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51 results for “Background analysis”
SPEAC Webinar: Use of background rates for observed / expected analysis
<p>The SPEAC team organized a webinar on April 24th, 2023, focused on the use of background rates for observed/expected analysis. The webinar featured three presentations, followed by a Q&A session. More details:</p> <ul> <li>Background rates of AESI for COVID-19, how have they been used? by Miriam Sturkenboom (SPEAC) - [00:00 - 10: 58]</li> <li>FDA COVID-19 vaccine safety surveillance: Use of background rates and observed/expected analyses by Brendan Day (US FDA-CBER) - [10:58 - 23:58]</li> <li>COVID-19 vaccines: Observed-to-expected analyses by Maria Gordillo Maranon and Catherine Cohet (EMA) - [23:58 - 23:58]</li> <li>Q&A - [39:57 - 58:59]</li> </ul> <p>The recording can be accessed below. </p>
Data from: Analysis of local-scale background concentrations of methane and other gas-phase species in the Marcellus Shale
The Marcellus Shale is a rapidly developing unconventional natural gas resource found in part of the Appalachian region. Air quality and climate concerns have been raised regarding development of unconventional natural gas resources. Two ground-based mobile measurement campaigns were conducted to assess the impact of Marcellus Shale natural gas development on local scale atmospheric background concentrations of air pollution and climate relevant pollutants in Pennsylvania. The first campaign took place in Northeastern and Southwestern PA in the summer of 2012. Compounds monitored included methane (CH4), ethane, carbon monoxide (CO), nitrogen dioxide, and Proton Transfer Reaction Mass Spectrometer (PTR-MS) measured volatile organic compounds (VOC) including oxygenated and aromatic VOC. The second campaign took place in Northeastern PA in the summer of 2015. The mobile monitoring data were analyzed using interval percentile smoothing to remove bias from local unmixed emissions to isolate local-scale background concentrations. Comparisons were made to other ambient monitoring in the Marcellus region including a NOAA SENEX flight in 2013. Local background CH4 mole fractions were 140 ppbv greater in Southwestern PA compared to Northeastern PA in 2012 and background CH4 increased 100 ppbv from 2012 to 2015. CH4 local background mole fractions were not found to have a detectable relationship between well density or production rates in either region. In Northeastern PA, CO was observed to decrease 75 ppbv over the three year period. Toluene to benzene ratios in both study regions were found to be most similar to aged rural air masses indicating that the emission of aromatic VOC from Marcellus Shale activity may not be significantly impacting local background concentrations. In addition to understanding local background concentrations the ground-based mobile measurements were useful for investigating the composition of natural gas emissions in the region.
Data and R script for "Fear and cultural background drive sexual prejudice in France – A sentiment analysis approach"
<p>Data:</p> <p>corpus_integral.csv</p> <p>FEEL_1.csv</p> <p>mauvais.txt</p> <p>neg_hetero_corrected.txt</p> <p>participant_info_used.txt</p> <p>pos_hetero_corrected.txt</p> <p>R script:</p> <p>polarities.R</p> <p>sentiments_discrete.R</p>
Analysis scripts and PFLOTRAN input files for "Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface"
<p>Supporting files for manuscript <em>Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface </em>(preprint published at https://arxiv.org/abs/2305.12575).</p> <p>Contents:</p> <ul> <li>PFLOTRAN input files (*.in) for the simulations that were used in the manuscript.</li> <li>The corresponding heterogeneous permeability fields for those simulations (*.h5).</li> <li>The CO2 property database for use with the PFLOTRAN MPHASE module.</li> <li>Python scripts for generation of the random fields and quantification of uptake rate.</li> <li>Jupyter notebooks for generation of summary figures.</li> <li>Excel spreadsheet summarizing the output of each of the simulations.</li> </ul>
Data from: Analysis of local-scale background concentrations of methane and other gas-phase species in the Marcellus Shale
Open the record for dataset details and reuse information.
Model data for: Analysis of the global atmospheric background sulfur budget in a multi-model framework
<p>The present dataset contains all model data used in the model intercomparison in ACP. All data is provided as monthly means. For more data, please contact the first author. V2 addresses inconsistencies in the time axes, vertical coordinates, and variable names between models.</p>
Raw data and background analysis used in 'On optimising cost and value in eScience'
<p>Raw data and background analysis used in 'On optimising cost and value in eScience'.</p> <p>contains:</p> <ul> <li>LOFAR publications per year, including impact per publication (i.e. impact factor of journal at time of publications)</li> <li>instructive artificial example</li> <li>top 500 list, including computational efficiencies (Nov 2017 edition)</li> <li>impact of spectre and meltdown on UDP/IP packet receive performance (raw data and summary)</li> <li>Titan awards and prizes</li> </ul>
Data from: Causes and consequences of genetic background effects illuminated by integrative genomic analysis
The phenotypic consequences of individual mutations are modulated by the wild-type genetic background in which they occur. Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist, nor about the mechanisms underlying it. We also lack knowledge on how mutations interact with genetic background to influence gene expression, and how this in turn mediates mutant phenotypes. Furthermore, how genetic background influences patterns of epistasis remains unclear. To investigate the genetic basis and genomic consequences of genetic background dependence of the scallopedE3 allele on the Drosophila melanogaster wing, we generated multiple novel genome-level datasets from a mapping-by-introgression experiment and a tagged RNA gene expression dataset. In addition we used whole genome re-sequencing of the parental lines—two commonly used laboratory strains—to predict polymorphic transcription factor binding sites for SD. We integrated these data with previously published genomic datasets from expression microarrays and a modifier mutation screen. By searching for genes showing a congruent signal across multiple datasets, we were able to identify a robust set of candidate loci contributing to the background-dependent effects of mutations in sd. We also show that the majority of background-dependent modifiers previously reported are caused by higher-order epistasis, not quantitative non-complementation. These findings provide a useful foundation for more detailed investigations of genetic background dependence in this system, and this approach is likely to prove useful in exploring the genetic basis of other traits as well.
Data from: Causes and consequences of genetic background effects illuminated by integrative genomic analysis
Open the record for dataset details and reuse information.
Whole transcriptomic analysis of zebrafish embryos of dyrk1aakrb1, dyrk1aa knock out mutant and Wild Type (WT) (+/+) in the background of Tg(kdrl:egfp) [32 hpf]
GEO Series GSE123026. Danio rerio. 4 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis of T. reesei CBS999.97, backcrossed female fertile strains in QM6a genetic background and QM6a upon mating
GEO Series GSE89104. Trichoderma reesei. 10 samples. Type: Expression profiling by array.
Single cell transcriptome analysis of intermediate neural progenitors (INPs) and type II neural stem cells (NSCII) from brat and control backgrounds isolated from Drosophila melanogaster larval brains
GEO Series GSE190133. Drosophila melanogaster. 7 samples. Type: Expression profiling by array.
Whole transcriptomic analysis of zebrafish embryos of dyrk1aakrb1, dyrk1aa knock out mutant and Wild Type (WT) (+/+) in the background of Tg(kdrl:egfp)
GEO Series GSE111280. Danio rerio. 4 samples. Type: Expression profiling by high throughput sequencing.
RNAseq analysis of Vibrio cholerae A1552 ∆rpoS (∆VC0534) from Smooth and Rugose backgrounds
GEO Series GSE255512. Vibrio cholerae O1 biovar El Tor. 12 samples. Type: Expression profiling by high throughput sequencing.
Gene expression analysis of HdhQ111 mice in a Pin1 knock-out background
GEO Series GSE64478. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis of T. reesei CBS999.97, backcrossed female fertile strains in QM6a genetic background and QM6a upon growth on cellulose
GEO Series GSE89103. Trichoderma reesei. 10 samples. Type: Expression profiling by array.
Gene and retrotransposon expression analysis in the F1 hybrid background of B6 and MSM for WT, Pld6 KO, and Dnmt3l KO male germ cells
GEO Series GSE78905. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq Analysis of CD1 background spermatogonia with NRRA treatment Transcriptomes
GEO Series GSE153273. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis of D. melanogaster developed in conditions of low radiation background laboratory and control group
GEO Series GSE159477. Drosophila melanogaster. 5 samples. Type: Expression profiling by high throughput sequencing.
Identification of genes regulated by the MADS transcription factor (TF), SEPALLATA3, in the context of the double sep1sep2 and triple sep1sep2sep3 mutant background, by RNA-Seq analysis
GEO Series GSE150605. Arabidopsis thaliana. 6 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.