bangla. Most contributions came from ages 23-27, indicating generational trends.

HOW TO USE THE DATA
The dataset can be used for ASR training in CNN, RNN, and Transformer models. It also improves NLP applications, chatbots, and speech-to-text systems. Linguists can study phonetic variations, and it enhances Bangla-English machine translation.

VALUE OF THE DATA
This dataset fills a gap in Bangla ASR research, supporting inclusive AI development and linguistic diversity preservation. It serves as a benchmark for Bangla speech-based AI, making voice technology more accessible
DOI: 10.17632/6pd2c48m66.1
title: BRWDS: A Multipurpose Dataset For Bangla Regional Word Detection

description: The BRDS (Bangla Regional Word Dataset) is a comprehensive collection of commonly used Bengali words that highlights the linguistic diversity across 8 distinct divisions in Bangladesh. This dataset aims to tackle the challenges posed by regional accents and variations in Bengali, which can create barriers to communication. The dataset covers words from the following divisions: Dhaka, Chittagong, Mymensingh, Sylhet, Rajshahi, Khulna, Barishal, and Rangpur. In total, it includes 347 Bengali words that are frequently used in daily conversations across these regions. While Bengali is spoken across all these divisions, each region has its own unique accent, leading to variations in pronunciation and word usage, which are captured in this dataset.

To create this dataset, 12 native speakers from the 8 divisions, as well as one additional district, contributed by providing word samples. The data is stored in XLSX format, making it easily accessible for further research. This dataset has several potential applications, including the development of systems that can automatically detect regional variations in Bengali text, enabling better localization and understanding of regional dialects. It can also help minimize communication barriers caused by accent differences within Bangladesh by offering a more standardized understanding of regional variations. Additionally, the dataset can be used to translate regional words into standard Bengali (Chaste Bengali), making it easier for people to understand each other. The dataset also supports research into linguistic diversity and provides a foundation for future advancements in speech and text processing technologies. The dataset has been reviewed and evaluated by 9 authentic speakers from each division to ensure its accuracy and proper representation of the regional language variations.

Looking forward, the dataset can be further enriched by adding voice data, which would support more advanced research in areas such as speech recognition, accent detection, and machine translation for regional language variants.

Data was situated in Bangla RDS.xlsv . In sheet 1 named Region wise data was collected and evaluated on other sheet named categorize data where all the data was categorized and organize according to common chaste words.
DOI: 10.17632/6pd2c48m66.2
title: BRWDS: A Multipurpose Dataset For Bangla Regional Word Detection

description: The BRWDS (Bangla Regional Word Dataset) is a comprehensive collection of commonly used Bengali words that highlights the linguistic diversity across 8 distinct divisions in Bangladesh. This dataset aims to tackle the challenges posed by regional accents and variations in Bengali, which can create barriers to communication. The dataset covers words from the following divisions: Dhaka, Chittagong, Mymensingh, Sylhet, Rajshahi, Khulna, Barishal, and Rangpur. In total, it includes 347 Bengali words that are frequently used in daily conversations across these regions. While Bengali is spoken across all these divisions, each region has its own unique accent, leading to variations in pronunciation and word usage, which are captured in this dataset.

To create this dataset, 12 native speakers from the 8 divisions, as well as one additional district, contributed by providing word samples. The data is stored in XLSX format, making it easily accessible for further research. This dataset has several potential applications, including the development of systems that can automatically detect regional variations in Bengali text, enabling better localization and understanding of regional dialects. It can also help minimize communication barriers caused by accent differences within Bangladesh by offering a more standardized understanding of regional variations. Additionally, the dataset can be used to translate regional words into standard Bengali (Chaste Bengali), making it easier for people to understand each other. The dataset also supports research into linguistic diversity and provides a foundation for future advancements in speech and text processing technologies. The dataset has been reviewed and evaluated by 9 authentic speakers from each division to ensure its accuracy and proper representation of the regional language variations.

Looking forward, the dataset can be further enriched by adding voice data, which would support more advanced research in areas such as speech recognition, accent detection, and machine translation for regional language variants.

Data was situated in Bangla RDS.xlsv . In sheet 1 named Region wise data was collected and evaluated on other sheet named categorize data where all the data was categorized and organize according to common chaste words.