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47,155 results for “evaluations”
Virtual Delivery Trees Evaluation Results
<p>The artifacts represent evaluation results of real world networks having more than 40 nodes from <a href="http://www.topology-zoo.org/">Network Topology Zoo</a>. The applied topologies are listed in following table, sorted in descending order by diameter (d) and nodal degree fluctuation (σ^2)).</p> <table> <thead> <tr> <th scope="col">Topology</th> <th scope="col">|V|</th> <th scope="col">|E|</th> <th scope="col"><k></th> <th scope="col">σ^2</th> <th scope="col">d</th> </tr> </thead> <tbody> <tr> <td>Chinanet</td> <td>42</td> <td>66</td> <td>1.5</td> <td>10.52</td> <td>4</td> </tr> <tr> <td>Litnet</td> <td>43</td> <td>43</td> <td>0.98</td> <td>5.04</td> <td>4</td> </tr> <tr> <td>Cernet</td> <td>41</td> <td>58</td> <td>1.32</td> <td>5.6 |</td> <td>5 |</td> </tr> <tr> <td>Ntt</td> <td>32</td> <td>65</td> <td>1.48</td> <td>7.07</td> <td>6</td> </tr> <tr> <td>Cesnet200706</td> <td>44</td> <td>51</td> <td>1.16</td> <td>6.27</td> <td>6</td> </tr> <tr> <td>Carnet</td> <td>44</td> <td>43</td> <td>0.98</td> <td>5.48</td> <td>6</td> </tr> <tr> <td>Dfn</td> <td>50</td> <td>78</td> <td>1.77</td> <td>5.31</td> <td>6</td> </tr> <tr> <td>Telcove</td> <td>71</td> <td>70</td> <td>1.59</td> <td>9.13</td> <td>7</td> </tr> <tr> <td>Forthnet</td> <td>62</td> <td>62</td> <td>1.41</td> <td>7.72</td> <td>7</td> </tr> <tr> <td>Bellsouth</td> <td>51</td> <td>66</td> <td>1.5 |</td> <td>7.55 |</td> <td>7 |</td> </tr> <tr> <td>Garr200902</td> <td>54</td> <td>68</td> <td>1.55</td> <td>5.13</td> <td>7</td> </tr> <tr> <td>Arnes</td> <td>41</td> <td>57</td> <td>1.3 |</td> <td>4.53 |</td> <td>7 |</td> </tr> <tr> <td>BeyondTheNetwork</td> <td>53</td> <td>65</td> <td>1.48</td> <td>3.98</td> <td>7</td> </tr> <tr> <td>Uunet</td> <td>49</td> <td>84</td> <td>1.91</td> <td>7.38</td> <td>8</td> </tr> <tr> <td>Tw</td> <td>71</td> <td>115</td> <td>2.61</td> <td>| 5.58</td> <td>| 8</td> </tr> <tr> <td>Uninett</td> <td>71</td> <td>97</td> <td>2.2</td> <td>3.12</td> <td>9</td> </tr> <tr> <td>Renater2010</td> <td>43</td> <td>56</td> <td>1.27</td> <td>3.08</td> <td>9</td> </tr> <tr> <td>Surfnet</td> <td>50</td> <td>68</td> <td>1.55</td> <td>3.36</td> <td>11</td> </tr> <tr> <td>Iris</td> <td>51</td> <td>64</td> <td>1.45</td> <td>2.16</td> <td>11</td> </tr> <tr> <td>Palmetto</td> <td>45</td> <td>64</td> <td>1.45</td> <td>2.57</td> <td>12</td> </tr> <tr> <td>BtLatinAmerica</td> <td>45</td> <td>50</td> <td>1.14</td> <td>1.87</td> <td>12</td> </tr> <tr> <td>Bellcanada</td> <td>48</td> <td>64</td> <td>1.45</td> <td>2.59</td> <td>13</td> </tr> <tr> <td>Sanet</td> <td>43</td> <td>45</td> <td>1.02</td> <td>1.66</td> <td>13</td> </tr> <tr> <td>LambdaNet</td> <td>42</td> <td>46</td> <td>1.05</td> <td>1.57</td> <td>13</td> </tr> <tr> <td>HiberniaGlobal</td> <td>55</td> <td>81</td> <td>1.84</td> <td>2.72</td> <td>16</td> </tr> <tr> <td>Ntelos</td> <td>47</td> <td>58</td> <td>1.32</td> <td>1.92</td> <td>17</td> </tr> <tr> <td>RedBestel</td> <td>84</td> <td>93</td> <td>2.11</td> <td>0.85</td> <td>28</td> </tr> <tr> <td>VtlWavenet2008</td> <td>88</td> <td>92</td> <td>2.09</td> <td>0.11</td> <td>31</td> </tr> </tbody> </table> <p>The evaluation results consist of three major parts:</p> <ol> <li><em>Raw Data</em>: Configuration and results of all simulation experiments as CSV files.</li> <li><em>Strategy Results</em>: Visualization of the test results for each topology.</li> <li><em>Best Strategies</em>: Highlighting of the best strategies across all topologies.</li> </ol> <p>Therein, the "Raw Data<em>"</em> comprise the configuration of or simulation experiments and the simulation results. Each line stands for a single simulation run.</p> <p><em>"</em>Strategy Results<em>" </em>and "Best Strategies" accompany the results presented in the paper. Result plots in the paper are excerpts from the plots in this repository. See below for further details.</p> <p><strong>Raw Data</strong></p> <p>Both, the configuration of a run and its results correspond to one line within a CSV file in subfolder <code>./raw</code>. Each file comprises the results of a replication.<br> </p> <pre><code>raw ├── results_0.csv ├── results_1.csv ├── ... └── results_9.csv</code></pre> <p>The raw data of a CSV file is structured as follows.</p> <table> <thead> <tr> <th scope="col">Column</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>topo</td> <td>Topology name.</td> </tr> <tr> <td>peers</td> <td>Number of nodes.</td> </tr> <tr> <td>edges</td> <td>Number of links.</td> </tr> <tr> <td>p_publishers</td> <td>Proportion of nodes acting as publisher (15% - 45%).</td> </tr> <tr> <td>p_subscriber</td> <td>Proportion of nodes acting as subscriber (15% - 45%).</td> </tr> <tr> <td>n_rules</td> <td>Number of allowed rules per switch.</td> </tr> <tr> <td>distances</td> <td>Flag for consideration of geographical distances (currently not used).</td> </tr> <tr> <td>strategy</td> <td>Applied virtual tree strategy.</td> </tr> <tr> <td>distribution</td> <td>Distribution method for client (uniform, distant, nearby)</td> </tr> <tr> <td>n_cluster</td> <td>Number of simulated clusters within the topology.</td> </tr> <tr> <td>p_change</td> <td>Churn rate of clients (0% - 100%).</td> </tr> <tr> <td>pub_change</td> <td>Flag for publisher migration (currently not used).</td> </tr> <tr> <td>tree_count</td> <td>Number of virtual trees installed in the network.</td> </tr> <tr> <td>selected_subscribers</td> <td>Avg. number of subscribers addressed by a publisher</td> </tr> <tr> <td>init_cost</td> <td>Avg. number of entries of a non-optimized distribution tree (per notification)</td> </tr> <tr> <td>trees</td> <td>Avg. proportion of tree entries per notification.</td> </tr> <tr> <td>stops</td> <td>Avg. proportion of stop entries per notification.</td> </tr> <tr> <td>hops</td> <td>Avg. proportion of hop entries per notification.</td> </tr> <tr> <td>final_cost</td> <td>Aggregated proportions (trees + stops + hops).</td> </tr> <tr> <td>datetime</td> <td>Timestamp of the simulation run.</td> </tr> </tbody> </table> <p><strong>Result Charts</strong></p> <p>The simulation results are visualized in <code>plots.md</code> or <code>plots.html</code>, ordered according above topology table.</p> <p>Each topology accompanys following:<br> - Topology figures with the computed <em>Clusters</em> therein.<br> - Line charts outlining the behavior of the strategies over changing <em>Number of Flow Rules</em>.<br> - Bar charts outlining the strategies' performance for different <em>Migration Scenarios</em>.</p> <p>Details of the figures and diagrams are described next.</p> <ul> <li>Clusters:<br> Visualization of exemplary groups within the topology, computed by `clusters` and `partitions` strategy. The clusters strategy assigns 60% of a network's nodes to cluster groups; the partition strategy, in contrast, assigns all nodes to groups. Both strategies are described in Sec. III.<br> </li> <li>Number of Flow Rules:<br> Results for varying number of rules (from 5 to 40) per switch, as described in Sec. IV. The charts are organized in a 3 x 3 matrix. A row of the matrix corresponds to different proportions of subscribers per publisher (15%, 30%, and 45%); a column corresponds to different distributions of clients (uniform, nearby and distant).<br> </li> <li>Migration Scenarios:<br> Results for different migration scenarios with a fixed number of rules (40 rules per switch), as described in Sec. V. Each bar group stands for a strategy and reflects the results of different migration rates (0%, 30%, 50%, 70%, 100%).</li> </ul> <p><strong>Best Strategies</strong></p> <p>Scatter plots in subfolder <code>./fluctuation</code> visualize the most efficient strategies for different migration scenarios by considering different proportions of subscribers per publisher (15%, 30%, and 45%). The plots show the results for a fixed number of subscribers (30% per publisher) and a churn rate of 100%. The strategies therein require the fewest labels in the header stack to encode a notification distribution tree, represented by the strategy's dot size</p>
Evaluation of background ionizing radiation dose variation in Ploiesti city area, 2022
<p>Measurements of background ionizing radiation dose rate, done in Ploiesti city area in 2022 (Romania), by the student Tripac Flavius Gabriel under supervision of Prof. Dr. Adrian Iftime as part of a diploma thesis project (C.Davila University of Medicine and Pharmacy). </p> <p>A graphical overview of the values is included (ploiesti_values_2022.png). <br> </p>
Evaluation of background ionizing radiation dose variation in Bucharest city area, 2022
<p>Measurements of background ionizing radiation dose rate, done in Bucharest city area in 2022, by the student Damalan Daria Liana under supervision of prof. Adrian Iftime as part of a diploma thesis project.</p> <p>A graphical overview of the values is included (bucharest_values_2022.png).</p>
Scans dataset for evaluation of AR-based assembly pipeline of half-timber dry-stone structures
<p><strong>Dataset description</strong></p> <p><em>The dataset is realized by the EPFL Laboratories of IBOIS, and EESD on the occasion of joint collaboration with the common goal of exploring augmented reality (AR)-based fabrication for half-timber dry-stone construction. It is open sourced and free to be used for anyone's own research.</em></p> <p>The current dataset has been employed to evaluate the designed digital fabrication pipeline. It consists of raw point clouds and reconstructed models of two digitized prototypes of one-layered dry-stone structures with a 1.7 m length, ~0.6 height, and 0.7 m width, each with a different composition of building units. The first wall presents 40 mineral by-products uniquely from by-products of quarry processes involving sawing and water-cutting. In contrast, the second wall features 30 mineral scraps issued from all mixed quarry's operations of transformations.</p> <p>The collection contains both the reconstructed models of the as-built artifacts and the corresponding recorded model of the AR-guided assembly processes for the two walls. The recorded model contains meshes of the placed stones accordingly to the developed geometric planner. By comparing the misalignment between each stone of both models (e.g. Root Mean Square Error distances between two set of points) is possible to obtain metrics about the performance of the proposed AR-assembly method.</p> <p> </p> <p><strong>Dataset labeling guide</strong></p> <table summary="test2"> <thead> <tr> <th scope="row">label</th> <th scope="col">content</th> </tr> </thead> <tbody> <tr> <th scope="row">01/02</th> <td>First or second wall.</td> </tr> <tr> <th scope="row">rs</th> <td>Raw data: unprocessed scans.</td> </tr> <tr> <th scope="row">ab</th> <td>As-built model: model presenting all the registered stones.</td> </tr> <tr> <th scope="row">rc</th> <td>Recorded model: collection of tracked stones during the construction. Each stone has being placed following the AR system is recorded in this sub-set.</td> </tr> <tr> <th scope="row">ev</th> <td>Evaluation set: documentation and working files for the evaluation of the developed AR assembly system.</td> </tr> <tr> <th scope="row">colored_stone</th> <td>Evaluation set outcome: a graphical representation of the misalignment between each stone of the as-built model (ab) with the recorded model (rc).</td> </tr> </tbody> </table> <p> </p> <p><strong>Equipement specs</strong></p> <p>All the raw scans present in the dataset have been obtained from a FARO Freestyle 2 equipped with a Mobile PC for live point cloud processing.</p> <pre><code>@manual{farofreestyle2, title = {FARO Freestyle 2 and Mobile PC}, year = 2022, month = feb, note = {User Manual}, organization = {FARO Technologies Inc.}, url = {https://downloads.faro.com/index.php/s/sqcRBipgSy9GaEq?dir=undefined&openfile=138985} } </code></pre> <p> </p> <p><strong>For extra info</strong></p> <p>The open-sourced code for the developed AR assembly:</p> <pre><a href="https://doi.org/10.5281/zenodo.7181087">https://doi.org/10.5281/zenodo.7181087</a></pre> <p>The complete dataset of digitized mineral scraps:</p> <pre><a href="https://doi.org/10.5281/zenodo.7189478">https://doi.org/10.5281/zenodo.7189478</a></pre> <p> </p> <p><strong>Version notes</strong></p> <p>- The current dataset is published as linked documentation to a future publication, yet not reviewed.</p> <p><strong>Change log</strong></p> <p>- Typos</p> <p>- Correct order of authors</p>
Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods
<p>EEG signals recorded from seven healthy subjects. On average, Seventy-three minutes of EEG data were recorded from 31 electrodes placed according to the extended 10-20 system. Signals are used in the paradigm-agnostic post-hoc labeled dataset generation framework for benchmarking of oscillatory neural decoding methods.</p>
TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image
<p>Unseparated Pulse Energy Spectrogram</p> <p>Processed audio data.</p> <p>Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it.</p> <p>I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification.</p> <p>For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied.</p> <p>I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning.</p> <p>Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.</p>
Histopathological Evaluation of Abdominal Aortic Aneurysms with Deep Learning: The HistAAA Dataset
<p>This dataset accompanies our manuscript "Histopathological Evaluation of Abdominal Aortic Aneurysms with Deep Learning" (<a title="Histopathological evaluation of abdominal aortic aneurysms with deep learning" href="https://www.medrxiv.org/content/10.1101/2024.04.23.24306178v1">1</a>) and comprises feature vectors extracted from digital whole-slide images (WSI) of abdominal aortic aneurysm (AAA) wall samples from 369 patients treated at three European centers and corresponding expert pathologist annotations. This dataset is intended to be used for computational modelling tasks including automated prediction of pathology-related variables such as inflammation, degradation of elastic fibers, and fibrosis (<a title="Histopathological evaluation of abdominal aortic aneurysms with deep learning" href="https://www.medrxiv.org/content/10.1101/2024.04.23.24306178v1">1</a>). </p> <p>Code for preprocessing is available at <a href="https://github.com/KatherLab/STAMP">https://github.com/KatherLab/STAMP</a>. Code for modeling is available at <a href="https://github.com/KatherLab/marugoto">https://github.com/KatherLab/marugoto</a>. Code for spatial heatmaps and top-attention tiles is available at <a href="https://github.com/KatherLab/highres-WSI-heatmaps/tree/AAA_heatmaps">https://github.com/KatherLab/highres-WSI-heatmaps/tree/AAA_heatmaps</a>. </p> <h3>Patient Cohort</h3> <p>Data from a total of 369 patients (84.6% male, mean age 69.1 ± 8.0 years, average maximum diameter 62.9 ± 15.7 mm) undergoing open AAA repair at the Technical University Munich (TUM, n = 287), the University Hospital Würzburg (UHW, n = 36) and the Medical University Vienna (MUV, n = 46), between 2005 and 2019, are included in this dataset. </p> <p>To generate annotations, aneurysm samples from the left anterior wall were independently analyzed by three pathologists as described previously (<a title="Abdominal aortic aneurysms harbor different histomorphology not associated with classic risk factors &ndash; the HistAAA study" href="https://www.medrxiv.org/content/10.1101/2024.04.16.24305904v1">2</a>). In brief, the following histopathological parameters were evaluated: Grade of inflammation in tunica media [none, minor, intermediate, major], grade of inflammation in adventitia [none, minor, intermediate, major], type of inflammation in adventitia and tunica media [none, acute, chronic], Histological Inflammation Scale of Aneurysms (HISA) grade (<a title="Inflammation of the abdominal aortic aneurysm wall" href="https://www.sciencedirect.com/science/article/abs/pii/S0950821X05801185">3</a>) [0, 1, 2, 3, 4], angiogenesis in tunica media [present, not present], calcification in tunica media [present, not present], grade of fibrosis in adventitia [minor, intermediate, major], remaining elastic fibers in tunica media [< 25%, > 25%]. In case of disagreement, consensus was reached through discussion. Hematoxylin and Eosin (HE)- and Elastica van Gieson (EvG)-stained slides were digitized using an Aperio AT2 (Leica, Wetzlar, Germany) slide scanner. Patient sex and smoking history were gathered as binary clinical parameters. </p> <h3>Preprocessing and Feature Extraction</h3> <p>The STAMP protocol was utilized to process the WSIs (<a title="From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology" href="https://arxiv.org/abs/2312.10944">4</a>). In brief, WSI were preprocessed by tessellation into 224 x 224 pixel patches at a magnification of 256 µm per pixel, followed by computational background rejection, and color normalization of HE-stained slides. EvG-stained slides were not color-normalized as no color normalization protocols exist for this staining. Subsequently, features were extracted using CTranspath (<a title="Transformer-based unsupervised contrastive learning for histopathological image classification" href="https://www.sciencedirect.com/science/article/abs/pii/S1361841522002043">5</a>), a pre-trained histology image encoder. </p> <p> </p> <h3>References</h3> <p>1. Kolbinger, F. R. et al. Histopathological Evaluation of Abdominal Aortic Aneurysms with Deep Learning. medRxiv 2024.04.23.24306178 (2024) doi:10.1101/2024.04.23.24306178. </p> <p>2. Nackenhorst, M. C. et al. Abdominal aortic aneurysms harbor different histomorphology not associated with classic risk factors – the HistAAA study. medRxiv 2024.04.16.24305904 (2024) doi:10.1101/2024.04.16.24305904. </p> <p>3. Rijbroek, A., Moll, F. L., von Dijk, H. A., Meijer, R. & Jansen, J. W. Inflammation of the abdominal aortic aneurysm wall. Eur. J. Vasc. Surg. 8, 41–46 (1994).</p> <p>4. El Nahhas, O. S. M. et al. From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology. arXiv [cs.CV] (2023).</p> <p>5. Wang, X. et al. Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022).</p>
Privacy-by-Design Maturity Model: literature review, coding, model creation and evaluation
<p>Results from two multivocal literature reviews (MLRs) and subsequent coding, formulation of capabilities and dependencies, creation of maturity matrix and evaluation results. Used in the creation of a PbD domain model and extraction of core activities for PbD in Information Systems design. Part of the <a href="https://www.privacymaturity.org/" target="_blank" rel="noopener">Privacy-by-Design Maturity</a> research project by the <a href="https://www.uu.nl/en/research/ai-labs/ai-lab-for-the-public-services" target="_blank" rel="noopener">AI Lab for Public Services</a>.</p>
Soil C models for evaluating the effect of cover crops
<p>Excel implementation and dataset for three C cycling models: </p> <ul> <li>Yasso20: monthly simulation version</li> <li>SOMIC 1.0: excel implementation with both simple time step and Euler-Heng iteration</li> <li>Single pool simulation model</li> <li>In addition input files for DNDC.Can.9.5.8 for one of the farms. </li> </ul> <p>There are three versions of the SOMIC model: </p> <ol> <li>The Zip file contains the operational version of the files for the cover crop experiment example and an </li> <li>_Euler-Heng version is an alternative version of the numerical simulation, which can be unstable (feel free to improve, do not use for simulation as such)</li> <li>The _MC version is a Monte Carlo uncertainty analysis implementation for one farm. (Using Simulacion 4.0 <a href="https://ucema.edu.ar/~jvarela/index_eng.htm">https://ucema.edu.ar/~jvarela/index_eng.htm</a>) </li> </ol> <p>The models are applied to an cover crop experiment, where four farms tested cover crops for 5 years. The corresponding article is submitted to Soil Use and Management. The C input estimation zip is used to translate recorded yield and cover crop NDVI data to time series of C inputs used for the models. </p> <ul> <li>The Yasso20 model implementation is based on Yasso20 model code: <a href="https://github.com/YASSOmodel/Yasso20/tree/main">https://github.com/YASSOmodel/Yasso20/tree/main</a></li> <li>The SOMIC model implementation is based on: <a href="https://github.com/domwoolf/somic1">https://github.com/domwoolf/somic1</a></li> <li>The single pool model is as described in: <a href="https://doi.org/10.1016/j.still.2021.105204">https://doi.org/10.1016/j.still.2021.105204</a></li> <li>The DNDC.Can model version can be downloaded from: <a href="https://github.com/BrianBGrant/DNDCv.CAN">https://github.com/BrianBGrant/DNDCv.CAN</a></li> </ul> <p>All the models are capable of simulating time series of soil C development over time, as influenced by C inputs, starting SOC and soil temperature and moisture. They are presented here for the purpose of further model development and comparison, not for making accurate forecasts. </p> <p> </p> <p></p> <p></p> <p></p>
Evaluation of transcription factor knockout impact on paclitaxel response for Triple Negative Breast Cancer
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11238552</div> <div> </div> <div>Two experimental formats included:</div> <div>'fixed' prefix: data from terminal time point of siRNA screen applied to HCC1143, HCC1806, and MDA-MB-468 Triple Negative Breast Cancer cell lines.</div> <div>'live' prefix: data from live-cell imaging of cell cycle reporter (HDHB-mClover/NLS-mCherry) HCC1143 Triple Negative Breast Cancer cell line.</div> <div>Note: 'live' level 1 data is available upon request (heiserl@ohsu.edu, calistri@ohsu.edu).</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate whether siRNA knockdown of transcription factors elevated during paclitaxel response impact cell count, cell morphology or cycling dynamics.</div> <div> </div> <div>Methods:</div> <div>siRNA Knockdown: Cells were plated in 90ul of serum free media per well of a 96 well plate. 24 hours later, siRNA knockdown mixture was prepared using a cell-line optimized concentration of Lipofectamine RNAiMAX (cat 13778075-075, Invitrogen) and siRNA (Horizon Discovery ON-TARGETplus) following RNAiMAX recommended protocol. The final concentration of siRNA per well was 1pmol and the final volume of RNAiMAX per well was 75nL for HCC1143, and 37.5nL for HCC1806 or MDA-MB-468 in 100uL of cell containing volume. 24 hours after siRNA transfection cells were treated with an addition of 100uL complete media containing either DMSO vehicle control or paclitaxel. </div> <div> </div> <div>Fixed-cell assays: Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask Green in PBS (H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 50, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>HDHB reporter live-cell assays: siRNA knockdown and drug treatment was performed as described above, and then the plate was loaded on an Incucyte S3 (Sartorious) and cells imaged every 15 minutes for 72 hours post drug treatment. At each timepoint 4 fields of view were captured at 20x magnificantion in each well using the phase, red and green channels. A cytoplasmic mask was computed from the mean of normalized red/green channel (cellpose parameters: diameter = 57, chan = mean(normalized(red), normalized(green)), and a nuclear mask was computed from the red channel (cellpose parameters: diameter = 30, chan = DAPI) using custom trained Cellpose models. Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0). An additional perinuclear ring mask was computed as the 11 pixel dilation from the nuclear mask, but still bound by the cytoplasmic mask. To determine mClover localization thresholds for cell cycle assignment, 250 cell images were randomly selected and manually assigned to the G1, S/G2 or M cell cycle state based on mClover localization. The mClover intensity ratios were then used to determine thresholds for automated cell cycle phase calling which was applied to the rest of the data set (Supplemental Figure 5A). Mononuclear cells with a Perinuclear:Nuclear mean intensity ratio greater than 0.8 and Nuclear:Cytoplasmic total intensity less than 0.5 were assigned to the S/G2 phase. Mononuclear and Multinuclear cells with a Nuclear:Cytoplasmic total intensity ratio greater than 0.8 and Perinuclear:Nuclear mean intensity ratio less than 0.8 were assigned to the ‘M’ phase. The remainder of mononuclear cells were assigned ‘G1’, and the remainder of multinucleated cells were assigned ‘Multinucleated’. </div> <div> </div> <div>Included files:</div> <div>fixed_level_1-plate_#.zip : Six .zip archives containing the raw images (DAPI/CellMask/Brightfield) from fixed-cell experiments.</div> <div>plate 1: HCC1143 cells treated with plate A schema</div> <div>plate 2: HCC1143 cells treated with plate B schema</div> <div>plate 3: HCC1806 cells treated with plate A schema</div> <div>plate 4: HCC1806 cells treated with plate B schema</div> <div>plate 5: MDA-MB-468 cells treated with plate A schema</div> <div>plate 6: MDA-MB-468 cells treated with plate B schema</div> <div>fixed_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>fixed_level_3: Data from 'fixed_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>fixed_incell_to_cellpose.rmd: R markdown code for converting original incell files (fixed_level_1) to RGB images for cellpose segmentation</div> <div>fixed_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (fixed_level_1)</div> <div>fixed_cellpose_models.zip: Archive including cellpose models used for fixed experiment</div> <div>live_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>live_level_3: Data from 'live_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>live_level_4: Data from 'live_level_3.csv' collapsed to the single condition level summarizing the number, multinucleation status and phase of cells at each time point.</div> <div>live_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (live_level_1).</div> <div>l ive_incu_archive2rgb.rmd: R markdown code for converting incucyte archive formatted data into RGB images, where the blue channel is the arithmetic mean of the min-max (0-1) normalized red and green channels.</div> <div>live_cellpose_models.zip: Archive including cellpose models used for live experiment.</div> <div> </div> <div> </div>
Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation"
<p>Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation".</p> <p><strong>Versions of dataset:</strong></p> <p><strong><span>V1: </span></strong><span>First dataset regarding the data used in the article.</span></p> <p><strong><span>V2:</span></strong><span> The dataset </span><span>was newly reorganized</span><span>, containing the </span><span>data,</span><span> that </span><span>were used</span><span> for the published article. </span><span>More information can be found</span><span> in the README file.</span></p> <p><strong>Article abstract</strong></p> <p>The urgent need for sustainable and innovative approaches to mitigate the increasing levels of atmospheric CO<sub>2</sub> necessitates the development of efficient methods for its removal. In this study, we focus on the new, innovative approach for synthesis and functionalization of metal-organic framework (MOF) ZIF-8 in one step at room temperature to enhance its capacity for CO<sub>2</sub> capture. Specifically, we investigated the impact of four amino-compounds, namely tetraethylenepentamine (TEPA), hexadecylamine (HDA), <a title="Learn more about ethanolamine from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemical-engineering/ethanolamine">ethanolamine</a> (ELA), and cyclopropylamine (CPA), on the <a title="Learn more about chemical structure from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/structure-composition">chemical structure</a>, size, surface area and porosity, and CO<sub>2</sub> capturing of ZIF-8 powder. By varying concentrations of the amino-compounds, we examined their influence on the ZIF-8 properties. Our findings demonstrate that each amino-compound and its respective concentration exhibit distinct effects on the characteristics of ZIF-8. Notably, the ZIF-8 sample functionalized with the highest presented concentration of TEPA exhibited significant improvement in CO<sub>2</sub> trapping efficiency, with a 33.3% enhancement. Moreover, least concentrated samples with added HDA or CPA demonstrated notable improvements with enhancements of 46.6% and 18.6%, respectively. These results highlight the potential of simple synthesis and functionalization techniques for MOFs in enhancing their CO<sub>2</sub> capture capabilities. The findings from this study offer new opportunities for the development of strategies to mitigate CO<sub>2</sub> emissions using MOFs.</p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
3D Stereo Body Pose Estimation - Evaluation Plots
<p>Evaluation plots for the "3D Stereo Body Pose Estimation" project, aimed at estimating 3d keypoints from humans captured using a OAK-D camera.</p> <p>The project is originated in the context of the activities of the postgraduate course IA904 - Model Project in Visual Computing, offered in the first semester of 2024, at Unicamp, under the supervision of Prof. Dr. Leticia Rittner and Prof. Paula D. Paro Costa, both from the Department of Computer and Automation Engineering (DCA) of the Faculty of Electrical and Computer Engineering (FEEC).</p> <p>The <a href="https://github.com/Disciplinas-FEEC/IA904-2024S1/tree/main/projetos/3DStereoBodyPoseEstimation">project page</a> have a full description of the project (portuguese).</p>
German Student Responses to Probability Theory and Statistics Bachelor Course (WuS24): Evaluated with Rubrics
<p><strong>Description:</strong></p> <p>This dataset contains questions and answers from an introductory computer science bachelor course on statistics and probability theory at Hochschule Bonn-Rhein-Sieg. The dataset includes three questions and a total of 90 answers, each evaluated using binary rubrics (yes/no) associated with specific scores.</p> <p> </p> <p><strong>Dataset Components:</strong></p> <ol> <li><em>questions.csv</em>: Contains the details of the three questions. <ul> <li>Columns: <ul> <li><em>question_id</em>: Unique identifier for each question</li> <li><em>question</em>: The text of the question</li> <li><em>solution</em>: The reference answer for the question</li> <li><em>max_score</em>: The maximum score for this question</li> </ul> </li> </ul> </li> <li><em>rubrics.csv</em>: Contains the grading rubrics for each question. <ul> <li>Columns: <ul> <li><em>question_id</em>: Unique identifier for each question</li> <li><em>rubric_id</em>: Unique identifier for each rubric within a question</li> <li><em>rubric</em>: The rubric phrased as a question</li> <li><em>score</em>: The score associated with fulfilling the rubric</li> </ul> </li> </ul> </li> <li><em>answers.csv</em>: Contains 90 student answers to the questions. <ul> <li>Columns: <ul> <li><em>answer_id</em>: Unique identifier for each answer</li> <li><em>question_id</em>: Unique identifier of the question that is answered</li> <li><em>answer</em>: The text of the student's answer</li> <li><em>score</em>: The score associated with fulfilling the rubric</li> </ul> </li> </ul> </li> <li><em>answer_rubrics.csv</em>: Contains the evaluations of rubrics for each answer.<br> <ul> <li>Columns: <ul> <li><em>answer_id</em>: The identifier of the answer.</li> <li><em>question_id</em>: The identifier of the question.</li> <li><em>rubric_id</em>: The identifier of the rubric for that question.</li> <li><em>label</em>: Indicates if the rubric crierion is fulfilled for the specific answer (true / false).</li> </ul> </li> </ul> </li> </ol> <pre><strong><br>Working with the Dataset:</strong> The easiest way to work with this dataset is using the class `RubricsDataset` defined in the file `dataloader.py`. Example:<br><br></pre> <pre><code>from dataloader import RubricsDataset<br><br>dataset = RubricsDataset.from_directory("data")<br> dataset.get_question(1) # Get a dictionary containing info about the first question, including rubrics dataset.get_answers(1) # Get all the answers for the first question as a list. Each answer is a dictionary with answer, score, rubrics.</code></pre>
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica
<p>This repository contains the waveform data used by Ho et al. (2024), along with all generated fine-tuned models and event catalogs. See the README file for a summary. The corresponding software packages are available on GitHub at <a href="https://github.com/jakewalter/easyQuake.git">https://github.com/jakewalter/easyQuake.git</a>, <a href="https://github.com/seisbench/seisbench">https://github.com/seisbench/seisbench</a>, and <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a>. See the README.md file at <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a> for additional details.</p>
Spectral- and image-based metics for evaluating cleaning tests on unvarnished painted surfaces
<p>Data spreadsheets, templates, and supporting image data associated to the article "Spectral- and image-based metics for evaluating cleaning tests on unvarnished painted surfaces", forming part of the doctoral thesis of Jan Dariusz Cutajar (University of Oslo). The following file types and files are included:</p> <p><strong>Templates</strong></p> <ul> <li>Template_dE00 calculation.xlsx</li> <li>Template_Skewness calculation.xlsx</li> </ul> <p><strong>Metric calculation tables</strong></p> <ul> <li>Metrics_CIELAB plots.xlsx</li> <li>Metrics_dE00 plots.xlsx</li> <li>Metrics_FTIR plots.xlsx</li> <li>Metrics_Gloss plots.xlsx</li> <li>Metrics_HSI (NDI) plots.xlsx</li> <li>Metrics_HSI (Supervised) plots.xlsx</li> <li>Metrics_HSI (Unsupervised) plots.xlsx</li> <li>Metrics_SEM-EDX particle count plots.xlsx</li> <li>Metrics_Skewness plots.xlsx</li> </ul> <p><strong>Extended result tables</strong></p> <ul> <li>Free liquid trial assessments.xlsx</li> <li>Cleaning score tables.xlsx</li> </ul> <p><strong>Processed images from photography, microscopy, FTIR and SEM</strong></p> <ul> <li>Processed images (VIS, FTIR, SEM).pdf</li> </ul> <p> </p>
Bloume: Evaluation Results for French Benchmarks
<p>These datasets contain the outputs of evaluation results for the BLOOM large language model tasked with a set of standard NLP benchmarks in French. The project is fully documented on github. See: https://github.com/fyvo/EvaluerBloom. </p>
Evaluation of the saturator efficiency of the low frost-point generator INRIM 03 Mark 1
<p>These datasets refers to the evaluation of the saturator efficiency of the low frost-point generator INRIM 03 developed at the Istituto Nazionale di Ricerca Metrologica. Alternating the inlet gas between a dry source and a moist gas source is it possible to test the capability of the generator to saturate the carrier gas (or condensate the excess water) at the corresponding saturation<br>temperature.</p> <p>This work has been carried out within the European Metrology Programme for Innovation and Research (EMPIR) Project ‘PROMETH2O—Metrology for trace water in ultra-pure process gases’.<br>This project (Grant No. 20IND06 PROMETH2O) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten GeoTIFF
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 0.5, 2, 5, 10, 15, 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 3,000 × 2,500 height samples each</p> <p>File format: GeoTIFF</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
SIMPATICO First Evaluation Galicia Dataset v1.1
<p>==SIMPATICO logs for the user evaluation of Galicia in project iteration 1.==</p> <p>The current package contains the Interaction LOG data captured in the Galicia evaluation of the results of H2020 project SIMPATICO that were undertaken between October 23rd 2017 and November 3rd 2017. This contains a total 374 user tests that were conducted, divided into 228 citizens for e-service BS607A, 130 citizens for e-service BS613B and 8 civil servants from Xunta de Galicia for each one of the e-services. </p> <p>The data is exported from the Elasticsearch instance that was used to log all of the interaction data. The data model for this can be found in project deliverable "D3.2 Basic Methods And Tools For User Interaction Automation". For more information about the setup for conducting the tests and the results achieved please consult project deliverable "D6.5 SIMPATICO Evaluation Report v1". All project deliverables, except where noted, are public and are available at Zenodo community reachable at https://zenodo.org/communities/h2020-simpatico-692819.</p> <p>=Change Log</p> <p>v1.0 - 2017-11-03 - Initial release</p> <p>v1.1 - 2018-02-14 - Update to initial release, prior dataset was incomplete</p> <p> </p> <p>Disclaimer: this data set was created by the consortium of project SIMPATICO (GA 692819, http://www.simpatico-project.eu). The data is provided here as-is with no liability due or claims by the authors for its completeness or validity. Recipients of this data can freely use it in any form following due contact with the project administration at info@simpatico-project.eu).</p> <p>(c) SIMPATICO - 692819 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 692819.</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.