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Genomics & Bioinformatics

DNA/RNA sequences, gene expression, protein structures, metagenomics, single-cell sequencing

22,522 datasets

Coral Recruitment and Temperature Data from Six Reefs in Cairns-Port Douglas, 2023-2025

Two years of relative coral recruitment data from six reefs in the Cairns-Port Douglas region, collected by JCU TropWATER from 2023/2024 to 2024/2025. The dataset includes GPS coordinates, recruit counts per tile and by coral family, tile orientation data, in-situ temperature logs, and Rapid Health Impact Survey results. Data was collected using limestone settlement tiles deployed during annual mass spawning periods.

TabularGeospatialGreat Barrier ReefCoral reefsRecruitment PatternsMarine EcologyHealthcareLarge ScaleTemperature Logging+1

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Genomics & Bioinformatics

LBA-ECO Modeled Amazon Deforestation Scenarios 2002-2050

NASA's LBA-ECO LC-14 dataset provides modeled future deforestation patterns for the Amazon Basin from 2002 to 2050. It contains results from two scenarios, Business-as-Usual and Governance, generated using the SimAmazonia model. The dataset includes 98 annual GeoTiff raster files and a comma-delimited file of model input data derived from satellite maps.

TabularGeospatialZIPAmazon BasinLand Use ChangeHabitat ConversionDeforestation ModelingGeospatial Simulation+1

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Genomics & Bioinformatics

Preprocedural Prediction Model for Failed Umbilical Venous Catheterization in Neonates

A retrospective cohort study of 123 neonates undergoing umbilical venous catheterization (UVC) between April 2023 and May 2025. The dataset was used to develop and internally validate an Elastic Net penalized logistic regression model predicting UVC failure based on preprocedural clinical and laboratory indicators. The model was created by Zhenzhen Ye and shared under a CC-BY-4.0 license.

TabularClinical PredictionNeonatal CareHealthcareMedical Device FailureRetrospective Cohort+1

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Genomics & Bioinformatics

MOVE-1: Smartwatch Monitoring Feasibility in Head and Neck Cancer Survivors

The MOVE-1 study dataset contains results from a cross-sectional feasibility study of smartwatch-based monitoring in head and neck cancer survivors. The study, authored by Karl Vietinghoff and shared on figshare, enrolled 35 participants with a median age of 63 years and collected data on recruitment rates, adherence, usability, and step counts. The dataset was last updated on June 3, 2026.

TabularCancer SurvivorsFeasibility StudySmartwatch MonitoringHealthcareHealth TechnologyPhysical Activity+1

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Genomics & Bioinformatics

MOVE-1: Smartwatch Monitoring Feasibility in Head and Neck Cancer Survivors

MOVE-1 was a cross-sectional feasibility study evaluating smartwatch-based monitoring in head and neck cancer survivors. The study enrolled 35 participants with a median age of 63 years, who wore a smartwatch for a median of 111 hours out of a target 168 hours. The dataset, authored by Karl Vietinghoff and shared under a CC-BY-4.0 license, contains results on recruitment, adherence, usability, and step counts.

TabularFeasibility StudySmartwatch MonitoringHealthcareHealth TechnologyPhysical Activity+1

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Genomics & Bioinformatics

MOVE-1 Study: Smartwatch Monitoring Feasibility in Head and Neck Cancer Survivors

Fifty percent of screened head and neck cancer survivors agreed to participate in a smartwatch monitoring study. The dataset, authored by Karl Vietinghoff and shared via figshare in 2026, contains results from the MOVE-1 cross-sectional feasibility study, including recruitment rates, adherence metrics, and usability scores. Thirty-five participants wore smartwatches for a median of 111 hours, with 60% using the display functions often.

TabularFeasibility StudyDigital healthSmartwatch MonitoringHealthcarePhysical Activity+1

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Genomics & Bioinformatics

MOVE-1 Study: Smartwatch Monitoring Feasibility in Head and Neck Cancer Survivors

A 2026 cross-sectional study by Karl Vietinghoff evaluated smartwatch-based monitoring in 35 head and neck cancer survivors. Participants wore a smartwatch for a median of 111 hours over seven days, with data on recruitment rate, adherence, usability, and daily step counts. The study concluded moderate-to-good adherence and good usability support future interventional research.

TabularCancer SurvivorsFeasibility StudySmartwatch MonitoringHealthcareHealth TechnologyPhysical Activity+1

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Genomics & Bioinformatics

MOVE-1 Study: Smartwatch Monitoring Feasibility in Head and Neck Cancer Survivors

A 2026 cross-sectional study by Karl Vietenhoff evaluated smartwatch-based monitoring in 35 head and neck cancer survivors. Participants wore a smartwatch for a median of 111 hours over a 7-day period, with data collected on heart rate, step count, and usability metrics. The study reported a recruitment rate of 50% and a median daily step count of 7,298.

TabularCancer SurvivorsFeasibility StudySmartwatch MonitoringHealthcareHealth TechnologyPhysical Activity+1

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Genomics & Bioinformatics

Audet et al. 2026: Drosophila Foreleg Development RNA-Seq Data and Analysis Scripts

A 64.0 MB repository of intermediate data and analysis scripts supporting a 2026 study on the genomic architecture of foreleg development in Drosophila prolongata. The dataset includes gene read counts for three Drosophila species (prolongata, carrolli, melanogaster) and R scripts for differential expression and PCA analysis. It was authored by Tyler Audet and colleagues and published on figshare under a CC-BY-4.0 license.

TabularZIPGene ExpressionRna SeqEvolutionary BiologyHealthcareGenomicsDrosophilaSynthetic+1

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Genomics & Bioinformatics

Vitamin Biomarker Risk Score for 90-Day Stroke Outcome in a 655-Patient Cohort

655 acute ischemic stroke patients from Huludao Central Hospital, admitted between November 2024 and July 2025, were retrospectively analyzed. The dataset includes demographic, clinical, and serum vitamin biomarker levels (vitamins D, E, A, K1, B12, folate, homocysteine) used to develop a nomogram for predicting 3-month functional outcome. The model was developed and internally validated by Yang Xu, with results published in May 2026.

TabularClinical NomogramBenchmarkHealthcareStroke PrognosisMedical ResearchVitamin BiomarkersRetrospective Cohort+1

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Genomics & Bioinformatics

Serum Resistin Levels and Coronary Heart Disease Severity from a Network Meta-Analysis

A 2026 network meta-analysis by Yicheng Ling, aggregating data from 26 studies with 9,169 subjects, quantifies the relationship between serum resistin levels and coronary heart disease severity. The analysis includes standardized mean differences and SUCRA rankings for disease stages from healthy controls to acute myocardial infarction. The dataset is derived from a systematic review registered with PROSPERO.

TabularNetwork Meta AnalysisBiomarker AnalysisHealthcareSerum ResistinCoronary Heart DiseaseClinical ResearchLarge Scale+1

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Genomics & Bioinformatics

Gryllus bimaculatus: Chromosome-Scale Genome Assembly and Annotation

Gryllus bimaculatus, the two-spotted cricket, is a key hemimetabolous model organism for developmental biology, neuroscience, and regeneration. This dataset provides a chromosome-scale nuclear genome assembly, mitochondrial genome sequence, and structural and functional annotation files for the white-eyed mutant strain. The assembly was generated using a hybrid strategy combining Oxford Nanopore, PacBio HiFi, Illumina, and Hi-C scaffolding, representing a major improvement over a previous 2021 draft.

TextTabularTSVExcelCricketGenome AssemblyModel OrganismAnnotationChromosome ScaleGenomeCricket GenomicsChromosome Scale Genome AssemblySynthetic+1

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Genomics & Bioinformatics

CT-Based Radiogenomic Prediction of NK Cytotoxicity Biomarkers in Kidney Cancer

143 patients with clear cell renal cell carcinoma (ccRCC) contributed preoperative CT images, with transcriptomic data from 538 TCGA-KIRC tumor samples. The dataset, authored by Xinwei Ma and last updated in June 2026, supports a framework linking 3,176 radiomic features per tumor to the expression of immune biomarkers ICAM1 and RAET1E. External validation was performed using tissue microarrays from 26 independent ccRCC cases.

TabularMedical ImagingBiomarkersComputer VisionImmunotherapyRenal Cell Carcinoma+1

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Genomics & Bioinformatics

Haplotype-Resolved Genome Assemblies and Annotations for Three Korean Rubus Species

Gene and transposable element annotations for six haplotype-resolved genome assemblies of three Korean native Rubus species: R. coreanus, R. crataegifolius, and R. phoenicolasius. The dataset includes 24 annotation files in GFF3 and PEP formats, totaling 682.0 MB, and was published by Hyeonseon Park on figshare in June 2026. Annotations were generated using BRAKER3 and EDTA tools, incorporating RNA-seq data and a pan-genome TE library.

TextRubus SpeciesGene AnnotationHealthcareGenomicsPlant GenomesHaplotype ResolvedSynthetic+1

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Genomics & Bioinformatics

PANDIA: Out-of-Distribution Validation for Infant Pain Assessment

2,847 infant records across four datasets were used to validate the PANDIA multimodal AI system for pain assessment in neonates aged 0–3 months. The system, developed by Oussama El Othmani and published on figshare in May 2026, achieved 87.3% accuracy and a 92.1% clinician acceptance rate for explanations. All code, trained models, and preprocessing pipelines are publicly available on GitHub.

TabularExcelInfant Pain AssessmentMultimodal DataNeonatal CareBenchmarkHealthcareComputer VisionClinical Ai+1

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Genomics & Bioinformatics

PANDIA: Infant Pain Assessment Performance Across Four Datasets

A dataset containing out-of-distribution performance analysis for a multimodal AI system designed for infant pain assessment. The system, PANDIA, was evaluated on 2,847 infants across four datasets and achieved 87.3% accuracy. The dataset was created by Oussama El Othmani and last updated on May 26, 2026.

TabularExcelInfant Pain AssessmentMultimodal AiNeonatal CareBenchmarkHealthcareComputer VisionClinical Data+1

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Genomics & Bioinformatics

PANDIA: Infant Pain Assessment Data for AI Model Evaluation

2,847 infants across four datasets were used to evaluate the PANDIA AI system for pain assessment in neonates aged 0–3 months. The dataset, associated with a paper by Oussama El Othmani, was last updated on 2026-05-26. All code, trained models, and preprocessing pipelines are publicly available via GitHub.

TabularExcelInfant Pain AssessmentMultimodal AiNeonatal CareBenchmarkHealthcareComputer VisionClinical Data+1

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Genomics & Bioinformatics

PANDIA: Ablation Study Results for Infant Pain Assessment AI

Oussama El Othmani published ablation study results for the PANDIA AI system on May 26, 2026. The system was evaluated on 2,847 infants across four datasets, achieving 87.3% accuracy and a 12.4% improvement over baselines. All code, trained models, and preprocessing pipelines are publicly available.

TabularExcelMultimodal DataClinical ValidationBenchmarkHealthcareComputer VisionNeonatal Pain AssessmentMedical Ai+1

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Genomics & Bioinformatics

PANDIA: Multimodal AI System for Infant Pain Assessment

A dataset likely containing metadata or summary information related to the PANDIA AI system for infant pain assessment. The system was evaluated on 2,847 infants across four datasets and achieves 87.3% accuracy. The dataset was authored by Oussama El Othmani and last updated in May 2026.

TabularExcelInfant Pain AssessmentMultimodal AiNeonatal CareBenchmarkHealthcareComputer VisionClinical DataExplainable Ai+1

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Genomics & Bioinformatics

PANDIA: Infant Pain Assessment Performance Metrics

Performance analysis data for the PANDIA AI system, which assesses pain in infants aged 0–3 months. The dataset likely contains evaluation metrics from a study involving 2,847 infants across four datasets. The system was developed by Oussama El Othmani and results were published in 2026.

TabularExcelEdge DeploymentInfant Pain AssessmentMultimodal AiNeonatal CareBenchmarkHealthcareComputer VisionClinical Data+1

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Genomics & Bioinformatics Datasets | DataSalon