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1,899 results for “Running”
CUT&RUN Profiling in CBF-Glis2 AMLs Identifies the Chromatin Remodeler Brg1 as a Key Dependency
GEO Series GSE239849. Homo sapiens. 33 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing.
Soleus muscle response to level running
GEO Series GSE8321. Rattus norvegicus; Homo sapiens. 12 samples. Type: Expression profiling by array.
Snip1 and PRC2 coordinate the intrinsic apoptosis, cell division, and neurogenesis in the developing brain [CUT&RUN]
GEO Series GSE212444. Mus musculus. 60 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
GATA3 Cut and Run in naïve human T cells
GEO Series GSE172346. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
CUT&RUN to map WT1 binding in M15 cell lines that are genome edited for the KTS isoforms.
GEO Series GSE189871. Mus musculus. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
CUT&RUN chromatin profiling of H3K4me2 and H3K79me2 in hPSC-derived cortical neurons
GEO Series GSE172543. Homo sapiens. 9 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Functional interplay between SWI/SNF complexes underlies BRD9 dependency in SMARCB1-mutant cancers [CUT & RUN]
GEO Series GSE210634. Homo sapiens. 10 samples. Type: Other.
Identification of chromatin states during zebrafish gastrulation using CUT&RUN and CUT&Tag
GEO Series GSE178343. Danio rerio. 9 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Hit-and-run silencing of endogenous DUX4 by targeting DNA hypomethylation on D4Z4 repeats in facioscapulohumeral muscular dystrophy [RNA-seq]
GEO Series GSE201178. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Computational Skills Students Need to Hit the Ground Running
<p>Data from survey of computational physics researchers about which skills students need to know to get started in computational research versus skills they can learn along the way. </p> <p>Possible responses for each skill are:<br> Ideally, students have prior experience with these;<br> Students learn these in natural course of research with me;<br> Students get focused training on these;<br> This skill is unnecessary for my work;</p> <p>Full list of skills:<br> Algorithmic thinking skills [Converting a problem to a step-by-step procedure amenable to coding]<br> Basic coding skills and knowledge [Programming in some language at "hello, world!" level]<br> Basic coding skills and knowledge [Calculation/visualization environment (e.g. Mathematica, Jupyter notebook, Matlab)]<br> Basic coding skills and knowledge [ Numerical arithmetic (e.g. machine precision, data types)]<br> Basic coding skills and knowledge [Flow control (loops, conditionals, etc.)]<br> Basic coding skills and knowledge [Scoping rules particular to a programming language]<br> Basic coding skills and knowledge [Selecting and manipulating data structures]<br> Software engineering [Whiteboarding]<br> Software engineering [Pseudocoding & diagramming ]<br> Software engineering [Software lifecycle best practices (design-code-implement-validate-utilize-archive)]<br> Software engineering [Keeping a TODO list]<br> Software engineering [Documenting code and keeping documentation current]<br> Software engineering [camelType and other strong typing]<br> Software engineering [Interfacing homegrown code with external libraries]<br> Software engineering [Object oriented design principles]<br> Software engineering [Fluency in an object oriented language]<br> Software engineering [Parsing/understanding legacy or inherited code]<br> Software engineering [Extending functionality of existing code]<br> Software engineering [Version control]<br> Software engineering [Creating modular, well-structured code easily passed on to other students]<br> Software engineering [Using an IDE]<br> Scientific project and data management [Keeping a lab notebook or electronic log]<br> Scientific project and data management [In situ documentation (README files, etc)]<br> Scientific project and data management [Recording input parameters when codes are run in production mode]<br> Scientific project and data management [Recording version of code used to produce any set of data]<br> Scientific project and data management [Naming conventions of output files]<br> Scientific project and data management [Backing up and knowing how to restore data/codes]<br> Scientific project and data management [Sharing code effectively with others]<br> Scientific project and data management [Proactive communication of results and issues]<br> Scientific project and data management [Collaborative mindset]<br> Fundamental scientific computing skills [Running an executable code one is given]<br> Fundamental scientific computing skills [Asking people for help with running or writing code]<br> Fundamental scientific computing skills [Reading code documentation]<br> Fundamental scientific computing skills [Googling to answer coding questions]<br> Fundamental scientific computing skills [Locating existing code and libraries on web-based servers]<br> Fundamental scientific computing skills [Terminal / unix shell programming]<br> Fundamental scientific computing skills [Unix shell scripting]<br> Fundamental scientific computing skills [Accessing and utilizing remote platforms (ssh, scp, etc.)]<br> Fundamental scientific computing skills [Submitting jobs to a queue]<br> Fundamental scientific computing skills [Using a terminal-based text editor]<br> Fundamental scientific computing skills [Compiling and making]<br> Fundamental scientific computing skills [Reading formatted data]<br> Fundamental scientific computing skills [Writing formatted data]<br> Fundamental scientific computing skills [Understanding compile-time and runtime errors]<br> Fundamental scientific computing skills [Visualizing line data (plots)]<br> Fundamental scientific computing skills [Visualizing image data (2D)]<br> Fundamental scientific computing skills [Visualizing volumetric data (3D)]<br> Fundamental scientific computing skills [Creating animations]<br> More advanced computational skills [High performance computing]<br> More advanced computational skills [Profiling code and optimizing execution]<br> More advanced computational skills [Specialized hardware (GPUs, etc)]<br> More advanced computational skills [Using a debugging environment]</p>
Fig. 9 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 9. Mass sets used for the Tyrannosaurus turning body analysis; shown for Models 1, 30, and 3.
Metabolomics Runs Raphael and Amira
<p>Microflow metabolomics runs of natural extracts, analyzed in Data dependent analysis on a Q Exactive HF.</p>
Samples runs_13062023_Maria-Paula Schröder
<p>Samples ran in random duplicates, mode= positive stepped CE (25, 35, 45) Res MS1= 30K, MS2=15k Fragment= Top 5 ions</p>
MCMC running scripts (polychord)
<p>Running scripts generated to produce MCMC. The jupyter notebook to generate them can be found at <a href="https://gitlab.euclid-sgs.uk/pf-ist-likelihood/likelihood-mcmc-generator">IST Euclid Gitlab (likelihood-mcmc-generator)</a></p> <p>Updated 19th April 2024: fixed bug in the definition of derived bgamma3 parameters for GCsp.</p> <p>Updated 2nd April 2024: updated polychord set up (higher number of nlives), added scripts for LCDM_gamma case, updated set-up for GCsp cases (purity parameter priors with 1% uncertainty, fixed counter terms, higher k_max) </p>
#running in #mountain #massacarrara
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1656262216.4073.jpg">https://4dcity.org/imgupload/1656262216.4073.jpg</a> <br>
v2.0.2 data for MCMC runs
<p>Input files, data vectors and covariance matrices for the MCMC runs with CLOE v2.0.2 <br><br>- <em>nzTabSPV3.dat:</em> n(z) for the 13 equipopulated redshift bins case<br>- <em>nuiTabSPV3.dat:</em> fiducial values of the nuisance parameters (#bin centre, source number density, galaxy bias, logaritmic slope of the luminosity function, shift in bin centre, variance of the zp - z relation)<br> </p> <p>- <em>Cls_zNLA_PosPos_C00.dat:</em> GCph data vector<br>- <em>Cls_zNLA_PosShear_C00.dat:</em> GGL data vector (in the order GL)<br>- <em>Cls_zNLA_ShearShear_C00.dat:</em> WL data vector<br> </p> <p>- <em>CovMat-3x2pt-Gauss-32Bins.npy:</em> Gauss only 3x2pt covariance matrix</p> <p>- <em>CovMat-3x2pt-GaussSSC-32Bins.npy:</em> Gauss + SSC 3x2pt covariance matrix</p> <p>- <em>CovMat-3x2pt-BNT-Gauss-32Bins.npy:</em> Gauss only 3x2pt covariance matrix, BNT-transformed</p> <p>- <em>CovMat-3x2pt-BNT-GaussSSC-32Bins.npy:</em> Gauss + SSC 3x2pt covariance matrix, BNT-transformed</p> <p>- <em>BNT-matrix.npy</em>: BNT matrix used for transforming the covariance matrices</p> <p><br>- <em>power_galaxies_EFT_benchmark_z1..fits:</em> GCsp data vector for bin centred on z = 1.0</p> <p>- <em>power_galaxies_EFT_benchmark_z1.2.fits:</em> GCsp data vector for bin centred on z = 1.2</p> <p>- <em>power_galaxies_EFT_benchmark_z1.4.fits:</em> GCsp data vector for bin centred on z = 1.4</p> <p>- <em>power_galaxies_EFT_benchmark_z1.65.fits:</em> GCsp data vector for bin centred on z = 1.65</p> <p> </p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1..fits:</em> GCsp Gauss only covariance for bin centred on z = 1.0</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.2.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.2</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.4.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.4</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.65.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.65</p>
Catalog of Lensed SNe Ia in 10 year of LSST run
<p>This data release is a part of arxiv 2404.15389. The catalog provides all lensed SNe Ia to be present in 10-year LSST run. </p>
Run input files for the simulation of a C. jejuni inner membrane model
<p>Run input files for the simulation of a C. jejuni inner membrane model (Chapter 5 of Kahlan Newman's Doctoral Thesis). Full trajectories can be shared on request. </p>
CUT&RUN in the chromatin landscape of healthy and injured cell types in the human kidney
<p><strong>*** To access this data, please go to <a href="https://www.kpmp.org/doi-collection/10-48698-hhe6-yv15">Kidney Precision Medicine Project (KPMP)</a> </strong></p> <p>CUT&RUN of human kidney nephrectomy tissue using 66,000-200,000 cells/reaction. H3K4me3 (1 biological replicate, with 3 technical replicates). H3K27ac (4 biological replicates, one with 3 technical replicates). H3K4me1 (1 biological replicate, with 3 technical replicates). H3K27me3 (6 biological replicates, one with 3 technical replicates, and two with isogenic replicates). Liquid nitrogen or OCT-embedded frozen kidney tissue from 6 donors was processed according to the protocol from Epicypher (https://www.epicypher.com/content/documents/protocols/cutana-cut&run-protocol.pdf) with modification. The full modified protocol is published on protocols.io and available here: https://www.protocols.io/view/cut-amp-run-chromatin-profiling-of-human-kidney-ti-bp2l615o1vqe/v1. Antibodies used in this study for CUT&RUN reactions: H3K27ac (Cell Signaling, 8173), H3K27me3 (Cell Signaling, 9733), H3K4me1 (Cell Signaling, 5326), H3K4me3 (Cell Signaling, 9751), and IgG (Cell Signaling, 2729). Up to 3ng of DNA from KPMP donor samples (21-020, 20-687, 20-688) was used to prepare sequencing libraries using the Ion Plus Fragment Library Kit (Thermo Fisher Scientific, 4471252), following manufacturer’s instructions, using 17 cycles to amplify the library. Up to 0.5ng of DNA from KTRC donor samples (3399, 3409, 3447) was used to prepare sequencing libraries using NEBNext Ultra II Library Kit (NEB, E7645), following manufacturer’s instructions, using 18 cycles to amplify the library. Sequencing: 100pM KPMP donor libraries were sequenced on the Ion Torrent Proton to a sequencing depth of 30 million reads (2-3X genome coverage). 0.8 pm KTRC donor libraries were sequenced on the NovaSeq 6000 (Illumina) targeting 100 million paired-end reads. Detailed bioinformatic analysis with command line examples for Illumina sequences can be found here: https://www.protocols.io/view/cut-amp-run-chromatin-profiling-of-human-kidney-ti-bp2l615o1vqe/v1?step=36. Briefly, trimmed fastq files were aligned to the hg38 reference genome using Bowtie2 (for Illumina sequenced) or TMAP (for Ion Torrent sequenced). Aligned reads were extracted and converted to a sorted bam file using SAMtools. Scaled bigWig files were generated using Deeptools, and peaks were called using Macs2.</p> <p>Published in: DOI: <a href="https://doi.org/10.1038/s41467-023-44467-6" target="_blank" rel="noopener"> 10.1038/s41467-023-44467-6 </a></p>
Genome-wide distribution of PRDM16, H3K4me1 and H3K4me2 in mouse ES cell line or EB day 6th [Cut&Run]
GEO Series GSE153773. Mus musculus. 24 samples. Type: Other.
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.