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”
Large-scale purification of Q23 HTT-HAP40 from Sf9 expression system with contaminating nucleic acid material 2019/09/16
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Large-scale purification of Q23 HTT-HAP40 from Sf9 expression system with contaminating nucleic acid material</p> <p><strong>Date completed:­ </strong>2019/09/16</p> <p><strong>Rationale:</strong> To purify HTT-HAP40 + Sf9 derived nucleic acid material for cryoEM analysis</p>
Large-scale purification of Q23 and Q54 HTT-HAP40 from Sf9 expression system in PBS 2019/09/16
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Large-scale purification of Q23 and Q54 HTT-HAP40 from Sf9 expression system in PBS.</p> <p><strong>Date completed:­ </strong>2019/09/16</p> <p><strong>Rationale: </strong>To purify HTT-HAP40 Q23 and Q54 in PBS to see if this reduces nucleic acid contamination.</p>
Gene expression for suboptimal mycorrhizal colonization in wild-type and jasmonic acid deficient spr2 tomato mutants
<pre>Data were obtained from mycorrhizhal colonized roots of wild-type and and spr2 mutants tomato plants, at 32 and 45 days after mycorrhizhal inoculation. Amplifications were performed using SYBR Green detection chemistry and run in triplicate in 96-well reaction plates with the CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). The data was analyzed by the delta delta ct method. </pre>
Life in the fastlane? A comparative analysis of gene expression profiles across annual, semi-annual, and non-annual killifishes (Cyprinodontiformes: Nothobranchiidae)
<ol> <li>Read Counts per gene</li> <li>All read counts by inidividual and species</li> <li>Species abbreviations key</li> <li>R script for data analyses</li> </ol>
Figure 1 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 1. Distribution of Cq values of candidate reference genes in date mussel (L. lithophaga).
Figure 2 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 2. Average expression stability (M-value) of reference genes evaluated by geNorm.
Assessment of changes in circRNA expression based on transcripts of genes encoding ADAMTS proteins in patients with non-small cell lung carcinoma compared to normal tissue
Open the record for dataset details and reuse information.
The identified bow shock crossings based on Venus Express observations
<div>Bow shock locations and upstream parameters observed by Venus Express.</div> <div> </div> <div>The columns are structured as follows:</div> <div> 1: time of bow shock crossings dd-mmm-yyyy HH:MM:SS</div> <div> 2: X-VSE [Rv]</div> <div> 3: Y-VSE [Rv]</div> <div> 4: Z-VSE [Rv]</div> <div>5: Solar wind magnetosonic Mach number</div> <div> 6: EUV [mW/m^2]</div> <div> 7: IMF Bx in VSE [nT]</div> <div>8: IMF By in VSE [nT]</div> <div> 9: IMF intensity |B| [nT]</div> <div> 10: IMF cone angle [degree]</div> <div>11: Solar wind dynamic pressure [nPa]</div> <div>12: The angle between the IMF and the shock normal, thetaBn [degree]</div> <div> </div> <div>Please contact xuqi01@nssc.ac.cn if you have questions.</div>
Data from: Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression
<p>Supporting data for the article "Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression", published in Frontiers in Neuroscience (DOI: 10.3389/fnins.2023.1260409).</p>
Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks
<p>Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks” </p> <p>Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle </p> <p>Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health</p> <p>Description: </p> <p>This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:</p> <ul> <li> <p>all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study. </p> </li> <li> <p>all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration </p> </li> <li> <p>precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.</p> </li> <ul> <li> <p>The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds</p> </li> <li> <p>For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X. </p> </li> <li> <p>We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”</p> </li> <li> <p>The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables. </p> </li> </ul> <li> <p>WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors. </p> </li> </ul>
CRISPRi with barcoded expression reporters dissects regulatory networks in human cells
<p>Genome-wide CRISPR screens have emerged as powerful tools for uncovering the genetic underpinnings of diverse biological processes. Incisive screens often depend on directly measuring molecular phenotypes, such as regulated gene expression changes, provoked by CRISPR-mediated genetic perturbations. Here, we provide quantitative measurements of transcriptional responses in human cells across genome-scale perturbation libraries by coupling CRISPR interference (CRISPRi) with barcoded expression reporter sequencing (CiBER-seq). To enable CiBER-seq in mammalian cells, we optimize the integration of highly complex, barcoded sgRNA libraries into a defined genomic context. CiBER-seq profiling of a nuclear factor kappa B (NF-κB) reporter delineates the canonical signaling cascade linking the transmembrane TNF-alpha receptor to inflammatory gene activation and highlights cell-type-specific factors in this response. Importantly, CiBER-seq relies solely on bulk RNA sequencing to capture the regulatory circuit driving this rapid transcriptional response. Our work demonstrates the accuracy of CiBER-seq and its potential for dissecting genetic networks in mammalian cells with superior time resolution.</p>
Supplementary files for "Tristetraprolin Affects Invasion-Associated Genes Expression and Cell Motility in Triple-Negative Breast Cancer Model"
<p>Track1 and Track3 - raw numerical data on cell tracking; Morphology-DXR treated - raw images of the cells, treated with DXR; Morphology ecTTP+WT - morphology of wild-type and TTP-overexpressing cells; RAW data qPCR - rew data of gene expression experiments</p>
Gene Expression Raw Data - Manuscript 1 CAREQiPSC
<p>This Excel file contains the delta delta Ct (cycle threshold) values from gene expression experiments conducted during the generation and characterisation of equine induced pluripotent stem cells, as part of the project CAREQiPSC.</p> <p>Data is grouped as the figures presented in a manuscript pending of publication in a peer-reviewed journal, which will be linked to this dataset.</p>
Table 2 in Perinatal exposure to a high-fat diet alters proopiomelanocortin, neuropeptide Y and dopaminergic receptors gene expression and the food preference in offspring adult rats
<p><b>Table 2.</b> Relative food intake of the high-fat diet and control diet during the food preference study in offspring exposed or not a control diet or high-fat diet during perinatal and/or postnatal period.</p><table><tbody><tr><th></th><th><i>High-fat diet (g/100 g body weight)</i></th><th></th></tr></tbody><tbody><tr><th></th><td><i>102º day</i></td><td><i>110º day</i></td><td><i>116º day</i></td></tr><tr><th><i>CC</i></th><td>12.6 ± 0.6</td><td>11.3 ± 0.2</td><td>11.1 ± 0.7</td></tr><tr><th><i>CH</i></th><td>12.8 ± 0.6</td><td>12.4 ± 0.3</td><td>11.4 ± 0.4</td></tr><tr><th><i>HC</i></th><td>16.7 ± 1.1 <b>a.b*</b></td><td>13.7 ± 0.9</td><td>10.0 ± 0.5</td></tr><tr><th><i>HH</i></th><td>15.3 ± 0.7 <b>a.b*</b></td><td>11.7 ± 0.9</td><td>10.7 ± 0.5</td></tr><tr><th></th><td><i>Control diet (g/100 g body weight)</i></td><td></td></tr><tr><th></th><td><i>102º day</i></td><td><i>110º day</i></td><td><i>116º day</i></td></tr><tr><th><i>CC</i></th><td>1.4 ± 0.2</td><td>1.1 ± 0.1</td><td>1.1 ± 0.3</td></tr><tr><th><i>CH</i></th><td>2.1 ± 0.2</td><td>1.5 ± 0.3</td><td>0.9 ± 0.1</td></tr><tr><th><i>HC</i></th><td>1.8 ± 0.2</td><td>1.0 ± 0.3</td><td>1.0 ± 0.3</td></tr><tr><th><i>HH</i></th><td>1.9 ± 0.2</td><td>1.1 ± 0.2</td><td>0.7 ± 0.1</td></tr></tbody></table><p>Values are presented as mean + SEM using two-way ANOVA followed by the Bonferroni multiple-comparison test. *p<0,005; “a”: compared to CC, “b”: compared to CH; “c”: compared to HC; “d”: compared to HH.</p>
Table 1 in Perinatal exposure to a high-fat diet alters proopiomelanocortin, neuropeptide Y and dopaminergic receptors gene expression and the food preference in offspring adult rats
<p><b>Table 1.</b> Experimental design. The rats were fed commercial standard diet for rodents control diet or high-fat diet during pregnancy, lactation and post-weaning (up to 100 days of life).During the feeding period, rats consumed both diets (control diet and high-fat diet). The numbers in parentheses indicate the animals number in each nutritional group.</p><table><tbody><tr><th><b>Experimental Design</b></th></tr></tbody><tbody><tr><th><b>Genitors (n)</b></th><td><b>Gestation and lactation</b></td><td><b>Offspring during lactation (n)</b></td><td><b>Offspring (n)</b></td><td><b>Post-weaning (21-100° life’s day)</b></td><td><b>Food preference (102-116º life’s day)</b></td></tr><tr><th></th><td></td><td></td><td>CC (12)</td><td>Control diet</td><td>Control diet</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td>High-fat diet</td></tr><tr><th>GC (5)</th><td>Control diet</td><td>C (23)</td><td>CH (11)</td><td>High-fat diet</td><td>Control diet</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td>High-fat diet</td></tr><tr><th></th><td></td><td></td><td>HC (11)</td><td>Control diet</td><td>Control diet</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td>High-fat diet</td></tr><tr><th>GH (5)</th><td>High-fat diet</td><td>H (23)</td><td>HH (12)</td><td>High-fat diet</td><td>Control diet</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td>High-fat diet</td></tr></tbody></table>
Table 1 in Tertiary hospital sewage as reservoir of bacteria expressing MDR phenotype in Brazil
<p><b>Table 1.</b> Antibiotic resistance profile and multiresistant phenotype of bacteria isolated in sewage from a tertiary hospital located in Ribeirão Preto, São Paulo, Brazil.</p><table><tbody><tr><th><b>Sampling point</b></th><th><b>Species</b></th><th><b>Antibiotic resistance profile*</b></th><th><b>Resistance phenotype</b></th></tr></tbody><tbody><tr><th>Ambulatory</th><td><i>Proteus mirabilis</i></td><td>AMO, AMC, AMP, CAZ, ERT, SXT, TET</td><td>MDR</td></tr><tr><th>care</th><td><i>Yersinia enterocolitica</i></td><td>AMO, AMC, AMP, CTX, CFO, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Enterococcus faecalis</i></td><td>CIP, CLI, GEN, SXT, TET, VAN</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP, CAZ, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th>Patient wards</th><td><i>Enterobacter cloacae</i> Complex</td><td>AMI, AMP, ASB, CPM, CFO, CAZ, CRO, CRX, CIP, ERT, GEN, IPM, MER, TZP</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella ozaenae</i></td><td>AMO, AMP, CTX, CFO, CAZ, CIP, ERT, GEN, IPM, MER, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, ERT, GEN, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella ozaenae</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, CIP, ERT, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMC, AMP, CTX, CAZ, ERT, IPM, MER, TET</td><td>MDR</td></tr><tr><th></th><td><i>Staphylococcus aureus</i></td><td>AMP, CIP, CLI, GEN, SXT, TCP, TET, VAN</td><td>MDR</td></tr><tr><th></th><td><i>Citrobacter youngae</i></td><td>AMO, AMC, AMP, CFO</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMC, AMP, SXT</td><td>-</td></tr><tr><th></th><td><i>Enterococcus gallinarum</i></td><td>CLI, SXT, TET, VAN</td><td>-</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>CAZ, CIP, GEN</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>CAZ</td><td>-</td></tr><tr><th>Confluent point</th><td><i>Klebsiella pneumoniae</i></td><td>AMP, ASB, CPM, CFO, CAZ, CRO, CRX, CIP, ERT, GEN, IPM, MER, TZP</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, ERT, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Serratia liquefaciens</i></td><td>AMO, AMP, CTX, CFO, CAZ, ERT, IPM, MER</td><td>MDR</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>AMI, AMP, ASB, CPM, CFO, CAZ, CRO, CRX</td><td>-</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP, CTX, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>CTX, CAZ, ERT, IPM, MER</td><td>-</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>CFO, CAZ, TET</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP, CAZ</td><td>-</td></tr><tr><th></th><td><i>Enterococcus faecium</i></td><td>CLI, SXT</td><td>-</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMP</td><td>-</td></tr></tbody></table>
Table 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
<p><b>Table 1:</b> Details of target genes (<i>hif-1α</i>, <i>hsp70</i>, <i>ras</i>, <i>mstn</i>, <i>acly</i>, <i>per-1</i>, <i>cry-1</i>, <i>ube3a</i> and <i>ogt</i>) and reference genes (<i>β- tubulin</i> and <i>β- actin</i>) primers.</p><table><tbody><tr><th><b>Gene</b></th><th><b>Length (bp)</b></th><th><b>R</b> <b>2</b></th><th><b>Efficiency (%)</b></th><th><b>Primers sequence (5ʹ-3ʹ) forward/reverse</b></th></tr></tbody><tbody><tr><th><i>tubulin</i> -F</th><td>20</td><td>0.99</td><td>109.5</td><td>GACGTGGTGCCCAAAGATGT</td></tr><tr><th><i>tubulin</i> -R</th><td>18</td><td>TGGATGGTGCGCTTGGT</td></tr><tr><th><i>β- actin</i> -F</th><td>21</td><td>0.99</td><td>100.5</td><td>GCTGTTTTCCCCTCCATTGTT</td></tr><tr><th><i>β- actin</i> -R</th><td>19</td><td>TCCCATGCCAACCATCACT</td></tr><tr><th><i>hif-1α</i> -F</th><td>20</td><td>0.99</td><td>105.2</td><td>CTTCTGAGCTCTGATGAGGC</td></tr><tr><th><i>hif-1α</i> -R</th><td>20</td><td>GAAAGCACCATCAGGAAGCC</td></tr><tr><th><i>hsp-70</i> -F</th><td>20</td><td>0.99</td><td>100.9</td><td>GCAAGGAGAACAAGATCACC</td></tr><tr><th><i>hsp-70</i> -R</th><td>19</td><td>CACTCCGTTGCACTTGTCC</td></tr><tr><th><i>mstn</i> -F</th><td>20</td><td>0.98</td><td>100.5</td><td>AATCCAAGCGAGGGAAAAGC</td></tr><tr><th><i>mstn</i> -R</th><td>22</td><td>CCTCCATCACCTGAAAGGTCTT</td></tr><tr><th><i>ras</i> -F</th><td>20</td><td>0.97</td><td>99.31</td><td>CCAGTACATGAGGACAGGAG</td></tr><tr><th><i>ras</i> -R</th><td>20</td><td>CAAGCACCATTGGCACATCG</td></tr><tr><th><i>acly</i> -F</th><td>19</td><td>0.99</td><td>100.7</td><td>ATCATCTCCCGCACTACAG</td></tr><tr><th><i>acly</i> -R</th><td>19</td><td>TACCTCCAATCTCTCCCAG</td></tr><tr><th><i>ube3a</i> -F</th><td>21</td><td>0.98</td><td>103.3</td><td>GCCATAAGCAAGCAGCACAAC</td></tr><tr><th><i>ube3a</i> -R</th><td>19</td><td>CCAGTCAGTCCGCACATCG</td></tr><tr><th><i>per-1</i> -F</th><td>20</td><td>0.98</td><td>104.1</td><td>TGTTGAAGTTTGTGCCCCAG</td></tr><tr><th><i>per-1</i> -R</th><td>18</td><td>CAGTCCAGATGCTCCTCC</td></tr><tr><th><i>cry-1</i> -F</th><td>19</td><td>0.99</td><td>103.6</td><td>GTCCAACAGCCCTCAAACT</td></tr><tr><th><i>cry-1</i> -R</th><td>18</td><td>TACGCCAAGCACTCCAGA</td></tr><tr><th><i>ogt</i> -F</th><td>19</td><td>0.99</td><td>104.1</td><td>CCTCCCTTTGCTGTGTTCC</td></tr><tr><th><i>ogt</i> -R</th><td>20</td><td>TGTCTGCTTTCCGCTTTCGC</td></tr></tbody></table>
A fMRI dataset in response to large number of short natural dynamic facial expression videos
<pre>#A fMRI dataset in response to large number of short natural dynamic facial expression videos<br>Natural facial expressions dataset (NFED),a dataset of functional magnetic resonance imaging (fMRI) responses to 1,320 short (3s) facial expression video clips.NFED offers researchers fMRI data that enables them to investigate the neural mechanisms involved in processing emotional information communicated by facial expression videos in real-world environments.<br>The dataset contains raw data, pre-processed volume data,pre-processed surface data and suface-based analyzed data.<br>To get more details, please refer to the paper at {website} and the dataset at https://openneuro.org/datasets/ds005047<br><br>## Preprocess procedure<br>The MRI data were preprocessed by using Kay et al, combining code written in MATLAB and certain tools from FreeSurfer, SPM,and FSL(http://github.com/kendrickkay). We used FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to construct the pial and white surfaces of participants from the T1 volume. Additionally, we established an intermediate gray matter surface between the pial and the white surfaces for all participants.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/volume_pre-process/**<br>Detailed usage notes are available in codes, please read carefully and modify variables to satisfy your customed environment.<br>## GLM of main experiment<br>We utilized a single-trial General Linear Model (GLMsingle) (https://github.com/cvnlab/GLMsingle) approach, an advanced denoising approach in MATLAB R2019a, to model the pre-processed fMRI data from main experiment. For single trials, the method of GLM was developed to offer estimations of BOLD response magnitudes ('betas'). GLMsingle requires only fMRI time series data and a design matrix as inputs, integrating three techniques to enhance the accuracy of experimental GLM beta estimates. Firstly, for each voxel, a custom HRF is identified from a library of candidate functions. Secondly, cross-validation is utilized to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Thirdly, to improve the stability of beta estimates for closely spaced trials, ridge regression is employed on a voxel-wise basis to regularize the betas. In this study, three betas were calculated by analyzing the BOLD response corresponding to individual video onset ranging from 1 to 3 seconds with 1-second intervals. We produced individual GLMsingle models for each session (consisted of 4 training runs and 2 test runs). In general, for each video within the training set, 2 (repetitions) x 3 (seconds) betas were acquired. Similarly, for each video within the testing set, 10 (repetitions) x 3 (seconds) betas were acquired. The utilization of repetitions enabled us to acquire video-evoked responses with a high signal-to-noise ratio (SNR).<br><br>The regressors in the GLMsingle toolbox mainly include the following categories:<br>1.Experimental Design Matrix: This is constructed based on the experimental design, including experimental conditions, task events, etc., and is used to estimate the BOLD (Blood - Oxygen - Level - Dependent) signal response.<br>2.Data-driven nuisance regressors: The data-driven nuisance regressors used in the GLMdenoise technique. These are identified by analyzing the data itself and are used to remove noise and improve the accuracy of beta estimation.<br>3.Physiological noise regressors: May include indicators of physiological signals such as heart rate and respiration, and are used to correct the influence of physiological noise on the BOLD signal.<br>4.Movement parameter regressors: Usually include head movement parameters, such as translation and rotation, and are used to correct signal changes caused by head movement.<br>5.Polynomial regressors: Polynomial terms used to o characterize the baseline signal level.<br><br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/GLMsingle-main-experiment/matlab/NFED_GLMsingle.m**<br><br>#### retinotopic mapping<br><br>The fMRI data from the the population receptive field experiment were analyzed by a pRF model implemented in the analyzePRF toolbox (http://cvnlab.net/analyzePRF/) to characterize individual retinotopic representation. Make sure to download required software mentioned in the code.<br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_prf.m**<br><br>#### fLoc experiment<br><br>We used GLMdenoise,a data-driven denoising method,to analyze the pre-processed fMRI data from the fLoc experiment.We used a "condition-split" strategy to code the 10 stimulus categories, splitting the trials related to each category into individual conditions in each run. Six response estimates (beta values) for each category were produced by using six condition-splits.To quantify selectivity for various categories and domains,we computed t-values using the GLM beta values after fitting the GLM.The regions of interest with category selectivity for each participant were defined by using the resulting maps.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_floc.m**<br><br><br>## Validation<br>### Basic quality control<br>The fundamental quality control suggests that the data displays good quality. To evaluate the quality of the structural data obtained from NFED, we employed four crucial metrics:Coefficient of Joint Variation(CJV), Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio in Grey Matter (SNR_GM), and Signal-to-Noise Ratio in White Matter (SNR_WM). Specifically, the CJV is between white matter(WM) and grey matter(GM).The CNR assesses the relationship between the contrast of GM and WM with the noise present in the image. The SNR assesses the connection between the mean signal measurements and the noise present in the image. The SNR assessment is conducted individually for GM and WM.For quality control of the NFED’s functional scans, we assessed the amount of head motion for each participant and the temporal signal-to-noise ratio (tSNR) of the time-series data, separately.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/T1_Image-quality-metrics/T1data/noiseeval/cal_SNRindex.m**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/FD/FD.py**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/tSNR/tSNR.py**<br>### The visual cortex exhibits reliable BOLD responses to natural facial expression videos stimuli in main experiment.<br>In the main experiment, there were 5 participants who accomplished 10 sessions, each consisting of of 4 training runs and 2 test runs, making a total of 60 runs(40 training and 20 test runs) in all. For each run, 30 (videos)*2 (repetitions) x 3 (seconds) betas were acquired. Z-score normalization was performed on the raw betas at each voxel for each run.The betas were then averaged across stimulus repetitions to generate a vector of betas. In general, 44 runs (40 training runs and 4 test runs)*90 betas were acquired. Hence, the test-retest reliability of responses to these videos in main experiment were evaluated through computing the Pearson correlation between the 90 betas obtained from the even runs and odd runs on each vertex.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/reliability_face/reliability_face.py**<br>### noise celling<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/noise_celling/sub-xx" store the intermediate files required for running the program<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/noise_celling/Noise_Ceiling.py**<br><br>### Correspondence between human brain and DCNN<br>The code are available at https://openneuro.org/datasets/ds005047. We combined the data from main experiment and functional localizer experiments to build an encoding model to replicate the hierarchical correspondences of representation between the brain and the DCNN. The encoding models were built to map artificial representations from each layer of the pre-trained VideoMAEv2 to neural representations from each area of the human visual cortex as defined in the multimodal parcellation atlas.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/dnnbrain/**<br><br>### Semantic metadata of action and expression labels reveal that NFED can encode temporal and spatial stimuli features in the brain<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/semantic_metadata/xx_xx_semantic_metadata" store the intermediate files required for running the program.<br>**code:https://openneuro.org/datasets/ds005047/derivatives/validation/code/semantic_metadata/**<br><br>## results<br> The results can be viewed at "https://openneuro.org/datasets/ds005047/derivatives/validation/results/brain_map_individual".<br><br>## Whole-brain mapping<br>The whole-brain data mapped to the cerebral cortex as obtained from the technical validation.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/results/show_results_allbrain/Showresults.m**<br><br>## Mannually prepared environment<br>We provide the *requirements.txt* to install python packages used in these codes. However, some packages like *GLM* and *pre-processing* require external dependecies and we have provided the packages in the corresponding file.<br><br>## stimuli<br>The video stimuli used in the NFED experiment are saved in the "stimuli" folders.<br><br></pre>
Assessment of the expression level of selected genes and protein phosphorylation of the PI3K/Akt signaling pathway in patients with colorectal cancer
Open the record for dataset details and reuse information.
Venus Express IMA solar wind data for VeRa radio science observations
<p>The Venus Express (VEX) Analyser of Space Plasmas and Energetic Atoms (ASPERA-4)<br>ion mass spectrometer (IMA) observed the the pristine solar wind parameters<br>at Venus between 2006 and 2014. Details can be found in<br>Barabash et al. (2007) Planetary and Space Science, Vol. 55 (12), pp. 1772-1792<br>The Analyser of Space Plasmas and Energetic Atoms (ASPERA-4) for the <br>Venus Express mission.</p> <p>This file is based on the VEX_IMA_SW_NVP_20060101.cef data file provided<br>by M. Fränz (MPI for Solar System Research, Goettingen, Germany) and contains only those ASPERA-4<br>solar wind observations used in the publication Peter et al. (2024) Icarus<br>The variability of the topside ionospheres of Venus and Mars<br>as seen by recent radio science observations (Appendix A4 Fig. 2).</p> <p>Data are based on hourly average IMA spectra obtained when VEX was at larger distance than<br>1 Venus radius from nominal bow shock (Martinecz 2009, PhD). Density and total velocity<br>are obtained by integration over the IMA_EXTRA proton spectrum. Dynamic pressure<br>by product of density and velocity. This file contains only orbits used for VERA analyis.</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.