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470 results for “Evaluation dataset”
Ground Truth for DCASE 2020 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This data is the ground truth for the "<a href="https://zenodo.org/record/3841772">evaluation dataset</a>" for the <strong>DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" </strong><a href="http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds">[task description]</a>. </p> <p>In the task, three datasets have been released: "<a href="http://zenodo.org/record/3678171">development dataset</a>", "<a href="https://zenodo.org/record/3727685">additional training dataset</a>", and "<a href="https://zenodo.org/record/3841772">evaluation dataset</a>". The evaluation dataset was the last of the three released and includes around 400 samples for each Machine Type and Machine ID used in the evaluation dataset, none of which have any condition label (i.e., normal or anomaly). This ground truth data contains the condition labels.</p> <p> </p> <p><strong>Data format</strong></p> <p>The ground truth data is a CSV file like the following:</p> <p>---------------------------------</p> <p>fan<br> id_01_00000000.wav,normal_id_01_00000098.wav,0<br> id_01_00000001.wav,anomaly_id_01_00000064.wav,1<br> ...</p> <p>id_05_00000456.wav,anomaly_id_05_00000033.wav,1<br> id_05_00000457.wav,normal_id_05_00000049.wav,0<br> pump<br> id_01_00000000.wav,anomaly_id_01_00000049.wav,1<br> id_01_00000001.wav,anomaly_id_01_00000039.wav,1<br> ...</p> <p>id_05_00000346.wav,anomaly_id_05_00000052.wav,1<br> id_05_00000347.wav,anomaly_id_05_00000080.wav,1<br> slider<br> id_01_00000000.wav,anomaly_id_01_00000035.wav,1<br> id_01_00000001.wav,anomaly_id_01_00000176.wav,1<br> ...</p> <p>---------------------------------</p> <p>"Fan", "pump", "slider", etc mean "Machine Type" names. The lines following a Machine Type correspond to pairs of a wave file in the Machine Type and a condition label. The first column shows the name of a wave file. The second column shows the original name of the wave file, but this can be ignored by users. The third column shows the condition label (i.e., 0: normal or 1: anomaly).</p> <p> </p> <p><strong>How to use</strong></p> <p>A system for calculating AUC and pAUC scores for the "evaluation dataset" is available on the Github repository <a href="https://github.com/y-kawagu/dcase2020_task2_evaluator">[URL]</a>. The ground truth data is used by this system. For more information, please see the Github repository.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was created jointly by <strong>NTT Corporation</strong> and <strong>Hitachi, Ltd.</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Publication</strong></p> <p>If you use this dataset, please cite <strong>all the following three papers</strong>:</p> <p>Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. <a href="https://ieeexplore.ieee.org/document/8937164">[pdf]</a></p> <p>Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. <a href="http://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf">[pdf]</a></p> <p>Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring<em>,"</em> in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Koizumi_3.pdf">[pdf]</a></p> <p><br> <strong>Feedback</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Yuma Koizumi, <a href="mailto:koizumi.yuma@ieee.org">koizumi.yuma@ieee.org</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>
Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability
<p>The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EI<sub>R</sub>) between consecutive years (e.g. HSi= |∆ EI<sub>R</sub> /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016. A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years. For each map, where HSi >1, regions are classified as <strong><em>Sensitive</em></strong> and for HSi ≤1, <strong><em>Resilient</em></strong>. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive. </p> <p><strong>This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against latitudinal change.</strong> We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees.</p> <p><strong>Contents:</strong></p> <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> <p>Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names.</p> <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>
Dataset for the project "Evaluation of the effects of trace elements from street dust under urban – industrial conditions on the ecophysiology of Acer platanoides L. and Tilia cordata Mill.
<p>Description of the project: The rapid growth of cities, industry and transport has significantly deteriorated environmental quality, especially in areas with the highest population densities. It applies to water, soil, and air, especially in urban areas. Air pollutants include particulate matter (PM), which harms human health. According to WHO reports (2021), PM pollution is the cause of cardiovascular and respiratory diseases, leading to 4.2 million premature deaths worldwide in 2016. Although improving every year, the situation in Poland is still worse than in many European countries. The particulate matter also includes heavy metals, which have a toxic effect on plants. Plants in urban areas are particularly vulnerable, especially trees, which perform several vital functions, including mitigating climate change, filtering pollutants, and improving air quality. The aim of the project was to determine and compare the morphological and physiological responses of selected tree species to particulate pollution stress under urban conditions. Tree leaves are an essential barrier to atmospheric dust by trapping it on their surface. However, this may come at the cost of reduced light absorption, increased leaf temperature, damage to leaf blades and consequently impaired photosynthesis and plant productivity. However, the ability to absorb dust varies between tree species. It depends on the leaf surface structure, and the response may be due to the species' sensitivity to pollutants. Investigations were conducted in the Upper Silesian Industrial Area around various emission sources, such as heavy metal smelters, combined heat and power plants, and busy streets. The research focused on two tree species common in urban areas, the Norway maple (<i>Acer platanoides</i>) and the small-leaved lime (<i>Tilia cordata</i>). It included measurement of heavy metal concentrations in leaf blades and dust collected on their surface, analysis of concentrations of selected pigments and ascorbic acid as markers of environmental stress. The study provided a preliminary assessment of the impact of particulate pollution on tree function under harsh urban conditions and determined the potential of the studied species to reduce atmospheric dust.</p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
Datasets of synthetic workflows for evaluating a multi-objective and multi-constrained scheduling approach for cyber-physical applications
<p>These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors with different processing core failure rates and varied sensing/actuating or other specialized capabilities. Our objectives were the minimization of the overall latency, the minimization of the overall energy consumption, and the maximization of the overall reliability of the workflow application in the specific CPS, under deadline, reliability, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random task graphs with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability, reliability threshold) were included post-generation, using appropriate values. More details are provided in README.txt.<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p>
Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"
<p>Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".</p> <p><em><strong>Inputs</strong></em>: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.</p> <ul> <li><em><strong>Real-life</strong></em>: preprocessed event logs (<em>xes</em> and <em>csv</em>) corresponding to the real-life processes used in the evaluation. Process models (<em>pnml</em>) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (<em>txt</em>) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>).</li> <li><em><strong>Synthetic</strong></em>: simulated event logs (<em>csv</em>) corresponding to the synthetic processes used in the evaluation. Designed process models (<em>bpmn</em> and <em>pnml</em>). Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>). Ongoing cases with injected noise as described in the publication (under folders <em>noise_1</em>, <em>noise_2</em>, and <em>noise_3</em>).</li> </ul>
Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system
<p>This dataset contains the raw and processed images from a low-cost high-throughput plant phenotyping (HTP) system, as well as the raw and processed images that were manually acquired for comparison. The HTP images were automatically and wirelessly acquired for entire benches of plants with a system composed of a Raspberry Pi and eight GoPro cameras. The entire file system of each GoPro camera was copied directly into a subfolder of finalGoProImages (numbered by camera). The raw HTP images were processed by correcting for lens distortion, computing the "greenness index" for each individual pixel, and filtering out extreme high and low values. These processed HTP images were then saved in the "greenness" subfolder of finalGoProImages. The manually acquired images in the finalDSLR folder each represent an individual plant from one of five time points during the same greenhouse experiment. The raw manually acquired images were processed in the same manner as the raw HTP images by computing the greenness index for each individual pixel and filtering out extreme high and low values. The two tab-delimited text files include the number of green pixels and mean greenness index for each HTP (greennessGoProTable2.txt) and manually acquired (greennessDSLRTable2.txt) image.</p>
Datasets for paper "Evaluating the PurpleAir monitor as an aerosol light scattering instrument"
<p>The data sets included will allow the user to reproduce the plots and analyses described in Ouimette et al. (2022). The Collocated*csv file contains data from multiple collocated PurpleAirs that sampled for a few days. The data in this file was used in the precision analysis in section 2.2.9 of the paper. The other files contain nephelometer and PurpleAir data from Mauna Loa (MLO) and Table Mountain (BOS) and DMPS size distribution files from BOS. Their contents are described in the README.TXT file.</p>
Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</p>
Dataset for evaluation of unrealistic optimism in time of pandemic COVID-19 on a Polish sample
<p>This dataset contains the data used in a project called "Unrealistic optimism in the eye of the storm. Positive bias towards the consequences of COVID-19 during the second and third waves of the pandemic. ". The project concerns the occurrence of a cognitive bias - unrealistic optimism - with regard to contracting the coronavirus. The following information are attached to the dataset: codebooks with variables' names; analysis codes to replicate our results; supplementary materials with the description of procedures. </p>
Dataset for CoordiNet D6.2 - Chapter 4 - Evaluation of Combinations of Coordination Schemes and Products for Grid Services
<p>This is a supporting material for chapter 4 of CoordiNet D6.2. The deliverable is available at:</p> <p><a href="https://coordinet-project.eu/publications/deliverables">CoordiNet deliverables (coordinet-project.eu)</a></p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line, which has capacity of 1.0. The interconnected system is fully represented in "Network.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_18, DN_69, DN_141);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected. The generated orderbook is presented in "OrderbookTN" (transmission system) and "OrderbookDN" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_18, DN_69, DN_141) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>In addition, table check_system contains the forecasted flows over lines before flexibility activation. For the transmission system, the SFTN is used and only active power is calculated. For the distribution systems, a linearized model of the power flow is used, thus active and reactive power are calculated:</p> <ul> <li>System: the system (TN, DN_18, DN_69, DN_141) where the line is located;</li> <li>From/to: bus numbers of the connection;</li> <li>Flow: forecasted active flow over transmission system lines;</li> <li>Flow P/Flow Q: forecasted active/reactive flow over distribution system lines (also for interface flows TN-DN);</li> <li>Congestion: if congestion is forecasted over the line.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Dataset and scripts for the paper with title Evaluating Programming Models for the HPC GPU Ecosystem
<p>Dataset and scripts for the paper with title Evaluating Programming Models for the HPC GPU Ecosystem</p>
Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data - Datasets
<p>Includes raw and processed copies of the scRNA-seq datasets used for the paper: '<strong>How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data.'</strong></p> <p><strong>Real scRNA-seq.zip </strong>contains the Abundant (subset1) and Rare (subset 2) subsets generated to represent discretely structured datasets (sourced from<strong> </strong> Wegmann et al. 2019) and the continuously structured data (sourced from Popescu et al. 2019).</p> <p><strong>Simulated scRNA-seq.zip</strong> contains the Abundant, Moderately-Rare and Ultra-Rare subsets for discretely and continuously structured datasets. All data was simulated using the PROSSTT package in Python 3.8, as well as the dataset containing the labels to re-produce Figure 3 of the manuscript.</p> <p><strong>Results.zip </strong>contains the results for all datasets from the full analysis, in a pickled python dictionary. Code to read in and visualise results is available on the projects github</p> <p>The scripts for the dataset generation, processing and visualisation of results are available at <a href="https://github.com/Ebony-Watson/scProximitE">our github for the scProcimitE package</a>, and documentation is available <a href="https://ebony-watson.github.io/scProximitE/">here</a>.</p>
Dataset of EnergyPlus models to evaluate the impact of modeling the hysteresis phenomenon of phase change materials on the building energy performance
<p>This dataset is the research data generated to evaluate the impact of modeling the hysteresis phenomenon of phase change materials (PCM) on the building performance simulation, which includes:<br> - A series of EnergyPlus models representing the medium office of the Prototype Building Models developed by DOE. These are the original model without PCM (Baseline), and four models with different PCM modeling approaches (melting-curve, solidification-curve, mean-curve, hysteresis-model).<br> - The typical meteorological year (TMY) for Frankfurt city that was used to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/).</p>
Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models - Dataset
<p>Main data used in the following paper: Revaz, T.; Porté-Agel, F. Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3745. https://doi.org/10.3390/en14133745</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>
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>
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>
Supporting dataset for: Repository optimisation & techniques to improve discoverability and web impact : an evaluation
<p>This dataset supports the working paper, "Repository optimisation & techniques to improve discoverability and web impact : an evaluation", currently under review for publication and available as a preprint at: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a>. </p> <ul> <li>Macgregor, G. (2018). <em>Repository optimisation techniques to improve discoverability and web impact: an evaluation</em>. (pp. 1-13). Glasgow: University of Strathclyde [Strathprints repository]. Available: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a></li> </ul> <p>The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing seven data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and Google Scholar, and usage statistics from IRStats2. All data relate to the EPrints repository, Strathprints, based at the University of Strathclyde.</p>
ScienceDex guides
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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.