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30,947 results for “Profile”
Example dataset and expected outcomes of Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues
<p>Here are the example datasets and expected outcomes included in "<strong>Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues</strong>" from Ren et al. Please refer to the README.txt file for more detailed information. Corresponding computational tools are available at <a href="https://github.com/wanglab-broad/starfinder">https://github.com/wanglab-broad/starfinder.</a></p>
Dataset 2. Functional genomic profiling of O-GlcNAc reveals its context-specific interplay with RNA polymerase II
Open the record for dataset details and reuse information.
Transcriptome profiling associated with CARD11 overexpres-sion in Colorectal Cancer implicates a potential role for Tumour Immune Microenvironment and Cancer pathways modulation via NF-κB
<p>Images for CARD11 study in IJMS</p>
Gene expression profiling of normothermic machine perfusion of human livers
<p>Normothermic machine perfusion (NMP) has been successfully implemented in clinical routine of liver transplantation over the past years. However, little is known about the mechanisms how NMP impacts on the transcriptome of a human donor liver. We herein examined gene expression profiles in transplanted and non-transplanted livers over NMP time. 50 livers subjected to NMP were included in this study. 30 were transplanted after a maximum of 20 hours (h) perfusion, while 15 were discarded due to poor performance. Biopsies were collected before NP (PRE), 1h, 6h, 12h, 20h of NMP and after reperfusion. Next-generation sequencing was applied in liver biopsies to assess differential gene expression over perfusion time. Perfusate samples were collected regularly to monitor liver function. Significantly differentially expressed genes between each timepoint and PRE (LIVER_NMP_TIMEPOINT_PRE_DEG.xlsx) as well as between non-transplanted livers (NTP) and transplanted livers (TP) (LIVER_NMP_NTP_TP_DEG.xlsx) were determined using the R package DESeq2 and included in this dataset.</p>
FIGURE 4 in DNA profiles to identify Dillenia species (Dilleniaceae) in Thailand
FIGURE 4. The phylogenetic tree with bootstrap support levels from psbA-trnH spacer sequences of 13 species and the Dillenia sp. by MEGA6.
FIGURE 2. The dendrogram constructed from RAPD bands from 14 in DNA profiles to identify Dillenia species (Dilleniaceae) in Thailand
FIGURE 2. The dendrogram constructed from RAPD bands from 14 primers in 28 individuals of 14 species and of the Dillenia sp. by NTSYS-pc version 2.1 UPGMA.
FIGURE 1. The 13 in DNA profiles to identify Dillenia species (Dilleniaceae) in Thailand
FIGURE 1. The 13 identified Dillinia species and one unidentified species, namely Dillenia aurea (A,B), D. excelsa (C,D), D. grandifolia (E), D. hookeri (F), D. indica (G,H), D. obovata (I), D. ovata (J,K), D. parviflora (L,M), D. pentagyna (N,O), D. philippinensis (P,Q), D. pulchella (R,S), D. reticulata (T,U), D. suffruticosa (V,W), and Dillenia sp. (X,Y).
FIGURE 3 in DNA profiles to identify Dillenia species (Dilleniaceae) in Thailand
FIGURE 3. The phylogenetic tree with bootstrap support levels from rbcL gene sequences of 13 species and of the Dillenia sp. by MEGA6.
Nice to Meet You: The Role of Communicative Signaling in GitHub Profile Biographies
<p>This repository serves as the online appendix for the paper "Nice to Meet You: The Role of Communicative Signaling in GitHub Profile Biographies".</p>
Nice to Meet You: The Role of Communicative Signaling in GitHub Profile Biographies
<p>This repository serves as the online appendix for the paper "Nice to Meet You: The Role of Communicative Signaling in GitHub Profile Biographies".</p>
Label-Free Quantification (LFQ) LC-MS Data for the Profiling of Domain-specific VCP/p97 Interactions in Living Cells
<p>This deposit includes LFQ LC-MS data for tandem-IP samples from VCP-L278AbK and VCP-D592AbK crosslinking experiments, their VCP-L278BocK and VCP-D592BocK controls, and SpectroMine output files.</p>
The taxonomy profiles of 25 additional SBP negative samples
<p>This upload includes the MetaX taxonomy profiles of 25 additional SBP samples as negative controls for the ascites metagenomic study. The study includes 26 negative controls in total, with one profile already provided in the paper's supplementary materials. Here are the remaining 25.</p>
Seismic reflection data of profile across the Central Basin Fault and the Kyushu-Palau Ridge
<p>Seismic reflection profiles were acquired in 2020 by the R/V "Dayanghao" (Second Institute of Oceanology, Ministry of Natural Resources, China) using a 6040 cubic inch air gun array, discharged at 37.5 m intervals, and a 6-km-long, 480-channel streamer. Data were recorded for 14 s at 2 ms sampling intervals. Seismic data processing involved noise reduction, signal enhancement via filtering and deconvolution, velocity analysis, time migration, and depth migration. </p>
Bioactive compounds, antioxidant activity, and sensory profile of sorghum tisane
<p>This dataset provides the result of bioactive compounds, antioxidant activity, sensory acceptance, and sensory profile measurements towards sorghum tisane with different pericarp colors.</p>
Dataset for "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet"
<p>The dataset supports the article "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet", which is accepted to be published in the Environmental Data Science journal. <br><br>The description of the files is as follows:</p> <ol> <li>ERA5.nc: <br> <ul> <li>Dimensions: (location: 11, time: 166560)<br>Coordinates:<br> longitude (location) float32 ...<br> latitude (location) float32 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2018-12-31T23:00:00<br> year (time) int64 ...</li> <li>Variables: 10ws, 100ws, 100alpha, 975ws, 950ws, 975wsgrad, 950wsgrad, zust, i10fg, t2m, skt, stl1, d2m, msl, blh, cbh, ishf, ie, tcc, lcc, cape, cin, bld, t_975, t_950, 2mtempgrad, sktempgrad, dewtempsprd, 975tempgrad, 950tempgrad, sinHR, cosHR, sinJDAY, cosJDAY, 10ws_delta1, 10ws_delta2, 10ws_delta3, 10ws_delta4, 10ws_delta5, 10ws_delta6, 100ws_delta1, 100ws_delta2, 100ws_delta3, 100ws_delta4, 100ws_delta5, 100ws_delta6, 975ws_delta1, 975ws_delta2, 975ws_delta3, 975ws_delta4, 975ws_delta5, 975ws_delta6, 950ws_delta1, 950ws_delta2, 950ws_delta3, 950ws_delta4, 950ws_delta5, 950ws_delta6</li> </ul> </li> <li>2000.nc: <ul> <li>Dimensions: (obs: 11, time: 8784, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2000-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> </ul> </li> <li> 2001.nc: <ul> <li>Dimensions: (obs: 11, time: 8760, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2001-01-01 ... 2001-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> <li>Data is the wind speed at multiple height levels</li> </ul> </li> </ol>
Driven two-fluid slow magnetoacoustic waves in the solar chromosphere with a realistic ionisation profile
<p>An animation showing log(Ti) overlaid by magnetic field lines. The initial atmosphere (at t = 0 s) is described by a hydrostatic equilibrium supplemented by the Saha equation and embedded in a fanning magnetic field. This initial equilibrium is perturbed by a monochromatic driver which operates in the chromosphere on the vertical components of the ion and neutral velocities with period P = 5 s.</p>
Safe cardiac action potential test (www.scaptest.com) : a database describing the in silico cardiac safety profile of drugs and their propensity to induce early afterdepolarization - Part II : description of 50 additional drugs
<p><span>This file describes the<em> in silico</em> cardiac safety profile of 50 additional drugs (in addition to the 150 drugs already described in the zenodo file 7541554) in order to complete the “scaptest” database.<span> </span>The aim of this database is to describe the <em>in silico</em> cardiac safety profile of drugss and their propensity to induce early afterdepolarization.<span> </span>This is based on the study of the effects of drugs on the non-failing human ventricular myocyte action potential (endo-, mid- and epicardial subtypes) reconstructed by computational simulation (O’Hara-Rudy dynamic algorithm) in order to identify cardiac action potential abnormalities such as high variations and/or occurrence of resting membrane potential, action potential amplitude, maximal rate of action potential rise, action potential duration, triangulation, early afterdepolarization, transmural dispersion of repolarization, reverse use dependence, qNet or minimal rate of action potential decrease at early afterdepolarization take-off voltage. These various parameters are useful in order to assume a more accurate predictability of pro-arrhythmic liabilities of new drug candidate in the cardiac safety pharmacology screening process, which is the aim of the comprehensive in vitro pro-arrhythmia assay (CiPA) initiative. The <em>in silico</em> cardiac safety profile of each drug is illustrated by a separate page describing the effects induced by each compound on these various parameters. The results are summarized regarding the expected pro-arrhythmia profile of the various compounds as described by the CredibleMeds classification evaluating their propensity to induce torsade de pointes.</span></p>
ROIs NanoString Digital Spatial Profiler protein assay van Hijfte et al. 2024
<p><span>Images that show ROIs of the NanoString Digital Spatial Profiler protein assay from Van Hijfte et al. 2024. See associated publication for more information.</span></p>
Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA
<p>Scripts and methylation frequencies for "Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA" manuscript.</p>
Evaluating the Overhead of the Performance Profiler Cloudprofiler With MooBench
<p>The dataset is the experimental results of the paper "Evaluating the Overhead of the Performance Profiler Cloudprofiler With MooBench". The results include Cloudprofiler and all target performance frameworks of MooBench in the current main branch. The experimental environment is an on-premise, bare metal server with two Intel Xeon E5-2650 CPUs and 64 GiB RAM on each NUMA domain. It runs the Debian 12.6 Linux on a single 480 GB SSD. The JVM-based performance benchmarks ran with OpenJDK 17.0.2, and Cloudprofiler's C++ library was built with GCC 12.2.0. The Kieker Python framework ran with Python 3.11.2.</p> <p>MooBenchs target frameworks and benchmark scripts are maintained separately under "<code>frameworks/<target benchmkark></code>" (the <target_benchmark> label is the placeholder for each performance frameworks), and the MooBench execution results are stored under "<code>frameworks/<target benchmkark>/results-<target_benchmark></code>". This dataset is a single compressed archive, which contains all MooBench execution results from each target_benchmark:</p> <p><code>results-Cloudprofiler-java/raw-${LOOP_NUM}-${REC_DEPTH}-${CONF_NUM}.csv</code><br><code>results-inspectIT-java/raw-*.csv</code><br><code>results-Kieker-java/raw-*.csv</code><br><code>results-Kieker-python/raw-*.csv</code><br><code>results-OpenTelemetry-java/raw-*.csv</code></p> <p>The experimental data is separated as files by the current configuration number <code>${CONF_NUM}</code>, the current loop number <code>${LOOP_NUM}</code>, and the current recursion depth <code>${REC_DEPTH}</code>.</p> <p>The overall results are depicted as boxplots in the results.pdf file. The Y-axis is the execution time in microseconds, and the configurations on the X-axis are ordered by <code>${CONF_NUM}</code> for each target framework.</p> <p>Cloudprofiler is hosted at <a href="https://github.com/shinhyungyang/cloud_profiler">https://github.com/shinhyungyang/cloud_profiler</a>, and MooBench is hosted at <a href="https://github.com/kieker-monitoring/moobench">https://github.com/kieker-monitoring/moobench</a>.</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.