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5,155 results for “Data Base”
Sequence-based microsatellite data of Anadenanthera colubrina (Leguminosae)
<p>The file contains SSRseq genotyping data of <em>Anadenanthera colubrina</em> populations. Individuals from two life stages were scored at 25 SSRseq loci. Goncalves AL, García MV, Chancerel E, Lepais O, Heuertz M. High-throughput sequence-based microsatellite genotyping for the non-model Neotropical tree species <em>Anadenanthera colubrina</em> (Leguminosae).</p> <p>The file contains</p> <p>- Two different data sets:</p> <p>GS: Genotypes based on sequence identity.<br>GL: Genotypes based on amplicon length.</p> <p>- Allele sequence information</p>
Data to "Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer"
<p>This record includes the DICOM images and analysed data that were used in the multicenter QA program for quantitative MRI in cervical cancer as published (<a href="https://www.sciencedirect.com/science/article/pii/S0167814020307854?via%3Dihub">https://doi.org/10.1016/j.radonc.2020.09.013</a> ).</p> <p>The DICOM data includes the acquired DICOM data for each institute selected to those that were used in the publication. Acquisitions that were not used were removed. Data was anonymized with conquest dicom server tools.</p> <p>The analyzed data files are included giving per measurement the estimated quantitative parameter values as well as the position of the ROIs and extracted signal intensity values per phantom sample. An explanation of the structure of the files is added in the readme file. The analysis was done with in-house written code in matlab.</p> <p>Included are a description of the sequence parameters for each institute (IQEMBRACE_PhantomQA_OverviewInstitutionalSequenceParameters_20241114) and details on the choices in the analysis of the data (IQEMBRACE_PhantomQA_OverviewPhantomData_20241114). As background also the description of the measurements was added, giving more information on how the measurements were performed.</p> <p>This work was in preparation for the IQ-EMBRACE trial (clinicaltrials.gov NCT03210428)</p>
Replication data for "The uncertain future of protected lands and waters" - protected area base layer for Amazonia
<p>We created a database of terrestrial and coastal protected areas (PAs) for all nine Amazonian countries following the IUCN definition for PAs and including only state-designated and state-managed PAs. We used the best available sources of archival data, including original legal documents, to confirm information about PAs. We included PAs that currently exist, as well as those that existed previously but have been degazetted. We note that this database differs from the World Database of Protected Areas (WDPA) for several reasons:</p> <p>• we focus on nationally-designated PAs and omit international or local designations</p> <p>• we include previously protected areas</p> <p>• we exclude other area-based conservation interventions other than state-designated and state-managed PAs (such as indigenous lands, privately protected areas, recreational sites, and community based natural resource management areas) which are included in the WDPA in certain countries</p> <p>• We use the establishment date as provided in each PA’s gazettement legal document, rather than the Status Year field in the WDPA, which lists the year that the PA’s current designation was established (46)</p> <p>• We use the spatial extent as provided in each PA’s gazettement legal document, rather than the spatial extent provided in the WDPA. The spatial extent in the WDPA (Rep_Area) is reported by nations and may represent the area as measured in GIS or paper maps, rather than the legally gazetted area.</p> <p>See Table S16 for detailed information by country describing the sources of PA data used for the nine Amazonian countries. </p> <p>Citation of original paper: Golden Kroner, R. E., Qin, S., Cook, C. N., Krithivasan, R., Pack, S. M., Bonilla, O. D., Cort-Kansinally, K. A., Coutinho, B., Feng, M., Martínez Garcia, M. I., He, Y., Kennedy, C. J., Lebreton, C., Ledezma, J. C., Lovejoy, T. E., Luther, D. A., Parmanand, Y., Ruíz-Agudelo, C. A., Yerena, E., … Mascia, M. B. (2019). The uncertain future of protected lands and waters. <em>Science</em>, <em>364</em>(6443), 881–886. <a href="https://doi.org/10.1126/science.aau5525">https://doi.org/10.1126/science.aau5525</a></p>
Data for "Nano onions based on an amphiphilic Au3(pyrazolate)3 complex"
<p>This upload contains raw data (NMR, DLS, Zeta Potential) files for the article: </p> <p><strong>Nano onions based on an amphiphilic Au<sub>3</sub>(pyrazolate)<sub>3</sub> complex</strong></p> <p>Nanoscale, 2024, Advance Article, <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4NR03901G">https://doi.org/10.1039/D4NR03901G</a></p>
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude</p> <p>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual.</p> <p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).</p> <p><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing
<p><strong>Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing</strong></p> <p><strong>Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip)</strong></p> <p>Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing</p> <p>Open code and documentation of prediction models available via <a href="https://github.com/major-s/mouse-mcao-outcome-predictor">https://github.com/major-s/mouse-mcao-outcome-predictor</a></p> <p><strong>Content:</strong></p> <p>README.txt</p> <p>This information</p> <p><strong>dat</strong></p> <p>Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/<br>Files used for the manuscript:<br>t2.nii: t2 weighted image acquired 24 h post stroke<br>masklesion.nii: manually delineated lesion<br>x_masklesion.nii: lesion in atlas space<br>ix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)<br>Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.nii<br>Overlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region</p> <p><strong>prediction_models</strong></p> <p>Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.<br>The files have following sturcture:<br>Column 1: animal ID<br>Columns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)<br>Column 538: lesion volume<br>Column 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6<br>Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficit<br>Column 541: residual performance/deficit<br>Column 542: test or training group<br>Consecutive rows contain data for each animal specified by the animal id</p> <p>The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.<br>The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.<br>The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigm<br>Folder 'subacute deficit prediction' contains:<br> - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume<br> - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mri<br>Folder 'long-term outcome prediction' contains:<br> - lesion_volume: prediction of residual deficit using lesion volume<br> - segmented_mri: prediction of residual deficit using segmented_mri<br> - initial_performance: prediction of residual deficit using subacute deficit<br>Folder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.</p> <p>Files with equal file name and different extension always contain the same data</p> <p><strong>templates</strong><br>Allen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2<br> </p>
Data and Results of eELib Simulations for the User-Based Multi-Use of Battery Storage Systems
<p>The dataset contains the configuration for the eElib models (model_data.json) and computed simulation results (.hdf5-files). The following scenarios were computed:</p> <ul> <li>ave_A_static-eq</li> <li>ave_A_static</li> <li>ave_A_dynamic_charging</li> <li>ave_A_fully_dynamic</li> <li>ave_B_static-eq_bss</li> <li>ave_B_static_bss</li> <li>ave_B_dynamic_charging_bss</li> <li>ave_B_fully_dynamic_bss</li> <li>MELANI_static</li> <li>MELANI_static_equal</li> <li>MELANI_dynamic_charging</li> <li>MELANI_fully_dynamic</li> </ul> <p><strong>Description of syntax of simulation results:</strong></p> <ul> <li>average (ave_B is half the size of the BSS of ave_A) and MELANI describe the considered multi-family house</li> <li>static/ static / dynamic_charging / fully_dynamic are the three developed operating strategies for the user-based multi-use</li> <li>static-equal: the allocation keys are equally, i.e., the PVS and BSS capabilities are equally distributed among the households of the multi-family house</li> </ul> <div> <div><strong>As part of the publication:</strong></div> <div>Henrik Wagner, Constantin von Lützow, Marcel Lüdecke, Michel Meinert, Bernd Engel "Empowering Collective Self-Consumption in Multi-Family Houses: User-Based Multi-Use of Residential Battery Storage Systems", 23rd Wind & Solar Integration Workshop 2024, Helsinki, Finland, doi: 10.1049/icp.2024.3901</div> <div> </div> <div><strong>Changelog:</strong></div> <div>v2: Added doi for WIW 2024 conference paper to improve citation possibilities</div> </div>
Interplanetary shock data base
<p>This interplanetary shock data base was compiled with Wind, Advanced Composition Explorer (ACE), and Deep Space Climate Observatory (DSCOVR) observations collected at the Lagrangian point L1. The list ranges from January 1995 to December 2024 with 650 events. Many shock parameters are included, such as shock impact angle, shock speed, Mach numbers, compression ratios, and IMF (interplanetary magnetic field) Bz in the upstream and downstream regions. The list also brings geomagnetic activity information such as minimum SMR values in a time interval of 2 hours after shock impact. The author intends to update this list annually.</p> <p>There are three files: (i) full_shock_list_2024.txt, a text file with 650 events; (ii) full_shock_params.cdf, a cdf file with detailed information about each specific shock events; and (iii) read_shock.py, a file that contains a short python routine to read information about a specific shock event. The SpacePy package (<a href="https://spacepy.github.io/spacepy.html#:~:text=SpacePy%3A%20Space%20Science%20Tools%20for,at%20the%20space%20science%20community.">https://spacepy.github.io/spacepy.html#:~:text=SpacePy%3A%20Space%20Science%20Tools%20for,at%20the%20space%20science%20community.</a>) is required to extract shock information from the cdf file.</p> <p>Example (shock number 142, 26 June 2000):</p> <p>from read_shock import read_shock_cdf</p> <p>read_shock_cdf(142)</p> <p>------------------------------------------------------------------------------------<br>sn date UTS UTM<br><a href="tel:142 2000 06 23 1226">142 2000 06 23 1226</a> 1226<br>Spacecraft is ac<br>Position: X = 239.9 Re; Y = 36.7 Re; Z = -0.7 Re</p> <p>Time windows<br>Upstream: 5 to 10 minutes before shock<br>Downstream: 5 to 10 minutes after shock</p> <p>Solar wind plasma and IMF<br> Bx By Bz Vx Vy Vz N T<br>Upstream 5.448 -3.946 -4.568 -399.958 18.948 -14.969 6.941 99731.6<br>Downstream 14.317 -1.766 -16.125 -508.734 37.628 -117.578 17.429 224496.1</p> <p>Computed parameters<br> dp1 dp2 Xdp Xb Xn vs_rh vA cs<br>1.864 7.989 4.286 2.661 2.511 604.778 67.317 52.384</p> <p>Minimum SMR index in the 2-hour window following shock impact: -9.60 nT</p> <p> nx ny nz thxn phiyn thbn vs vfms Ma Ms<br>MC -0.323 -0.944 0.070 108.831 175.779 78.309 127.332 85.704 0.255 0.200<br>MX1 -0.796 0.191 -0.575 142.729 -71.590 72.348 578.268 86.195 3.681 2.874<br>MX2 -0.798 0.133 -0.587 142.962 -77.223 74.365 579.175 86.010 3.693 2.890<br>MX3 -0.798 0.107 -0.592 142.980 -79.739 75.273 578.920 85.933 3.694 2.894<br>VC -0.722 0.124 -0.681 136.205 -79.682 80.717 551.670 85.556 3.720 2.927</p> <p>Type cdf['SHOCK'].attrs for a description of all shock variables and parameters.</p> <p>More details about this list and methods for shock normal calculations can be found in:</p> <p>Oliveira, D. M. (2023). Interplanetary Shock Data Base. Frontiers in Astronomy and Space Science. (Under review) </p>
Wallhack1.8k Dataset | Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition
<p>This repository contains the <strong>Wallhack1.8k dataset</strong> for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the <strong>CAD models</strong> (of 3D-printable parts) of the WiFi systems proposed in [2].</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: <a href="https://github.com/StrohmayerJ/wallhack1.8k" target="_blank" rel="noopener">https://github.com/StrohmayerJ/wallhack1.8k</a></p> <p><strong>Dataset Description</strong></p> <p>The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).</p> <p>To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:</p> <ul> <li>LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system</li> <li>LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system</li> <li>NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system</li> <li>NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system</li> </ul> <p>These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector <em>H</em> being stored in the "data" field and the class label being stored in the "class" field. <em>H </em>is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of <em>H</em> (e.g., via <em>numpy.abs(H)</em>) yields the subcarrier amplitudes <em>A</em>.</p> <p>To extract the 52 L-LTF subcarriers used in [1], the following indices of <em>A</em> are to be selected:</p> <pre><code># 52 L-LTF subcarriers csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]</code></pre> <p>Additional 56 HT-LTF subcarriers can be selected via:</p> <pre><code># 56 HT-LTF subcarriers csi_valid_subcarrier_index += [i for i in range(66, 94)] csi_valid_subcarrier_index += [i for i in range(95, 123)]</code></pre> <p>For more details on subcarrier selection, see <a href="https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html">ESP-IDF</a> (Section Wi-Fi Channel State Information) and <a href="https://github.com/espressif/esp-csi">esp-csi</a>.</p> <p>Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the <em>spectrograms/</em> directory.</p> <p>The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label]</p> <ul> <li>Spectrogram index: [0, ..., n]</li> <li>Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."</li> <li>Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.</li> </ul> <p><strong>Dataset Overview:</strong></p> <p>Table 1: Raw WiFi packet sequences.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td><em>"no presence" / label 0</em></td> <td><em>"walking" / label 1</em></td> <td><em>"walking + arm-waving" / label 2</em></td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4</td> <td>20</td> <td>20</td> <td><strong>44</strong></td> </tr> </tbody> </table> <p>Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td> <p><em>"no presence" / </em> label 0</p> </td> <td> <p><em>"walking"</em> / label 1</p> </td> <td><em>"walking + arm-waving" / </em>label 2</td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>149</td> <td>154</td> <td>155</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>149</td> <td>160</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>148</td> <td>150</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>143</td> <td>147</td> <td>147</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>589</td> <td>611</td> <td>606</td> <td><strong>1,806</strong></td> </tr> </tbody> </table> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2].</p> <p>[1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, <em>In IFIP International Conference on Artificial Intelligence Applications and Innovations</em> (pp. 42-56). Cham: Springer Nature Switzerland<em>,</em> doi: <a href="https://doi.org/10.1007/978-3-031-63211-2_4" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-63211-2_4</a>.</p> <p>[2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: <a href="https://doi.org/10.1109/ICIP51287.2024.10647666" target="_blank" rel="noopener">https://doi.org/10.1109/ICIP51287.2024.10647666</a>.</p> <p>BibTeX citations:</p> <pre>@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}}<br><br>@INPROCEEDINGS{10647666,<br> author={Strohmayer, Julian and Kampel, Martin},<br> booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, <br> title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition}, <br> year={2024},<br> volume={},<br> number={},<br> pages={3594-3599},<br> keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32},<br> doi={10.1109/ICIP51287.2024.10647666}}<br><br><br></pre>
Supporting data to the paper "Modelling charge profiles of electric vehicles based on charges data"
<p>This dataset contains the<em> underling data</em> and the <em>extended data</em> for the paper Modelling charge profiles of electric vehicles based on charges data”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p>HISTORIC DATA 2019 ELECTROLINERES AMB.csv: contains information on the charge events at the public charging points managed by the municipality in the metropolitan area of Barcelona in 2019. Fields are: charging point name; connector typology and number; charge start time; charge stop time; charge duration in minutes, energy delivered in kWh; vehicle manufacturer (optional); vehicle model (optional).</p> <p>STATIC INFORMATION CHARGING POINTS AMB 29042020.csv: contains the information about the public charging points of the metropolitan area of Barcelona. Fields are: charger typology (Quick/Normal); Charging point name and address; OCCP version; charger location; longitude; latitude; 7 flag fields for the connector type; observations; charging point maker.</p> <p>Lataustapahtumat, julkiset latauslaitteet 2019.csv: contains the information about the Turku Energia charge events for the city of Turku in 2019. Fields are: date of record creation, Station ID, Station name, charge start time, charge stop time, charge duration in minutes, energy delivered in Wh, Plug type (AC 22 kW/DC 50 kW), Cumulative energy delivered in the year (Wh), Average charge power (W)</p> <p>EV.csv: containes data on battery size retrived from vehicle datasheet or manufacturer website. Fields are: record ID, vehicle manufacturer ; vehicle model; battery size in kWh.</p> <p>Charge2019_EV_AMB.csv: contains the data on charge requests ( HISTORIC DATA 2019 ELECTROLINERES AMB.csv ) combined with the information on vehicle battery (EV.csv).</p> <p> </p>
Underlying data - Digital Twin for Rainbow Trout (Oncorhynchus mykiss) land-based aquaculture
<p>Datasets for replicating Figures 5, 6, 7 and 8 of the article "Digital twins for land-based aquaculture: a case study for rainbow trout (<em>Oncorhynchus mykiss</em>)", by Adriano C. Lima, Edouard Royer, Matteo Bolzonella, and Roberto Pastres.</p>
MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles (Data)
<p>Origin project and figures used for the article "MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles", published in <em>Chemosensors</em> on 04 Aug 2021.</p>
VirHunter: a deep learning-based method for detection of novel RNA viruses in plant sequencing data
<p>This storage contains 2 archives: toy datasets to test the training of the VirHunter and weights of the fully trained VirHunter models for 3 host species (peach, grapevine, sugar beet) and for fragment sizes 500 and 1000. .</p> <p>The toy dataset consists of 3 archived files: 'viruses.fasta', 'host.fasta', 'bacteria.fasta'.</p> <p>'viruses.fasta' contains 10000 randomly selected plant viruses from the virus dataset described in the paper.</p> <p>'host.fasta' consists of peach chromosome 2.</p> <p>'bacteria.fasta' consists of 10 bacterial genomes selected randomly: GCF_000284415, GCF_000590555, GCF_001548055, GCF_002795265, GCF_003330825, GCF_003957805, GCF_005845345, GCF_009176625, GCF_010748935, GCF_014681765</p> <p> </p>
Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective
<p>Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. <em>Functional Ecology, </em>2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.</p>
Efficient embryoid-based method to improve generation of optic vesicles from human induced pluripotent stem cells data
<p>Animal models have provided many insights into ocular development and disease, but they remain suboptimal for understanding human oculogenesis. Eye development requires spatiotemporal gene expression patterns and disease phenotypes can differ significantly between humans and animal models, with patient-associated mutations causing embryonic lethality reported in some animal models. The emergence of human induced pluripotent stem cell (hiPSC) technology has provided a new resource for dissecting the complex nature of early eye morphogenesis through the generation of three-dimensional (3D) cellular models. By using patient-specific hiPSCs to generate <em>in vitro </em>optic vesicle-like models, we can enhance the understanding of early developmental eye disorders and provide a pre-clinical platform for disease modelling and therapeutics testing. A major challenge of <em>in vitro </em>optic vesicle generation is the low efficiency of differentiation in 3D cultures. To address this, we adapted a previously published protocol of retinal organoid differentiation to improve embryoid body formation using a microwell plate. Established morphology, upregulated transcript levels of known early eye-field transcription factors and protein expression of standard retinal progenitor markers confirmed the optic vesicle/presumptive optic cup identity of <em>in vitro </em>models between day 20 and 50 of culture. This adapted protocol is relevant to researchers seeking a physiologically relevant model of early human ocular development and disease with a view to replacing animal models.</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces'
<p>Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces' by Kuppusamy Senthil Kumar et al. The two versions of Fig. 4d datasets correspond to the preprint version (https://zenodo.org/record/4905692#.Ymj9odpBxaQ) and publication version (https://www.nature.com/articles/s41467-021-22383-x), since a new set of data was taken during the review process. </p>
Fine-scale population spatialization data of China in 2018 based on real location-based big data
<p><strong>This data contains a geospatial population raster layer in GeoTIFF format with 1*1 km resolution for 31 provincial regions (2851 counties) of China in 2018 (pop2018.tif). It also provides the Tencent positioning data in 2018 (TN_hSum2018.tif), the table of statistical population of 2851 counties (statistical_population_2018_china_county.xls) and its vector map (statisitcal_pop.shp) and codes (code.docx).</strong></p>
Survey Base Calculation tool. Raw data.
<p>Raw data from survey application of Perceptions towards the adoption of Bio-based fertilisers. </p>
Data and code for: A conceptual model-based sediment connectivity assessment for patchy agricultural catchments
<p>Authors: Pedro V G Batista, Peter Fiener, Simon Scheper, Christine Alewell</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Abstract</p> <p>The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. In specific, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the WaTEM/SEDEM model using high resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km<sup>2</sup>) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Metainformation</p> <p>This dataset includes:</p> <p>1 - The input data used for running the WaTEM/SEDEM model in the Baldegg catchment.</p> <p>2 - The discharge and sediment concentration data used for producing the sediment rating curves for the tributaries of the Lake Baldegg.</p> <p>3 - The model and sediment rating curve output data.</p> <p>4 - The R scripts for running the WaTEM/SEDEM model in the Baldegg catchment, the code for producing the sediment rating curves, and the code for summarising and analysing the model output data.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The sediment concentration and water discharge data were supplied by Robert Lovas, from the Department of Environment and Energy of the Canton of Lucerne.</p> <p>The model input data were adapted from freely available ©swisstopo geodata products:</p> <p>Swisstopo. SwissALTI3D. Das hoch aufgelöste Terrainmodell der Schweiz, 2014.</p> <p>Swisstopo. Swiss Map Vector 25 Beta, Das digitale Landschaftsmodell der Schweiz. 2018.</p> <p>Swisstopo. SwissTLM3D. Das grossmassstäbliche Topografische Landschaftsmodell der Schweiz, 2020.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further information we refer to our preprint: https://doi.org/10.5194/hess-2021-231</p> <p> </p> <p> </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.