Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
21
datasets available to search
ShareScore release 0.9.0
Dataset results
21 results for “Phenotypic methods”
Phenotype Driven Data Augmentation Methods for Transcriptomic Data
<p>This repository contains the data and associated results of all experiments conducted in our work "<em>Phenotype Driven Data Augmentation Methods for Transcriptomic Data</em>". In this work, we introduce two classes of phenotype driven data augmentation approaches – signature-dependent and signature-independent. The signature-dependent methods assume the existence of distinct gene signatures describing some phenotype and are simple, non-parametric, and novel data augmentation methods. The signature-independent methods are a modification of the established Gamma-Poisson and Poisson sampling methods for gene expression data. We benchmark our proposed methods against random oversampling, SMOTE, unmodified versions of Gamma-Poisson and Poisson sampling, and unaugmented data. <br> </p> <p>This repository contains data used for all our experiments. This includes the original data based off which augmentation was performed, the cross validation split indices as a json file, the training and validation data augmented by the various augmentation methods mentioned in our study, a test set (containing only real samples) and an external test set standardised accordingly with respect to each augmentation method and training data per CV split. </p> <p>The compressed files <code>5x5stratified_{x}percent.zip</code> contains data that were augmented on <code>x%</code> of the available real data. <code>brca_public.zip</code> contains data used for the breast cancer experiments. <code>distribution_size_effect.zip</code> contains data used for hyperparameter tuning the reference set size for the modified Poisson and Gamma-Poisson methods. </p> <p>The compressed file <code>results.zip</code> contains all the results from all the experiments. This includes the parameter files used to train the various models, the metrics (balanced accuracy and auc-roc) computed including p-values, as well as the latent space of train, validation and test (for the (N)VAE) for all 25 (5x5) CV splits.</p> <p><strong>PLEASE NOTE:</strong> If any part of this repository is used in any form for your work, please <strong>attribute</strong> the following, in addition to attributing the original data source - TCGA, CPTAC, GSE20713 and METABRIC, accordingly:</p> <pre>@article{janakarajan2025phenotype,<br> title={Phenotype driven data augmentation methods for transcriptomic data},<br> author={Janakarajan, Nikita and Graziani, Mara and Rodr{\'\i}guez Mart{\'\i}nez, Mar{\'\i}a},<br> journal={Bioinformatics Advances},<br> volume={5},<br> number={1},<br> pages={vbaf124},<br> year={2025},<br> publisher={Oxford University Press}<br>}</pre> <p> </p>
Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation (Part 1/2)
Open the record for dataset details and reuse information.
Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation (Part 2/2)
Open the record for dataset details and reuse information.
A high-precision method of segmenting complex postures in C. elegans and deep phenotyping to analyze lifespan
<p>The data for our paper, "A high-precision method of segmenting complex postures in <em>C. elegans</em> and deep phenotyping to analyze lifespan", includes the following three components:</p> <ol> <li>Synthetic image dataset, CSB-1 dataset, and MD dataset, which originate from the paper <em>"WormSwin: Instance segmentation of C. elegans using Vision Transformer"</em>.</li> <li>BBC010 dataset, sourced from the paper <em>"Annotated high-throughput microscopy image sets for validation".</em></li> <li>Training weights for the Synthetic image dataset, CSB-1 dataset, MD dataset, and BBC010 dataset, as well as the pretrained weights used for worm tracking. The training weights from the synthetic image dataset can serve as pretrained weights for training on other datasets.</li> </ol> <p>Our experimental results are based on the average of multiple training runs; here, we have only uploaded one set of weights per dataset to facilitate reproducibility for readers. For more detailed information on the datasets, please refer to the relevant papers.</p> <p> </p>
Data From: Effects of measurement methods and growing conditions on phenotypic expression of photosynthesis in seven diverse rice genotypes
<p class="p1"><strong>Introduction: </strong>Light response curves are widely used to quantify phenotypic expression of photosynthesis by measuring a single sample and sequentially altering light intensity within a chamber (sequential method) or by measuring different samples that are each acclimated to a different light level (nonsequential method). Both methods are often conducted in controlled environments to achieve steady-state results, and neither method involves equilibrating the entire plant to the speci<span class="s1">fi</span>c light level.</p> <p class="p1"><strong>Methods: </strong>Here, we compare sequential and non-sequential methods in controlled (greenhouse), semi-controlled (plant grown in growth chamber and acclimated to <span class="s1">fi</span>eld conditions 2-3 days before measurements), and <span class="s1">fi</span>eld environments. We selected seven diverse rice genotypes (<span class="s1">fi</span>ve genotypes from the USDA rice minicore collection: 310588, 310723, 311644, 311677, 311795; and 2 additional genotypes: Nagina 22 and Zhe 733) to understand (1) the limitations of different methods, and (2) phenotypic plasticity of photosynthesis in rice grown under different environments.</p> <p class="p1"><strong>Results:</strong> Our results show that the non-sequential method was time-ef<span class="s1">fi</span>cient and captured more variability of <span class="s1">fi</span>eld conditions than the sequential method, but the model parameters were generally similar between the two methods except for the maximum photosynthesis rate (A<sub>max</sub>). A<span class="s2"><sub>max</sub> </span>was signi<span class="s1">fi</span>cantly lower across all genotypes under greenhouse conditions compared to the growth chamber and <span class="s1">fi</span>eld conditions consistent with prior work, but surprisingly the apparent quantum yield (α) and the mitochondrial respiration (R<sub><span class="s2">d</span></sub>) were generally not different among growing environments or measurement methods.</p> <p class="p1"><strong>Discussion: </strong>Our results suggest that <span class="s1">fi</span>eld conditions are best suited to quantify phenotypic differences across different genotypes, and the nonsequential method was better at capturing the variability in photosynthesis.</p>
Data From: Effects of measurement methods and growing conditions on phenotypic expression of photosynthesis in seven diverse rice genotypes
Open the record for dataset details and reuse information.
Data from: Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture
Open the record for dataset details and reuse information.
Data from: A method for analysis of phenotypic change for phenotypes described by high-dimensional data
The analysis of phenotypic change is important for several evolutionary biology disciplines, including phenotypic plasticity, evolutionary developmental biology, morphological evolution, physiological evolution, evolutionary ecology and behavioral evolution. It is common for researchers in these disciplines to work with multivariate phenotypic data. When phenotypic variables exceed the number of research subjects—data called 'high-dimensional data'—researchers are confronted with analytical challenges. Parametric tests that require high observation to variable ratios present a paradox for researchers, as eliminating variables potentially reduces effect sizes for comparative analyses, yet test statistics require more observations than variables. This problem is exacerbated with data that describe 'multidimensional' phenotypes, whereby a description of phenotype requires high-dimensional data. For example, landmark-based geometric morphometric data use the Cartesian coordinates of (potentially) many anatomical landmarks to describe organismal shape. Collectively such shape variables describe organism shape, although the analysis of each variable, independently, offers little benefit for addressing biological questions. Here we present a nonparametric method of evaluating effect size that is not constrained by the number of phenotypic variables, and motivate its use with example analyses of phenotypic change using geometric morphometric data. Our examples contrast different characterizations of body shape for a desert fish species, associated with measuring and comparing sexual dimorphism between two populations. We demonstrate that using more phenotypic variables can increase effect sizes, and allow for stronger inferences.
Assessment of four in vitro phenotypic biofilm detection methods in relation to antimicrobial resistance in aerobic clinical bacterial isolates
<p><strong>Introduction</strong>: The lack of standardized methods for detecting biofilms continues to pose a challenge to microbiological diagnostics since biofilm-mediated infections induce persistent and recurrent infections in humans that often defy treatment with common antibiotics. This study aimed to evaluate diagnostic parameters of four in vitro phenotypic biofilm detection assays in relation to antimicrobial resistance in aerobic clinical bacterial isolates.</p> <p><strong>Methods</strong>: In this cross-sectional study, bacterial strains from clinical samples were isolated and identified following the standard microbiological guidelines. The antibiotic resistance profile was assessed through the Kirby-Bauer disc diffusion method. Biofilm formation was detected by gold standard tissue culture plate method (TCPM), tube method (TM), Congo red agar (CRA), and modified Congo red agar (MCRA). Statistical analyses were performed using SPSS version 17.0, with a significant association considered at p<0.05.</p> <p><strong>Result</strong>: Among the total isolates (n=226), TCPM detected 140 (61.95%) biofilm producers, with CoNS (9/9) (p<0.001) as the predominant biofilm former. When compared to TCPM, TM (n=119) (p<0.001) showed 90.8% sensitivity and 70.1% specificity, CRA (n=88) (p=0.123) showed 68.2% sensitivity and 42% specificity, and MCRA (n=86) (p=0.442) showed 65.1% sensitivity and 40% specificity. Juxtaposed to CRA, colonies formed on MCRA developed more intense black pigmentation from 24 to 96 hours. There were 77 multi-drug-resistant (MDR)-biofilm formers and 39 extensively drug-resistant (XDR)-biofilm formers, with 100% resistance to ampicillin and ceftazidime, respectively.</p> <p><strong>Conclusion</strong>: It is suggested that TM be used for biofilm detection. Unlike MCRA, black pigmentation in colonies formed on CRA declined with time. MDR- and XDR-biofilm formers were frequent among the clinical isolates.</p>
Double-method Comparative Study in Order to Predict the Use of Co-receptors From Type 1 HIV: Phenotypic Study (Trofile ESTA®) and Virologic Response to a CCR5 Antagonist in the Short Term
ClinicalTrials.gov study NCT01060618. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Can Methods From Computational Psychology be Used to Phenotype Individuals Most Likely to be Non-adherent to Fitness Goals?
ClinicalTrials.gov study NCT04783298. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: A method for analysis of phenotypic change for phenotypes described by high-dimensional data
Open the record for dataset details and reuse information.
Data from: A new method for testing evolutionary rate variation and shifts in phenotypic evolution
Open the record for dataset details and reuse information.
Assessment of four in vitro phenotypic biofilm detection methods in relation to antimicrobial resistance in aerobic clinical bacterial isolates
Open the record for dataset details and reuse information.
Data from: A new phylogenetic method for identifying exceptional phenotypic diversification
Open the record for dataset details and reuse information.
PHDs-seq: A Large-scale Phenotypic Screening Method for Drug Discovery through Parallel Multi-readout Quantification [Targeted RNA-seq: KF-b1_1204]
GEO Series GSE227387. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
PHDs-seq: A Large-scale Phenotypic Screening Method for Drug Discovery through Parallel Multi-readout Quantification [Targeted RNA-seq: KF-b3_0723]
GEO Series GSE227391. Homo sapiens. 131 samples. Type: Expression profiling by high throughput sequencing.
scONE-seq: A one-tube single-cell multi-omics method enables simultaneous dissection of genotype and phenotype heterogeneity from frozen tumor samples
GEO Series GSE185269. Homo sapiens. 621 samples. Type: Expression profiling by high throughput sequencing; Other.
PHDs-seq: A Large-scale Phenotypic Screening Method for Drug Discovery through Parallel Multi-readout Quantification
GEO Series GSE227392. Homo sapiens. 146 samples. Type: Expression profiling by high throughput sequencing.
PHDs-seq: A Large-scale Phenotypic Screening Method for Drug Discovery through Parallel Multi-readout Quantification [Targeted RNA-seq: KF-b2_0118]
GEO Series GSE227388. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.