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30,948 results for “Profile”
Fig. 7. Sketch profile from the locality 10 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
Fig. 7. Sketch profile from the locality 10 miles southwest of the Camp Margetts (Granger, 1930).
dataset relared to article "Childhood‑onset dystonia‑causing KMT2B variants result in a distinctive genomic hypermethylation profile"
<p>Sanger sequences (.abi files) of all patients of Fondazione Besta cohort.</p>
ESPRESSO spectra of HE0515-4414 and absorption profile fits
<p>VLT/ESPRESSO spectra of HE0515-4414 and absorption profile fits associated with Murphy et al. (2021, A&A, submitted).</p> <p>This is the first release of this repository. It was prepared just prior to submission of the associated paper to Astronomy & Astrophysics in mid-September 2021.</p>
Microbiome profile of patients in a referral gastrointestinal diseases centre in the Sudan
<p>Data set of 16S rRNA Nanopore sequencing of DNA extracted from faecal samples of Gastrointestinal tract diseases from Human. </p>
Comprehensive Chromatome Profiling Reveals Hundreds of Metabolic Enzymes in the Nucleus
<p>Data behind figures for <span>Comprehensive Chromatome Profiling Reveals </span><span>Hundreds of</span><span> Metabolic Enzymes in the Nucleus </span>paper</p>
A computational workflow for cell line profiling by Imaging Mass Cytometry.
<p>Imaging Mass Cytometry Data as 32-bit single TIFF with computational analysis from the manuscript: <strong>A computational workflow for cell line profiling by Imaging Mass Cytometry.</strong></p> <p><strong><span lang="EN-US">Breast cancer cell lines SKBR3 MCF7 HCC1143 IMC data and CellProfiler pipelines.zip</span></strong></p> <p><strong><span lang="EN-US">Elongated cell lines HeLa SKOV3 BJ IMC data and CellProfiler pipelines.zip:</span></strong></p> <p><strong><span lang="EN-US">Small cell lines A431 HT29 BxPC3 IMC data and CellProfiler pipelines.zip</span></strong></p> <p><strong><span lang="EN-US">U937 PMA-differentiated cells IMC data and CellProfiler pipeline.zip</span></strong></p> <p><strong><span lang="EN-US">A431 Cisplatin Study IMC data and CellProfiler pipeline.zip</span></strong></p> <p><span lang="EN-US">Contains 1 folder per cell line or drug treatment of single TIFF 32-bit markers exported from MCD/txt files (including Xe131 channel) and their respective cpproj. pipeline file for IMC Cell Line Profiler workstream reproducible analysis</span></p> <p><strong><span lang="EN-US">IMC Cell Line Profiler high dimensional and correlation analysis R scripts.zip</span></strong></p> <p><span lang="EN-US">Contains three adaptable R scripts for high dimensional analysis, correlation analysis and combination of both scripts for Machine Learning classified datasets.</span></p> <p><strong><span lang="EN-US">Breast cancer cell lines nuclear state classification by CellProfiler Analyst MLs.zip</span></strong></p> <p><span lang="EN-US">Contains SQLite databases, properties files, training datasets, nuclear classes visual rendering, and classifier model files with outputs for two machine learning classifiers (Random Forest and Fast Gentle Boosting) per breast cancer cell line for CellProfiler Analyst workflow reproducibility.</span></p> <p><strong><span lang="EN-US">A431 Cisplatin Study IMC data nuclear state classification by CellProfiler Analyst MLs.zip</span></strong></p> <p><span lang="EN-US">Contains SQLite databases, properties files, training datasets, classifier model with outputs for Fast Gentle Boosting and Random Forest per treatment for CellProfiler Analyst workflow reproducibility.</span></p> <p><strong><span lang="EN-US">IMC Cell Line Profiler pseudo-color images with Ki-67 marker Cytoplasm marker and Cell-ID nuclei (Fig2 Fig3), visual nuclei and whole-cell segmentation contours rendered images (Fig4).</span></strong></p> <p><strong><span lang="EN-US">Non-compensated and compensated multiTIFF 32-bit cells lines with Cellprofiler masks SCE objects and FCS files and Datatables.zip</span></strong></p> <p>Contains publicly available compensation matrix (<a href="https://zenodo.org/records/7575859">https://zenodo.org/records/7575859</a>) , R compensation script (<strong>Compensation IMC data with CATALYST.R)</strong>, compensated and non-compensated multiTIFF stacks 32-bit per cell line experiment, exported CellProfiler 16-bit masks per cell line dataset, R single cell experiment script (<strong>Conversion IMC data to Single Cell Experiments Objects and FCS.R)</strong> with inputs and outputs (fcs files, sce files, panel files, metadata files),R<strong> </strong>conversion single cell experiment to datatable script<strong> (Conversion SCE to Datatable and analysis.R)</strong>.</p> <p><strong><span lang="EN-US">Step-by-step guide to assist users with the IMC Cell Line Profiler computational workflow.</span></strong></p>
Internal proper motion dispersion profiles for 37 Milky Way globular clusters extracted from Gaia EDR3
<p>Internal proper motion dispersion profiles, in the radial and tangential components, extracted from Gaia EDR3 data for 37 Milky Way globular clusters.</p> <p>Created for, and used for model fitting in, the submitted manuscript: "Multimass modelling of Milky Way globular clusters - I. Implications on their stellar initial mass function above 1 M$_{\odot}$". Details on the creation of this dataset are available within this article.</p>
CHROMATIN ACTIVATION PROFILING OF STEREOTYPED CHRONIC LYMPHOCYTIC LEUKEMIAS
<p>We used chromatin-immunoprecipitation followed by sequencing (ChIP-Seq) with an antibody for the H3K27ac (a bona fide histone mark for regulatory element activation) in sorted CLL cells from 46 CLL, including 21 cases from stereotyped subsets #1, #2, #4, and #8, and 15 normal B cell subpopulations. To understand their functional impact, we integrated our data with 16 RNA-seq data.</p> <p><strong>Files description:</strong><br> - <a href="https://zenodo.org/api/files/fb018e48-adb4-4750-a3e7-5188ac0a4123/H3K27ac_freq2_FDR_consensus_2022.txt?versionId=887e281e-526d-403d-a59d-4ac3e5773305">H3K27ac_freq2_FDR_consensus_2022.txt</a> count matrix from H3K27ac signals<br> - <a href="https://zenodo.org/api/files/fb018e48-adb4-4750-a3e7-5188ac0a4123/H3K27ac_FDR_consensus_norm.Combat_2022.txt?versionId=4a17abb5-0c95-42cc-b573-d11e0de2f484">H3K27ac_FDR_consensus_norm.Combat_2022.txt</a> count matrix from H3K27ac signals after batch effect correction<br> - <a href="https://zenodo.org/api/files/fb018e48-adb4-4750-a3e7-5188ac0a4123/RNAseq_norm.Combat_results.txt">RNAseq_norm.Combat_results.txt</a> count matrix from RNAseq after batch effect correction<br> - <a href="https://zenodo.org/api/files/fb018e48-adb4-4750-a3e7-5188ac0a4123/signature_RESAMPLING_ss8VSucll_2022.txt?versionId=806b1776-1571-4675-bc1c-3f0c29bf892e">signature_RESAMPLING_ss8VSucll_2022.txt</a> differentially acetylated regions between U-CLL and stereotyped subset #8 applying random sampling 100 times with a range of samples from 5 to n-1 in each category. The regions showing frequency >=20% were considered significant regions.</p>
Monthly average profiles of Distributed Temperature Sensing at Thwaites Eastern Ice Shelf
<p>Monthly average profiles of Distributed Temperature Sensing (DTS) for Thwaites Eastern Ice Shelf used in Dotto et al. (under review). The profiles are from April 2020, September 2020 and February 2021 and cover the ice-shelf-ocean interface depths. DTS utilizes a fibre-optic cable installed within the ice and through the ocean, and it uses Raman backscattered photons to estimate the in situ temperature of the fiber (Hausner et al 2011). The DTS interrogators (Silixa XT-DTS, Silixa LLC. Elstree, UK) used a spatial sampling of 25 cm. Each DTS profile uses a 1-minute integration time to reduce signal to noise. Independent temperature measurements from the MicroCATs are employed to calibrate the backscatter signal (Tyler et al., 2013; Hausner et al 2011).</p> <p> </p> <p>Hausner, M. B., Suárez, F., Glander, K. E., van de Giesen, N., Selker, J. S. & Tyler, S. W. Calibrating single-ended fiber-optic Raman spectra distributed temperature sensing data. Sensors (Basel), 11(11), 10,859–10,879 (2011).</p> <p>Tyler, S. W., Holland, D. M., Zagorodnov, V., Stern, A. A., Sladek, C., Kobs, S., White, S., Suárez, F. & Bryenton, J. Using distributed temperature sensors to monitor an Antarctic ice shelf and sub-ice-shelf cavity. Journal of Glaciology, 59(215), 583–591 (2013).</p> <p>Dotto, T. S., Heywood, K., Hall, R. et al. Ocean variability beneath Thwaites Eastern Ice Shelf driven by the Pine Island Bay Gyre strength, 11 October 2022, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-1466534/v1]</p>
Data of "A workflow to study the microbiota profile of piglet's umbilical cord blood: from sampling to data analysis".
<p>The present study proposes a workflow – from the sampling method to DNA extraction, bioinformatics and data analysis – that characterises the bacterial profile of umbilical cord blood samples, taking into account the contaminants found throughout the procedure of bacterial DNA extraction and amplification.</p> <p>Ps_umbilical.rds: A phyloseq object file of data containing the amplicon sequences variants (ASVs) of thirteen umbilical cord samples and two negative control samples, created by DADA2.</p> <p>R-script.doc: A word document containing the scripts used to characterize the taxonomical composition of the fifteen umbilical cord samples and two negative control samples before and after the application of Decontam R-package (Davis et al., 2018).</p> <p>metadata.docx: meta data for R-script.doc</p>
Epidemiological profile and diabetes control of Type 1 Diabetes Mellitus patients in Karbala Governorate, Iraq
<p><strong>Epidemiological profile and diabetes control of Type 1 Diabetes Mellitus patients in Karbala Governorate, Iraq</strong></p>
Expanding the Miscanthus market in the UK: Growers in profile and experience, benefits and drawbacks of the bioenergy crop
<p>To achieve net zero greenhouse gas emission by 2050 as set out by the 2019 amendment to the 2008 UK Climate Change Act, a major shift towards renewable energy is needed. This includes the development of new methods along with improving and upscaling existing technologies. One example of new methods in bioenergy is developing new <em>Miscanthus</em> cultivars for electricity generation via thermal power station furnaces. <em>Miscanthus</em> is still relatively new compared to other agriculture practices, so market assessments and improvements are needed to reduce the barriers of entry for prospective growers. This publication provides a profile of UK <em>Miscanthus</em> growers and their businesses, their experiences of benefits and drawbacks of the crop, and what they see as potential barriers to entry for prospective farmers. A survey of current <em>Miscanthus</em> growers in England and Wales was conducted and indicated that most farmers were content with the crop and that its environmental and economic benefits were noted. However, it was evident that with a geographically limited UK market, growers wanted to see a better distribution of biomass processing stations to reduce the ongoing costs of transport. With growing demand for renewables, including bio-energy sources, it was determined important to provide information and support for stable farming operations and to incentivise the adoption of <em>Miscanthus</em>. Such incentives include ongoing development of new cultivars, focussing on traits such as production potential and stressor resilience, and growers indicated preference for an annual planting grant. These developments are predicted to further improve the crop's profit margin, making it a more cost-effective crop for farmers. Sensitively managed <em>Miscanthus</em> also has the potential to contribute to carbon sequestration, soil health and aspects of farmland biodiversity. Incentivising such management in government land-based environmental schemes would offer additional income streams and help to promote environmental positive crop planting.</p>
Acoustic Current Profiler data from Multi-Year (2020-2022) Autonomous Underwater Glider Surveys in the Anegada Passage
<p>This dataset contains glider acoustic doppler profiler observations from four glider deployments in the Anegada Passage region from 2020-2022. The 2020-2021 data are from a Nortek AD2CP and the 2022 data are from a Teledyne RDI Pathfinder. The RDI Pathfinder .PD0 files can be read directly in the code that is used for this analysis. The Nortek AD2CP .ad2cp files are processed using Nortek's MIDAS software to generate NetCDFs. <br> </p>
Seawater velocities from ship acoustic Doppler current profiler during PolarFront 2022-05 cruise
<p>Seawater velocities captured by Ocean Surveyor ADCP, RD Instruments, were recorded (blanking distance 8 m, bottom track on) during our entire study from 18-26 May.</p>
WaterKit: thermodynamic profiling of protein hydration sites (3/3)
<p><strong>Overview</strong></p> <p>This archive (3/3) contains the results (kith, nram and streptavidin) for the following study:</p> <ul> <li> <p>WaterKit: thermodynamic profiling of protein hydration sites (https://doi.org/10.26434/chemrxiv-2022-grlsr)</p> </li> </ul> <p>Each zip archive contains the following files:</p> <ul> <li>charmm-gui: output from CHARMM-gui</li> <li>xx_amber: GIST output from MD simulations</li> <li>figures: convergence, predictions per waterkit model plots, etc...</li> <li>pdbbind_ligands: contains all the ligands used to define the ligand binding site</li> <li>waterkit: contains all the results from the WaterKit calculations <ul> <li>All the WaterKit parameters tested: <ul> <li>ad : acceptor/donor anchor points</li> <li>all : used all hydrogen atoms as anchor points</li> <li>vina : used O_DA vina probe for the spherical model (otherwise TIP3P model)</li> <li>300 : temperature</li> <li>3 : number of hydration layers placed during the calculations</li> <li>min_100_2.5 : 100 steps of minimization with 2.5 kcal/mol/A**2 constraints</li> </ul> </li> <li>Each directory contains: <ul> <li>output from the GIST analysis (gist-*.dx)</li> <li>cluster.pdb: All the hydration sites identified</li> <li>protein.nc : WaterKit "trajectory"</li> <li>protein_system.prmtop : Amber top file</li> <li>pymol_*.pse : PyMol session with all the results (WK, MD)</li> <li>results_*.png : Comparison plots between WaterKit predictions and MD simulations per hydration sites</li> </ul> </li> </ul> </li> <li>analysis_xxx.ipynb: Jupyter notebook containing all the analysis (needs utils.py file)</li> <li>protein_prepared.pdbqt: structure used for the WK calculations</li> <li>results_md.csv : contains all the raw results from the MD simulations</li> <li>results_wk.csv : contains all the raw results from the WK calculations</li> <li>results_md_md_comp_energy_*.csv : comparison results between MD simulations (energy)</li> <li>results_md_md_comp_placement_*.csv : comparison results between MD simulations (placement)</li> <li>results_md_wk_comp_energy_*.csv : comparison results between MD simulations and WK (energy)</li> <li>results_md_wk_comp_placement_*.csv : comparison results between MD simulations and WK (placement)</li> <li>results_wk_wk_comp_energy_*.csv : comparison results between WK calculations (energy)</li> <li>results_wk_wk_comp_placement_*.csv : comparison results between WK calculations (placement)</li> </ul> <p> </p>
WaterKit: thermodynamic profiling of protein hydration sites (2/3)
<p><strong>Overview</strong></p> <p>This archive (2/3) contains the results (fa10 and pgh2) for the following study:</p> <ul> <li> <p>WaterKit: thermodynamic profiling of protein hydration sites (https://doi.org/10.26434/chemrxiv-2022-grlsr)</p> </li> </ul> <p>Each zip archive contains the following files:</p> <ul> <li>charmm-gui: output from CHARMM-gui</li> <li>xx_amber: GIST output from MD simulations</li> <li>figures: convergence, predictions per waterkit model plots, etc...</li> <li>pdbbind_ligands: contains all the ligands used to define the ligand binding site</li> <li>waterkit: contains all the results from the WaterKit calculations <ul> <li>All the WaterKit parameters tested: <ul> <li>ad : acceptor/donor anchor points</li> <li>all : used all hydrogen atoms as anchor points</li> <li>vina : used O_DA vina probe for the spherical model (otherwise TIP3P model)</li> <li>300 : temperature</li> <li>3 : number of hydration layers placed during the calculations</li> <li>min_100_2.5 : 100 steps of minimization with 2.5 kcal/mol/A**2 constraints</li> </ul> </li> <li>Each directory contains: <ul> <li>output from the GIST analysis (gist-*.dx)</li> <li>cluster.pdb: All the hydration sites identified</li> <li>protein.nc : WaterKit "trajectory"</li> <li>protein_system.prmtop : Amber top file</li> <li>pymol_*.pse : PyMol session with all the results (WK, MD)</li> <li>results_*.png : Comparison plots between WaterKit predictions and MD simulations per hydration sites</li> </ul> </li> </ul> </li> <li>analysis_xxx.ipynb: Jupyter notebook containing all the analysis (needs utils.py file)</li> <li>protein_prepared.pdbqt: structure used for the WK calculations</li> <li>results_md.csv : contains all the raw results from the MD simulations</li> <li>results_wk.csv : contains all the raw results from the WK calculations</li> <li>results_md_md_comp_energy_*.csv : comparison results between MD simulations (energy)</li> <li>results_md_md_comp_placement_*.csv : comparison results between MD simulations (placement)</li> <li>results_md_wk_comp_energy_*.csv : comparison results between MD simulations and WK (energy)</li> <li>results_md_wk_comp_placement_*.csv : comparison results between MD simulations and WK (placement)</li> <li>results_wk_wk_comp_energy_*.csv : comparison results between WK calculations (energy)</li> <li>results_wk_wk_comp_placement_*.csv : comparison results between WK calculations (placement)</li> </ul> <p> </p>
WaterKit: thermodynamic profiling of protein hydration sites (1/3)
<p><strong>Overview</strong></p> <p>This archive (1/3) contains the results (fabp4, fkb1a, hivpr, hs90a, ital) for the following study:</p> <ul> <li> <p>WaterKit: thermodynamic profiling of protein hydration sites (https://doi.org/10.26434/chemrxiv-2022-grlsr)</p> </li> </ul> <p>Each zip archive contains the following files:</p> <ul> <li>charmm-gui: output from CHARMM-gui</li> <li>xx_amber: GIST output from MD simulations</li> <li>figures: convergence, predictions per waterkit model plots, etc...</li> <li>pdbbind_ligands: contains all the ligands used to define the ligand binding site</li> <li>waterkit: contains all the results from the WaterKit calculations <ul> <li>All the WaterKit parameters tested: <ul> <li>ad : acceptor/donor anchor points</li> <li>all : used all hydrogen atoms as anchor points</li> <li>vina : used O_DA vina probe for the spherical model (otherwise TIP3P model)</li> <li>300 : temperature</li> <li>3 : number of hydration layers placed during the calculations</li> <li>min_100_2.5 : 100 steps of minimization with 2.5 kcal/mol/A**2 constraints</li> </ul> </li> <li>Each directory contains: <ul> <li>output from the GIST analysis (gist-*.dx)</li> <li>cluster.pdb: All the hydration sites identified</li> <li>protein.nc : WaterKit "trajectory"</li> <li>protein_system.prmtop : Amber top file</li> <li>pymol_*.pse : PyMol session with all the results (WK, MD)</li> <li>results_*.png : Comparison plots between WaterKit predictions and MD simulations per hydration sites</li> </ul> </li> </ul> </li> <li>analysis_xxx.ipynb: Jupyter notebook containing all the analysis (needs utils.py file)</li> <li>protein_prepared.pdbqt: structure used for the WK calculations</li> <li>results_md.csv : contains all the raw results from the MD simulations</li> <li>results_wk.csv : contains all the raw results from the WK calculations</li> <li>results_md_md_comp_energy_*.csv : comparison results between MD simulations (energy)</li> <li>results_md_md_comp_placement_*.csv : comparison results between MD simulations (placement)</li> <li>results_md_wk_comp_energy_*.csv : comparison results between MD simulations and WK (energy)</li> <li>results_md_wk_comp_placement_*.csv : comparison results between MD simulations and WK (placement)</li> <li>results_wk_wk_comp_energy_*.csv : comparison results between WK calculations (energy)</li> <li>results_wk_wk_comp_placement_*.csv : comparison results between WK calculations (placement)</li> </ul> <p> </p>
Accurate profiling of full length Fv in highly homologous antibody libraries using UMI tagged short reads
<p>Data consists of raw Illumina MiSeq 2x250bp reads from the Fab mini library Mix9A, and Illumina MiSeq 2x300bp reads from the Fab mini library Mix14 at 8 attomole as described in the journal publication "Accurate profiling of full length Fv in highly homologous antibody libraries using UMI tagged short reads".</p>
Rapid changes in transcriptomic profile and mitochondrial function in human soleus muscle after three-day dry immersion
<p>Supplemental Tables for a study <strong>Rapid changes in transcriptomic profile and mitochondrial function in human soleus muscle after three-day dry immersion</strong></p>
Hourly wind profile measurements at Beijing weather station for the year 2020
<p>This dataset contains the wind profiler from 0.14 to 10.38 km above ground level at 1-h intervals for the Beijing Observatory station, which is obtained from the measurements of the Radar wind profiler for the year 2020. It is stored in Matlab format, and is organized by a matrix of 21*8784. the field "h" represents its corresponding height.</p>
ScienceDex guides
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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.