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.
54,099
datasets available to search
ShareScore release 0.9.0
Dataset results
54,099 results for “Expression”
FIG. 3 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 3. — Scheme of inflorescence architecture of Sedum praealtum A. DC. (Crassulaceae). A, Basal-positioned floral buds; B, Distal-positioned floral buds.
FIG. 4 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 4. — Comparison between basal-positioned floral buds (black circles) and distal-positioned floral buds (white circles) in its probability of reaching anthesis during observation period. The S(t) abbreviation is the probability that a floral bud has not opened. Time until floral bud reaches anthesis refers to the time elapsed since the begin of the observation period. Solid line shows mean of time that basal-positioned floral buds open, and dashed line shows mean of time that distal-positioned floral buds open.
FIG. 2 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 2. — Floral traits in Sedum praealtum A. DC. (Crassulaceae). A, Floral longevity (days); B, Onset of female-phase flowers (days); C, Female-phase duration (days). Values are means ± SE.
FIG. 1 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 1. — Male-phase and female-phase flowers of Sedum praealtum A. DC. (Crassulaceae). A, Male-phase flower with dehiscent anthers; B, Female-phase flower with expansion of the stigmas. Photos by Angélica Hernández-Ramírez. Scale bars: 0.5 cm.
proportion expressed across transcripts (pext)
<p>Original file that was previously found here:</p> <pre><code class="language-http">https://storage.googleapis.com/gnomad-public/papers/2019-tx-annotation/pre_computed/all.possible.snvs.tx_annotated.022719.tsv.bgz</code></pre> <p>Now only a newer file is available:</p> <pre><code class="language-http">https://storage.googleapis.com/gnomad-public/papers/2019-tx-annotation/pre_computed/all.possible.snvs.tx_annotated.GTEx.v7.021520.tsv.bgz</code></pre> <p>which is differs from the original.</p> <p> </p> <p> </p>
Raw Data related to Research Article: Scaling of metal-clad InP nanodisk lasers: optical performance and thermal effects, Optics Express, volume 29, issue 3, 2021
<p>These are the plotted and raw data used to obtain figures shown in:</p> <p>P. Tiwari, P. Wen, D. Caimi, S. Mauthe, N. Vico Triviño, M. Sousa, and K. E. Moselund, Scaling of metal-clad InP nanodisk lasers: optical performance and thermal effects., Optics Express, volume 29, issue 3, 2021</p> <p>Please comply with copyright rules of the Optical Society of America under the terms of the OSA Open Access Publishing Agreement.:</p> <p>https://www.osapublishing.org/library/license_v1.cfm#VOR-OA</p> <p> </p>
Data from: Has gene expression neofunctionalization in the fire ant antennae contributed to queen discrimination behavior?
<p>Queen discrimination behavior in the fire ant <i>Solenopsis invicta</i> maintains its two types of societies: colonies with one (monogyne) or many (polygyne) queens, yet the underlying genetic mechanism is poorly understood. This behavior is controlled by two supergene alleles, <i>SB</i> and <i>Sb,</i> with ~600 genes. Polygyne workers, having either the <i>SB/SB</i> or <i>SB/Sb </i>genotype, accept additional <i>SB/Sb</i> queens into their colonies but kill <i>SB/SB</i> queens. In contrast, monogyne workers, all <i>SB/SB</i>, reject all additional queens regardless of genotype. Because the <i>SB</i> and <i>Sb</i> alleles have suppressed recombination, determining which genes within the supergene mediate this differential worker behavior is difficult. We hypothesized that the alternate worker genotypes sense queens differently because of the evolution of differential expression of key genes in their main sensory organ, the antennae. To identify such genes, we sequenced RNA from four replicates of pooled antennae from three classes of workers: monogyne <i>SB/SB</i>, polygyne <i>SB/SB,</i> and polygyne <i>SB/Sb</i>. We identified 81 differentially expressed protein-coding genes with 13 encoding potential chemical metabolism or perception proteins. We focused on the two odorant perception genes: an odorant receptor<i> SiOR463</i> and an odorant binding protein <i>Si</i><i>OBP12</i>. We found that <i>SiOR463</i> has been lost in the <i>Sb</i>-genome. In contrast, <i>SiOBP12</i> has an <i>Sb</i>-specific duplication, <i>SiOBP12b'</i>, which is expressed in the <i>SB/Sb</i> worker antennae, while both paralogs are expressed in the body. Comparisons with another fire ant species revealed that <i>SiOBP12b'</i> antennal expression is specific to <i>S. invicta</i> and suggests that queen discrimination may have evolved, in part, through expression neofunctionalization.</p>
Data from: Effects of multiple climate change stressors on gene expression in blue rockfish (Sebastes mystinus)
<p>Global climate change is predicted to increase the co-occurrence of high pCO2 and hypoxia in upwelling zones worldwide. Yet, few studies have examined the effects of these stressors on economically and ecologically important fishes. Here, we investigated short-term responses of juvenile blue rockfish (Sebastes mystinus) to independent and combined high pCO2 and hypoxia at the molecular level, using changes in gene expression and metabolic enzymatic activity to investigate potential shifts in energy metabolism. Fish were experimentally exposed to conditions associated with intensified upwelling under climate change: high pCO2 (1200 μatm, pH~7.6), hypoxia (4.0 mg O2/L), and a combined high pCO2/hypoxia treatment for 12 h, 24 h or two weeks. Muscle transcriptome profiles varied significantly among the three treatments, with limited overlap among genes responsive to both the single and combined stressors. Under elevated pCO2, blue rockfish increased expression of genes encoding proteins involved in the electron transport chain and muscle contraction. Under hypoxia, blue rockfish up regulated genes involved in oxygen and ion transport and down regulated transcriptional machinery. Under combined high pCO2 and hypoxia, blue rockfish induced a unique set of ionoregulatory and hypoxia responsive genes not expressed under the single stressors. Thus, high pCO2 and hypoxia exposure appears to induce a non-additive transcriptomic response that cannot be predicted from single stressor exposures alone, further highlighting the need for multiple stressor studies at the molecular level. Overall, lack of a major shift in cellular energetics indicates that blue rockfish may be relatively resistant to intensified upwelling conditions in the short term.</p>
Data from: Differential gene expression in relation to mating system in Peromyscine rodents
Behaviors that increase an individual's exposure to pathogens are expected to have important effects on immunoactivity. Because sexual reproduction typically requires close contact among conspecifics, mating systems provide an ideal opportunity to study the immunogenetic correlates of behaviors with high versus low risks of pathogen exposure. Despite logical links between polygynandrous mating behavior, increased pathogen exposure, and greater immunoactivity, these relationships have seldom been examined in nonhuman vertebrates. To explore interactions among these variables in a different lineage of mammals, we used RNAseq to study the gene expression profiles of liver tissue—a highly immunoactive organ—from sympatric populations of the monogamous California mouse (Peromyscus californicus) and two polygynandrous congeners (P. maniculatus and P. boylii). Differential expression and co‐expression analyses revealed distinct patterns of gene activity among species, with much of this variation associated with differences in mating system. This tendency was particularly pronounced for MHC genes, with multiple MHC Class I genes being upregulated in the two polygynandrous species, as expected if exposure to sexually transmitted pathogens varies with mating system. Our results underscore the role of mating behavior in influencing patterns of gene expression and highlight the use of emerging transcriptomic tools in behavioral studies of free‐living animals.
Data from "Corset: enabling differential gene expression analysis for de novo assembled transcriptomes"
<p>This dataset contains de novo transcriptome assemblies for three publicly available RNA-seq dataset (SRA055442, SRR453566-SRR453571 and GSE37704 ). For each assembly we also provide a table with the read counts per contig, the output from corset (clusters and counts), and the results from a genome-based analysis. This dataset was used to assess the performance of the corset software. More detail is provided in the paper: Nadia M Davidson and Alicia Oshlack,<strong> </strong>Corset: enabling differential gene expression analysis for de novo assembled transcriptomes, <em>Genome Biology</em> 2014, <strong>15</strong>:410. http://genomebiology.com/2014/15/7/410/abstract</p>
Cancer Gene Expression Datasets
<p>The attached files contain two datasets termed GCM and Acute Leukemia datasets. They have been used in our submitted paper termed 'Semi-supervised Nonnegative Matrix Factorization for Cancer Classification'. The file names imply its datasets. The details are listed as follows:</p> <p>GCM_Test.res --- The testing gene expression data of the GCM dataset<br /> GCM_Test.cls --- The ground-truth classes of the testing samples of the GCM dataset<br /> GCM_Training.res --- The training gene expression data of the GCM dataset<br /> GCM_Training.cls --- The ground-truth classes of the training samples of the GCM dataset</p> <p>GCM.mat --- The .mat file contains the training and testing data and their classes information.</p> <p>The GCM dataset were downloaded from the website</p> <p>http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi.</p> <p> </p> <p>Leukemia dataset</p> <p>Acute_Leukemia_feature.txt --- The feature names<br /> Acute_Leukemia_sampleLabel.txt --- The ground-truth classes of all samples<br /> Acute_Leukemia_sampleName.txt --- The sample names<br /> Acute_Leukemia1.txt --- The file is the gene expression of the first 1048 samples<br /> Acute_Leukemia2.txt --- The file is the gene expression of the subsequent 1048 samples<br /> Acute_Leukemia.mat --- The .mat file contains gene expression data of all the samples and their classes.</p> <p>The Leukemia dataset were obtained from the website : </p> <p>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13159</p> <p> </p>
Gene expression data and proliferation rates for NCI-60
<p>Data set dimensions: 57 rows x 54357 columns</p> <p> </p> <p>This is a data set contains RMA normalized log expression values for 54356 genes identified with their ENSEMBL ID (columns 1-54356) for 57 cancer cell lines and their respective proliferation rates (column 54357).</p> <p>Gene expression was obtained from the GSE29682 GEO HuEx 1.0 ST microarray data.</p>
Proteomic analysis of silenced cathepsin B expression suggests non-proteolytic cathepsin B functionality.
<p>A list of human protein uniprot IDs. The proteins were identified by LC-MS/MS in the cellular supernatant of MDA-MB-231 cells, originally published in:</p> <p>F.C. Sigloch, J.D. Knopf, J. Weißer, A. Gomez-Auli, M.L. Biniossek, A. Petrera, et al., Proteomic analysis of silenced cathepsin B expression suggests non-proteolytic cathepsin B functionality, Biochim. Biophys. Acta - Mol. Cell Res. 1863 (2016) 2700–2709. doi:10.1016/j.bbamcr.2016.08.005. https://www.ncbi.nlm.nih.gov/pubmed/27526672</p>
Differential gene expression in iPSC-derived macrophages after IFNg stimulation and Salmonella infection
<p>We used likelihood ratio test implemented in DESeq2 v1.10.0 (test = “LRT”) to test if a model that allowed different mean expression in each condition explained the data better than a null model assuming the same mean expression across conditions. See the manuscript for more details: http://www.biorxiv.org/content/early/2017/05/18/102392 .</p> <p>We used the following commands in DESeq2:<br> #Run DESeq2<br> dds = DESeq2::DESeqDataSetFromMatrix(combined_expression_data_filtered$counts, design, ~condition_name) <br> dds = DESeq2::DESeq(dds, test = "LRT", reduced = ~ 1)</p> <p>#Extract differentially expressed genes in each condition<br> ifng_genes = results(dds, contrast=c("condition_name","IFNg","naive")) <br> sl1344_genes = results(dds, contrast=c("condition_name","SL1344","naive")) <br> ifng_sl1344_genes = results(dds, contrast=c("condition_name","IFNg_SL1344","naive"))</p>
EEG and eye-tracking data from a go/no-go saccadic task based on facial expression cues
<p>Electroencephalographic (EEG) and eye-tracking data from 20 healthy individuals who performed a go/no-go saccadic task based on facial expression cues aimed at studying error monitoring processes.</p> <p> </p> <p>Version 2 includes only the EEG data, but with triggers of correct and erroneous actions. Version 2 has an error that was corrected for version 3.</p> <p> </p> <p>EEG Triggers</p> <table> <tbody> <tr> <td> <p><span>19</span></p> </td> <td> <p><span>Eye tracker starts recording</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Beginning of each trial</span></p> </td> </tr> <tr> <td> <p><span>200</span></p> </td> <td> <p><span>Gap between neutral and instruction</span></p> </td> </tr> <tr> <td> <p><span>99</span></p> </td> <td> <p><span>Fixation cross between instruction and saccade</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Instruction no-go happy</span></p> </td> </tr> <tr> <td> <p><span>21</span></p> </td> <td> <p><span>Target left no-go happy</span></p> </td> </tr> <tr> <td> <p><span>22</span></p> </td> <td> <p><span>Target right no-go happy</span></p> </td> </tr> <tr> <td> <p><span>121</span></p> </td> <td> <p><span>Response period for no-go happy after target left</span></p> </td> </tr> <tr> <td> <p><span>122</span></p> </td> <td> <p><span>Response period for no-go happy after target right</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Instruction no-go sad</span></p> </td> </tr> <tr> <td> <p><span>31</span></p> </td> <td> <p><span>Target left no-go sad</span></p> </td> </tr> <tr> <td> <p><span>32</span></p> </td> <td> <p><span>Target right no-go sad</span></p> </td> </tr> <tr> <td> <p><span>131</span></p> </td> <td> <p><span>Response period for no-go sad after target left</span></p> </td> </tr> <tr> <td> <p><span>132</span></p> </td> <td> <p><span>Response period for no-go sad after target right</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Instruction pro-right</span></p> </td> </tr> <tr> <td> <p><span>42</span></p> </td> <td> <p><span>Target right pro-right</span></p> </td> </tr> <tr> <td> <p><span>142</span></p> </td> <td> <p><span>Response period for pro-right</span></p> </td> </tr> <tr> <td> <p><span>5</span></p> </td> <td> <p><span>Instruction pro-left</span></p> </td> </tr> <tr> <td> <p><span>51</span></p> </td> <td> <p><span>Target left pro-left</span></p> </td> </tr> <tr> <td> <p><span>151</span></p> </td> <td> <p><span>Response period for pro-left</span></p> </td> </tr> <tr> <td> <p><span>6</span></p> </td> <td> <p><span>Instruction anti-left</span></p> </td> </tr> <tr> <td> <p><span>61</span></p> </td> <td> <p><span>Target left anti-left</span></p> </td> </tr> <tr> <td> <p><span>161</span></p> </td> <td> <p><span>Response period for anti-left</span></p> </td> </tr> <tr> <td> <p><span>7</span></p> </td> <td> <p><span>Instruction anti-right</span></p> </td> </tr> <tr> <td> <p><span>72</span></p> </td> <td> <p><span>Target right anti-right</span></p> </td> </tr> <tr> <td> <p><span>172</span></p> </td> <td> <p><span>Response period for anti-right</span></p> </td> </tr> <tr> <td> <p><span>199</span></p> </td> <td> <p><span>Final fixation period (1.5s black screen after last saccade)</span></p> </td> </tr> <tr> <td> <p><span>190</span></p> </td> <td> <p><span>Eye tracker stops recording</span></p> </td> </tr> <tr> <td> <p><span>proCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>proErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>nogoErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a no-go intruction</span></p> </td> </tr> </tbody> </table>
Critical Assessment of RNA-Seq Differential Expression
<p><strong>Warden and Wu Preprint</strong>: <a href="https://www.biorxiv.org/content/10.1101/2024.02.10.579728v1">v1</a></p> <p>In general, this primarily focuses on the following types of comparisons:</p> <ol> <li>Cell line experiments with over-expression or knock-down to define a known causal gene, with processing starting with public reads.</li> <li>Processed TCGA (The Cancer Genome Atlas) data for breast cancer (BRCA) to compare gene expression by immunohistochemistry status (ER/ESR1, PR/PGR, or HER2/ERBB2).</li> </ol> <p>Differential expression methods include the following:</p> <ul> <li><em>edgeR (GLM)</em></li> <li><em>edgeR-robust (GLM)</em></li> <li><em>edgeR (QL)</em></li> <li><em>edgeR-robust (QL)</em></li> <li><em>DESeq1</em></li> <li><em>DESeq2</em></li> <li><em>limma-voom</em></li> <li><em>limma-trend (CPM)</em></li> <li><em>limma-trend (FPKM/RPKM)</em></li> <li><em>ANOVA (log2 FRPKM/RPKM)</em></li> </ul> <p>The most common preprocessing strategies include STAR, TopHat2, and Salmon. However, a limited amount of additional processing with HISAT2, kallisto, Bowtie2 (+eXpress), and Bowtie1 (+RSEM) is also provided.</p> <p>Most STAR and TopHat2 alignments use htseq-count for quantification, as well as running cuffdiff (for single variable 2-group comparisons). However, a limited amount of additional processing with featureCounts is also provided.</p> <p>Most STAR and TopHat2 alignments start with the public <strong>forward</strong> reads, even if paired-end data was available.</p>
Placental Expression Quantitative Trait Loci In An East Asian Population
<p>Analysis script, full eQTL summary statistics, and fine-mapping statistics of article "Placental Expression Quantitative Trait Loci In An East Asian Population". This data contains workflow and result of 102 East Asian placental expression quantitative trait loci analysis.</p><p>Genotype from 102 cord blood used in the analysis is also included. Variants with minor allele frequencies less than 0.01 was filtered out to prevent personnel identification.</p>
Differential Gene Expression Datasets for "Identification of candidate repurposable drugs to combat COVID‑19 using a signature‑based approach"
<p>This dataset has the unfiltered transcriptome differential expression results used in the paper "Identification of candidate repurposable drugs to combat COVID‑19 using a signature‑based approach". </p>
Dataset underlying the study "Enhanced Susceptibility to Tomato Chlorosis Virus (ToCV) in Hsp90- and Sgt1-Silenced Plants: Insights from Gene Expression Dynamics"
<p>This dataset is underlying the scientific publication titled "Enhanced Susceptibility to Tomato Chlorosis Virus (ToCV) in Hsp90- and Sgt1-Silenced Plants: Insights from Gene Expression Dynamics", published in the <a href="https://www.mdpi.com/1999-4915/15/12/2370">Viruses</a> journal. </p><p>The dataset includes a time-course transcriptome analysis using RNA-seq of naïve (no whitefly and no virus), mock (non-viruliferous whiteflies) and ToCV (ToCV_viruliferous whiteflies)-treated tomato samples at 2, 7, and 14 days post-infection (dpi) and viral small RNAs derived from Tomato plants infected with ToCV at 14 dpi. The dataset provided here has been deposited in full by the authors in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB67704 (<a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB67704"><strong>https://www.ebi.ac.uk/ena/browser/view/PRJEB67704</strong></a><br><br>The provided information in the dataset are further discussed and interpreted in detail, as well as their subsequent results, in the scientific publication.</p><p>This research was conducted within the VIRTIGATION project, which is part of the EU Open Research Data pilot. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 101000570.</p>
Dataset from the analysis of biological activity of endophytic strain Serratia quinivorans KP32, the expression of biocontrol-related genes and the activity of antioxidant enzymes in bacterial cells treated with pathogenic fungi filtrates
<p>This dataset contains the data from the analyses published in the article entitled "Genetic Determinants of Antagonistic Interactions and the Response of New Endophytic Strain <i>Serratia quinivorans</i> KP32 to Fungal Phytopathogens" in the International Journal of Molecular Sciences (https://doi.org/10.3390/ijms232415561). The data consist of results collected for studies on the antifungal activity of KP32 strain towards four fungal phytopathogens, results of primer efficiency determination and studies on the expression of genes potentially involved in biocontrol after treatment of KP32 strain with the fungal phytopathogens filtrates. Additionally, absorbances from activity tests for catalase (CAT) and superoxide dismutase (SOD) in the strain treated with fungal pathogens are included.</p>
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.