cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/yiddxoxnipmzji-uhfffaoysa-n.1
title: 1,3-bis((4-fluorophenyl)ethynyl)benzene
title: InChI=1S/C22H12F2/c23-21-12-8-17(9-13-21)4-6-19-2-1-3-20(16-19)7-5-18-10-14-22(24)15-11-18/h1-3,8-16H

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C22H12F2/c23-21-12-8-17(9-13-21)4-6-19-2-1-3-20(16-19)7-5-18-10-14-22(24)15-11-18/h1-3,8-16H, and canonical SMILES descriptor[cheminf_000007]: Fc1ccc(cc1)C#Cc1cccc(c1)C#Cc1ccc(cc1)F, and by the IUPAC name[cheminf_000107]: .
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-54627

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 149.1 (°C)
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0000630 | infrared absorption spectroscopy (IR)
 
 CHMO:0001146 | 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
 
 CHMO:0000597 | 19F nuclear magnetic resonance spectroscopy (19F NMR)
 
 CHMO:0000470 | mass spectrometry (MS)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/yiddxoxnipmzji-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C22H12F2/c23-21-12-8-17(9-13-21)4-6-19-2-1-3-20(16-19)7-5-18-10-14-22(24)15-11-18/h1-3,8-16H

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/yiddxoxnipmzji-uhfffaoysa-n/chmo0001146
title: 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) ()
title: InChI=1S/C22H12F2/c23-21-12-8-17(9-13-21)4-6-19-2-1-3-20(16-19)7-5-18-10-14-22(24)15-11-18/h1-3,8-16H

description: dataset for 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
DOI: 10.14272/yiddxoxnipmzji-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C22H12F2/c23-21-12-8-17(9-13-21)4-6-19-2-1-3-20(16-19)7-5-18-10-14-22(24)15-11-18/h1-3,8-16H

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.14272/oyedttntnyksfx-uhfffaoysa-n/chmo0000292
title: ultraviolet-visible spectrophotometry (UV-VIS) (1,3-bis(2-phenylethynyl)benzene)
title: InChI=1S/C22H14/c1-3-8-19(9-4-1)14-16-21-12-7-13-22(18-21)17-15-20-10-5-2-6-11-20/h1-13,18H

description: dataset for ultraviolet-visible spectrophotometry (UV-VIS)
DOI: 10.14272/oyedttntnyksfx-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (1,3-bis(2-phenylethynyl)benzene)
title: InChI=1S/C22H14/c1-3-8-19(9-4-1)14-16-21-12-7-13-22(18-21)17-15-20-10-5-2-6-11-20/h1-13,18H

description: dataset for mass spectrometry (MS)
DOI: 10.14272/oyedttntnyksfx-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (1,3-bis(2-phenylethynyl)benzene)
title: InChI=1S/C22H14/c1-3-8-19(9-4-1)14-16-21-12-7-13-22(18-21)17-15-20-10-5-2-6-11-20/h1-13,18H

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.14272/oyedttntnyksfx-uhfffaoysa-n/chmo0001146
title: 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) (1,3-bis(2-phenylethynyl)benzene)
title: InChI=1S/C22H14/c1-3-8-19(9-4-1)14-16-21-12-7-13-22(18-21)17-15-20-10-5-2-6-11-20/h1-13,18H

description: dataset for 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
DOI: 10.14272/oyedttntnyksfx-uhfffaoysa-n.1
title: 1,3-bis(2-phenylethynyl)benzene
title: InChI=1S/C22H14/c1-3-8-19(9-4-1)14-16-21-12-7-13-22(18-21)17-15-20-10-5-2-6-11-20/h1-13,18H

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C22H14/c1-3-8-19(9-4-1)14-16-21-12-7-13-22(18-21)17-15-20-10-5-2-6-11-20/h1-13,18H, and canonical SMILES descriptor[cheminf_000007]: c1cc(C#Cc2ccccc2)cc(c1)C#Cc1ccccc1, and by the IUPAC name[cheminf_000107]: 1,3-bis(2-phenylethynyl)benzene.
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-54512

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 110.7 (°C)
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0001146 | 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0000630 | infrared absorption spectroscopy (IR)
 
 CHMO:0000470 | mass spectrometry (MS)
 
 CHMO:0000292 | ultraviolet-visible spectrophotometry (UV-VIS)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/oyedttntnyksfx-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (1,3-bis(2-phenylethynyl)benzene)
title: InChI=1S/C22H14/c1-3-8-19(9-4-1)14-16-21-12-7-13-22(18-21)17-15-20-10-5-2-6-11-20/h1-13,18H

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/oyedttntnyksfx-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (1,3-bis(2-phenylethynyl)benzene)
title: InChI=1S/C22H14/c1-3-8-19(9-4-1)14-16-21-12-7-13-22(18-21)17-15-20-10-5-2-6-11-20/h1-13,18H

description: dataset for infrared absorption spectroscopy (IR)
DOI: 10.6084/m9.figshare.31096369
title: Supplemental data for Frantz et al. (2026)

description: Meiotic crossover data from <i>Drosophila melanogaster</i> females with different dosages of the genes encoding crossover-associate RING (COR) proteins. Protocols are described and data are summarized in Frantz <i>et al.</i> (2026).
DOI: 10.6084/m9.figshare.31096369.v1
title: Supplemental data for Frantz et al. (2026)

description: Meiotic crossover data from <i>Drosophila melanogaster</i> females with different dosages of the genes encoding crossover-associate RING (COR) proteins. Protocols are described and data are summarized in Frantz <i>et al.</i> (2026).
DOI: 10.7910/dvn/l2c5e6
title: Replication Data for: Nasopharyngeal microbiome composition and its clinical correlates in children hospitalized with severe pneumonia in East Africa

description: &lt;p&gt;This is a replication dataset for the manuscript titled: "&lt;strong&gt;&lt;i&gt;Nasopharyngeal microbiome composition and its clinical correlates in children hospitalized with severe pneumonia in East Africa&lt;/i&gt;&lt;/strong&gt;."&lt;/p&gt;

&lt;p&gt;This dataset is part of a study that investigated Children’s Oxygen Administration Strategies Trial (COAST): A randomised controlled trial of high flow versus oxygen versus control in African children with severe pneumonia. This dataset contains the patients’ sociodemographic characteristics, clinical features (symptoms, underlying comorbidities, diagnosis of pneumonia, outcomes, and respiratory viruses diagnosis).The dataset is used to generate partial 16s rRNA sequence data on the MiSeq platform for 876 children.&lt;/p&gt;
DOI: 10.5281/zenodo.18469022
title: فاطمه آقائي ، محقق و پژوهشگر ، كارشناس تحقيق و توسعه
title: تحلیل دلایل شکست استارتاپ های مبتنی بر شیمی کاربردی

description: با وجود پیشرفت‌های قابل‌توجه در حوزه شیمی کاربردی، بخش بزرگی از ایده‌ها و دستاوردهای آزمایشگاهی هرگز به مرحله تجاری‌سازی نمی‌رسند. این مسئله به‌ویژه در استارتاپ‌های مبتنی بر شیمی نمود بیشتری دارد، جایی که فاصله میان منطق علمی آزمایشگاه و الزامات بازار به یکی از دلایل اصلی شکست تبدیل می‌شود. هدف این مقاله تحلیل مفهومی و ساختاری دلایل ایجاد «شکاف آزمایشگاه تا بازار» در استارتاپ‌های شیمی‌محور است. پژوهش حاضر با روش تحلیلی–توصیفی و بر اساس مرور منابع علمی، گزارش‌های صنعتی و تجربیات کارآفرینانه، عوامل فنی، اقتصادی و مدیریتی مؤثر بر این شکاف را بررسی می‌کند. نتایج نشان می‌دهد که تمرکز بیش از حد بر کمال علمی، نادیده‌گرفتن مقیاس‌پذیری واکنش‌ها، هزینه‌های پنهان تولید و ضعف در مدل کسب‌وکار از مهم‌ترین موانع تجاری‌سازی ایده‌های شیمیایی هستند. در پایان، راهکارهایی جهت کاهش این فاصله و افزایش شانس موفقیت استارتاپ‌های شیمی کاربردی ارائه می‌شود
DOI: 10.5281/zenodo.18469021
title: فاطمه آقائي ، محقق و پژوهشگر ، كارشناس تحقيق و توسعه
title: تحلیل دلایل شکست استارتاپ های مبتنی بر شیمی کاربردی

description: با وجود پیشرفت‌های قابل‌توجه در حوزه شیمی کاربردی، بخش بزرگی از ایده‌ها و دستاوردهای آزمایشگاهی هرگز به مرحله تجاری‌سازی نمی‌رسند. این مسئله به‌ویژه در استارتاپ‌های مبتنی بر شیمی نمود بیشتری دارد، جایی که فاصله میان منطق علمی آزمایشگاه و الزامات بازار به یکی از دلایل اصلی شکست تبدیل می‌شود. هدف این مقاله تحلیل مفهومی و ساختاری دلایل ایجاد «شکاف آزمایشگاه تا بازار» در استارتاپ‌های شیمی‌محور است. پژوهش حاضر با روش تحلیلی–توصیفی و بر اساس مرور منابع علمی، گزارش‌های صنعتی و تجربیات کارآفرینانه، عوامل فنی، اقتصادی و مدیریتی مؤثر بر این شکاف را بررسی می‌کند. نتایج نشان می‌دهد که تمرکز بیش از حد بر کمال علمی، نادیده‌گرفتن مقیاس‌پذیری واکنش‌ها، هزینه‌های پنهان تولید و ضعف در مدل کسب‌وکار از مهم‌ترین موانع تجاری‌سازی ایده‌های شیمیایی هستند. در پایان، راهکارهایی جهت کاهش این فاصله و افزایش شانس موفقیت استارتاپ‌های شیمی کاربردی ارائه می‌شود
DOI: 10.15468/dl.esax79
title: Occurrence Download

description: A dataset containing 1345318 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Fagus sylvatica",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1345318 records from 2166 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009586-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/w1nyf9td
title: Loan Approval Dataset

description: The dataset used in the paper is a simulation study for a loan approval case, measuring the financial impact of different choices on the worst-off group.
DOI: 10.13145/bacdive170110.20250331.9.3
title: Pseudovibrio flavus Goldberg et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170110.20251217.10
title: Pseudovibrio flavus Goldberg et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.17632/xz3wtpc4r9.1
title: Results of the Pegadaian Application Research Questionnaire.

description: This data presents the factors that influence trust and satisfaction in the Tring! By Pegadaian user application.
DOI: 10.13145/bacdive170111.20250331.9.3
title: Jiella flava Chen et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/vuichuhy
title: DUNE-like survey dataset

description: The dataset used in this paper for studying the impact of systematics on future tomographic cosmic shear measurements.
DOI: 10.13145/bacdive170111.20251217.10
title: Jiella flava Chen et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170112.20250331.9.3
title: Acuticoccus kalidii Tian et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170112.20251217.10
title: Acuticoccus kalidii Tian et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.17632/y7zgrm95fn
title: STEM_QA_Test

description: The full corpus is curated across multiple STEM/Non-STEM disciplines and structured for use in LLM training, evaluation, and instruction tuning (SFT/RLHF). This sample represents the structure and quality of the larger dataset.

Dataset composition (full corpus):

Text corpus: 1.6B+ words of curated STEM and Non-STEM educational content across 22000+ texbooks in 7 languages(English, Hindi, Arabic, Bahasa, Tamil, Telegu, Kannada)
Question–Answer pairs: 6.5M+ high-quality Q&A pairs of STEM and Non-STEM in (English, Arabic, Hindi and Indic languages)
Video data: 100K+ hours of STEM Videos and 30K+ hours of UGC.
Audio data: 821K+ hours of Podcasts and Call Center data(Dual Channel)
Medical datasets: 30M+ files including clinical and diagnostic data like CT Scan, MRI, X-ray, Pathology, EHRs, USG Reports and Echo Reports.
This repository includes:

A small preview subset of the STEM English Q&A data
Flat, viewer-friendly schema for inspection
Parquet files suitable for benchmarking and evaluation
Purpose of this dataset:

Dataset preview and validation
Model evaluation and experimentation
Schema and format inspection before full-scale access
⚠️ Note: This repository contains sample data only. Access to the complete dataset is available separately under appropriate licensing or partnership terms. Note: This is not the full dataset.

For further details contact [Email: Ph: ]
DOI: 10.57702/sqf8rd33
title: Minimum Spanning Tree

description: The dataset used in the paper is the Minimum Spanning Tree problem, a combinatorial optimization problem where the goal is to find a minimum spanning tree of a graph.
DOI: 10.13145/bacdive170113.20250331.9.3
title: Pelagibacterium xiamenense He et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170113.20251217.10
title: Pelagibacterium xiamenense He et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.17632/y7zgrm95fn.1
title: STEM_QA_Test

description: The full corpus is curated across multiple STEM/Non-STEM disciplines and structured for use in LLM training, evaluation, and instruction tuning (SFT/RLHF). This sample represents the structure and quality of the larger dataset.

Dataset composition (full corpus):

Text corpus: 1.6B+ words of curated STEM and Non-STEM educational content across 22000+ texbooks in 7 languages(English, Hindi, Arabic, Bahasa, Tamil, Telegu, Kannada)
Question–Answer pairs: 6.5M+ high-quality Q&A pairs of STEM and Non-STEM in (English, Arabic, Hindi and Indic languages)
Video data: 100K+ hours of STEM Videos and 30K+ hours of UGC.
Audio data: 821K+ hours of Podcasts and Call Center data(Dual Channel)
Medical datasets: 30M+ files including clinical and diagnostic data like CT Scan, MRI, X-ray, Pathology, EHRs, USG Reports and Echo Reports.
This repository includes:

A small preview subset of the STEM English Q&A data
Flat, viewer-friendly schema for inspection
Parquet files suitable for benchmarking and evaluation
Purpose of this dataset:

Dataset preview and validation
Model evaluation and experimentation
Schema and format inspection before full-scale access
⚠️ Note: This repository contains sample data only. Access to the complete dataset is available separately under appropriate licensing or partnership terms. Note: This is not the full dataset.

For further details contact [Email: Ph: ]
DOI: 10.57702/rayn1xbf
title: AQI-36

description: The dataset contains hourly sampled PM2.5 observations from 36 stations in Beijing, covering a total of 12 months.
DOI: 10.13145/bacdive170114.20250331.9.3
title: Microvirga lenta Du et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170114.20251217.10
title: Microvirga lenta Du et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170115.20250331.9.3
title: Microvirga roseola Du et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170115.20251217.10
title: Microvirga roseola Du et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.17632/9vpy7ycnkh
title: STEM_QA_English

description: The full corpus is curated across multiple STEM/Non-STEM disciplines and structured for use in LLM training, evaluation, and instruction tuning (SFT/RLHF). This sample represents the structure and quality of the larger dataset.

Dataset composition (full corpus):

Text corpus: 1.6B+ words of curated STEM and Non-STEM educational content across 22000+ texbooks in 7 languages(English, Hindi, Arabic, Bahasa, Tamil, Telegu, Kannada)
Question–Answer pairs: 6.5M+ high-quality Q&A pairs of STEM and Non-STEM in (English, Arabic, Hindi and Indic languages)
Video data: 100K+ hours of STEM Videos and 30K+ hours of UGC.
Audio data: 821K+ hours of Podcasts and Call Center data(Dual Channel)
Medical datasets: 30M+ files including clinical and diagnostic data like CT Scan, MRI, X-ray, Pathology, EHRs, USG Reports and Echo Reports.
This repository includes:

A small preview subset of the STEM English Q&A data
Flat, viewer-friendly schema for inspection
Parquet files suitable for benchmarking and evaluation
Purpose of this dataset:

Dataset preview and validation
Model evaluation and experimentation
Schema and format inspection before full-scale access
⚠️ Note: This repository contains sample data only. Access to the complete dataset is available separately under appropriate licensing or partnership terms. Note: This is not the full dataset.

For further details, Please connect [Em: vipul.mishra@infobay.ai ph: 8303174762]
DOI: 10.57702/m9zaro2c
title: LibriMix

description: The LibriMix dataset is a large corpus of mixed speech, designed for training and evaluating speech separation systems.
DOI: 10.13145/bacdive170116.20240510.9
title: Undibacter mobilis Jung et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170116.20251217.10
title: Undibacter mobilis Jung et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/qvb66owq
title: Phoenix dataset

description: The Phoenix dataset is an attempt to take both the new advances in event data described above, along with decades of knowledge regarding best practices, in order to create a new iteration of event data.
DOI: 10.13145/bacdive170117.20250331.9.3
title: Bradyrhizobium australafricanum Klepa et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.17632/9vpy7ycnkh.1
title: STEM_QA_English

description: The full corpus is curated across multiple STEM/Non-STEM disciplines and structured for use in LLM training, evaluation, and instruction tuning (SFT/RLHF). This sample represents the structure and quality of the larger dataset.

Dataset composition (full corpus):

Text corpus: 1.6B+ words of curated STEM and Non-STEM educational content across 22000+ texbooks in 7 languages(English, Hindi, Arabic, Bahasa, Tamil, Telegu, Kannada)
Question–Answer pairs: 6.5M+ high-quality Q&A pairs of STEM and Non-STEM in (English, Arabic, Hindi and Indic languages)
Video data: 100K+ hours of STEM Videos and 30K+ hours of UGC.
Audio data: 821K+ hours of Podcasts and Call Center data(Dual Channel)
Medical datasets: 30M+ files including clinical and diagnostic data like CT Scan, MRI, X-ray, Pathology, EHRs, USG Reports and Echo Reports.
This repository includes:

A small preview subset of the STEM English Q&A data
Flat, viewer-friendly schema for inspection
Parquet files suitable for benchmarking and evaluation
Purpose of this dataset:

Dataset preview and validation
Model evaluation and experimentation
Schema and format inspection before full-scale access
⚠️ Note: This repository contains sample data only. Access to the complete dataset is available separately under appropriate licensing or partnership terms. Note: This is not the full dataset.

For further details, Please connect [Em: vipul.mishra@infobay.ai ph: 8303174762]
DOI: 10.13145/bacdive170117.20251217.10
title: Bradyrhizobium australafricanum Klepa et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170118.20240510.9
title: Bradyrhizobium cenepequi Klepa et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170118.20251217.10
title: Bradyrhizobium cenepequi Klepa et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170119.20250331.9.3
title: Bradyrhizobium hereditatis Klepa et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/o33wix1s
title: Materials Project database

description: The dataset is a collection of materials with their properties and constituent components.
DOI: 10.13145/bacdive170119.20251217.10
title: Bradyrhizobium hereditatis Klepa et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive170120.20250331.9.3
title: Bradyrhizobium semiaridum Klepa et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.