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321 results for “performance tests”
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 7 . Test Accuracy with prograess generation
<p>In the training phase, the neural network weights errors are minimized and network design<br> problem which the objective function to an acceptable level. In test step we have better results<br> because weights of neural network are adjusted by genetic algorithm and back propagation method.<br> Of course achievement to accuracy with 83.5% is reason using of good feature with minimum error.</p>
Multisensor measurement of healthy adult performance during standardised motor function test battery
<p>This dataset contains inertial data from 4 wearable sensor nodes and 1 wearable patch worn by 20 healthy adult participants performing a series of physical functioning tests (including the short physical performance battery, the timed up and go test, a walking test and balance tests). Details of patient demographics, the physical functioning tests and of each sensor are contained in files in the main folder.</p> <p>Inertial data (accelerometer and gyroscope) is contained in two folders relating to each sensor type. The start and end time for each sensor can be taken from the details in each folder structure, as detailed below. The times given are specific to each sensor's monitoring system which are not exactly synchronised. As such, a manual synchronisation shaking protocol was followed where all sensors were strapped together and shaken three times in succession at the start of each data collection period. The physical functioning test times will also need to be synchronised.</p> <p>-> Inertial sensor data / (subject id).zip / (subject id) / (date_time_crossTest_SD_session#) /<br> -> Wearable inertial patch / (subject id) / (date)T(time) /</p>
Results from Performance Evaluation and Testing of Virtual Infrastructure Managers
<p>NFV leverages Cloud Computing principles to move the data-plane network functions from expensive, closed and proprietary hardware to so-called Virtual Network Functions (VNFs). We deal with the management of virtual computing resources (Unikernels) for the execution of VNFs. This functionality is performed by the Virtual Infrastructure Manager (VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. In this data set we report the results of a performance evaluation we have realized of three open source VIMs, namely OpenStack, Nomad and OpenVIM; both considering stock and the tuned versions. The VIMs and the performance evaluation tools that we employ are provided openly and can be downloaded from our repositories (<a href="https://github.com/superfluidity/openvim4unikernels">https://github.com/superfluidity/openvim4unikernels</a> and <a href="https://github.com/netgroup/vim-tuning-and-eval-tools">https://github.com/netgroup/vim-tuning-and-eval-tools</a>).</p>
Supplementary Materials to paper: Performance Testing of istSOS Under High Load Scenarios
<p>IPython notebook with data used for the analysis and generation of plots for the paper "Performance Testing of istSOS Under High Load Scenarios".</p>
Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network
<p>This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h2>Methods </h2> <p><em>Field trials </em></p> <p>The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566).</p> <p>Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m<sup>2</sup> plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m<sup>2</sup>, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator. </p> <p>The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the <em>extenso</em> conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.</p> <p> </p> <p><em>Data collection </em></p> <p>For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured.</p> <p>The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then.</p> <p>At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).</p>
Codes and catalogs for: Parametric testing of EQTransformer's performance against a high-quality, manually-picked catalog for reliable and accurate seismic phase picking
<p><strong>Codes and Catalogs for:</strong> "Parametric Testing of EQTransformer's Performance Against a High-Quality, Manually-Picked Catalog for Reliable and Accurate Seismic Phase Picking."</p> <p><strong>Codes:</strong></p> <ol> <li><strong>overlap_check.py:</strong> This script tests the overlap parameter of EQTransformer to help minimize detection inconsistencies.</li> <li><strong>picks_comparison_other_networks.py:</strong> This code evaluates the probability threshold of EQTransformer by obtaining the time differences between picks from a catalog and EQTransformer.</li> <li><strong>test_seisbench_eq_eqt.py:</strong> A comparative analysis between the native EQTransformer and its implementation in SeisBench.</li> </ol> <p><strong>Catalogs:</strong></p> <ol> <li><strong>picks_differences_all_years_0.01_mag_cat.csv:</strong> This catalog presents pick differences for the central Alpine Fault using the SAMBA network and manual picks from Michailos et al. (2019).</li> <li><strong>sed_picks.csv:</strong> A catalog that showcases pick differences derived from data obtained from the Swiss Seismological Service (SED).</li> </ol> <p><strong>Note:</strong> Versions <1.0 represent pre-acceptence files and should not be used.</p>
Dataset for triaxial monotonic and cyclic laboratory tests on sand HN31 with fines performed at Université Gustave Eiffel/GERS/SRO
<p>Data from monotonic and triaxial tests obtained during the thesis :</p> <p>Gobbi, S. Caractérisation de paramètres mécaniques d'un sol saturé à partir d'essais de laboratoire et calibration de lois de comportement sous charge dynamique par modélisation numérique, PhD thesis, Université Gustave Eiffel, 2020</p> <p>https://theses.hal.science/tel-03268600</p> <p> </p>
Supplementary material and supplementary data files for: Handling logical character dependency in phylogenetic inference: Extensive performance testing of assumptions and solutions using simulated and empirical data
Open the record for dataset details and reuse information.
An archive of data from Resonant Column and Cyclic Torsional Shear Tests performed on Italian Clays
<p>A large data-set of index and dynamic parameters measured from resonant column (RC) and cyclic torsional shear(CTS) tests on 170 undisturbed isotropically consolidated fine-grained specimens deriving from 90 sites in Central and Northern Italy is made available. Tests were all performed over the past 20 years at the Geotechnical Laboratory of the Civil and Environmental Engineering Department of the Florence University using the same apparatus and following the same standardized procedures.</p> <p>The experimental data are organized in an excel file (named as “Italian_Clays_Archive.xlsx”). For each tested sample, the main physical, index and dynamic properties measured are archived with the code number of the sample (No) in the sheet named as “Dataset” as well as any information available about the borehole from which the sample has been taken. The list and the meaning of the symbols used can be found in the sheet named as “Legend”. Other sheets containing borehole stratigraphy are named as “XX-ST” (where “XX” stands as the bore-hole code, BH) and they can be recalled directly from the “Dataset” sheet. Note that stratigraphy is given in its original format, when available. However, depth and thickness of each layer can be easily deduced by the figure provided and the soil lithology is well represented by the symbol used that are those generally adopted internationally. Finally, the sheets named as "YY-CTS-STEPZ" (where “YY” and “Z” stand as the sample code, No, and the step number, respectively) contain the shear stress and strain values measured after CTS tests at different steps (i.e. amplitudes of the cyclic dynamic torsional loading applied) during the 1st, 5th, 15th, 20th and 25<sup>th</sup>.and/or and/or the corresponding shear modulus and damping ratio calculated from the same cycles.</p> <p>The selected samples were taken mostly in Holocene and Pleistocene fluvio-lacustrine soil deposits at depths ranging from 1 m to 75 m below ground level and they mainly consist of normally and over-consolidated clayey silts or clays (1 < OCR < 9.4) of medium-to-high plasticity (4 < PI < 84), with very low-to high consistency (-1< Ic < 1.9) and initial void ratio, e<sub>0</sub>, ranging between 0.175 and 2.456. The database also includes some samples of organic clays of low consistency, very high water content and void ratio and low unit weight. The initial (small strain) values of shear modulus, G<sub>0</sub>, and damping ratio, D<sub>0</sub>, range between 21 MPa and 292 MPa and between 0.8% and 5.1%, respectively. The smallest and the largest shear strain values induced by RC and CTS tests are 1.9x10<sup>-5</sup> % and 6.3x10<sup>-1</sup>%, respectively.</p>
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance
<p>This Zenodo repository contains 100 copies of the model BERT fine-tuned on the MNLI dataset, created for the paper "BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance." Please see the project GitHub page for more details about using these models and how to cite any such usage: https://github.com/tommccoy1/hans/tree/master/berts_of_a_feather</p>
WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction - A Model-Driven Approach for Session-Based Application Systems.
<p>Supplementary material for the paper: "WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction".</p> <p>Included in the supplementary material are the evaluation results.</p> <p>The WESSBAS software relevant to the paper is available via https://github.com/Wessbas/</p> <p>The WESSBAS UI is available as a password-protected (password: wessbasui) ZIP file:</p> <p>https://dl.dropboxusercontent.com/u/81621779/wessbas.ui.zip (--- WESSBAS GUI (license confirmation pending, i.e., not on GitHub, yet))</p>
Dataset: Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: a moment of truth?
<p><span>Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; <em>Δ</em>) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (<em>Gallus gallus domesticus</em>) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues,<em> δ</em><sup>13</sup>C and <em>δ </em><sup>15</sup>N values differed among dietary groups. <em>Δ</em><sup>13</sup>C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For <em>Δ</em><sup>15</sup>N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type</span><span><span>, </span></span><span><span>with f</span></span><span>emales having higher liver <em>Δ</em><sup>15</sup>N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, they demonstrate experimentally that these types of models reliably characterize consumed diets when appropriately parameterized.<span> </span></span></p>
PsySuite: Performing multimodal psychophysical testing within the Android environment
<p>Data used for either the hardware or the behavioral validation of the Android APP <em>PsySuite</em>.</p>
Data from Test Performance Study Euphresco project 2019-A-327
<p>Data of the test performance study organised in the framework of the Euphresco project 2019-A-327 'Validation of molecular tests for the detection of tomato brown rugose fruit virus<em> </em>(ToBRFV) in seed of tomato and pepper'</p>
Diurnal variation in Uchikomi fitness test performance: Influence of warm-up protocols
<table> <tbody> <tr> <td>group</td> <td>time</td> <td>total scores</td> <td>uft(a+b)</td> <td>heart rate</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>31</td> <td>11</td> <td>183.83</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>42</td> <td>14</td> <td>178.17</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>27</td> <td>9</td> <td>175.17</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>42</td> <td>14</td> <td>179.33</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>42</td> <td>14</td> <td>179.67</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>42</td> <td>15</td> <td>182.50</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>42</td> <td>14</td> <td>181.67</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>42</td> <td>15</td> <td>182.50</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>42</td> <td>14</td> <td>178.17</td> </tr> <tr> <td>NWU</td> <td>morning</td> <td>42</td> <td>14</td> <td>179.67</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>46</td> <td>16</td> <td>185.17</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>43</td> <td>15</td> <td>186.67</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>49</td> <td>17</td> <td>177.33</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>41</td> <td>14</td> <td>182.67</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>49</td> <td>17</td> <td>179.17</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>48</td> <td>16</td> <td>187.00</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>41</td> <td>14</td> <td>158.50</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>48</td> <td>16</td> <td>187.00</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>43</td> <td>15</td> <td>186.67</td> </tr> <tr> <td>NWU</td> <td>evening</td> <td>49</td> <td>17</td> <td>179.17</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>38</td> <td>14</td> <td>183.83</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>53</td> <td>22</td> <td>187.00</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>33</td> <td>12</td> <td>175.17</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>52</td> <td>19</td> <td>188.67</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>41</td> <td>14</td> <td>173.00</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>68</td> <td>30</td> <td>176.67</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>49</td> <td>18</td> <td>166.67</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>68</td> <td>30</td> <td>176.67</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>53</td> <td>22</td> <td>187.00</td> </tr> <tr> <td>FWU</td> <td>morning</td> <td>41</td> <td>14</td> <td>173.00</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>44</td> <td>16</td> <td>183.83</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>55</td> <td>20</td> <td>188.50</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>51</td> <td>16</td> <td>172.50</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>62</td> <td>27</td> <td>185.17</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>63</td> <td>27</td> <td>173.83</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>68</td> <td>25</td> <td>185.83</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>52</td> <td>21</td> <td>156.83</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>68</td> <td>25</td> <td>185.83</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>55</td> <td>20</td> <td>188.50</td> </tr> <tr> <td>FWU</td> <td>evening</td> <td>63</td> <td>27</td> <td>173.83</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>55</td> <td>19</td> <td>169.50</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>62</td> <td>23</td> <td>173.50</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>55</td> <td>19</td> <td>169.50</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>56</td> <td>19</td> <td>179.33</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>57</td> <td>20</td> <td>168.33</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>68</td> <td>24</td> <td>176.33</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>50</td> <td>18</td> <td>158.67</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>68</td> <td>24</td> <td>176.33</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>62</td> <td>23</td> <td>173.50</td> </tr> <tr> <td>SWU</td> <td>morning</td> <td>57</td> <td>20</td> <td>168.33</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>61</td> <td>21</td> <td>171.67</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>66</td> <td>24</td> <td>174.50</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>60</td> <td>20</td> <td>171.00</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>61</td> <td>22</td> <td>180.33</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>57</td> <td>20</td> <td>167.83</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>68</td> <td>23</td> <td>176.33</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>54</td> <td>18</td> <td>160.83</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>70</td> <td>24</td> <td>177.00</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>62</td> <td>23</td> <td>173.50</td> </tr> <tr> <td>SWU</td> <td>evening</td> <td>57</td> <td>20</td> <td>168.33</td> </tr> </tbody> </table>
A Thirty Minute Nap Enhances Performance in Running Based Anaerobic Sprint Test During and After Ramadan Observance
<table> <tbody> <tr> <td> <p>Age</p> </td> <td> <p>height</p> </td> <td> <p> weight</p> </td> <td> <p>BMI</p> </td> <td> <p>MAX POWER</p> <p>(Watt)-DURING RAMADAN NO NAP</p> </td> <td> <p>MİNİMUM POWER</p> <p>(Watt)-DURING RAMADAN NO NAP</p> </td> <td> <p>AVERAGE POWER</p> <p>(Watt)-DURING RAMADAN NO NAP</p> </td> <td> <p>FATİGUE İNDEX</p> <p>(%)-DURING RAMADAN NO NAP</p> </td> <td> <p>MAX POWER</p> <p>(Watt)- AFTER RAMADAN NO NAP</p> </td> <td> <p>MİNİMUM POWER</p> <p>(Watt)- AFTER RAMADAN NO NAP</p> </td> <td> <p>AVERAGE POWER</p> <p>(Watt)- AFTER RAMADAN NO NAP</p> </td> <td> <p>FATİGUE İNDEX</p> <p>(%)- AFTER RAMADAN NO NAP</p> </td> <td> <p>MAX POWER</p> <p>(Watt)- DURING RAMADAN 30M NAP</p> </td> <td> <p>MİNİMUM POWER</p> <p>(Watt)- DURING RAMADAN 30M NAP</p> </td> <td> <p>AVERAGE POWER</p> <p>(Watt)- DURING RAMADAN 30M NAP</p> </td> <td> <p>FATİGUE İNDEX</p> <p>(%)- DURING RAMADAN 30M NAP</p> </td> <td> <p>MAX POWER</p> <p>(Watt)- AFTER RAMADAN 30M NAP</p> </td> <td> <p>MİNİMUM POWER</p> <p>(Watt)- AFTER RAMADAN 30M NAP</p> </td> <td> <p>AVERAGE POWER</p> <p>(Watt)- AFTER RAMADAN 30M NAP</p> </td> <td> <p>FATİGUE İNDEX</p> <p>(%)- AFTER RAMADAN 30M NAP</p> </td> </tr> <tr> <td> <p>24.00</p> </td> <td> <p>177.00</p> </td> <td> <p>89.00</p> </td> <td> <p>28.40</p> </td> <td> <p>388</p> </td> <td> <p>303</p> </td> <td> <p>334</p> </td> <td> <p>2.05</p> </td> <td> <p>432</p> </td> <td> <p>312</p> </td> <td> <p>365</p> </td> <td> <p>2.97</p> </td> <td> <p>453</p> </td> <td> <p>326</p> </td> <td> <p>392</p> </td> <td> <p>3.23</p> </td> <td> <p>466</p> </td> <td> <p>350</p> </td> <td> <p>411</p> </td> <td> <p>3.02</p> </td> </tr> <tr> <td> <p>23.00</p> </td> <td> <p>178.00</p> </td> <td> <p>70.00</p> </td> <td> <p>22.10</p> </td> <td> <p>513</p> </td> <td> <p>399</p> </td> <td> <p>469</p> </td> <td> <p>3.33</p> </td> <td> <p>539</p> </td> <td> <p>428</p> </td> <td> <p>485</p> </td> <td> <p>3.27</p> </td> <td> <p>560</p> </td> <td> <p>451</p> </td> <td> <p>513</p> </td> <td> <p>3.29</p> </td> <td> <p>606</p> </td> <td> <p>468</p> </td> <td> <p>535</p> </td> <td> <p>4.24</p> </td> </tr> <tr> <td> <p>21.00</p> </td> <td> <p>170.00</p> </td> <td> <p>76.00</p> </td> <td> <p>26.30</p> </td> <td> <p>563</p> </td> <td> <p>444</p> </td> <td> <p>506</p> </td> <td> <p>3.47</p> </td> <td> <p>585</p> </td> <td> <p>456</p> </td> <td> <p>516</p> </td> <td> <p>3.80</p> </td> <td> <p>605</p> </td> <td> <p>482</p> </td> <td> <p>533</p> </td> <td> <p>4.82</p> </td> <td> <p>640</p> </td> <td> <p>475</p> </td> <td> <p>538</p> </td> <td> <p>4.92</p> </td> </tr> <tr> <td> <p>22.00</p> </td> <td> <p>174.00</p> </td> <td> <p>67.00</p> </td> <td> <p>22.10</p> </td> <td> <p>711</p> </td> <td> <p>376</p> </td> <td> <p>507</p> </td> <td> <p>10.12</p> </td> <td> <p>733</p> </td> <td> <p>402</p> </td> <td> <p>538</p> </td> <td> <p>10.24</p> </td> <td> <p>791</p> </td> <td> <p>432</p> </td> <td> <p>575</p> </td> <td> <p>11.34</p> </td> <td> <p>866</p> </td> <td> <p>448</p> </td> <td> <p>613</p> </td> <td> <p>13.48</p> </td> </tr> <tr> <td> <p>20.00</p> </td> <td> <p>170.00</p> </td> <td> <p>72.00</p> </td> <td> <p>24.90</p> </td> <td> <p>673</p> </td> <td> <p>367</p> </td> <td> <p>503</p> </td> <td> <p>9.02</p> </td> <td> <p>697</p> </td> <td> <p>356</p> </td> <td> <p>534</p> </td> <td> <p>10.24</p> </td> <td> <p>741</p> </td> <td> <p>381</p> </td> <td> <p>570</p> </td> <td> <p>11.03</p> </td> <td> <p>808</p> </td> <td> <p>412</p> </td> <td> <p>608</p> </td> <td> <p>12.38</p> </td> </tr> <tr> <td> <p>19.00</p> </td> <td> <p>172.00</p> </td> <td> <p>62.00</p> </td> <td> <p>21.00</p> </td> <td> <p>566</p> </td> <td> <p>290</p> </td> <td> <p>434</p> </td> <td> <p>8.13</p> </td> <td> <p>553</p> </td> <td> <p>305</p> </td> <td> <p>445</p> </td> <td> <p>7.35</p> </td> <td> <p>579</p> </td> <td> <p>311</p> </td> <td> <p>468</p> </td> <td> <p>8.11</p> </td> <td> <p>604</p> </td> <td> <p>346</p> </td> <td> <p>502</p> </td> <td> <p>7.99</p> </td> </tr> <tr> <td> <p>21.00</p> </td> <td> <p>171.00</p> </td> <td> <p>73.00</p> </td> <td> <p>25.00</p> </td> <td> <p>769</p> </td> <td> <p>295</p> </td> <td> <p>513</p> </td> <td> <p>13.84</p> </td> <td> <p>789</p> </td> <td> <p>304</p> </td> <td> <p>527</p> </td> <td> <p>14.26</p> </td> <td> <p>824</p> </td> <td> <p>329</p> </td> <td> <p>563</p> </td> <td> <p>14.91</p> </td> <td> <p>878</p> </td> <td> <p>390</p> </td> <td> <p>610</p> </td> <td> <p>15.17</p> </td> </tr> <tr> <td> <p>19.00</p> </td> <td> <p>179.00</p> </td> <td> <p>76.00</p> </td> <td> <p>23.70</p> </td> <td> <p>908</p> </td> <td> <p>305</p> </td> <td> <p>551</p> </td> <td> <p>17.56</p> </td> <td> <p>944</p> </td> <td> <p>330</p> </td> <td> <p>574</p> </td> <td> <p>18.18</p> </td> <td> <p>982</p> </td> <td> <p>362</p> </td> <td> <p>600</p> </td> <td> <p>18.67</p> </td> <td> <p>1029</p> </td> <td> <p>394</p> </td> <td> <p>647</p> </td> <td> <p>19.57</p> </td> </tr> <tr> <td> <p>22.00</p> </td> <td> <p>174.00</p> </td> <td> <p>71.00</p> </td> <td> <p>23.50</p> </td> <td> <p>717</p> </td> <td> <p>358</p> </td> <td> <p>517</p> </td> <td> <p>10.72</p> </td> <td> <p>735</p> </td> <td> <p>385</p> </td> <td> <p>543</p> </td> <td> <p>10.63</p> </td> <td> <p>753</p> </td> <td> <p>399</p> </td> <td> <p>565</p> </td> <td> <p>10.93</p> </td> <td> <p>806</p> </td> <td> <p>434</p> </td> <td> <p>606</p> </td> <td> <p>11.74</p> </td> </tr> <tr> <td> <p>21.00</p> </td> <td> <p>183.00</p> </td> <td> <p>77.00</p> </td> <td> <p>23.00</p> </td> <td> <p>1001</p> </td> <td> <p>499</p> </td> <td> <p>675</p> </td> <td> <p>15.91</p> </td> <td> <p>1028</p> </td> <td> <p>540</p> </td> <td> <p>711</p> </td> <td> <p>15.78</p> </td> <td> <p>1035</p> </td> <td> <p>592</p> </td> <td> <p>760</p> </td> <td> <p>14.67</p> </td> <td> <p>1100</p> </td> <td> <p>606</p> </td> <td> <p>804</p> </td> <td> <p>16.66</p> </td> </tr> </tbody> </table> <p> </p>
Academic Excellence, Website Quality, SEO Performance: Is there a Correlation? - Dataset of measurements, test results and calculated ratings.
<p>This Dataset, in two files of xlsx format, contains the data of all measurements, test results and calculated ratings as they are described in the methodology of the research article "Academic Excellence, Website Quality, SEO Performance: Is there a Correlation".</p>
Testing the Skill-based Approach: Consolidation strategy impacts Attentional Blink performance
<p>Data accompanying the journal article Testing the Skill-based Approach: Consolidation strategy impacts Attentional Blink performance published in PLOS ONE. It contains the datasets (without personal information) for both experiments and a small R file that can be used to read in the data.</p>
MongoDB Performance Test Result Dataset
<p>This artifact contains performance test results from MongoDB’s internal performance testing system.</p>
Assessing the prevalence of Female Genital Schistosomiasis and comparing the acceptability and performance of health worker-collected and self-collected cervical-vaginal swabs using PCR testing among women in North-Western Tanzania: the ShWAB study
<p>Female genital schistosomiasis (FGS) is a severe neglected disease, caused by infection with <em>Schistosoma haematobium</em>. The WHO has prioritized the improvement of diagnostics for FGS and previous studies have explored the PCR-based detection of <em>Schistosoma</em> DNA on genital specimens, with encouraging results. We aimed to determine the prevalence of FGS among women living in an endemic district in North-western Tanzania, applying and preliminary comparing self-collected and operator-collected cervical-vaginal swabs followed by PCR, and to assess the acceptability of these sampling procedures.</p>
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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.