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54,099 results for “Expression”
Gene Expression and Tree Growth in the CTFS-ForestGEO Plot at Harvard Forest 2017-2019
Major goals in ecosystem ecology have been to scale from leaves to canopies and to determine whether individual-level, intra-species and inter-specific variation is critical for models projecting ecosystem processes now and in the future. The project is important in that it examines these issues in detail considering genotypes and levels of gene expression all the way up to canopy level CO2 flux. Ecological genomics and transcriptomics are nascent fields that have been primarily restricted to model species in natural and (mostly) controlled environments. To date, we have very few studies of non-model organisms in nature and/or studies of functional genomics through space and time. The research is producing extraordinarily rich datasets regarding the gene expression of trees across populations, through space in each population, across the growing season and across years and linking this information to growth and gas exchange. It will, therefore, provide tremendous insights into how much variation exists in nature thereby guiding sampling designs in future ecological 'omics projects. More importantly, it will provide unusually detailed phenotypic information for important non-model species that have large impacts on the CO2 flux of eastern US forests.
Expression-based polygenic score from the amygdala 5HTT gene network
<p>This pipeline intends to facilitate the calculation of biologically informed polygenic scores from collected genomic data. This template can be adapted to create other expression-based polygenic risk scores. Data is 1) step by step description and 2) a list of genes that compose the gene network.</p>
Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay
<p>The dataset supplements the publication `Optimization of the <em>TeraTox</em> assay for preclinical teratogenicity assessment`. </p> <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> <p>Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” <em>Toxicological Sciences</em> 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.</p>
Lab513/Landscape of Gene Expression Dataset
<p>Datasets from the article:</p> <p><strong>A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies.</strong></p> <p>by Zoran S Marinkovic<sup>1,2,3</sup>, Clément Vulin<sup>1,4,5</sup>, Mislav Acman<sup>1,3</sup>, Xiaohu Song<sup>2</sup>, Jean Marc Di Meglio<sup>1</sup>, Ariel B. Lindner<sup>*,2,3</sup>, Pascal Hersen<sup>*,1</sup></p> <p>eLife 2019;8:e47951 DOI: <a href="https://doi.org/10.7554/eLife.47951">10.7554/eLife.47951</a></p> <p>first versions on BioRxiv : <a href="https://www.biorxiv.org/content/10.1101/527846v2">https://www.biorxiv.org/content/10.1101/527846v2</a></p> <p> </p>
Plasma circulating microRNA-expression quantitative trait loci (eQTLs) data in the Rotterdam Study
<p>The dataset contains GWAS summary statistics for 2,083 plasma circulating microRNAs, obtained from nearly 2,178 participants of the Rotterdam Study. The dataset includes three files, as outlined below:</p> <p><strong>File1: SNP_reference_file_maf0.01_Rsq0.7.txt</strong></p> <p>A reference file for SNPs with good imputation quality (Rsq > 0.7) and minor allele frequency > 0.01 among participants included in our GWAS in the Rotterdam Study (N=2,178). The headers are:</p> <p>SNP: rsID</p> <p>chr: chromosome number according to GRCh37</p> <p>bp: basepair position according to GRCh37</p> <p>effect_allele: effect allele</p> <p>other_allele: other allele</p> <p>eaf: effect allele frequency</p> <p><strong>File2: miReQTLs_1e-5_maf0.01_Rsq0.7.txt</strong></p> <p>Summary statistics for all SNPs significantly associated with 2083 miRNAs (p-value < 1e-5), filtered by minor allele frequency > 0.01 and Rsq > 0.7. The headers are:</p> <p>SNP: rsID</p> <p>beta: effect estimate</p> <p>se: standard error</p> <p>pval: p-value</p> <p>miRNA: miRNA ID</p> <p><strong>File3: miReQTLs_nominal_sig.csv.gz</strong></p> <p>Summary statistics for all SNPs nominally associated with 2083 miRNAs (p-value < 0.05). The headers are:</p> <p>RSID: SNP ID</p> <p>p-value: p-value</p> <p>phenotype: miRNA</p> <p>SE: standard error</p> <p>BETA: effect estimate</p> <p> </p> <p>The SNP allelic information and frequency can be found in the reference file (<strong>File1</strong>). </p> <p><br>For more information, please contact: m.ghanbari@erasmusmc.nl</p>
Light-regulated gene expression and alternative splicing data from rice seedlings.
<p>This data contains analyzed data from the experiment conducted on rice seedlings under dark and light conditions. Seeds of rice (Oryza sativa spp. japonica cv. Nipponbare) were sown in the dark and germinated on day 2 and continued to grow in the dark for another 6 days. 3 biological replicates of the dark-grown etiolated shoots were harvested on day 8 after sowing. The remaining dark-grown seedlings were exposed to continuous white light at 120 mol/m2/sec for 48 hours or another 2 days (Days 9 and 10 after sowing). Three replicates of the light-treated green-colored seedling samples were harvested at the end of day 10. Harvested samples were frozen in liquid nitrogen and stored at -80C until further processing.</p>
Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention
<p>Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention</p>
Data from: Visual pigment chromophore usage in Nicaraguan Midas cichlids: Phenotypic plasticity and genetic assimilation of cyp27c1 expression
<p>Code and Data associated with "Visual pigment chromophore usage in Nicaraguan Midas cichlids: Phenotypic plasticity and genetic assimilation of <em>cyp27c1</em> expression"</p> <h2><span>Abstract</span></h2> <p><span>The wide-ranging photic conditions found across aquatic habitats may act as selective pressures potentially driving rapid evolution and diversity in the visual system of teleost fishes. Fine-tuning of visual sensitivities in many fish species relies on regulating the two components of visual pigments, the opsin protein and the chromophore. Many studies have focused on opsin gene expression or opsin sequence divergence in fishes inhabiting contrasting habitats. However, variation in chromophore usage across photic habitats has received less attention. Species from the Nicaraguan Midas cichlid complex, <em>Amphilophus </em>cf <em>citrinellus </em>[Günther 1864], have independently colonized seven isolated crater lakes of varying photic conditions resulting in repeated examples of small adaptive radiations. Here, we investigate variation in <em>cyp27c1</em>, the main enzyme involved in chromophore exchange, in response to photic environments in the wild, we measure its genetic component using laboratory-reared fish and test the effect of different rearing light conditions on <em>cyp27c1</em> expression. We found that photic environments significantly predict variation in <em>cyp27c1</em> expression in wild populations and that this variation seems to be genetically assimilated in two populations. We found that light-induced <em>cyp27c1</em> expression is variable across populations (i.e., genotype-by-environment interactions) and correlated with local photic conditions thus highlighting <em>cyp27c1</em> as a key factor of visual ecology in cichlid fishes.</span></p> <p><span>Keywords: <em>cyp27c1 </em>gene expression, sensory ecology, visual plasticity, Neotropical cichlids </span></p>
Dataset of "TWIST1 expression is associated with high-risk neuroblastoma and promotes primary and metastatic tumor growth"
<p>The embryonic transcription factors TWIST1/2 are frequently overexpressed in cancer, acting as multifunctional oncogenes. Here we investigate their role in neuroblastoma (NB), a heterogeneous childhood malignancy ranging from spontaneous regression to dismal outcomes despite multimodal therapy. We first reveal the association of TWIST1 expression with poor survival and metastasis in primary NB, while TWIST2 correlates with good prognosis. Secondly, suppression of TWIST1 by CRISPR/Cas9 results in a reduction of tumor growth and metastasis in immunocompromised mice. Moreover, TWIST1 knock-out tumors displays a less aggressive cellular morphology and a reduced disruption of the extracellular matrix (ECM) reticulin network. Additionally, we identify a TWIST1-mediated transcriptional program associated with dismal outcome in NB and involved in the control of pathways mainly linked to the signaling, migration, adhesion, the organization of the ECM, and the tumor cells versus tumor stroma crosstalk. Taken together, our findings identified TWIST1 as novel therapeutic target in NB.</p>
Dataset for "TWIST1 expression is associated with high-risk neuroblastoma and promotes primary and metastatic tumor growth"
<p>The embryonic transcription factors TWIST1/2 are frequently overexpressed in cancer, acting as multifunctional oncogenes. Here we investigate their role in neuroblastoma (NB), a heterogeneous childhood malignancy ranging from spontaneous regression to dismal outcomes despite multimodal therapy. We first reveal the association of TWIST1 expression with poor survival and metastasis in primary NB, while TWIST2 correlates with good prognosis. Secondly, suppression of TWIST1 by CRISPR/Cas9 results in a reduction of tumor growth and metastasis in immunocompromised mice. Moreover, TWIST1 knock-out tumors displays a less aggressive cellular morphology and a reduced disruption of the extracellular matrix (ECM) reticulin network. Additionally, we identify a TWIST1-mediated transcriptional program associated with dismal outcome in NB and involved in the control of pathways mainly linked to the signaling, migration, adhesion, the organization of the ECM, and the tumor cells versus tumor stroma crosstalk. Taken together, our findings confirm TWIST1 as promising therapeutic target in NB.</p> <p>This dataset comprise images (.ndpi files) of anti-F4/80 IHC staining used for the quantification of macrophages in subcutaneous and orthotopic neuroblastoma xenografts derived from SK-N-Be2c cells expressing TWIST1 or knocked out for TWIST1 through CRISR/Cas9.</p>
EEG Data for Emotive Response to Robot Facial Expressions
<p>This dataset consists of EEG recorded during visual human-robot interaction from 10 healthy participants to investigate the emotive response in EEG to different robot facial expressions. Participants observed four different facial expressions (angry, happy, sad and surprised along with neutral expression) displayed by the social robot Miko on its digital screen. EEG was recorded from 16 unipolar channels in frontal, central, temporal, parietal, and occipital locations . During each trial, an emotion stimulus was displayed for approximately 4s followed by 4s break during which the Miko robot displayed neutral expression and blinked regularly. Emotions were displayed in random order. Total of 240 EEG trials were recorded from each participant with 60 trials per emotion. The dataset provides raw minimally filtered EEG along with cleaned EEG with artefacts removal using ICA with sampling frequency of 128 Hz, and corresponding stimulus onset markers. Please refer to README file for further details and example code.</p> <p><em>Please cite the original publication:</em></p> <p>M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <em>IEEE Internet of Things Journal</em>, DOI: <a href="https://doi.org/10.1109/JIOT.2021.3097592">10.1109/JIOT.2021.3097592</a>.</p> <p><em>Preprint: </em></p> <p>M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <a href="https://arxiv.org/abs/2012.04511">arXiv:2012.04511</a></p>
ATEPP: A Dataset of Automatically Transcribed Expressive Piano Performance
<p>ATEPP is a dataset of expressive piano performances by virtuoso pianists. The dataset contains 11742 11677 performances (~1000 hours) by 49 pianists and covers 1580 movements by 25 composers. All of the MIDI files in the dataset come from the piano transcription of existing audio recordings of piano performances. Scores in MusicXML format are also available for around half of the tracks. The dataset is organized and aligned by compositions and movements for comparative studies. For more details, please check <a href="https://github.com/BetsyTang/ATEPP">here</a>. </p>
Evaluation of PD-L1 expression in vulvar cancer
<p><strong>background & Aims: </strong>Vulvar cancer accounts for about 4% of all female genital malignancies. Squamous cell carcinoma is the most common histological type.PD-L1 is a membranous protein of nucleated cells. By binding to its PD-1 receptor, PD-L1 inactivates T lymphocytes. It plays a key role in the treatment of some cancers. Our aims were to study the expression profile of PD-L1 in vulvar cancer by immunohistochemistry and to correlate its expression with survival rates. <strong>Methods:</strong> A retrospective study was conducted in the Pathology Department of Saleh Azaiez Institute, involving 55 patients followed for vulvar cancer over a period of 13 years from January 2008 to December 2021. Clinicopathologic data were collected from medical records and pathology reports. Immunohistochemical analysis was performed using an automaton (Leica Biosystems™). <strong>Results:</strong> PD-L1 expression in vulvar squamous cell carcinoma was observed in 44% of cases. This expression was noted in 11% of cases at the level of lymphocytes whose dissection was negative in all cases with a maximum tumor size of 40 mm. PDL1 was expressed in tumor cells in 33% of cases where there was a positive inguinal dissection in 22% of cases and a maximum tumor size of 90 mm. <strong>Conclusions:</strong> The immune checkpoint inhibitor PD-1/PD-L1 seems to have an important prognostic effect on the development of some cancers (breast cancer, lymphoma). Indeed, the expression of PD-L1 in tumors is often considered a factor of poor prognosis due to its immunosuppressive activity in tumor tissues. Many studies have attempted to determine the prognostic factors of vulvar cancer in order to optimize therapeutic management. PD-L1 expression in vulvar squamous cell carcinoma has not been studied before, hence the interest of our study which suggested, as in the literature, a prognostic role for this protein.</p>
Gene expression ATLAS of Arabidopsis thaliana (accession Columbia) across its lifecycle
<p><strong>Abstract: </strong>Arabidopsis thaliana (accession- Columbia) is an important model plant. RNA-Seq based study of 36 gene expression libraries was carried out to explore transcriptional programs operating in different plant parts (seedling, rosette, root, inflorescence, flower, fruit silique, and seed) and developmental stages (2-leaf stage, 6-leaf stage, 12-leaf stage, senescence stage, dry mature and imbibed seed stage). For each tissue type and developmental stage, three individual plants were used as biological replicates.</p> <div><strong><span>Organism part: </span></strong><span>inflorescence, whole plant, seed, root, silique fruit, flower, rosette</span></div> <div> </div> <div><span><strong>Developmental stage:</strong> </span><span>LP.02 two leaves visible stage, IL.00 inflorescence just visible stage, fruit size 30 to 50% stage, LP.12 twelve leaves visible stage, root development stage, fruit size 70% to final stage, LP.06 six leaves visible stage, dry seed stage, flowering stage, seed imbibition stage, sporophyte senescent stage, inflorescence development stage</span></div> <div> </div> <div> <div><strong><span>Organism: </span></strong><span>Arabidopsis thaliana</span></div> <div> </div> <div><span><strong>Ecotype:</strong> </span><span>Col-0</span></div> <div> </div> <div><strong><span>Genotype: </span></strong><span>wild type genotype</span></div> <div> </div> <div><span><strong>Age:</strong> Samples are from </span><span>20-day, 49-day, 39-day, 15-day, 21-day, 9-day, 22-day, 55-day, 26-day, 45-day</span></div> <div> </div> <div><span><strong><span>Experimental Designs: </span></strong><span>growth chamber study<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://purl.obolibrary.org/obo/EO_0007269" target="_blank" rel="noopener"> EFO</a></span>, <span>development or differentiation design<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0001746" target="_blank" rel="noopener"> EFO</a></span>, <span>organism part comparison design<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0001750" target="_blank" rel="noopener"> EFO</a></span></span></div> <div> </div> <div><span>For more description of the data and sample types see the file <a href="../api/records/11133989/draft/files/PRJEB24664_Sample_descriptors.xlsx/content" target="_blank" rel="noopener noreferrer">PRJEB24664_Sample_descriptors.xlsx or visit </a> or visit <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422/sdrf">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422/sdrf</a></span></div> <div> </div> <div><span>Original data was submitted from </span></div> <div> <ul> <li><span>EMBL-EBI ArraExpress: <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422</a></span></li> <li><span>NCBI SRA: <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24664">https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24664</a></span></li> </ul> <p><strong><span>Protocol description:</span></strong></p> <table> <tbody><tr> <th>Name</th> <th>Type</th> <th>Description</th> <th>Hardware</th> </tr> </tbody><tbody> <tr> <td>P-MTAB-71349</td> <td><span>growth protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0003789" target="_blank" rel="noopener"> EFO</a></span></td> <td>Seeds were planted in pots containing commercial potting mix with fertilizers. Pots were covered with clear perforated plastic wrap and kept at 4 degrees celsius for 3 days to break the dormancy. After 3 days plants were transferred to the Intellus Ultra growth chamber (Percival Scientific, IA, USA) which was set to temperature 22-23 degrees celsius, light intensity 120-150 micromol/m2sec under the cycle of 16h light and 8h dark. Soil was kept moist by gently spraying with water every 72 hours to maintain humidity to 50-60%. Sampling time point is given in days after germination.</td> <td> </td> </tr> <tr> <td>P-MTAB-71350</td> <td><span>nucleic acid extraction protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0002944" target="_blank" rel="noopener"> EFO</a></span></td> <td>Total RNA from frozen samples was extracted as a method described in Filichkin et al., 2010. Total RNA was used to isolate large RNA as per manufacturer's protocol for miRNeasy Mini kits (Qiagen Inc., USA), and RNase-free DNase (Life Technologies Inc., USA).</td> <td> </td> </tr> <tr> <td>P-MTAB-71351</td> <td><span>nucleic acid library construction protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0004184" target="_blank" rel="noopener"> EFO</a></span></td> <td>True-Seq kit (Illumina Inc.) was used to prepare RNA-seq libraries, according to the manufacturer’s protocol.</td> <td> </td> </tr> <tr> <td>P-MTAB-71352</td> <td><span>nucleic acid sequencing protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0004170" target="_blank" rel="noopener"> EFO</a></span></td> <td>101bp paired-end sequencing of mRNA was performed by using the standard protocols on Illumina HiSeq 3000.</td> <td>Illumina HiSeq 3000</td> </tr> </tbody> </table> </div> </div>
Energy and Emissions Impacts of Atlanta's Reversible Express Toll Lanes and High-Occupancy Toll Lanes
<p>This dataset is the MOVES-Matrix emission rates for the NCST project of Energy and Emissions Impacts of Atlanta’s Reversible Express Toll Lanes and High-Occupancy Toll Lanes, developed by our research team at Georgia Institute of Technology.</p> <p> </p> <p>The abstract of the project is as follows.</p> <p>This report summarizes the impact on corridor-level energy use and emissions associated with the 2018 opening of the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA. The research team tracked changes in vehicle throughput on the managed lane corridors (extracted from GDOT’s Georgia NaviGAtor machine vision system after comprehensive QA/QC) and performed a difference-in-difference analysis to exclude regional changes, pairing test sites vs. control sites not influenced by the openings. The results show a large increase in overall peak-period vehicle throughput on the NWC, especially on I-575, due to the congestion decrease (20 mph speed increases at some locations). The increase in corridor-level energy use and emissions was smaller than vehicle throughput, but still significant. Predicted downwind maximum CO concentrations only increased from 1.81 ppm to 1.93 ppm(which remains extremely low). The increase in morning peak activity on the corridor likely resulted from diversion of some traffic into the peak from the shoulder periods, diversion of some traffic from other nearby freeway corridors, and diversion of local road traffic into the corridor. Unfortunately, without overall control volume totals and/or pre-and-post travel behavior surveys for the alternative commute routes, it is not possible to quantify the likely reductions in traffic flow and emissions that occurred along the other corridors that likely resulted from morning commute shifts. Hence, the team cannot draw reliable conclusions related to net regional or sub-regional impacts associated with the new managed lane corridors. The impact observed on the I-85 corridor was much smaller than on the NWC, especially at Indian Trail/Lilburn Road (far from the Express Lane Extension). After the Express Lanes opened, energy use and emission rates at Old Peachtree Road increased slightly (as uncongested vehicle speeds increased), but this increase may be short-lived as traffic on the corridor changes over time.</p> <p> </p>
Arabidopsis thaliana circadian mRNA-seq gene expression processed tables from Romanowski et al., TPJ 2020.
<p>This dataset is an add-on for Romanowski et al., TPJ 2020 (https://doi.org/10.1111/tpj.14776) containing processed files for the circadian RNAseq data in tab delimited txt format.</p> <p><br> Here, you can the raw counts file, the normalized CPM values, and the full JTK result (without recalculated circadian phases, just the original ones). All genes with a read density > 0.05 in at least one timepoint were considered expressed. The read density is calculated as the amount of reads divided by the effective length of a gene (total reads / length). Genes rd file is also included.</p> <p>Some useful notes:<br> 1) Counts were assigned using ASpli and the AtRTDv2 annotation (34,212 genes).<br> 2) After filtering by rd we had a total of 18,503 expressed genes.<br> 3) 13,256 genes passed the QL F-tests.<br> 4) 9,127 genes were rhythmic according to JTK_cycle. </p> <p>For detailed protocols, please see Romanowski et al., TPJ 2020 (https://doi.org/10.1111/tpj.14776)</p> <p>The RNA-seq raw data supporting the conclusions of this article have been deposited in ArrayExpress (Kolesnikov et al., 2015) at EMBL-EBI (www.ebi.ac.uk/arrayexpress), under accession numbers E-MTAB-7933.</p> <p>All relevant custom r scripts are available at https://github.com/aromanowski/Circadian_rhythms_and_alternative_splicing</p>
Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br> Planet: <strong>Venus</strong><br> Radius: <strong>R<sub>V</sub> = 6051.8 km</strong> (volumetric mean planetary radius)<br> Spacecraft: <strong>ESA/Venus Express</strong><br> Spacecraft coordinates system: <strong>Venus Solar Orbital (VSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>VSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>VSO</sub></em> towards Venus’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>VSO</sub></em>–<em>Y<sub>VSO</sub></em> plane passing through the centre of Venus,</li> <li><em>Y<sub>VSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/04/2006 to 25/11/2014</strong><br> Total number N of candidate bow shock crossings in the database: <strong>N = 4950</strong><br> Number of quasi-parallel bow shock crossings: <strong>N<sub>||</sub> = 844</strong><br> Number of quasi-perpendicular bow shock crossings: <strong>N<sub><span class="math-tex">\(\perp\)</span></sub> = 4106</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br> The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br> To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in:<br> Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a></p> <p>They consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane <span class="math-tex">\(\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)\)</span>performed on the datasets of <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> <p>We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted <span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub></span> (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that:</p> <ul> <li><span class="math-tex">45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup></span>: quasi-perpendicular shock condition</li> <li><span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub><span class="math-tex">\(\geq\)</span>135<sup>∘</sup></span>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>For details, see <strong>Simon Wedlund et al. (2022)</strong> above, §2.3 pp. 10-12.</p> <p><strong>VARIABLES DESCRIPTION</strong></p> <p>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in Venus Express' database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius <em>R</em><sub>V </sub>(<em>R</em><sub>V</sub> = 6051.8 km):<br> <em>X<sub>VSO</sub></em>,<sub> </sub><em>Y<sub>VSO</sub></em>, <em>Z<sub>VSO</sub></em> and Euclidean distance <span class="math-tex">\(R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}\)</span> (in <em>R<sub>V</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = <span class="math-tex">\(\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}\)</span> (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface <span class="math-tex">\(\theta_{Bn}\)</span> (ThetaBn, in º, calculated with atan2(norm(cross(<strong>B</strong>,<strong>ñ</strong>),dot(<strong>B</strong>,<strong>ñ</strong>)), with <strong>B</strong> the magnetic field vector and <strong>ñ</strong> the vector normal to the shock surface): <ul> <li>45 < ThetaBn < 135 deg: quasi-<span class="math-tex">\(\perp\)</span> shock</li> <li>ThetaBn <span class="math-tex">\(\leq\)</span> 45 deg & ThetaBn <span class="math-tex">\(\geq\)</span> 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, <em>B<sub>x</sub></em>, <em>B<sub>y</sub></em>, <em>B<sub>z</sub></em> (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath <span class="math-tex">\(\longrightarrow\)</span> solar wind (2447 events)</li> <li>flag = 1: solar wind <span class="math-tex">\(\longrightarrow\)</span> magnetosheath (2503 events)</li> </ul> </li> </ul> <p><strong>WARNING</strong></p> <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of:<br> <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 R<sub>V</sub> (with R<sub>V</sub> = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br> C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. </p> <p><strong>LICENSE AND RIGHTS</strong><br> This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences, 2022-10-05<br> Contact email: cyril.simon.wedlund@gmail.com</p>
Supplemental data files: Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans
<p>This dataset holds all non-GEO-hosted supplemental data files for manuscript "Beyond the reference: gene expression variation and transcriptional response to RNAi in <em>C. elegans</em>". Please see the linked preprint/publication for full details.</p> <p>The PDF _guide_to_datafiles.pdf gives details on the format and content of each of the included files.</p>
TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about HNSC, a type of cancer that originates in the squamous cells lining the mucosal surfaces of the head and neck region, including the oral cavity, throat, and larynx. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</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)
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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.