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.
3,272
datasets available to search
ShareScore release 0.7.1
Dataset results
3,272 results for “microarray”
Infrared Chemical Image of a Breast Cancer Tissue Microarray
<p>This data set relates to an open access paper published in Analyst <em>Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets</em> by Jiayi Tang, Alex Henderson* and Peter Gardner. <a href="https://doi.org/10.1039/D0AN02155E"> https://doi.org/10.1039/D0AN02155E</a></p> <p>The files in this archive are mid-infrared spectroscopy chemical images of a breast cancer tissue microarray. The tissue microarray is BR20832 from Biomax. <a href="http://www.biomax.us/tissue-arrays/Breast/BR20832">http://www.biomax.us/tissue-arrays/Breast/BR20832</a></p> <p>Processed versions of these data in MATLAB file format can be found in another Zenodo archive at <a href="https://doi.org/10.5281/zenodo.4730312">https://doi.org/10.5281/zenodo.4730312</a></p> <p>This processed data refers to a paper published in Analyst</p>
ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'
<p>ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'</p>
Tissue microarray data and processing scripts for The molecular consequences of androgen activity in the human breast
<p>This repository contains raw and processed data from the CODEX imaging dataset in this publication.</p> <p>The RAW data tables provide the resulting nuclei and membrane staining signals obtained from the nuclei segmentation described in the Methods.</p> <p>The processed data file provides the clustered and annotated version described in Methods.</p> <p>The repository also contains two scripts describing the processing of snRNA-seq and snATAC-seq data.</p>
The raw microarray data and the differential expression analysis results from "Manipulating the growth environment through co-culture to enhance stress tolerance and viability of probiotic strains in the gastrointestinal tract".
<p>The signal data for each spot were subsequently quantified by using Feature Extraction software (Agilent Technologies).M1.txt to M5.txt: monoculture; C1.txt to C5.txt: co-culture; P1.txt to P5.txt: pH-controlled monoculture. The differential expression analysis results were obtained by using limma.</p>
Raw microarray gene expression datasets included in the eQTL Catalogue
<p>Raw microarray intensity values for five datasets:</p> <ul> <li>CEDAR</li> <li>Fairfax_2012</li> <li>Fairfax_2014</li> <li>Naranbhai_2015</li> <li>Kasela_2017</li> </ul>
HLA Class II specificity assessed by high-density peptide microarray interactions
<p>The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery; something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I (MHC-I) prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC-I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Since MHC-II restricted CD4+ T cells control and orchestrate most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data is key to the solution of this problem; a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse and representative data. Here, we have used recombinant HLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, <em>in casu</em> containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated. Collectively, with a cost per peptide reduced to a few cents combined with the flexibility of recombinant HLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.</p>
Breast Cancer TGCA Microarray and RNA-seq Data for RNA-seq Titration Project
<p>Breast cancer from The Cancer Genome Atlas (TCGA; Cancer Genome Atlas Network, 2012) gene expression data from two platforms, microarray and RNA-seq, in PCL format and accompanying clinical data. Used as a test case for cross-platform normalization for machine learning applications because these data contain "matched" samples that were run on both platforms.</p>
Analysis of heme and iron influence on Porphyromonas gingivalis A7436 and ATCC 33277 strains genes expression (microarray results)
<p>The aim of this study was to analyze phenotypic differences between <i>P. gingivalis</i> more virulent A7436 and less virulent ATCC 33277 (33277) strains. The analysis comprised the influence of heme and iron on <i>P. gingivalis</i> gene expression. </p><p><i>P. gingivalis</i> A7436 and 33277 strains were cultured in basal medium (3% trypticase soy broth and 0.5% yeast extract), supplemented with 3.6 mM L-cysteine hydrochloride, and 0.5 mg/l menadione, in anaerobic conditions (80% N2, 10% H2 and 10% CO2). To generate heme and iron-limited conditions, the medium was supplemented with 0.16 mM of the iron chelator 2,2-dipyridyl (DIP conditions). To generate heme and iron-rich conditions, the medium was supplemented with 0.0077mM hemin chloride (Hm conditions). Three sample replicates of A7436 and 33277 strains were grown in Hm or DIP conditions for 20 hours. RNA isolation and microarray analysis were performed in IMGM laboratories (Martinsried, Germany), as described by Śmiga et al. (2023).</p><p>The online tool eArray (http://earray.chem.agilent.com/; Agilent Technologies, Santa Clara, CA, USA) was used to design an Agilent Custom <i>Porphyromonas gingivalis</i> A7436 Gene Expression Microarray (8×15K format). Probes were prepared based on <i>P. gingivalis</i> transcriptome information derived from the NCBI reference sequence NZ_CP011995.1. Total RNA isolation, RNA quantity, and quality were determined as described by Curaszkiewicz et al. 2014. For internal labeling control, the total RNA was spiked with <i>in vitro </i>synthesized polyadenylated transcripts (One-Color RNA Spike-In Mix; Agilent Technologies). Subsequently, samples were reverse transcribed into cDNA and then converted into cyanine-3-labeled complementary RNA (cRNA) with Low Input Quick-Amp Labeling Kit One-Color (Agilent Technologies). For microarray hybridization, a Gene Expression Hybridization Kit (Agilent Technologies) was used. Labeled cRNA was hybridized for 17 hours at 65℃ on Agilent Custom GE 8×15K Microarrays, washed according to the manufacturer's protocol, and dried with acetonitrile (Sigma-Aldrich). The fluorescence of samples was detected with Scan Control A.8.4.1 software (Agilent Technologies) on the Agilent DNA Microarray Scanner (Agilent Technologies) and extracted from the images using Feature Extraction 10.7.3.1 software (Agilent Technologies). For data analysis, Feature Extraction 10.7.3.1 (Agilent Technologies), GeneSpring GX 13.1.1 (Agilent Technologies), and Excel 2010 (Microsoft, Redmond, WA, USA) were used. For statistical analysis, Welch's approximate <i>t</i>-test was used. Differences in gene expression are shown as fold change values (FC). The average was calculated from the normalized signal values and they were transformed from the log2 to the linear scale. Increases and decreases in gene expression are shown as positive and negative numbers, respectively. The fold change in gene expression was considered significant for FC ≥ 2 or FC ≤ -2 and <i>P</i>-value ≤ 0.05</p><ul><li>Ciuraszkiewicz J, Śmiga M, Mackiewicz P, Gmiterek A, Bielecki M, Olczak M, Olczak T. 2014. Fur homolog regulates <i>Porphyromonas gingivalis </i>virulence under low-iron/heme conditions through a complex regulatory network. Mol Oral Microbiol 29:333-353. doi: 10.1111/omi.12077.</li><li>Śmiga M, Ślęzak P, Olczak T. 2023. Comparative analysis of <i>Porphyromonas gingivalis</i> A7436 and ATCC 33277 strains reveals differences in the expression of heme acquisition systems. Microbiol Spectr (revised manuscript under revision).</li></ul>
Supplementary material for the manuscript "Simple synthesis of massively parallel RNA microarrays via enzymatic conversion from DNA microarrays"
<p>This dataset contains:</p> <ul> <li> a .txt file with the design of the Agilent SurePrint DNA microarray AMADID 086693, containing all sequences and their position on the surface </li> <li> the following raw microarray scans:</li> </ul> <ol> <li>Image of T7RNAP crystal structure, scanned at 635 nm (Cy5) (polymerase) ("01_T7RNAP image_Cy5")</li> <li>Image of T7RNAP crystal structure, scanned at 532 nm (Cy3) (dsDNA template strand) ("02_T7RNAP image_Cy3")</li> <li>Image of T7RNAP crystal structure, scanned at 488 nm (FAM) (RNA product strand) ("03_ T7RNAP image_FAM")</li> <li>Scan of the Agilent SurePrint DNA microarray (AMADID 086693) after hybridization with a Cy3-labeled oligonucleotide to untreated DNA (Block 1) and RNA as the product of the conversion process (Block 2) ("04_AgilentArray - Block 1 (untreated) vs Block 2 (converted)")</li> </ol>
Human breast tissue microarray stained for PAICS
<p>These are the raw images of human breast tissue microarrays (TMA) stained for PACS, Ki-67, and DNA (DAPI). The file format czi is a file format developed by Zeiss; it combines the imaging data with all relevant meta information into one compact file. A number of open source programs are able to use these files.</p>
New methods for the genotyping of Legionella pneumophila - Establishment, validation and implementation of a DNA-based microarray and a core genome multilocus sequence typing
<p>This data presented here are part a doctoral thesis with the focus on new genotyping methods for the human pathogen <em>Legionella pneumophila</em>. The data are partially published in articles. </p> <p>The thesis can be downloaded: update of the URL is coming soon</p>
Data and Analysis Files Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
<p>Data and Analysis Files from "Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics"</p> <p>ArraySeq_Method.zip contains the following folder and contents:</p> <ul> <li>STARSolo: All code and count matrix output from fastq spatial barcode demultiplexing. </li> <li>Images: All resolution-downsampled H&E image scans from analyzed tissues</li> <li>Space_Ranger: All 10x Space Ranger output from Visium datasets generated in the paper. </li> <li>Analysis: All scripts for analyzing and plotting Array-seq and Visium datasets generated in this paper. Also contains output h5ad files. </li> </ul> <p>ArraySeq_Barcode_generation_n12.rmd: The script used to generate the Array-seq probes with 12-mer spatial barcodes. </p>
Galaxy Training Data for "End-to-End Tissue Microarray Image Analysis with Galaxy-ME"
<p>This dataset provides the inputs used in the Galaxy Training Network (GTN) training 'End-to-End Tissue Microarray Image Analysis with Galaxy-ME'. The tutorial demonstrates how to use the Galaxy-ME tool suite for primary image processing, data analysis, and interactive visualization of multiple tissue imaging datasets. Original data was published by <a href="https://pubmed.ncbi.nlm.nih.gov/34824477/">Schapiro <em>et al</em></a>.</p>
Axiom canine microarray data from Australian dingoes and domestic dogs for admixture and population structure analysis
<p>Admixture between species is a cause for concern in wildlife management. Canids are particularly vulnerable to inter-specific hybridisation, and genetic admixture has shaped their evolutionary history. Microsatellite DNA testing, relying on a small number of genetic markers and geographically restricted reference populations, has identified extensive domestic dog admixture in Australian dingoes and driven conservation management policy. There has been concern that geographic variation in dingo genotypes could confound ancestry analyses that use a small number of genetic markers. Here we apply genome-wide single nucleotide polymorphism (SNP) genotyping to a set of 385 wild and captive dingoes from across Australia and then carry out comparisons to domestic dogs, and perform ancestry modelling and biogeographic analyses to characterize population structure in dingoes and investigate the extent of admixture between dingoes and dogs in different regions of the continent. We show that there are at least five distinct dingo populations across Australia. We observed limited evidence of dog admixture in wild dingoes, challenging previous reports regarding the occurrence and extent of dog admixture in dingoes, as our ancestry analyses show that previous assessments severely overestimate the degree of domestic dog admixture in dingo populations, particularly in southeastern Australia. These findings strongly support the use of genome-wide SNP genotyping as a refined method for wildlife managers and policy makers to assess and inform dingo management policy and legislation moving forwards.</p>
Microarray analysis of Quail Embryo Pharyngeal Pouch Endoderm
<p>Anterior endoderm region was isolated from Coturnix japonica embryos at qE3 and 2PP and 3/4PP endoderm separated from central pharynx. Total RNA was isolated from two biological replicate of 2PP and 3/4PP endoderm samples. Transcription profiles were obtained using GeneChip® Chicken Genome Array.</p>
Treatment of Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma (CLL/SLL): DNA Microarray Gene Expression Analysis
ClinicalTrials.gov study NCT00001586. IPD Sharing: Not stated. Countries: 1. Publications: 5.
DNA microarray of long-lived neutrophils versus control
Open the record for dataset details and reuse information.
HLA Class II specificity assessed by high-density peptide microarray interactions
Open the record for dataset details and reuse information.
Axiom canine microarray data from Australian dingoes and domestic dogs for admixture and population structure analysis
Open the record for dataset details and reuse information.
Microarray analysis of EZH2 knockout HaCat cell lines
Open the record for dataset details and reuse information.
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.