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Mueller matrix imaging combining optical parameters of mice non-melanoma skin cancer tissue
<p>The dataset consists of the Mueller matrix elements and optical parameters acquired from the backscattered light using a CCD camera and Mueller matrix imaging technique.</p><p>This dataset contains 90 samples including 20 feature vectors for SCC, 33 feature vectors for normal and 37 feature vectors for papilloma.</p>
ColoPola: A dataset of colorectal cancer polarimetric images (Mueller matrix elements) for colorectal cancer detection
<p><strong>ColoPola</strong> dataset is <strong>Colo</strong>rectal cancer <strong>Pola</strong>rimetric images dataset</p> <p>The dataset consists of 572 slices (specimens) with 20,592 images, 284 slices of which were designated as cancer samples and 288 as normal samples.</p> <p>Each sample has 36 polarimetric images (i.e., HH, HV, HP, HM, HR, HL, VH, VV, VP, VM, VR, VL, PH, PV, PP, PM, PR, PL, MH, MV, MP, MM, MR, ML, RH, RV, RP, RM, RR, RL, LH, LV, LP, LM, LR, and LL).</p> <p>Each folder in the <strong>ColoPola</strong> dataset consists of 36 polarimetric images. Each image is 1280x1024 pixels in size and was created in the TIF file format (HH.tif, HV.tif, ..., LL.tif). </p>
Non-perturbative phase structure of the bosonic BMN matrix model --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice calculations investigating the phase structure of the bosonic part of the Berenstein--Maldacena--Nastase matrix model. See the README for further information.</p>
Dataset: Effective T-matrix of a cylinder filled with a random 2D particulate
<p>This data is the one used in the paper "Effective T-matrix of a cylinder filled with a random 2D particulate", currently submitted to the Proceedings A of the Royal Society. A preprint version of this paper can be found at: https://arxiv.org/abs/2308.13338</p> <p>This dataset contains the numerically computed effective T-matrix of a cylinder filled with a random 2D particulate. Since the T-matrix is diagonal, only the diagonal elements T_n are computed. Values of T_n are provided for various set of parameters (frequency, particle type and volume fraction). Furthermore, for each set of parameters, T_n is computed with three different methods:</p> <p>1) The Monte Carlo method (MC), which requires computing the scattered field for several configurations of particles.</p> <p>2) The Effective Waves Method (EWM), based on results on random particulate materials. It provides a formula of the effective T-matrix with an effective wavenumber.</p> <p>3) A simplified version of the EWM when only monopole scattering is accounted for (EWM-MA). </p> <p>This dataset includes the following files: metadata_MC.csv and per each parameter one csv file with data records. The notations used in the headers of the files MCx.csv are described in the file header_notations.pdf.</p>
Helsinki Region Travel Time Matrix 2018-2023
<p><strong>Introduction</strong></p> <p>This travel time matrix records <strong>travel times</strong> and travel distances for routes between all centroids (N = 13132) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by <strong>walking</strong>, <strong>cycling</strong>, <strong>public transportation</strong>, and <strong>private car</strong>. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below.</p> <p>The grid follows the geometric properties and enumeration of the versatile <a href="https://ckan.ymparisto.fi/dataset/ykr-ruutuaineisto"><em>Yhdyskuntarakenteen seurantajärjestelmä</em> (YKR) grid</a> used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.</p> <p> </p> <p><strong>Data formats</strong></p> <p>The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.</p> <ul> <li><em>Helsinki_Travel_Time_Matrix_2023.csv.zst</em>: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the <a href="https://facebook.github.io/zstd/">Zstandard algorithm</a>, that many data science libraries, for instance, pandas, <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">support transparently, directly, and automatically</a>.</li> <li><em>Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip</em>: an <a href="https://www.ogc.org/standard/geopackage/">OGC GeoPackage standard</a> file containing all data columns <em>and</em> the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., <a href="https://gdal.org/drivers/vector/gpkg.html">GDAL/OGR</a>, <a href="https://docs.qgis.org/">QGIS</a>, or <a href="https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html">geopandas</a>, support natively.</li> <li><em>Helsinki_Travel_Matrix_2023_travel_times.csv.zip</em>: a set of 13132 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern <em>Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv</em> where <em>5787545</em> is replaced by the <em>to_id</em> by which the rows in the file are grouped. Use the <em>from_id</em> column to join with the geometries from one of the files below.</li> </ul> <p>Geometry, only:</p> <ul> <li><em>Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip</em>: an <a href="https://www.ogc.org/standard/geopackage/">OGC GeoPackage standard</a> file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the <em>from_id</em> and <em>to_id</em> columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., <a href="https://gdal.org/drivers/vector/gpkg.html">GDAL/OGR</a>, <a href="https://docs.qgis.org/">QGIS</a>, or <a href="https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html">geopandas</a>, support natively.</li> <li><em>Helsinki_Travel_Time_Matrix_2023_grid.shp.zip</em>: an <a href="https://gdal.org/drivers/vector/shapefile.html">ESRI Shapefile</a> archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the <em>from_id</em> and <em>to_id</em> columns of the data files.</li> </ul> <p> </p> <p><strong>Table structure</strong></p> <p><em>from_id</em>: ID number of the origin grid cell <br><em>to_id</em>: ID number of the destination grid cell <br><em>walk_avg</em>: Travel time in minutes from origin to destination by walking at an average speed <br><em>walk_slo</em>: Travel time in minutes from origin to destination by walking slowly <br><em>bike_avg</em>: Travel time in minutes from origin to destination by cycling at an average speed<br><em>bike_fst</em>: Travel time in minutes from origin to destination by cycling fast<br><em>bike_slo</em>: Travel time in minutes from origin to destination by cycling slowly<br><em>pt_r_avg</em>: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed <br><em>pt_r_slo</em>: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed <br><em>pt_m_avg</em>: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed <br><em>pt_m_slo</em>: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed <br><em>pt_n_avg</em>: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed <br><em>pt_n_slo</em>: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed <br><em>car_r</em>: Travel time in minutes from origin to destination by private car in rush hour traffic <br><em>car_m</em>: Travel time in minutes from origin to destination by private car in midday traffic <br><em>car_n</em>: Travel time in minutes from origin to destination by private car in nighttime traffic <br><em>walk_d</em>: Distance from origin to destination, in meters, on foot</p> <p> </p> <p><strong>Data for 2013, 2015, and 2018</strong></p> <p>At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations' results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.</p> <p>For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to <a href="https://doi.org/10.5281/zenodo.3247563">DOI:10.5281/zenodo.3247563</a>.</p> <p> </p> <p><strong>Methodology</strong></p> <p>Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. 'Rush hour' refers to an 1-hour window between 8 and 9 am, 'midday' to 12 noon to 1 pm, and 'nighttime' to 2-3 am.</p> <p>All routes have been calculated using <a href="https://github.com/r5py/r5py">r5py</a>, a Python library making use of the <a href="https://github.com/conveyal/r5">R5</a> engine by <a href="https://conveyal.com">Conveyal</a>, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:</p> <p><em>Walking</em></p> <p>Walking speeds, and in turn walking times, are based on the findings of <a href="https://doi.org/10.1016/j.jtrangeo.2022.103521">Willberg et al., 2023,</a> in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for <em>walk_avg</em> (as well as the respective <em>pt_*_walk_avg</em>), and the slowest quintile of all measured walker across all conditions for <em>walk_slo</em> (and the respective <em>pt_*_walk_slo</em>).</p> <p><em>Cycling</em></p> <p>Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from <a href="https://metro.strava.com/">Strava data</a>, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the <a href="https://www.hsl.fi/kaupunkipyorat">Helsinki City Bike bike-share system</a>, and the mean between the two.</p> <p>Further, in line with the values observed by <a href="https://blogs.helsinki.fi/saavutettavuus/files/2013/01/Py%C3%B6r%C3%A4ily_osana_joukkoliikennett%C3%A4_kaupunkipy%C3%B6rien_vaikutus_alueiden_saavutettavuuteen_p%C3%A4%C3%A4kaupunkiseudulla.pdf">Jäppinen (2012)</a>, we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination.</p> <p><em>Public Transport</em></p> <p>We used public transport schedules in General Transit Feed Specification (GTFS) format published by the <a href="https://www.hsl.fi/hsl/avoin-data">Helsinki Regional Transport Authority</a>, and adjusted the walking speeds (for connections between vehicles, as well as for access and egress to and from public transport stops) using the same methods as described above for <em>walking</em>.</p> <p><em>Private motorcar</em></p> <p>To represent road speeds actually driven in the Helsinki metropolitan region, we used floating car data of a representative sample of the roads in the region to derive the differences between the speed limit and the driven speed on different road classes, and by speed limit, see <a href="https://hdl.handle.net/10138/358181">Perola (2023)</a> for a detailed description of the methodology. Because these per-segment speeds factor in potential waiting times at road crossings, we eliminated turn penalties from R5.</p> <p> </p> <p>Our modifications were carried out in two ways: some changes can be controlled by preparing input data sets in a certain way, or by setting model parameters outside of R5 or r5py. Other modifications required more profound changes to the source code of the R5 engine.</p> <p>You can find a fully patched fork of the R5 engine in the Digital Geography Lab's GitHub repositories at <a href="https://github.com/DigitalGeographyLab/r5">github.com/DigitalGeographyLab/r5</a>. The code that handles input data mangling and model parameter estimations is kept together with the logic to read input parameters and to collate output data, in the repository at <a href="https://github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices/">github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices</a>.</p>
Dateset: Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix
<p>This dataset provides complementary material to the scientific research presented in the paper "<strong>Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix</strong>"<strong>,</strong> accepted for publication in PLOS ONE under number PONE-D-18-07662R1.</p> <p>A draft version of the paper is available at <a href="https://arxiv.org/abs/1803.05336">https://arxiv.org/abs/1803.05336</a></p> <p>This paper presents two studies targeting two groups of countries:</p> <ul> <li>[40] the set 40 worldwide countries set ;</li> <li>[EU] the set of 27 European Union countries as of February 2013.</li> </ul> <p>Data is derived using Reduced Google matrix analysis on the Wikipedia English, Arabic, Russian, German and French editions collected in February 2013. Networks representing each edition are available here:</p> <p><a href="http://www.quantware.ups-tlse.fr/QWLIB/topwikipeople/index.html">http://www.quantware.ups-tlse.fr/QWLIB/topwikipeople/index.html</a></p> <p>The following files are given:</p> <ul> <li>[GRedured_40.zip] GReduced matrix and its decomposition for [40] countries set</li> <li>[GRedured_EU.zip] GReduced matrix and its decomposition for [EU] countries set</li> <li>[PageRank_vs_CheiRank.zip] PageRank versus CheiRank figures for RuWiki and ArWiki</li> <li>[Sensitivity.xlsx] and [Sensitivity_html.xlsx] Sensitivity values for both [EU] and [RU] in either .xlsx or in .html format</li> </ul>
Radar and Lidar scattering lookup tables for atmospheric hydrometeors using a T-Matrix method and a Mie theory
<h2>Overview</h2> <p>The database includes text files containing the scattering amplitude matrices for single spherical/nonspherical particles for radar and lidar. They are the lookup tables used for calculating radar and lidar observables in the Cloud-Resolving Radar Simulator (Oue et al. 2020). The radar scattering properties were calculated for several hydrometeor categories using a T-matrix method proposed by Mishchenko (2000) accounting for incident angles, scattering direction (forward and backward), polarimetry (horizontally (H) and vertically (V) polarized waves), particle aspect ratio, phase (liquid or ice), bulk density, temperature, particle size, and radar frequency. The lidar scattering properties at a vertical incidence were calculated for spherical liquid or ice particles using the BHMIE Mie code (Bohrean and Hyffman,1998) accounting for lidar wavelength, temperature, and bulk density. The hydrometeor categories are commonly used for cloud resolving models employing bulk microphysical schemes (e.g., cloud, rain, ice cloud, snow aggregates, and graupel). Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v3.34).</p> <h2>Data structure</h2> <p>The data files are arranged and zipped every hydrometeor types. The names of the tar-zipped directories under the top directory LLUT3 represents the hydrometer type.<br>For lidar scattering, the following directories are included:<br>ceilo: Ceilometer lidar backscatter properties at a wavelength of 905 nm<br>mpl: Micropulse lidar (MPL) backscatter properties at wavelengths of 353 and 532 nm</p> <p>For radar scattering, the following hydrometer types are included:<br>cloud: Radar scattering for liquid cloud droplets (spherical shape)<br>raina: Radar scattering for raindrops with the aspect ratio model proposed by Andsager et al. (1999)<br>rainb: Radar scattering for raindrops with the aspect ratio model proposed by Brandes et al (2002)<br>ice_ar0.90: Radar scattering for cloud ice with an aspect ratio of 0.9<br>ice_ar0.20: Radar scattering for cloud ice with an aspect ratio of 0.2<br>smallice: Radar scattering for spherical cloud ice particles<br>snow_ar0.60: Radar scattering for snowflakes with an aspect ratio of 0.6<br>graupel_ar0.60: Radar scattering for graupel particles with an aspect ratio of 0.6<br>graupel_ar0.80: Radar scattering for graupel particles with an aspect ratio of 0.8<br>graupel: Radar scattering for spherical graupel particles<br>gh_ryzh: Radar scattering for graupel particles with the graupel aspect ratio model proposed by Ryzhkov et al (2011)<br>unrimedice_ar0.40: Radar scattering for unrimed ice particles with an aspect ratio of 0.4<br>unrimedice_ar0.60: Radar scattering for unrimed ice particles with an aspect ratio of 0.6<br>unrimedice_ar0.80: Radar scattering for unrimed ice particles with an aspect ratio of 0.8<br>unrimedice: Radar scattering for spherical unrimed ice particles<br>partrimedice_ar0.40: Radar scattering for partially rimed ice particles with an aspect ratio of 0.4<br>partrimedice_ar0.60: Radar scattering for partially rimed ice particles with an aspect ratio of 0.6<br>partrimedice_ar0.80: Radar scattering for partially rimed ice particles with an aspect ratio of 0.8<br>partrimedice: Radar scattering for partially rimed spherical ice particles </p> <h2>The file name convention </h2> <p>For lidar scattering data, each file name has the following format:<br>[hydrometeor type]_[instrument name]_ [wavelength in nm]_[phase ID]_d[bulk density in kg m-3].dat<br>The hydrometeor type shows: 1) ‘cld’ for liquid cloud droplets, and 2) ‘ice’ for ice particles. The phase ID shows: 1) ‘p25’ for ceilometer liquid cloud, 2) ‘p20’ for MPL lidar liquid cloud, and 3) ‘m30’ for MPL lidar ice. </p> <p>For radar scattering data, each file name has the following format.<br>[hydrometeor type]_fr[frequency in GHz]GHz_t[temperature in K]_rho[bulk density in kg m-3]_el[elevation angle in degree].dat<br>The hydrometeor type follows the directory name presented above.</p> <h2>Format of the data files</h2> <p>Line 1: Wavelength in mm<br>Line 2: Temperature in K<br>Line 3: Refractive index (real and imaginary)<br>Line 4: Number of radii calculated and number of elevation angles<br>Line 6: Incident angle and scattered angle in degrees<br>Line 7: Radius in mm and aspect ratio<br>Line 8: Forward scattering amplitude for co-polarization VV and HH (complex number)<br>Line 9: Backward scattering amplitude for co- and cross polarizations VV, VH, HV, HH (complex number) <br>Line 10 to the end of file: Repeat Line 7 to Line 9 with different radii until the maximum radius.</p>
STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.2 System Level Matrix dataset
<p>This dataset contains the final set of indicators selected for the system level of the new monitoring system. The data is part of the D3.2 "Report on additional indicators<br>of monitoring system". The indicators are organised by category, principles, criteria, requirements and references. </p> <p> </p>
Block-wise sparse matrix-vector product dataset and convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product
<p><strong>Introduction</strong></p> <p><strong>SpMV-CNN</strong> is a set of Convolutional Neural Networks (CNNs) that provide accurate estimations of the performance and energy consumption of the SpMV kernel. The proposed CNN-based models use a block-wise approach to make the CNN architecture independent of the matrix size. These models cat be trained to estimate run time as well as total, package and DRAM energy consumption at different processor frequencies.</p> <p><strong>Prerequisites</strong></p> <p><strong>SpMV-CNN</strong> requires Python3 with the following packages:</p> <pre><code>keras==2.1.6 tensorflow==1.8.0 h5py==2.7.1 matplotlib==2.1.1 scikit-learn==0.19.1 </code></pre> <p><strong>Obtaining the dataset</strong></p> <p>The execution time and energy consumption data corresponding to the SpMV operation on a set of sparse matrices from the SuiteSparse Matrix Collection have been obtained on an Intel Xeon E5-2630 core running at frequencies 1.2, 1.6, 2.0, 2.4 GHz. The energy consumption measurements are obtained via the Intel RAPL interface and gathered at three different levels (total, package and DRAM, where total = package + DRAM) for this specific processor.</p> <p>The <code>spmv-cnn-dataset.tgz</code> archive contains the whole dataset, including the following HDF5 files:</p> <pre><code>$ tree . |-- test | |-- f_1200000_b250 | | |-- output_2cubes_sphere_1200000.h5 | | |-- output_apache2_1200000.h5 | | |-- output_bcsstk36_1200000.h5 | | |-- output_cfd1_1200000.h5 | | |-- output_cfd2_1200000.h5 | | |-- output_ct20stif_1200000.h5 | | |-- output_denormal_1200000.h5 | | |-- output_Dubcova2_1200000.h5 | | |-- output_Dubcova3_1200000.h5 | | |-- output_ecology2_1200000.h5 | | |-- output_gyro_1200000.h5 | | |-- output_gyro_k_1200000.h5 | | |-- output_msc10848_1200000.h5 | | |-- output_msc23052_1200000.h5 | | |-- output_nasasrb_1200000.h5 | | |-- output_nd3k_1200000.h5 | | |-- output_offshore_1200000.h5 | | |-- output_oilpan_1200000.h5 | | |-- output_olafu_1200000.h5 | | |-- output_parabolic_fem_1200000.h5 | | |-- output_qa8fm_1200000.h5 | | |-- output_raefsky4_1200000.h5 | | |-- output_s3dkq4m2_1200000.h5 | | |-- output_s3dkt3m2_1200000.h5 | | |-- output_ship_001_1200000.h5 | | |-- output_ship_003_1200000.h5 | | |-- output_shipsec1_1200000.h5 | | |-- output_shipsec5_1200000.h5 | | |-- output_shipsec8_1200000.h5 | | |-- output_smt_1200000.h5 | | |-- output_thermomech_dM_1200000.h5 | | |-- output_thread_1200000.h5 | | `-- output_vanbody_1200000.h5 | |-- f_1600000_b250 | | |-- output_2cubes_sphere_1600000.h5 | | |—- ... | | `-- output_vanbody_1600000.h5 | |-- f_2000000_b250 | | |-- output_2cubes_sphere_2000000.h5 | | |—- ... | | `-- output_vanbody_2000000.h5 | `-- f_2400000_b250 | |-- output_2cubes_sphere_2400000.h5 | |—- ... | `-- output_vanbody_2400000.h5 |-- test_pagerank | |-- f_1200000_b250 | | |-- output_adaptive_1200000.h5 | | |-- output_cit-HepPh_1200000.h5 | | |-- output_delaunay_n22_1200000.h5 | | |-- output_email-Enron_1200000.h5 | | |-- output_email-EuAll_1200000.h5 | | |-- output_europe_osm_1200000.h5 | | |-- output_hugebubbles-00020_1200000.h5 | | |-- output_rgg_n_2_24_s0_1200000.h5 | | |-- output_road_usa_1200000.h5 | | |-- output_Stanford_1200000.h5 | | |-- output_wb-edu_1200000.h5 | | |-- output_web-BerkStan_1200000.h5 | | |-- output_web-Google_1200000.h5 | | |-- output_web-NotreDame_1200000.h5 | | |-- output_wiki-Talk_1200000.h5 | | `-- output_wiki-Vote_1200000.h5 | |-- f_1600000_b250 | | |-- output_adaptive_1600000.h5 | | |—- ... | | `-- output_wiki-Vote_1600000.h5 | |-- f_2000000_b250 | | |-- output_adaptive_2000000.h5 | | |—- ... | | `-- output_wiki-Vote_2000000.h5 | `-- f_2400000_b250 | |-- output_adaptive_2400000.h5 | |—- ... | `-- output_wiki-Vote_2400000.h5 `-- train |-- merged_energy_train_shuffle_f1200000_250.h5 |-- merged_energy_train_shuffle_f1600000_250.h5 |-- merged_energy_train_shuffle_f2000000_250.h5 `-- merged_energy_train_shuffle_f2400000_250.h5 </code></pre> <p>The matrices contained in the merged training files (<code>merged_energy_train_shuffle_fXX00000_250.h5</code>) are the following:</p> <pre><code>$ tree . |-- output_af_0_k101_1200000.h5 |-- output_af_1_k101_1200000.h5 |-- output_af_2_k101_1200000.h5 |-- output_af_3_k101_1200000.h5 |-- output_af_4_k101_1200000.h5 |-- output_af_5_k101_1200000.h5 |-- output_af_shell10_1200000.h5 |-- output_af_shell1_1200000.h5 |-- output_af_shell2_1200000.h5 |-- output_af_shell3_1200000.h5 |-- output_af_shell4_1200000.h5 |-- output_af_shell5_1200000.h5 |-- output_af_shell6_1200000.h5 |-- output_af_shell7_1200000.h5 |-- output_af_shell8_1200000.h5 |-- output_af_shell9_1200000.h5 |-- output_atmosmodd_1200000.h5 |-- output_atmosmodj_1200000.h5 |-- output_atmosmodl_1200000.h5 |-- output_audikw_1_1200000.h5 |-- output_BenElechi1_1200000.h5 |-- output_bmw3_2_1200000.h5 |-- output_bmw7st_1_1200000.h5 |-- output_bmwcra_1_1200000.h5 |-- output_bone010_1200000.h5 |-- output_boneS01_1200000.h5 |-- output_boneS10_1200000.h5 |-- output_bundle_adj_1200000.h5 |-- output_cage14_1200000.h5 |-- output_cage15_1200000.h5 |-- output_circuit5M_1200000.h5 |-- output_circuit5M_dc_1200000.h5 |-- output_CO_1200000.h5 |-- output_consph_1200000.h5 |-- output_CoupCons3D_1200000.h5 |-- output_crankseg_1_1200000.h5 |-- output_crankseg_2_1200000.h5 |-- output_CurlCurl_2_1200000.h5 |-- output_CurlCurl_3_1200000.h5 |-- output_CurlCurl_4_1200000.h5 |-- output_dielFilterV2real_1200000.h5 |-- output_dielFilterV3real_1200000.h5 |-- output_Emilia_923_1200000.h5 |-- output_ESOC_1200000.h5 |-- output_F1_1200000.h5 |-- output_F2_1200000.h5 |-- output_Fault_639_1200000.h5 |-- output_Freescale1_1200000.h5 |-- output_Freescale2_1200000.h5 |-- output_FullChip_1200000.h5 |-- output_G3_circuit_1200000.h5 |-- output_Ga10As10H30_1200000.h5 |-- output_Ga19As19H42_1200000.h5 |-- output_Ga3As3H12_1200000.h5 |-- output_Ga41As41H72_1200000.h5 |-- output_Ge87H76_1200000.h5 |-- output_Ge99H100_1200000.h5 |-- output_Geo_1438_1200000.h5 |-- output_gsm_106857_1200000.h5 |-- output_Hardesty3_1200000.h5 |-- output_hood_1200000.h5 |-- output_Hook_1498_1200000.h5 |-- output_human_gene1_1200000.h5 |-- output_human_gene2_1200000.h5 |-- output_inline_1_1200000.h5 |-- output_JP_1200000.h5 |-- output_kkt_power_1200000.h5 |-- output_ldoor_1200000.h5 |-- output_Long_Coup_dt0_1200000.h5 |-- output_Long_Coup_dt6_1200000.h5 |-- output_mat_104_10000_1200000.h5 |-- output_mat_104_1000_1200000.h5 |-- output_mat_104_5000_1200000.h5 |-- output_mat_112_10000_1200000.h5 |-- output_mat_112_1000_1200000.h5 |-- output_mat_112_5000_1200000.h5 |-- output_mat_120_10000_1200000.h5 |-- output_mat_120_1000_1200000.h5 |-- output_mat_120_5000_1200000.h5 |-- output_mat_128_10000_1200000.h5 |-- output_mat_128_1000_1200000.h5 |-- output_mat_128_5000_1200000.h5 |-- output_mat_16_10000_1200000.h5 |-- output_mat_16_1000_1200000.h5 |-- output_mat_16_5000_1200000.h5 |-- output_mat_24_10000_1200000.h5 |-- output_mat_24_1000_1200000.h5 |-- output_mat_24_5000_1200000.h5 |-- output_mat_32_10000_1200000.h5 |-- output_mat_32_1000_1200000.h5 |-- output_mat_32_5000_1200000.h5 |-- output_mat_40_10000_1200000.h5 |-- output_mat_40_1000_1200000.h5 |-- output_mat_40_5000_1200000.h5 |-- output_mat_48_10000_1200000.h5 |-- output_mat_48_1000_1200000.h5 |-- output_mat_48_5000_1200000.h5 |-- output_mat_56_10000_1200000.h5 |-- output_mat_56_1000_1200000.h5 |-- output_mat_56_5000_1200000.h5 |-- output_mat_64_10000_1200000.h5 |-- output_mat_64_1000_1200000.h5 |-- output_mat_64_5000_1200000.h5 |-- output_mat_72_10000_1200000.h5 |-- output_mat_72_1000_1200000.h5 |-- output_mat_72_5000_1200000.h5 |-- output_mat_80_10000_1200000.h5 |-- output_mat_80_1000_1200000.h5 |-- output_mat_80_5000_1200000.h5 |-- output_mat_8_10000_1200000.h5 |-- output_mat_8_1000_1200000.h5 |-- output_mat_8_5000_1200000.h5 |-- output_mat_88_10000_1200000.h5 |-- output_mat_88_1000_1200000.h5 |-- output_mat_88_5000_1200000.h5 |-- output_mat_96_10000_1200000.h5 |-- output_mat_96_1000_1200000.h5 |-- output_mat_96_5000_1200000.h5 |-- output_memchip_1200000.h5 |-- output_ML_Laplace_1200000.h5 |-- output_mouse_gene_1200000.h5 |-- output_msdoor_1200000.h5 |-- output_m_t1_1200000.h5 |-- output_nd12k_1200000.h5 |-- output_nd24k_1200000.h5 |-- output_nd6k_1200000.h5 |-- output_nlpkkt120_1200000.h5 |-- output_nlpkkt80_1200000.h5 |-- output_PFlow_742_1200000.h5 |-- output_pwtk_1200000.h5 |-- output_rajat31_1200000.h5 |-- output_RM07R_1200000.h5 |-- output_Rucci1_1200000.h5 |-- output_Serena_1200000.h5 |-- output_Si34H36_1200000.h5 |-- output_Si41Ge41H72_1200000.h5 |-- output_Si87H76_1200000.h5 |-- output_SiO2_1200000.h5 |-- output_sls_1200000.h5 |-- output_StocF-1465_1200000.h5 |-- output_TEM152078_1200000.h5 |-- output_TEM181302_1200000.h5 |-- output_thermal2_1200000.h5 |-- output_tmt_sym_1200000.h5 |-- output_torso1_1200000.h5 |-- output_Transport_1200000.h5 |-- output_TSOPF_FS_b300_c2_1200000.h5 |-- output_TSOPF_FS_b300_c3_1200000.h5 |-- output_TSOPF_RS_b2383_1200000.h5 |-- output_TSOPF_RS_b2383_c1_1200000.h5 |-- output_TSOPF_RS_b678_c2_1200000.h5 `-- output_x104_1200000.h5 f_1600000_b250 |-- output_af_0_k101_1600000.h5 |—- ... `-- output_x104_1600000.h5 f_2000000_b250 |-- output_af_0_k101_2000000.h5 |—- ... `-- output_x104_2000000.h5 f_2400000_b250 |-- output_af_0_k101_2400000.h5 |—- ... `-- output_x104_2400000.h5 </code></pre> <p><strong>Creating your own dataset</strong></p> <p>If you wish to create your own training/testing dataset on a different target architecture you need to take the following steps:</p> <ol> <li> <p>Build the SpMV driver:</p> <ol> <li> <p>Go to <code>cd SpMV-driver/src</code></p> </li> <li> <p>Edit makefile and set the PAPI and HDF5 install prefixes.</p> </li> <li> <p>Build the driver via <code>make.</code></p> </li> </ol> </li> <li> <p>Run the SpMV driver: </p> <p><code>./driver <arg0> <arg1> ...</code></p> <p>List of driver arguments:</p> <pre><code>matrix = audikw_1.rb # Input matrix in rb format reps = 10000 # Number of repetitions of the operation to avoid overhead block_size_ini = 250 # Minimum block size block_size_end = 1000 # Maximum block size increment = 250 # Increment between block sizes base = 0 # Starting nnz of the matrix freq = [2400000, 2000000, 1600000, 1200000] # Operating frequency sym = 1 # If 1 the matrix is symmetric. If 0 the matrix is no-symmetric.</code></pre> <p>Example:</p> <p><code>numactl --membind 0 taskset -c 0 ./src/driver audikw_1.rb 10000 250 1000 250 0 2400000</code></p> <p>Note that <code>numactl</code> and <code>taskset</code> utilities are used to guarantee both NUMA and process-to-core affinity.</p> </li> <li> <p>Generating the dataset:</p> <ol> <li> <p>Edit the <code>SpMV-driver/run_all.sh</code> and uncomment the line <code>matrices =</code> in order to launch the driver for Train_symmetric / Train_noSymmetric / Test_symmetric / Test_noSymmetric matrices.</p> </li> <li> <p>Edit the 3rd parameter in the command SpMV-driver/run_driver.sh: 1 for symmetric matrices, 2 for unsymmetric matrices.</p> </li> <li> <p>Edit the command in SpMV-driver/run_driver.sh to select the input parameters of the driver as explained before.</p> </li> <li> <p>Run <code>SpMV-driver/run_all.sh</code> to obtain <code>hdf5</code> files that will create the dataset.</p> </li> </ol> </li> <li> <p>Merging the dataset:</p> <p>Run the script</p> <p><code>python3 SpMV-driver/merge_train_matrices.py /path/to/hdf5/matrix/files /output/path</code></p> <p>to obtain a single <code>hdf5</code> file containing all data from individual <code>hdf5</code> files obtained in the previous step. This merged file is the training dataset.</p> </li> </ol> <p><strong>Hyperparameter search</strong></p> <p>The script <code>spmv_cnn_hyperas.py</code> performs the hyperparameter search via the Hyperas tool. This script requires the hdf5 file dataset in the directory <code>dataset/train/</code> and produces both a <code>best_model_*.json</code> and <code>best_run_*.json </code>files in the <code>results/models/</code> directory containing the model structure and hyperparameters of the best performing configuration.</p> <p>This script can be invoked in the following way:</p> <p><code>python3 spmv_cnn_hyper.py 2400000 Time</code></p> <p>where <code>2400000</code> is the operating processor frequency (2.4 GHz) at which the dataset was generated and <code>Time</code> the modeled metric. According to the labels in the dataset, the hyperparameter search can also be performed with the <code>Energy</code>, <code>EPKG</code> and <code>EDRAM</code> metrics, corresponding to the energy measured by the Intel RAPL counters from our Intel Xeon Haswell core. In our case, however, we only search hyperparameters for the <code>Time</code> and <code>Energy</code> metrics at 2.4 GHz. Other metrics and frequencies inherit the best performing model and settings from the previous configuration.</p> <p><strong>Training</strong></p> <p>The script <code>spmv_cnn_train.py</code> performs the training on the best performing models obtained on the previous step. For that, it uses both the <code>best_model_*.json</code> and <code>best_run_*.json</code> files obtained in the hyperparameter search.</p> <p>This script can be invoked in the following way:</p> <p><code>python3 spmv_cnn_train.py 2400000 Time</code></p> <p>where <code>2400000</code> is the operating processor frequency (2.4 GHz) and <code>Time</code> the modeled metric. The training should be performed per metric and frequency. The training produces a file that contains the trained weights, so the model is ready for performing inference (testing).</p> <p><strong>Testing</strong></p> <p>The script <code>spmv_cnn_test.py</code> performs the test on the set of testing matrices involved in the SpMV operation.</p> <p>This script can be invoked in the following way:</p> <p><code>python3 spmv_cnn_test.py 2400000 Time</code></p> <p>where <code>2400000</code> is the operating processor frequency (2.4 GHz) and <code>Time</code> the modeled metric. The test should be performed per metric and frequency. The training produces two files in the <code>results/tests/</code> directory:</p> <ul> <li><code>Pred_*.txt</code>: This file contains the real measurements and the predictions obtained by the CNN for the individual vpos blocks of the testing matrices.</li> <li><code>Test_*.txt</code>: This file summarizes the information of <code>Pred_*.txt</code> file, showing the average relative error among the blocks of each test matrix and the total relative error, which is computed by summing up the real measurements and the predictions for all the blocks of a same matrix and computing the relative error upon those values.</li> </ul> <p><em>Note that this testing step and the two previous steps (hyperparameter search and training) can be performed at once using the <code>run.sh</code> script.</em></p> <p><strong>References</strong></p> <p>Publications describing <strong>SpMV-CNN-Model</strong>:</p> <ul> <li>Barreda, M., Dolz, M.F., Castaño, M.A. et al. Performance modeling of the sparse matrix–vector product via convolutional neural networks. J Supercomputing (2020). <a href="https://doi.org/10.1007/s11227-020-03186-1">https://doi.org/10.1007/s11227-020-03186-1</a></li> </ul> <p><strong>Acknowledgments</strong></p> <p>The <strong>SpMV-CNN-Model</strong> research has been partially supported by:</p> <ul> <li> <p>Project TIN2017-82972-R <strong>“Agorithmic Techniques for Energy-Aware and Error-Resilient High Performance Computing”</strong> funded by the Spanish Ministry of Economy and Competitiveness (2018-2020).</p> </li> <li> <p>Project CDEIGENT/2017/04 <strong>“High Performance Computing for Neural Networks” </strong>funded by the Valencian Government.</p> </li> <li> <p>Project UJI-A2019-11 <strong>“Energy-Aware High Performance Computing for Deep Neural Networks”</strong> funded by the Universitat Jaume I.</p> </li> </ul>
Simulated TSCH dataset using different slotframe matrix configurations
<p>The current dataset was obtained by using a specifically developed simulator, to analyze the behavior of a time slotted channel hopping (TSCH) wireless sensor network (WSN), under different operating conditions.</p> <p>The configuration of the simulator, the characteristics of the network, and the generated traffic patterns are reported in [1].<br>Different configurations of the slotframe matrix, which allows slots to be reserved for specific pairs of nodes, are analyzed.</p> <p>File "<code>network_topology.pdf</code>" reports the topology of the simulated wireless network.</p> <p>The four analyzed configurations, which are deeply described in [1], are:</p> <ul> <li><strong>Star</strong>, whose slotframe matrix configuration is reported in the "<code>Star.conf</code>" file.</li> <li><strong>Load</strong>, whose slotframe matrix configuration is reported in the "<code>Load.conf</code>" file.</li> <li><strong>Parallel</strong>, whose slotframe matrix configuration is reported in the "<code>Parallel.conf</code>" file.</li> <li><strong>LPR</strong>, whose slotframe matrix configuration is reported in the "<code>LPR.conf</code>" file.</li> </ul> <p>A typical "<code>.conf</code>" file has the following format:<br><code># Offset src dest</code><br><code>0 4 1</code><br><code>0 6 2</code><br><code>0 8 3</code><br><code>0 10 9</code><br><code>1 1 0</code><br>where the first column represents the slot offset, i.e., the time slot in the slotframe matrix (which repeats periodically over time), in which a transmission opportunity is scheduled. Since many concurrent transmissions between different couple of nodes and different channels are possible simultaneously, more than one transmission could be scheduled at the same time. In the example, four transmission opportunities are scheduled in slot offset number 0.<br>The second column of each row represents the source node, while the third column represents the destination node. For instance, the schedule "<code>0 10 9</code>" represents the scheduled transmission at slot offset 0 from the source node 10 to the destination node 9.</p> <p> </p> <p>For each configuration, a corresponding file with the extension "<code>.dat</code>" contains the log generated in the simulation. An example is the following: <br><code>00083 72204 FLOW: 6 11 -> 0 LOST: 0 TRIES: 3 LATENCY: 540</code><br><code>00082 72156 FLOW: 5 10 -> 0 LOST: 0 TRIES: 4 LATENCY: 3460</code><br><code>00084 78013 FLOW: 0 4 -> 0 LOST: 0 TRIES: 2 LATENCY: 1240</code><br>where the transmission in a path from the source node (e.g., 11) to a destination node (e.g., the root node 0) is summarized with a single line in the log.</p> <p>Each line is composed of the following fields:</p> <ul> <li><em><packet number></em>: an integer number (e.g., <code>00083</code>) that uniquely identifies a packet transmitted in a multi-hop fashion from the source node to the destination node.</li> <li><em><queuing_time></em>: the queuing time expressed in terms of number of slots. In the simulation, slots have a length of 20 ms.</li> <li><em><flow_index></em>: the word "<code>FLOW:</code>" followed by an integer number identifying the flow. The simulation contains seven periodic flows with periods 6001, 6003, 6005, 6007, 6011, 6013, and 6017 expressed in terms of number of slots, for flows with index 0, 1, 2, 3, 4, 5, 6, respectively. For instance, "FLOW: 6" has a period of 6017 slots, which corresponds to 120.34 s (i.e., about 2 minutes).</li> <li><em><path></em>: an integer value representing the source node of the path, followed by the characters "<code>-></code>", followed by another integer value representing the destination node. For instance, "<code>11 -> 0</code>" represents the transmission in the path between node 11 and node 0.</li> <li><em><lost></em>: is an indication if the packet was lost in the path ("<code>LOST: 1</code>") or the packet arrived correctly at the destination ("<code>LOST: 0</code>"). A packet is lost if on a given link reached the maximum number of retransmissions.</li> <li><em><tries></em>: is the sum of the transmissions performed in each link. For instance, the link "<code>10 -> 0</code>" is composed of 3 hops. The value "<code>TRIES: 4</code>" means that a retransmission was performed for one of the links in the path.</li> <li><em><latency></em>: the transmission latency of the packet from when it was queued to when it reached its destination. The latency is expressed in ms.</li> </ul> <p>For each condition, the number of logged packets (i.e., lines) is 36,742,162, corresponding to 20 years of simulation.</p> <p> </p> <p>In addition, the code of the simulator is provided in the file "<code>TSCHmodeler.zip</code>".</p> <p>To run the simulations reported in [1], you have to execute the command:</p> <ul> <li>For experiment in Section IV.A <ul> <li><code>python3 -m TSCHmodeler conf/simple.conf</code></li> <li><code>python3 -m TSCHmodeler conf/simple_1week.conf</code></li> </ul> </li> <li>For experiment in Section IV.B <ul> <li><code>python3 -m TSCHmodeler conf/star_minimal.conf</code> for the <strong>star</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_load.conf</code> for the <strong>load</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_parallel.conf</code> for the <strong>parallel</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_LPR.conf</code>for the <strong>LPR</strong> minimal configuration</li> </ul> </li> <li>For experiment in Section IV.C <ul> <li><code>python3 -m TSCHmodeler conf/large_40_nodes.conf</code></li> <li><code>python3 -m TSCHmodeler conf/large_121_nodes.conf</code></li> </ul> </li> </ul> <p> </p> <p>References:<br>[1] S. Scanzio, P. Chiavassa, G. Formis, G. Paolini and G. Cena, “A Lightweight Simulation Environment for TSCH-Based Wireless Sensor Networks,” in IEEE Transactions on Industrial Cyber-Physical Systems, 2025. doi: <a title="https://doi.org/10.1109/TICPS.2025.3620370" href="https://doi.org/10.1109/TICPS.2025.3620370" target="_blank" rel="noopener">10.1109/TICPS.2025.3620370</a></p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "Three new species of Torrent Treefrogs (Anura: Hylidae) of the Hyloscirtus bogotensis group from the eastern Andean slopes and the biogeographic history of the genus"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "Three new species of Torrent Treefrogs (Anura: Hylidae) of the Hyloscirtus bogotensis group from the Amazon foothills and the biogeographic history of the genus"</p> <p>The matrix is in NEXUS format and has 3259 bp and 25 terminals.</p> <p>Partitions are as follows:</p> <div>charset 12S = 1-955;</div> <div>charset ND1_nonCoding1 = 956-1279;</div> <div>charset ND1_Pos1 = 1280-2240\3;</div> <div>charset ND1_Pos2 = 1281-2241\3;</div> <div>charset ND1_Pos3 = 1282-2242\3;</div> <div>charset ND1_nonCoding2 = 2243-2361;</div> <div>charset cmyc_Pos1 = 2362-2779\3;</div> <div>charset cmyc_Pos2 = 2363-2780\3;</div> <div>charset cmyc_Pos3 = 2364-2781\3;</div> <div>charset Rag1_Pos1 = 2782-3415\3;</div> <div>charset Rag1_Pos2 = 2783-3416\3;</div> <div>charset Rag1_Pos3 = 2784-3417\3;</div>
Gene expression count matrix for 4 T cell subtypes from ROSMAP participants
<p><span>Peripheral blood mononuclear cells (PBMCs) from participants in the Rush Religious Orders Study/Memory and Aging Project (ROSMAP) were isolated by Ficoll gradient centrifugation, then sorted by high-speed flow cytometry into the following T cell subtypes:<span> </span>CD4+CD45RO-, CD4+CD45RO+, CD8+CD45RO-, and CD8+CD45RO+.<span> </span>Total RNA was extracted using buffer TCL (Qiagen), then RNA-seq libraries were prepared according to the Single Cell RNA Barcoding and Sequencing method originally developed for single-cell RNA-seq</span><span>, adapted for extracted total RNA.<span> </span>RNA libraries were collected on a single 384-well plate and sequenced on the Illumina HiSeq </span><span>using the High-throughput 3<span>’</span> Digital Gene Expression (DGE) library</span><span>.<span> The "RNA count matrix" file is the raw counts from the 384-well plate, while the "ROSMAP_Tcell_DGE_PlateMap" file contains metadata for the wells on the plate, by well position.</span></span></p>
Data description: Deprivation of loading during early healing of rat Achilles tendons affects extracellular matrix composition and structure, and reduces cell density and cell alignment
<p><a name="_Hlk158643946"></a><strong>Data description: Deprivation of loading during early healing of rat Achilles tendons affects extracellular matrix composition and structure, and reduces cell density and cell alignment</strong></p> <p><em>Malin Hammerman, Maria Pierantoni, Hanna Isaksson<sup> *</sup>, Pernilla Eliasson <sup>*</sup></em></p> <p><em><sup>* </sup></em><em>joint<sup> </sup>last authors</em></p> <p>This dataset contains microscope images obtained from sections of healing and intact rat Achilles tendons undergoing different in vivo loading protocols and different time points post-transection. The data presented are the full resolution microscope images available in lower resolution in the accompanying manuscript’s Supplementary Figures 4-6.</p> <p>Each zipped folders contain images (tif-files) from all time-points for each respective staining and loading group. </p> <ul> <li>Col1: Sections stained with Collagen 1 antibodies</li> <li>Col3: Sections stained with Collagen 3 antibodies</li> <li>Elastin: Sections stained with Elastin antibodies</li> <li>Full_loading: Free cage activity</li> <li>Reduced_loading: Paralysis of the calf muscle with Botox</li> <li>Minimal_loading: Botox combined with joint fixation using a steel-orthosis</li> <li>Intact_reference: Contralateral uninjured Achilles tendons, used as reference</li> </ul> <p>More description of the datasets inside the zipped files are available below and in the file 'Data Description.pdf'</p> <p> </p> <p><strong>Brief re-cap of methods</strong></p> <p>Histological analysis was performed on healing Achilles tendons from Female Sprague-Dawley rats, specific-pathogen free (11-12 weeks, weight 299 ± 15 g), that had undergone full transection [13] of the right Achilles tendon, and been exposed to different levels of loading. Altered loading was imposed through two mechanisms. Reduced loading involved intramuscular Botox injections in the right calf muscles to induce plantar flexor muscle paralysis [24]. Additionally, the rats in the minimal loading group received a steel-orthosis around their right hindlimb directly after surgery [24].</p> <p>Snap frozen tendons in OCT were sectioned longitudinally (7 μm thickness) and stained with immunofluorescent staining for collagen 1, collagen 3, or elastin. Sections were counterstained with DAPI followed by mounting. The tissue sections were imaged under a microscope (DMi8, Leica Microsystems, Wetzlar, Germany, with a Hamamatsu Orca LT Flash sCMOS camera) where fluorescence was detected at 550 nm (secondary antibody Alexa Fluor 594), 470 nm (secondary antibody Alexa Fluor 488) and 385 nm (DAPI), and exposure time was held constant for each color channel regarding magnification and staining.</p> <p>Mapping images of the entire tendon were obtained for one section per group (n=1 per healing time, loading group and ECM matrix protein). All images were adjusted to the negative control, where the primary antibody was omitted, to correct for unspecific antibody detection.</p> <p><strong>Microscope images and description of file-names </strong></p> <p>All data is presented in the form of .tif files. Please refer to the scale bars in the images. All image-files are named using the following abbreviations, as described below. As an example, the file name “Tendon_col1_FL_1W_col1.tif” refers to a tendon section stained for collagen 1 from a rat exposed to full loading for a period of 1 week after tendon transection, where only the channel for collagen 1 is shown, whereas “Tendon_col1_FL_1W_merged.tif” includes the channels for both staining for collagen 1 and DAPI of the same section.</p> <p>Col1: Sections stained with Collagen 1 antibodies<br>Col3: Sections stained with Collagen 3 antibodies<br>Elastin: Sections stained with Elastin antibodies<br>dapi: Sections stained with 4',6-Diamidino-2-Phenylindole Dihydrochloride.<br>FL: Full loading (free cage activity),<br>RL: Reduced loading (paralysis of the calf muscle with Botox),<br>ML: Minimal loading (Botox combined with joint fixation using a steel-orthosis)<br>IT: Intact contralateral Achilles tendons, used as reference.</p> <p>1W: Healing time point 1 week after transection<br>2W: Healing time point 2 weeks after transection<br>3W: Healing time point 3 weeks after transection<br>20W: Healing time point 20 weeks after transection</p> <p><strong>Settings for brightness and contrast</strong></p> <p><em>Collagen 1</em><br>1w FL 2000-12 000, UL 4000-10 000, ML 4000-12 000<br>2w FL 2500-10 000, UL 4000-10 000, ML 5000-12 000<br>3w FL 2000-12 000, UL 3500-13 000, ML 3500-14 000<br>12w FL 3000-12 000<br>20w FL 3000-11 000<br>IT 2000-8 000</p> <p>Collagen 3<br>1w FL 3000-12 000, UL 4000-10 000, ML 4000-13 000<br>2w FL 2000 - 7 000, UL 2500-12 000, ML 2000-12 000<br>3w FL 2000-12 000, UL 3500-13 000, ML 3500-14 000<br>12w FL 3000-12 000<br>20w FL 2000-12 000<br>IT 3000-12 000</p> <p>Elastin<br>1w FL 4000-10 000, UL 5000 - 8000, ML 3500-12 000<br>2w FL 3000-12 000, UL 3000-12 000, ML 3000-12 000<br>3w FL 2500-12 000, UL 2000-12 000, ML 2500-12 000,<br>12w FL 3500-12 000<br>20w FL 3500-12 000<br>IT 2000-12 000</p>
COSI-Article matrix: linking ISCB Communities of Special Interest to Wikipedia
<p>Wikipedia is regarded as one of the most important channels for the public communication of science; English Wikipedia has around 1,500 articles relating to computational biology, which are frequently accessed as an educational resource. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further quality improvement, primarily while comparing to related scientific fields such as genetics and medicine. Facilitating the involvement of members from ISCB COSIs (Communities of Special Interest) would improve a vital open educational resource in computational biology, additionally allowing COSIs to provide a quality educational resource particular to their subfield.</p> <p>This first version of the COSI-Article matrix is a binary matrix identifying relevant ISCB COSIs for all Wikipedia articles relating to computational biology, defining a domain-specific open educational resource for each COSI. In addition, quality and importance ratings for each article allow identification of areas where domain experts could improve computational biology representation.</p>
Eremohaplomydas, Haplomydas, and Lachnocorynus matrix-based key in SDD-format
<p>Matrix-based, multi-entry key to species of the genera Eremohaplomydas, Haplomydas, and Lachnocorynus (Diptera: Mydidae) developed with Lucid Builder v4 in XML Structure of Descriptive Data (SDD) format.</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "A new glassfrog of the genus Centrolene (Amphibia: Centrolenidae) from the Subandean Kutukú Cordillera, eastern Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "A new glassfrog of the genus Centrolene (Amphibia: Centrolenidae) from the Subandean Kutukú Cordillera, eastern Ecuador"</p> <p>The matrix is in NEXUS format and has 6626 bp and 239 terminals.</p> <p>Partitions are as follows:</p> <div> <div>charset 12S = 1-967;</div> <div>charset 16S = 968-2130;</div> <div> </div> <div>charset BNDFcodonPos1 = 2133-2829\3;</div> <div>charset BNDFcodonPos2 = 2131-2830\3;</div> <div>charset BNDFcodonPos3 = 2132-2828\3;</div> <div> </div> <div> </div> <div>charset ND1codonPos1 = 2832-3786\3;</div> <div>charset ND1codonPos2 = 2833-3787\3;</div> <div>charset ND1codonPos3 = 2831-3788\3;</div> <div> </div> <div> </div> <div>charset CXCR4codonPos1 = 3790-4144\3;</div> <div>charset CXCR4codonPos2 = 3791-4142\3;</div> <div>charset CXCR4codonPos3 = 3789-4143\3;</div> <div> </div> <div> </div> <div>charset cmyccodonPos1 = 4145-4547\3;</div> <div>charset cmyccodonPos2 = 4146-4548\3;</div> <div>charset cmyccodonPos3 = 4147-4549\3;</div> <div> </div> <div> </div> <div>charset POMCcodonPos1 = 4551-5160\3;</div> <div>charset POMCcodonPos2 = 4552-5161\3;</div> <div>charset POMCcodonPos3 = 4550-5162\3;</div> <div> </div> <div> </div> <div>charset RAG1codonPos1 = 5163-5616\3;</div> <div>charset RAG1codonPos2 = 5164-5617\3;</div> <div>charset RAG1codonPos3 = 5165-5618\3;</div> <div> </div> <div> </div> <div>charset SLC8A1codonPos1 = 5620-6160\3;</div> <div>charset SLC8A1codonPos2 = 5621-6158\3;</div> <div>charset SLC8A1codonPos3 = 5619-6159\3;</div> <div> </div> <div> </div> <div> </div> <div>charset SLC8A3codonPos1 = 6162-6627\3;</div> <div>charset SLC8A3codonPos2 = 6163-6625\3;</div> <div>charset SLC8A3codonPos3 = 6161-6626\3;</div> </div> <p> </p>
Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach
<p>This is the dataset used in the analysis "Identification of Southeast Asian <em>Anopheles </em>mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach". It consists in 3584 raw mass spectra (mzXML file format) of the head of 359 <em>Anopheles </em>mosquito specimens collected in Karen (Kayin state) in Myanmar between 2020 and 2022 and associated metadata (Rdata file format) including sample information (taxonomy.Rdata) and spectra information (metadata.Rdata).</p>
CATCH-EyoU Work Package 2 Dataset 2.1a - Full Consortium Collection of Literature Matrix
<p>The dataset includes <strong>one master spreadsheet containing literatures searched, catalogued and summarised in the fields of Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology,</strong> which contains <strong>770 selected texts.</strong></p> <p> </p> <p>The aims of the data collected are to produce an integrated theory that builds on the findings of different disciplines (Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology) focused on the understanding of factors and processes (from the macro social level to the social and psychological level), within the different life contexts, that promote or hinder youth active citizenship in EU.</p> <p> </p> <p>It is possible that similar databases of literature around Europe, Young People and Active Citizenship across the fields of Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology exist in other forms, perhaps collected for studies on one or more of the included disciplines, but we do not currently have access to a similar repository.</p> <p> </p> <p>With that said, it is highly unlikely that an exact dataset corresponding to the specifics of this study exist in any form elsewhere, thus justifying the creation of new data for this study in the absence of suitable existing data. Data collected here will bridge the gap between global aggregated literatures on youth and citizenship separated by discipline on the one hand, and a new dataset offering an integrated literature analysis of different fields of study.</p> <p> </p> <p>The data sources are available in bibliographic format and attached via csv document.</p> <p> </p> <p>The dataset relies on the following information taken from the data sources: specific identifying information about the text itself (title/author/year/publisher); and abstract or summarizing information either taken directly from the text or summarized by the researcher.</p> <p> </p> <p>Finally, the aggregated literature review spreadsheet constitutes raw data which can be reused by researchers who want to compare our data with similar data collected in different countries, or to perform textual analysis (content analysis and/or data mining) on our data.</p>
STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.3 Content Level Matrix dataset
<p>This dataset contains the final set of indicators selected for the content level of the new monitoring system. It is part of the D3.2 "Report on additional indicators of monitoring system" of the STAR4BBS Project (Appendix 6.3). The list of indicators are accompanied by guidance notes, the specific sector and value chain to which they apply as well as potential examples of indicators are suggested. </p>
Social Accounting Matrix for Lithuania, 2017 (with disaggregated Rubber and Plastics activity)
<p>The dataset is based on doi: 10.5281/zenodo.5077893 but includes Rubber and plastics activity disaggregated to depict production of plastic bags with more details.</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.