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615 results for “Tuning”
Melodic Features for Meertens Tune Collections and ESSEN Folksong Collection
<p>The <a href="http://www.liederenbank.nl/mtc/">Meertens Tune Collections</a> (MTC) and the <a href="http://www.esac-data.org">Essen Folk Song Collections</a> include various data sets with melodic data. The melodies are provided in Humdrum **kern encoding and as MIDI sequences. In many cases, a representation of the melodies as sequences of feature values is needed rather than encoded scores. The present dataset provides such feature sequences. It is accompanied by a Python module that offers functionality to load and filter the sequences: <a href="https://pvankranenburg.github.io/MTCFeatures/">MTCFeatures</a>. The documentation of MTCFeatures contains a detailed description of the features.</p> <p>The following melody collections are included:</p> <ul> <li> <p>MTC-ANN-2.0.1 - A small set of 360 richly annotated melodies from Dutch sources.</p> </li> <li> <p>MTC-FS-INST-2.0 - A large set of c. 18 thousand melodies from Dutch sources.</p> </li> <li> <p>ESSEN Folksong Collection - A set of more than 8 thousand folk song melodies mainly from Germany.</p> </li> </ul> <p>For more information on the contents of the Meertens Tune Collections, please visit <a href="http://www.liederenbank.nl/mtc/">http://www.liederenbank.nl/mtc/</a>.</p> <p>For the Essen Folk Song Collection, the features were extracted from the **kern files in the zip-archive as provided by the Center for Computer Assisted Research in the Humanities at Stanford University (<a href="https://kern.humdrum.org/cgi-bin/browse?l=/essen">https://kern.humdrum.org/cgi-bin/browse?l=/essen</a>).</p>
Case studies related to the manuscript Tuning Trains Speed in Railway Scheduling
<p>This dataset is dedicated to the case studies related to the manuscript <strong>Tuning Trains Speed in Railway Scheduling</strong> by Étienne André, published in the proceedings of the 25th International Conference on Formal Engineering Methods (ICFEM 2024).</p> <p>See README.md for more information.</p>
Sharpening emitter localization in front of a tuned mirror - NPC dataset
<p>This is a depository for two single molecule localisation microscopy datasets of nuclear pore complex (NPC) structures for single particle averaging. The data was published in: </p> <p>Heil, H.S., Schreiber, B., Götz, R. <em>et al.</em> Sharpening emitter localisation in front of a tuned mirror. <em>Light Sci Appl</em> <strong>7, </strong>99 (2018). https://doi.org/10.1038/s41377-018-0104-z</p> <p>Both datasets have two different levels of localisation precision as one is a conventional STORM experiment and the second a mirror-enhanced STORM experiment. A detailed description of the sample preparation and imaging conditions can be found in the related publication. In short the NPC structures are placed on the surface of a glas coverslip or nano-mirror coated coverslip by manual isolation and spreading of nuclear envelopes from xenopus laevis oocytes, fixed and stained by indirect immunolabeling. The primary antibody targets GP210, the secondary F(ab')<sub>2</sub> fragment is conjugated with Alexa Fluor 647. </p> <p>In this depository I'm providing the raw images data, localisation data and super-resolved reconstruction for the two experiments, as well as the localisation data and super-resolved reconstruction of single NPC rings. </p> <p>I'm also providing a MatLab script that allows to select single NPC positions in the super-resolved image and export the localization data of the single NPC ROI: <strong>P01_ImageAlignment_PickElements.m</strong></p> <p>Information about the dataset is also available here: <strong>NPC Image Alignment Dataset_Info.pdf.</strong></p> <p>Image parameters: 102 nm pixel size, EM Gain 100, Photoelectrons per A/D count: 15.01</p> <p>Column structure of the localisation text files: </p> <p>Id,Frame, x [nm], y [nm], sigma [nm], intensity [photon], offset [photon], bkgstd [photon], chi2, Uncertainty [nm], detections</p> <p>Files: </p> <ul> <li><strong>NPCData_glass_EPI.tif</strong></li> </ul> <p>-> NPC on glass coverslip, low power EPI illumination, widefield image, 20 ms exposure</p> <ul> <li><strong>NPCData_glass_STORM.tif</strong></li> </ul> <p>-> NPC on glass coverslip, high power EPI illumination, 5 ms exposure, 20000 frames</p> <ul> <li><strong>NPCData_glass_STORM_loc.csv</strong></li> </ul> <p>-> ThunderSTORM Localisation data of NPCData_glass_STORM.tif, parameters specified NPCData_glass_STORM_loc-protocol.txt</p> <ul> <li><strong>NPCData_glass_STORM_20xNormalizedGaussian.tif</strong></li> </ul> <p>-> 20x Nomalized Gaussian reconstruction of localization data from NPCData_glass_STORM.tif (ThunderSTORM), pixelsize 5.1 nm</p> <ul> <li><strong>NPCData_glass_STORM_singleRings.zip</strong></li> </ul> <p>-> Localisation data and 20x 20x Nomalized Gaussian reconstruction of single NPC ROIs picked out of the NPCData_glass_STORM dataset, ROI size is 240*240 nm<sup>2</sup></p> <ul> <li><strong>NPCData_nanomirror_EPI.tif</strong></li> </ul> <p>-> NPC on nanomirror coated coverslip, low power EPI illumination, widefield image, 20 ms exposure</p> <ul> <li><strong>NPCData_nanomirror_STORM.tif</strong></li> </ul> <p>-> NPC on nanomirror coated coverslip, high power EPI illumination, 5 ms exposure, 20000 frames</p> <ul> <li><strong>NPCData_nanomirror_STORM_loc.csv</strong></li> </ul> <p>-> ThunderSTORM Localisation data of NPCData_nanomirror_STORM.tif, parameters specified NPCData_nanomirror_STORM_loc-protocol.txt</p> <ul> <li><strong>NPCData_nanomirror_STORM_20xNormalizedGaussian.tif</strong></li> </ul> <p>-> 20x Nomalized Gaussian reconstruction of localisation data from NPCData_nanomirror_STORM.tif (ThunderSTORM), pixelsize 5.1 nm</p> <ul> <li><strong>NPCData_nanomirror_STORM_singleRings.zip</strong></li> </ul> <p>-> Localisation data and 20x 20x Nomalized Gaussian reconstruction of single NPC ROIs picked out of the NPCData_nanomirror_STORM dataset, ROI size is 240*240 nm<sup>2</sup></p>
Virtual audiovisual scenes for hearing device fine-tuning
<p>The virtual audiovisual scenes in this upload were developed to be used during hearing device fine-tuning. By letting patients try out the settings of their hearing devices in a number of different daily-life situations, they can experience what it sounds like in different situations. The audiologist can then ask specific questions and make further adjustments to the settings. This may result in a better fit. The development of the virtual audiovisual scenes is documented in a paper presented at Forum Acusticum (Hendrikse, Dingemanse, Grimm, Hohmann, & Goedegebure, 2023). The development and evaluation of the fine-tuning procedure using these virtual audiovisual scenes will be the focus of future work, and the publication will be added to the related identifiers. This document provides a description of the virtual audiovisual scenes, software & hardware requirements, installation and usage instructions, and information about the licensing.</p> <p>For further information, contact Maartje Hendrikse (<a href="mailto:m.hendrikse@erasmusmc.nl">research.audiologie@erasmusmc.nl</a>).</p>
BCC-Cu nanoparticles: from a transient to a stable allotrope by tuning size and reaction conditions
<p>Open data for "BCC-Cu nanoparticles: from a transient to a stable allotrope by tuning size and reaction conditions"</p>
Fine-Tuning of Colloidal Polymer Crystals by Molecular Simulation
<p>Data archive corresponding to the manuscript "Fine-Tuning of Colloidal Polymer Crystals by Molecular Simulation" by M. Herranz et al., Phys. Rev. E 107, 064605 (2023); DOI: 10.1103/PhysRevE.107.064605</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All snapshots have been generated and successively analyzed by the Simu-D software.</p>
Boron-, carbon-, and silicon-bridged 1,12-dihydroxy-perylene bisimides with tuned structural and optical properties
<p>Additional data to report <a href="https://doi.org/10.1039/D3CC03704E">https://doi.org/10.1039/D3QO01389H</a></p> <p>Establishing suitable design strategies to tailor the functional properties of perylene bisimide (PBI) dyes are critical for their successful application in various devices. Herein, we report a new synthetic strategy to tune their structural and fluorescence properties by employing 1,12-bay-substitution pattern that has been seldomly investigated in the past. Central to the strategy is the use of 1,12-dihydroxy-PBI as a starting compound and the subsequent bridging of these hydroxy bay-functional groups with either a boron, carbon or silicon atom resulting in derivatives with rigidified perylene core. This is followed by a detailed exploration of synthetic possibilities to functionalize the unsubstituted 6,7-positions at the opposite bay area to achieve novel perylene dyes with excellent structural and optical properties. The fluorescence color could be tuned from green to dark-orange while retaining the almost unity fluorescence quantum yield in solution. Moreover, a strong fluorescence with quantum yields as high as 40% has been observed for powders, which clearly illustrates the potential of the presented structural design to obtain new solid-state emitters.</p>
Data for "Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis"
<p>The files named <code>df_scran.csv</code>, <code>df_seurat.csv</code>, <code>df_zinbwave.csv</code>, <code>df_dca.csv</code>, and <code>df_scvi.csv</code> contain one row per configuration that we ran successfully.</p> <p>The files named <code>DATASET.METHOD.h5ad</code> are encoded with anndata <code>v0.7.0</code> (be careful as they are not readable with previous versions) and contain 100 embeddings each. The embeddings are in the <code>obsm</code> attribute of the object. All the embeddings can be listed with the <code>obsm_keys()</code> method. The name of the embedding contains the parameters used to generate that embedding and are written like that <code>method=zinbwave.dims=10.epsilon=1000.features=300.gene_covariate=0</code>.</p> <p> </p> <p>For questions on this dataset please contact fraimundo@google.com</p>
Sun compass neurons are tuned to migratory orientation in monarch butterflies
Every autumn, monarch butterflies migrate from North America to their overwintering sites in Central Mexico. To maintain their southward direction, these butterflies rely on celestial cues as orientation references. The position of the sun combined with additional skylight cues are integrated in the central complex, a region in the butterfly's brain that acts as an internal compass. However, the central complex does not solely guide the butterflies on their migration but helps monarchs in their non-migratory form manoeuvre on foraging trips through their habitat. By comparing the activity of input neurons of the central complex between migratory and non-migratory butterflies, we investigated how a different lifestyle affects the coding of orientation information in the brain. During recording, we presented the animals with different simulated celestial cues and found that the encoding of the sun was narrower in migratory compared to non-migratory butterflies. This feature might reflect the need of the migratory monarchs to rely on a precise sun compass to keep their direction during their journey. Taken together, our study sheds light on the neural coding of celestial cues and provides insights into how a compass is adapted in migratory animals to successfully steer them to their destination.
Supplementary Data: "Dark matter, fine-tuning and mu(g-2) in the pMSSM" (arxiv 2104.03245)
<p>The data consists of the output files of all the runs that were done. The files are organized in iterations, according to the iteration they were produced in. The map name within one iteration is labeled by the "Barbieri-Giudice fine-tuning measure"_"Electroweak fine-tuning measure"_"time stamp". </p> <p>The files that are contained include:</p> <ul> <li> <p>spheno_in.dat, spheno_out.dat, feynhiggs14.dat, slhafile.dat, slhafileGUT.dat, susyhit.dat: in and outputs of the spectrum generators that were used. The slhafile.dat is obtained from softsusy with inputs defined at the SUSY scale, fed to FeynHiggs (produces the feynhiggs14.dat file) and then fed to SUSYHIT to produce susyhit.dat. The slhafileGUT.dat is the same spectrum but the input parameters are run to the GUT scale (defined by the scale where the unification of the coupling constants happens).The susyhit.dat file is used as input for prospino, micromegas, SUSY-AI, the fine-tuning calculation, superiso, gm2calc and ddcalc. The spheno files are used to cross-check our spectra, but not used in the results of the paper;</p> </li> <li> <p>DDcalcv2.out: output of the DDcalc program;</p> </li> <li> <p>Micromegas5_2_1.out, micro.out: micromegas output (version 5.2.1 and version 5.0.8);</p> </li> <li> <p>Prospino.dat: output containing the cross sections for chi1pm chi1pm, chi1pm, neu2, and slepton slepton production;</p> </li> <li> <p>SUSY_AI.dat: output from SUSY-AI;</p> </li> <li> <p>ft_contri.dat: electro-weak fine-tuning calculation, contains the number and the source of the dominant contribution;</p> </li> <li> <p>gm2_out.dat: output of GM2Calc;</p> </li> <li> <p>output_superiso, output_superiso_true: command-line output (output_superiso_true) and the slha-file (output_superiso).</p> </li> </ul> <p> </p> <p>The data used in our plots is stored in a CSV file (datagm2_right_omegah2_only.csv). This contains all the files that have the right omegah^2. </p> <p>This contains:</p> <ul> <li>dir_name: name of saving directory;</li> <li>bsmumu_spheno,btaunu_spheno,bsgamma_spheno,gm2_spheno: calculations done by Spheno of Br(b_s -> mu+ mu-), Br(b->tau nu), Br(b->s \gamma), g-2_mu;</li> <li>flag_prospino.dat_valid,flag_gm2_out.dat_valid,flag_ft_contri.out_valid,flag_DDcalcv2.out_valid,flag_susyhit.dat_valid,flag_spheno_out.dat_valid,flag_micromegas5_2_1.out_valid,flag_output_superiso_true_valid,flag_SUSY_AI.dat_valid: flags to check whether the files were present in the output;</li> <li>flag_LSP: flag to check whether the LSP is the neutralino;</li> <li>sigmachan_5,sigmaSIn_5,sigmaSIp_5,sigmaSDn_5,sigmaSDp_5,sigmav_5,omegachan_5,sigmacontri_5,omegacontri_5,omegah_mic5,omega_chan_label,sigma_chan_label: micromegas 5.2.1 output of the dominant annihilation channel for sigmav, sigmaSIn, sigmaSIp, sigmaSDn, sigmaSDp, sigmav, dominant annihilation channel for omegah^2, contribution of the dominant annihilation channel for sigmav, contribution of the dominant annihilation channel for omegah^2, omegah^2, label for omegah dominant annihilation channel (used for plotting), label for sigmav dominant annihilation channel (used for plotting);</li> <li>pval_DarkSide,pval_XENON,pval_PICO,pval_CRESST: p-values for DarkSide, XENON, PICO, CRESST;</li> <li>gm2_mic5,dmunu_mic5,bsgnlo_mic5,dtaunu_mic5,bsmumu_mic5,btaunu_mic5: output of micromegas 5.2.1 for g-2, D->munu, b->s gamma, D->tau nu, B_s -> mu mu, B->tau nu;</li> <li>zdec_neutralinos_5: output of micromegas 5.2.1 for branching fraction of the invisible decay of the Z boson to neutralinos;</li> <li>dtaunu_iso,bsmumu_iso,btaunu_iso,bsmumuuntag_iso,bsgamma_iso,dmunu_iso,gm2_iso: the low-energy observables as calculated by SuperIso;</li> <li>susyaistopexcl,susyai8pval,susyaistoppval,susyaiewexcl,susyai13excl,susyaiewpval,susyai8excl,susyai13pval: p-values for the SUSY-AI exclusions;</li> <li>n12,n13,n11,n23,n14,n24,n31,n32,n21,n34,n22,n33,n41,n43,u11,u12,v22,v21,u21,v12,u22,n44,n42,v11: neutralino (n) and chargino (u,v) components of the mixing matrices;</li> <li>alpha_higgs: higgs mixing angle;</li> <li>mgluino,md2,mu1,md1,mu2,mc2,mc1,msneutrinotau,mH,mstop2,msbottom2,msbottom1,mstop1,mslepton1,mHpm,mDM,mn2,mn3,mn4,mstau1,mstau2,,msneutrinoe,mA0,mslepton2: masses of the pMSSM spectrum;</li> <li>MeL,M2,AB,M1,M3,MeR,Mq3L,MuR,MuL,SUSYSCALE,AT,MdR,ATAU,MbR,Ml3R,MA,MtR,tanb,mu,Ml3L: input parameters of the pMSSM spectrum;</li> <li>mhiggsFeyn: mass of the SM-like higgs boson as computed by FeynHiggs;</li> <li>FTEW: value for electro-weak fine-tuning;</li> <li>PandaX (2017),DarkSide 50,omegah_ddcalc,XENON1T (2018),DarkSide 20k,DARWIN,LZ,PICO-500,PICO-60 (2017): p-value outputs of DDcalc;</li> <li>gm2calc,gm2calc_unc: GM2Calc calculation for g-2 and the uncertainty;</li> <li>xsec_lRlR,xsec_c1pn2,xsec_lLlL,xsec_c1c1,xsec_c1mn2,xsec_stau1stau1: cross sections for slepton_R sleptonR, slepton_L, slepton_L, chi1pm chi1mp, chi1pm neu2, stau_1 stau_1;</li> <li>excl: number to determine whether the channel is excluded and for what reason (> 0.5 = excluded, < 0.5 = not excluded, 0.4 = sensitive to PICO-500, 0.26 = sensitive to LZ, 0.13 = sensitive to Darwin, 0.0 = not sensitive to any proposed DMDD future experiment). </li> </ul> <p> </p> <p> </p>
Matrix multiplication software and results bundle for paper "Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library" for P^3MA submission
<p>This is the archive containing the matrix multiplication software and the results of the publication "<em>Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library</em>" submitted to the P^3MA workshop 2017.</p> <p><strong>The archive has the following content:</strong></p> <ul> <li>Source code for the (tiled) matrix multiplication in "src": <ul> <li>regular version in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-compatible-alpaka-0-1-0</li> <li>Commit: a63ba4810d6bfcca62c68dd57408af15028e78a3</li> </ul> </li> <li>forked version for XL in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-xl-workaround</li> <li>Commit: 1fee028eccb8cf7b677e8071233e08aa9f81846a</li> </ul> </li> </ul> </li> <li>The compiled binaries and the results of the tuning and scaling runs are in "runs" in sub folders for each type of run and architectures.</li> </ul>
Tuning-less Object Naming with a Foundation Model - Data recorded during testing
<p>We implement a real-time object naming system that enables learning a set of named entities never seen. Our approach employs an existing foundation model that we consider ready to see anything before starting. It turns seen images into relatively small feature vectors that we associate with index to a gradually built vocabulary without any training of fine-tuning of the model. Our contribution is using the association mechanism known from transformers as attention. It has features that support generalization from irrelevant information for distinguishing the entities and potentially enable associating with much more than indices to vocabulary. As a result, the system can work in a one-shot manner and correctly name objects named in different contents. We also outline implementation details of the system modules integrated by a blackboard architecture. Finally, we investigate the<br>system's quality, mainly how many objects it can handle in this way.</p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 4 relative to Figure 7 – ArhGEF11 CRISPR interference
<p><span>Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to <strong>Figure 7B </strong>and<strong> Figure 7 - Figure Supplement 6</strong> (see <strong>Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells</strong>).</span></p> <p><span>The source data comprises for each 48 - 55 hpf <em>Tg(kdrl:eGFP-JAM3b; kdrl:nls-mKate2)</em> zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 2 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.</span></p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 3 relative to Figure 7 – ArhGEF11 morpholino splicing interference
<p><span>Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to <strong>Figure 7A </strong>and<strong> Figure 7 - Figure Supplement 5</strong> (see <strong>Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells</strong>).</span></p> <p><span>The source data comprises for each 48 - 55 hpf <em>Tg(kdrl:eGFP-JAM2a; kdrl:nls-mKate2)</em> zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 3 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.</span></p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 1 relative to Figure 3
<p><span>Raw image files (TIFF format) relative <strong>to Figure 3</strong> (see <strong>Materials and Methods — Dt-runx1 phenotype analysis – cell count</strong>).</span></p> <p><span>The source data comprises for each 52 - 55 hpf zebrafish embryo 3 z-stack (segments 1 to 3) encompassing the whole length of the aorta, for control condition (<em>Tg(Kdrl:Gal4;UAS:RFP), </em>n = 3 individuals) and mutant condition (<em>Tg(kdrl:Gal4;UAS:RFP;4xNR:dt-runx1-eGFP), </em>n = 7 individuals). For control condition, one fluorescence channel was acquired, corresponding to the cytoplasmic RFP expressed in endothelial cells. For mutant condition, two fluorescence channels were acquired, corresponding first to the cytoplasmic RFP expressed in endothelial cells using the same reporter as for the control condition, and second the cleaved cytoplasmic GFP reporting the expression of our dt-runx1 mutant construct in endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm.</span></p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 2 relative to Figure 4
<p><span>Raw image files (TIFF format) and segmented 3D images (.ims, Imaris proprietary files) relative to <strong>Figure 4</strong> and <strong>Figure 4 Figure Supplement 3</strong> (see <strong>Materials and Methods — RNAscope image analysis – Pard3</strong>).</span></p> <p><span>The source data comprises for each 52 - 55 hpf zebrafish embryos 2 z-stack (segments 1 to 2) encompassing the whole length of the aorta, for control condition (<em>Tg(Kdrl:eGFP), </em>n = 7 individuals) and mutant condition (<em>Tg(kdrl:Gal4; 4xNR:dt-runx1-eGFP), </em>n = 12 individuals). For both control and mutant conditions, two fluorescence channels are displayed, corresponding to the cytoplasmic GFP expressed in endothelial cells (in green) and the RNAscope signal (OPAL-570, in magenta). Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.4 µm. The .ims files contain the 3D rendering of the z-stacks as well as the segmentations of Pard3ba mRNA RNAscope spots (in magenta), in the aorta (Spots 1 Selection) or outside (Spots 1), as well as the segmentation of endothelial cells (green) (Cells 1) and hemogenic endothelial cells (Cells 1 Cell export).</span></p>
scGeneAI bone marrow fine-tune dataset
<div>The input bone marrow fine-tune dataset used in the full-size examples in scGenAI is uploaded here</div> <div> <div> <p> </p> </div> </div>
Dataset of the publication: Reversible tuning of luminescence and magnetism in a structurally flexible erbium-anilato MOF
<p>Dataset of the publication: Reversible tuning of luminescence and magnetism in a structurally flexible erbium-anilato MOF</p> <p>DOI: 10.1039/d2sc00769j</p> <p>N. Monni, J. J. Baldoví, V. García-López, M. Oggianu, E. Cadoni, F. Quochi, M. Clemente-León, ML. Mercuri, E. Coronado</p> <p>Chem. Sci., 13, 25,7419-7428 (2022)</p>
Dataset of the publication: Spin-crossover tuning of the luminescence in 2D Hofmann-type compounds in bulk and exfoliated flakes
<p>Dataset of the publication: Spin-crossover tuning of the luminescence in 2D Hofmann-type compounds in bulk and exfoliated flakes</p> <p>DOI: 10.1039/d3tc03693f</p> <p>V. García-López, F. Marques-Moros, J. Troya, J. Canet-Ferrer, M. Clemente. León, E. Coronado</p> <p>J. Mater. Chem. C, 12, 161-169 (2024)</p>
Data for "Unconventional pairing in few-fermion systems tuned by external confinement"
<p>This upload includes data shown in Figures 1-7 and in Table I of the paper "Unconventional pairing in few-fermion systems tuned by external confinement", arXiv:2105.12519, doi:10.1103/PhysRevResearch.3.043105. Also included are gnuplot scripts to recreate the figures. </p>
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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.