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3,576 results for “strain”
Strain diversity of human-residential Lactiplantibacillus plantarum across children cohorts of two ethnic groups geographically isolated
<p>This file includes seven genes, groEL-ileS-murC-murE-pheS-pyrG-recA, and has been used for multi-site sequence typing (MLST) studies.</p>
Scanning electron microscopy datasets -- Emiliania huxleyi strains from naturally high and low CO2 waters responding to high and low CO2 in the lab
<p>Study question: How do Emiliania huxleyi strains isolated from naturally high CO2 waters or low CO2 waters respond to exposure to high and low CO2 levels?</p> <p> </p> <p>Associated article:<br> Peter von Dassow, Francisco Díaz-Rosas, El Mahdi Bendif, Juan-Diego Gaitán-Espitia, Daniella Mella-Flores, Sebastian Rokitta, Uwe John, and Rodrigo Torres. 2018. Over-calcified forms of the coccolithophore <em>Emiliania huxleyi </em>in high-CO2 waters are not preadapted to ocean acidification. Biogeosciences. <a href="https://doi.org/10.5194/bg-15-1-2018">https://doi.org/10.5194/bg-15-1-2018</a></p> <p> </p> <p>Technical notes:</p> <p>Three electron microscopes were used:</p> <ol> <li>TM3000 (Hitachi High-Technologies, Tokyo, Japan) in the Unidad de Microscopía Avanzada of the Facultad de Ciencias Biológicas, Pontificia Univesidad Católica de Chile. The Hitachi microscope is not of high quality, and, when available, other electron microscopes were used.</li> <li>Quanta 250 (FEI, Hillsboro, Oregon, USA) in the Facultad de Geología, Universidad de Chile</li> <li>Quanta FEG 250 (FEI, Hillsboro, Oregon, USA) in the laboratory CIEN-UC, Facultad de Física, Pontificia Universidad Católica de Chile.</li> </ol> <p> </p> <p>Data set 1: Data-sharing-SEM_Calfuco-CO2 experiment.zip</p> <p>Scanning electron microscopy images of E. huxleyi strains after bubbling with 1200 µatm CO2 and 400 µatm CO2 air/CO2 mixes.</p> <p> </p> <p>Data set 2: Field-SEM-2011-2013.zip</p> <p>Scanning electron microscope images of filters of plankton samples taken during field campaigns. See article for methodology. For the samples from ElQuisco_2012 and JuanFernandez_2011, note that the last two digits in the sample name refer to the depth from which the sample was obtained (ej., “FQ.01.01.05D” is from 5 m and “FQ.01.01.15D” is from 15 m). Tables are provided to associate counts and taxonomic identifications to environmental variables from the samples for which data was used in statistical analysis. Note also that images do not correspond to all counts reported, as sometimes counts were made without capturing images due to time pressure for microscope use. </p>
Development of a specific PCR to detect thiocyanate-oxidizing Thioalkalivibrio strains in their environment
<p>Data repository for my thesis named 'Development of a specific PCR to detect thiocyanate-oxidizing <em>Thioalkalivibrio</em> strains in their environment' at the University of Amsterdam. The folder consists of 2 subfolders, namely the alignment, in both AA and NA forms, of the TcDH-sequences of the 10 Thioalkalivibrio strains, shown in <em>Figure 21</em> (1) and the alignment of the TcDH-sequences and names of the organisms used while making the phylogenetic tree of <em>Figure 2</em> (2).</p>
Additional data for "In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load"
<p>The following document is an extension of the <em>In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiments and bundle of carbon fiber ones. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
static stress-strain behaviour of hardened and tempered steel 52100 (100Cr6)
<p>this file contains the recorded data (time, nominal stress, strain, actuator position) of a tensile test as long as the used strain gauge worked</p>
Dataset for "Improved genome of Agrobacterium radiobacter type strain provides new taxonomic insight into Agrobacterium genomospecies 4"
<p>Please read the README.txt file in each folder prior to doing any analysis.</p> <p>1. Agro_CDHIT.tar.gz contains files and a script to generate the input files that can be submitted to http://bioinfogp.cnb.csic.es/tools/venny/ for Venn Diagram generation.</p> <p>2. Agro_Roary.tar.gz contains files (gff) and a script to perform identification and alignment of core and accessory genes using the Roary software.</p> <p> </p>
Dataset for "Co-Localization of Microstructural Damage and Excessive Mechanical Strain at Aortic Branches in Angiotensin-II Infused Mice"
<p>This dataset contains geometries and simulation files for the manuscript "Co-Localization of Microstructural Damage and Excessive Mechanical Strain at Aortic Branches in Angiotensin-II Infused Mice", to be published in the journal "Biomechanics and Modeling in Mechanobiology".</p> <p>For each animal described in the study, the following data are uploaded:</p> <p>[mouse name]_Strain.vtp contains the Eulerian strain estimation for each abdominal aorta mapped back on the ex vivo undeformed configuration.</p> <p>[mouse name]_Scan.vtp is the abdominal aorta in the ex vivo undeformed configuration, as scanned using PCXTM imaging.</p> <p>[mouse name]_Exitron.vtp is the distribution of contrast agent infiltration along the aorta (can be superimposed to the corresponding ‘scan.vtp’ file), which serves a surrogate for vascular damage.</p> <p>The aortic partitioning described in the paper’s method section was performed on all of the above .vtp files for every animal. .vtp files can be visualized in the open-source Paraview or similar software, and used for centerline calculations using the open-source vmtk software.</p>
Dataset for strain-localisation in helium implanted tungsten: CPFE, Laue diffraction, AFM
<p>The folder includes dataset for helium-implanted tungsten and pure tungsten. The data includes measurement of the following for both materials as observed experimentally, and predicted using crystal-plasticity simulations:</p> <p>1. Surface morphology of nano-indents,</p> <p>2. Lattice-distortions around and under nano-indents</p> <p>3. Computed geometrically necessary dislocations field around and under nano-indents in both materials</p> <p>4. Load-displacement curves</p> <p>Guidelines for using dataset:</p> <p>1. Extracting the folder will generate five individual folders</p> <p>2. In the AFM plots folder, use the matlab code and dataset in the the folder to generate the surface morphology of nano-indents.</p> <p>3. In the "CPFE implanted sample data" folder --> use "CPFE implanted matlab code" --> load "variables2" --> run the code (raw data is also provided in the folder)</p> <p>4. In the "CPFE unimplanted sample data" folder --> use "CPFE unimp matlab code" --> load "variables3" --> run the code (raw data is also provided in the folder)</p> <p>5. In the "Laue unimplanted data" folder --> use matlab code with relevant raw data provided in folder</p> <p>6. In the "Laue implanted data" folder --> use matlab code with relevant raw data provided in folder</p> <p>7. Excel sheet provides nano-indentation and CPFE predictions of load-displacement curves</p> <p>8. The folder "HR-EBSD code and data" includes related raw data and codes.</p>
Maternal antibodies provide strain-specific protection against infection with the Lyme disease pathogen in bank voles
<p>Raw data for manuscript titled, "Maternal antibodies provide strain-specific protection against infection with the Lyme disease pathogen in bank voles". This manuscript was submitted to Applied and Environmental Microbiology and was assigned the manuscript ID number AEM01887-19R1.</p>
Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets
<p>Transmission electron microscopy data and processing scripts related to the journal article "Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets": <a href="https://doi.org/10.1002/smll.201904738">https://doi.org/10.1002/smll.201904738</a></p> <p><strong>Data files</strong></p> <p>The data is contained within the 00N_....hdf5 files, which can be accessed using an HDF5 reader. Note that these datasets are very large, and trying to load one of them directly will most likely lead to your computer crashing.</p> <p>Loading the data in python with h5py:</p> <pre><code class="language-python">import h5py f = h5py.File('003_stripe1.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>Exploring the datasets lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: STEM-DPC processing</li> <li>l00N_...: lattice size processing</li> <li>s00N_...: rotation "simulations" to find the relation between the scan and detector rotation</li> </ul> <p>Several of the scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p>This will most likely take several hours to complete.</p>
Comparative whole genome phylogeny of animal, environmental and human strains confirms the genogroups organization and the diversity of Stenotrophomonas maltophilia
<p>Reannotation of Smc genomes from Refseq (Prokka v1.13) and gene presence and absence spreadsheet from Roary.</p>
Antimicrobial molecules derived from kefir, effective against P aeruginosa and MRSA strains
<p>These files contain the NGS data of the partial 16.S rRNA gene (V3-V4 region) amplicon from Kefir grains used within the manuscript "Antimicrobial molecules derived from kefir, effective against P aeruginosa and MRSA strains"</p>
Velocity and strain rate fields of the Southern Tibetan Plateau
<p>This data set contains velocity and strain rate fields over the southern Tibetan Plateau, which are derived from Sentinel-1A and -1B synthetic aperture radar satellite data (SAR) and NETCDF (.grd) formats.</p> <p>This repository contains:</p> <p>(1) asc.grd : the InSAR LOS velocity field in the ascending tracks in a resolution of ~1000 m.</p> <p>(2) desc.grd : the InSAR LOS velocity field in the descending tracks in a resolution of ~1000 m.</p> <p>(3) dilatation_strain_rate.grd : the dilatational strain rate calculated from the interpolated GNSS Vn and InSAR-derived Ve.</p> <p>(4) second_invariant_horizontal_strain_rate.grd: the second invariant horizontal strain rate calculated from the interpolated GNSS Vn and InSAR-derived Ve.</p>
Raw Data and Codes for the Article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion"
<p>Dataset for the article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion", containing:</p> <ul> <li>The data and the codes used to generate the figures</li> </ul>
Aladdin: High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D Cine MRI
<p>The uploaded files include high-resolution 3D images of the left atrium from 18 individuals—10 healthy volunteers and 8 patients with various cardiovascular diseases—along with their corresponding left atrium segmentation maps. Additionally, a deformation atlas based on the 10 healthy cases is also provided.</p> <p>For more information, visit: <a href="https://github.com/cgalaz01/aladdin_cmr_la" target="_new" rel="noopener">https://github.com/cgalaz01/aladdin_cmr_la</a></p>
Dataset from the paper entitled "Linking Lattice Strain and Fractal Dimensions to Non-Monotonic Volume Changes in Irradiated Nuclear Graphite"
<p>Dataset from the paper entitled "Linking Lattice Strain and Fractal Dimensions to Non-Monotonic Volume Changes in Irradiated Nuclear Graphite". The dataset includes the small angle X-ray scattering measurements, the wide-angle X-ray scattering measurements, and the fitting parameters. Please see the included Readme file for more detail on the organization. </p>
Data in support of : Volumetric Deformation Feedback on Fault Stability and Strain Localization Precursor to Stick-Slip Behavior in Laboratory Earthquakes.
<p>This folder contains 3 .txt files:</p> <p> </p> <p>LP1 — low pressure data set.</p> <p>HP1 — High confining pressure experiement.</p> <p>HP2 — High cinfining pressure repeat experiment.</p> <p> </p> <p>The .txt files contain the following columns: Time (s), Pc (bar), VerticalLoad (kN), Horizontalload (kN), DisplacementOE (mm), StrainSG_xx, StrainSG_yy, StrainSG_xy.</p> <p>Strain gauge data are pre-filtered. All other data are raw.</p>
Strain partitioning, interseismic coupling, and shallow creep along the Ganzi-Yushu fault from Sentinel-1 InSAR data
<p>The dataset includes the InSAR velocity data and fault coupling model in the article "Strain partitioning, interseismic coupling, and shallow creep along the Ganzi-Yushu fault from Sentinel-1 InSAR data" (<a href="https://doi.org/10.1029/2024GL111469">https://doi.org/10.1029/2024GL111469</a>). The "insardata.zip" file includes original data of 5 tracks export from MintPy software, and the detailed format of the data can be found in the instruction provided by the MintPy software (<a href="https://github.com/insarlab/MintPy">GitHub - insarlab/MintPy: Miami InSAR time-series software in Python</a>). The "couplingmodel.gmt" is the fault coupling distribution along the Ganzi-Yushu fault, formatted for utilization in GMT software (<a href="https://github.com/GenericMappingTools/gmt">GitHub - GenericMappingTools/gmt: The Generic Mapping Tools</a>).</p>
Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS
<p>InSAR Line-of-Sight (LOS) velocities and their associated uncertainties in the southeastern Tibetan Plateau, along with the strain rate fields.</p> <p><br>Citations:</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS. Geophysical Research Letters.</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13731812</p>
Sequential Bayesian Inference of Finite-strain Visco-elastic Visco-plastic model parameters of 22-month aged PA12 bulk material printed along different directions
<p>These are the data related to aged PA12 (22 months) following the methodology described in the following publication in which non-aged PA12 has been tested:</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br>journal = "International Journal of Solids and Structures",<br>year = "2023",<br>volume = "283",<br>pages = "112470",<br>doi = "10.1016/j.ijsolstr.2023.112470",<br>author = "Wu, Ling and Anglade, Cyrielle and Cobian, Lucia and Monclus, Miguel and Segurado, Javier and Karayagiz, Fatma and Freitas, Ubiratan and Noels Ludovic"</p> <p>Contrarily to the non-aged material, since high-strain-rate tests are not available, only 5 Maxwell's branches are considered herein.</p> <h1>Description</h1> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a></p> <p>The sequential BI is described in [WU23] The experimental protocol is reported in [COB22,COB22b] but is herein applied on aged PA12</p> <p>To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a></p> <p>If you use these data or model, we would be grateful if you could cite the related papers:</p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: <a href="https://doi.org/10.1016/j.ijsolstr.2023.112470" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.ijsolstr.2023.112470</a></li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: <a href="https://doi.org/10.1016/j.polymertesting.2022.107556" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.polymertesting.2022.107556</a> (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” <a href="http://dx.doi.org/10.5281/zenodo.6136935" target="_blank" rel="nofollow noreferrer noopener">http://dx.doi.org/10.5281/zenodo.6136935</a> (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; <a href="https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008" target="_blank" rel="nofollow noreferrer noopener">https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008</a>, Open access: <a href="https://orbi.uliege.be/handle/2268/197898" target="_blank" rel="nofollow noreferrer noopener">https://orbi.uliege.be/handle/2268/197898</a></li> </ul> <h1>Directories</h1> <p>All the codes and experimental results are in three directories:</p> <ol> <li>Experiment_PA12_AGED: experimental data of aged material, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters<br>2.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range<br>2.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.<br>2.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>2.1.3. Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI<br>2.2. VE_V and VE_H: BI for viscoelastic properties of "V" specimen (VE_V) and "H" specimen (VE_H)<br>2.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat<br>2.2.2. WarmStart = True is used to restart an inference<br>2.2.3. MCMC_VE_....dat in the VE_V and VE_H directories are the BI results<br>2.3. CheckBayRes: to visualize predictions of a BI sample and experimental curves 2.3.1. MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line 7)<br>2.3.2. ResKGEmu.py plots the evolution of elastic properties with time<br>2.3.3. uses as input VE_V/MCMC_VE_....dat or VE_H/MCMC_VE_....dat<br>2.3.4. uses local ViscoElasticTest.py, line.geo, line. msh as interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code<br>2.3.5. uses local functions plotExp.py<br>2.4. ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V and VE_H to call the VEVP model</li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters<br>3.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges<br>3.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.<br>3.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>3.1.3. Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI<br>3.2. VP_V and VP_H: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V) and "H" specimen (VP_H)<br>3.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat<br>3.2.2. WarmStart = True is used to restart an inference 3.2.3. It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3. CheckBayRes: visualize predictions of a BI sample and experimental curves<br>3.3.1. MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples (inclusing MAP, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.2. plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.3. Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?_1Step.dat (? being H or V) 3.3.4. ResKGEmu.py plots the evolution of elastic properties with time<br>3.4. VEVPTest.py: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> <h1>Figures (reference to the number in [WU23] but for aged PA12)</h1> <ul> <li>Fig. 5 (Selected observations): From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,14,18,22,23,24,25]</li> <li>Fig. 8 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 15B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 16B: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 19B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> </ul> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015.</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.