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470 results for “Evaluation dataset”
Evaluation datasets for DCASE 2018 Bird Audio Detection
<p>Evaluation data audio for <a href="http://dcase.community/challenge2018/task-bird-audio-detection">the DCASE 2018 Bird Audio Detection task (Task 3)</a>.</p> <ol> <li> <p><strong>Crowdsourced dataset, UK ("warblrb10k")</strong> - a held-out set of 2,000 recordings from the same conditions as the Warblr development dataset.</p> </li> <li> <p><strong>Remote monitoring dataset, Chernobyl ("Chernobyl")</strong> - 6,620 audio clips collected from unattended remote monitoring equipment in the Chernobyl Exclusion Zone (CEZ). This data was collected as part of the <a href="https://wiki.ceh.ac.uk/display/NRT/NERC+RATE+TREE+Home">TREE</a> (Transfer-Exposure-Effects) research project into the long-term effects of the Chernobyl accident on local ecology. The audio covers a range of birds and includes weather, large mammal and insect noise sampled across various CEZ environments, including abandoned village, grassland and forest areas.</p> </li> <li> <p><strong>Remote monitoring night-flight calls, Poland ("PolandNFC")</strong> - 4,000 recordings from Hanna Pamuɫa's PhD project of monitoring autumn nocturnal bird migration. The recordings were collected every night, from September to November 2016 on the Baltic Sea coast, Poland, using Song Meter SM2 units with microphones mounted on 3–5 m poles. For this challenge, we use a subset derived from 15 nights with different weather conditions and background noise including wind, rain, sea noise, insect calls, human voice and deer calls.</p> </li> </ol>
Development, evaluation and application of NicheNet: datasets
<p>Here you can find the datasets used for development, validation and application of NicheNet, a method that links ligands to target genes to model intercellular communictation. The software to use NicheNet (i.e. R package nichenetr) is available at:</p> <p><a href="https://github.com/saeyslab/nichenetr">https://github.com/saeyslab/nichenetr</a></p> <p>Short description of individual files:</p> <p>* data for development of NicheNet: </p> <p>- lr_network.rds: network containing ligand-receptor interactions</p> <p>- signaling_network.rds: network containing signaling interactions</p> <p>- gr_network.rds: network containing gene regulatory interactions</p> <p>- weighted networks.rds: integrated weighted ligand-signaling and gene regulatory network</p> <p>* data for use of NicheNet:</p> <p>- ligand_tf_matrix.rds: matrix containing ligand-gene signaling scores</p> <p>- tf_target_matrix.rds: matrix containing gene-gene regulatory scores</p> <p>- ligand_target_matrix.rds: matrix containing ligand-target gene regulatory potential scores</p> <p>* datasets for validation of NicheNet:</p> <p>- expression_settings.rds: 111 expression datasets profiling the transcriptional response to a ligand</p> <p>* datasets to demonstrate application possibilities of NicheNet:</p> <p>- pemt_signature.txt: p-EMT geneset for which upstream regulators will be predicted</p> <p>- hnscc_expression.rds: single-cell expression data of malignant and non-malignant cells from head and neck squamous cell carcinoma tumors</p>
SNEMI3D dataset pre-processed for evaluation
<p>These files are pre-processed .tif files for the evaluation of my workflow to predict 3D segmentation of neurons from EM image data.</p> <p>The original dataset was published in the context of the IEEE International Symposium on Biomedical Imaging 2013. Biological findings and further information about hte dataset can be found in following article:</p> <p>Kasthuri, N., Hayworth, K. J., Berger, D. R., Schalek, R. L., Conchello, J. A., Knowles-Barley, S., ... & Roberts, M. (2015). Saturated reconstruction of a volume of neocortex. <em>Cell</em>, <em>162</em>(3), 648-661.</p>
GAED: Game Acceptance Evaluation Dataset
<p>The Game Acceptance Evaluation Dataset (GAED) contains statistical data aswell training and validation sets used in our experiments on <em>Neural Networks to evaluate Video Games Acceptance</em>.</p> <p>Please consider citing the following references if you found this dataset useful:</p> <p>[1] Augusto de Castro Vieira, Wladmir Cardoso Brandão. Evaluating Acceptance of Video Games using Convolutional Neural Networks for Sentiment Analysis of User Reviews. In Proceedings of the 30th ACM Conference on Hypertext and Social Media. 2019.</p> <p>[2] Augusto de Castro Vieira, Wladmir Cardoso Brandão. GA-Eval: A Neural Network based approach to evaluate Video Games Acceptance. In Proceedings of the 18th Brazilian Symposium on Computer Games and Digital Entertainment. 2019.</p> <p>[3] Augusto de Castro Vieira, Wladmir Cardoso Brandão. (2019). GAED: The Game Acceptance Evaluation Dataset (Version 1.0) [Data set]. Zenodo. </p>
Trace-Share Dataset for Evaluation of Statistical Characteristics Preservation
<p>The dataset contains all data used during the evaluation of statistical characteristics preservation. Archives are protected by password "<strong>trace-share</strong>" to avoid false detection by antivirus software.</p> <p>For more information, see the project repository at <strong><a href="https://github.com/Trace-Share">https://github.com/Trace-Share</a></strong>.</p> <p> </p> <p><strong>Selected Attack Traces</strong></p> <p>We selected 72 different traces of network attacks obtained from various internet databases. File names refer to common names of contained vulnerabilities, malware, or attack tools.</p> <p> </p> <p><strong>Background Traffic Data</strong></p> <p>Publicly available dataset <a href="https://www.unb.ca/cic/datasets/ids-2018.html">CSE-CIC-IDS-2018</a> was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.</p> <p> </p> <p><strong>Evaluation Results and Dataset Structure</strong></p> <ul> <li>Traces variants (<a href="https://zenodo.org/record/3553063/files/traces-normalized.zip">traces-normalized.zip</a>, <a href="https://zenodo.org/record/3553063/files/traces-adjusted.zip">traces-adjusted.zip</a>) <ul> <li>./traces-normalized/ — normalized PCAP files and details in YAML format;</li> <li>./traces-adjusted/ — configuration files for traces combination in YAML format.</li> </ul> </li> <li>Computed statistics (<a href="https://zenodo.org/record/3553063/files/statistics.zip">statistics.zip</a>) <ul> <li>./statistics-background/ — background traffic statistics computed by ID2T;</li> <li>./statistics-combination/ — combined traces statistics computed by ID2T for all adjust options (selected only combinations where ID2T provided all statistics files);</li> <li>./statistics-difference/ — computed mean and median differences of background and combined traffic traces.</li> </ul> </li> <li>Evaluation results <ul> <li><a href="https://zenodo.org/record/3553063/files/statistics-difference.ipynb">statistics-difference.ipynb</a> — file containing visualization of statistics differences.</li> </ul> </li> </ul>
Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms
<p><strong>Synthetic Lunar Terrain (SLT) </strong>is a dataset based on a reconstruction of a typical <strong>cratered lunar surface landscape </strong>at the <a href="https://set.adelaide.edu.au/atcsr/space-research/exterres-laboratory" target="_blank" rel="noopener">EXTERRES Laboratory</a> at University of Adelaide, Roseworthy Campus. On a surface area of <strong>3.6m x 4.8m</strong>, multiple synthetic craters with different sizes and geometries were sculpted into<strong> lunar regolith simulant</strong>. A <strong>9kW metal-halide lamp</strong> illuminated the scene, providing high contrast drop-shadows from the rims of craters and similar surface features that are characteristic for the Earth's moon.</p> <p>The purpose of this dataset is to provide multimodal recordings of visual information to develop and test algorithms on a hardware analogue of the moon rather than relying on computer simulations. In particular, comparisons between <strong>neuromorphic vision sensors</strong> like <strong>event-based cameras</strong> and imaging with <strong>conventional monocular cameras</strong> are at the core of this work. For this purpose, an event-based camera (Gen4 Prophesee with Prophesee-Sony IMX636 sensor) was mounted downward-pointing next to a optical camera (Basler a2A1920-160ucPRO with Sony IMX392 sensor) on an extendable rod which was moved above the surface in a slow and continuous sweep. In total, SLT consists of camera recordings from 21 different positions/settings, with clockwise and anti-clockwise motions under varying, extreme lighting conditions.</p> <p>The event-stream and grayscale image data can be further referenced via a detailed <strong>3D point cloud</strong> obtained by a FARO Focus S70 3D Scanner. This 3D Scan was post-processed, realigned and resampled into a 3D point cloud of <strong>~6.25M points, </strong>with a surface density of <strong>1.862 p/mm²</strong>, providing a ground-truth for the crater geometries.</p> <p>In detail, SLT contains the following:</p> <ul> <li>eventbased.zip: <ul> <li><strong>42 camera orbits</strong> in the binary <strong>EVT 3.0</strong> format (<a href="https://docs.prophesee.ai/stable/data/encoding_formats/evt3.html" target="_blank" rel="noopener">Prophesee docs</a>) </li> <li>corresponding <strong>.mp4 </strong>event-frame video rendering for visualization purposes (33.333ms accumulation time at 30FPS)</li> <li>corresponding<strong> .bias</strong> file containing settings used during recording</li> </ul> </li> <li>code.zip: <ul> <li>Standalone C++ code of the <strong>metavision EVT3-to-RAW file decoder</strong>, allowing to convert the binary EVT 3.0 format into a plaintext <strong>.csv </strong>that includes <ul> <li>the coordinates of the event-pixel,</li> <li>the polarity change,</li> <li>and the time-stamp of the event.</li> </ul> </li> <li>This code is an unmodified redistribution from the <a href="https://www.prophesee.ai/metavision-intelligence/" target="_blank" rel="noopener">Metavision SDK</a>, version 4.6.0, released by Prophesee under Apache License 2.0.</li> </ul> </li> <li> optical.zip: <ul> <li><strong>42 image sequences</strong> in <strong>.tif</strong> format (LZW, 1920x1200px, 8bit, grayscale) <ul> <li>Length of image sequences varies between about 300 to 700 images per sequence</li> </ul> </li> </ul> </li> <li>3d_scan.zip: <ul> <li><strong>SLT3d_scan.ply:</strong> 3D point cloud of the scene Stanford Polygon File Format</li> <li><strong>SLT3d_scan.xyz:</strong> 3D point cloud with plaintext x y z coordinates, white-space separated</li> </ul> </li> <li>cratermap.png: <ul> <li>Annotations of <strong>130 different surface features</strong> that have been manually identified as crater-like with approximate x,y-coordinates.</li> </ul> </li> <li>positionmap.png: <ul> <li>Illustration of the different positions from which the rod was moved over the scene (not to scale).</li> </ul> </li> <li>sample.zip: <ul> <li>A sample containing 1 event-camera orbit with the corresponding image sequence (for convenience only, to test the dataset without the need to download it's entirety)</li> </ul> </li> </ul> <p>The global coordinate frame of this dataset puts the origin at the centre of the scene. The shorter side of the terrain is roughly aligned with the x-axis, the longer side with the y-axis. The z-axis represents height/depth (compare with <strong>cratermap.png</strong>). The different conditions (compare with <strong>positionmap.png</strong>) from which the data was taken are encoded as follows:</p> <ul> <li><strong>A1, ..., A9</strong> refer to the left side of the scene (negative x)</li> <li><strong>B1, ..., B9 </strong>refer to the right side of the scene (positive x)</li> <li><strong>S1, S2, S3</strong> and <strong>S4 </strong>describe special lighting conditions and/or parameter settings</li> <li><strong>CW </strong>refers to a "clockwise" sweeping of the camera-rod, relative to the position</li> <li><strong>ACW</strong> refers to an "anti-clockwise" sweeping of the camera-rod, relative to the position</li> </ul> <p>The light from the metal-halide lamp was directed through a small opening, shining along the positive y-axis. In some of the setups, an obstacle was placed between the surface and the opening, blocking out part of the light to create a light-dark separator on the surface, emulating the <strong>terminator</strong> on the Moon between it's day and night side, resulting in highly contrastive images.</p> <p>We encourage you to consult and cite our related publication, should you find SLT useful.</p> <ul> <li>Märtens, M., Farries, K., Culton, J. and Chin, TJ. "<strong>Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms</strong>", Proceedings of "<em>International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS), 2024</em>", pp. 609-614</li> </ul> <p> </p>
Expansion Cones Dataset for Robust Tetrahedral Mapping Evaluation
<p>This record stores the dataset used by the <a href="https://www.algohex.eu/publications/expansion-cones/">"Expansion Cones"</a> paper.<br>This paper presents a volumetric mapping method called <em>Shrink-And-Expand</em> (SAE for short).<br>Its robustness was evaluated by mapping ~3'000 ball-topology meshes to 4 different target boundaries (spherical, tetrahedral, "stiff" tetrahedral and random star-shaped). Other tetrahedral mapping methods can be evaluated by using the rest meshes and either of the target boundaries.</p> <p>The record is structured as follows:</p> <ul> <li>`ball_topology_tet_wild_ovm_format`: all input meshes in their original shapes, in <a href="https://www.graphics.rwth-aachen.de/software/openvolumemesh/">.ovm</a> format. These consists of the <a href="https://github.com/Yixin-Hu/TetWild">TetWild</a> dataset, restricted to ball-topology meshes. If you need to convert the meshes to another format, you can use Martin Heistermann's <a href="https://github.com/mheistermann/meshio">fork</a> of <a href="https://pypi.org/project/meshio/">meshio</a>.<br>NOTE: Those meshes were pre-processed by splitting all <strong>interior</strong> edges connecting two boundary vertices and all <strong>interior</strong> faces connecting three boundary vertices. This was done to satisfy the prerequisites of our method.</li> <li>`XXX_boundary_conditions.zip`: boundary conditions for the 4 boundary shapes: tetrahedral, "stiff" tetrahedral, ball and random star-shape. Please read the related paper for more details about the boundary types.<br>Those boundary conditions are stored as `.txt` files, each containing a list of boundary vertices indices, along with their prescribed positions.</li> </ul> <p>The other archives contain data used to compare SAE with two other volumetric mapping methods called <a href="https://github.com/duxingyi-charles/lifting_simplices_to_find_injectivity"><em>Total Lifted Conten</em>t (TLC)</a> and <a href="https://dl.acm.org/doi/abs/10.1145/3450626.3459847">FoldOver-Free (FOF)</a></p> <ul> <li> `SAE_results/expansion_data`: All `.json` files corresponding to executions of our method (SAE) on those meshes.</li> <li> `SAE_results/quality_stats`: conformal and volumetric distortion values for _all_ cells of _all_ meshes.</li> <li>`TLC_FOF_results`: For each mesh and each competitor method (and each boundary map), there are 1 to 3 files, depending on the results of running those methods. <ul> <li>`TLC/FOF_timing_s.txt` gives the number of seconds it took to run</li> <li>`TLC/FOF_stats.txt` stores the following data: `[mesh name, #vertices, #edges, #faces, #cells, #degenerate cells after running the method, #flipped cells after running the method]`. This file only exists if the method terminated within the 12hours time limit.</li> <li>`TLC/FOF_quality_stats.txt` lists the conformal and volumetric distortion of each individual cell in the resulting map produced by the competitor methods (one line per cell), This file only exists if the method produced a valid map.</li> </ul> </li> </ul> <p> </p> <p><strong>NOTE</strong>: If you want to download the codomain meshes, with prescribed boundary positions already set. you can find them in the v2.0 of this same dataset.</p>
Dataset: Seeking feedback and taking action: An evidence based evaluation of the impact of using practice tests with inbuilt feedback in a first-year business class
<p><strong>This data set includes both qualitative and quantitative data.</strong></p> <p><strong>Qualitative, survey data sought student opinions on the purpose of feedback and the actions taken following receipt of feedback.</strong></p> <p>1. Survey data collection began with a 13 question survey used in classes in early February 2020. This initial feedback survey was conducted via MS Forms. The data from this is available in the following files: a. 2020-2-17_feedbacksurvey_1_rawdata.csv b. 2020-2-17_feedbacksurvey_1_how-many-times-got-feedback.csv c. 2020-2-17_feedbacksurvey_1_take-action-after-feedback.csv d. 2020-2-17_feedbacksurvey_1_type-feedback-you-prefer.csv e. 2020-2-17_feedbacksurvey_1_purpose-of-feedback.csv f. 2020-2-17_feedbacksurvey_1_define-feedback.csv g. 2020-2-17_feedbacksurvey_1_what-makes-feedback-useful.csv h. 2020-2-17_feedbacksurvey_1_know-how-feedback-effective.csv i. 2020-2-17_feedbacksurvey_1_what-can-institute-do-to-improve-feedback.csv</p> <p><br>2. The first practice test was followed by a short survey of approximately 3 questions (created using MS Forms, a mix of closed and open questions). The data from this is available in the following files: a. 2020-3-2_feedbacksurvey_2_rawdata.csv b. 2020-3-2_action-comments-coded.csv c. 2020-3-2_other-comments-coded.csv d. 2020-3-2_open-question-comments.csv</p> <p>3. The second practice test followed by a 3 question short survey (created using MS Forms, all open questions) The data from this is available in the following files: a. 2020-5-12_feedbacksurvey_3_rawdata.csv b. 2020-5-12_feedbacksurvey_3_what-learn-by-participating.csv c. 2020-5-12_feedbacksurvey_3_what-action-taking.csv d. 2020-5-12_feedbacksurvey_3_has-module-changed-view-of-feedback.csv<br><br><strong>Quantitative data was gathered from Learning Management System, Canvas. This data shows the relationship between number of practice tests taken by students and their resultant grades.</strong></p> <p><br>4. I gathered the number of times students had taken the practice test before the midterm summative exam and compared with the student grades from the 30% midterm summative exam. The data from this is available in the following files: a. 2020-11-26_Canvasdata_midterm.csv</p> <p>5. Following the completion of the second summative exam (worth 70%), Canvas data showing the number of times the students had taken the second practice test and the grade outcomes for the students for the summative 70% test was gathered. The data from this is available in the following files: a. 2020-11-26_Canvasdata_endterm.csv</p> <p><br>6. A regression model was created to examine the impact of number of times that the practice test was taken on final grades, whilst controlling for midterm grades. The data from this is available in the following files: a. 2024-2-12_Anonymised_mid_and_final_grades_with_number_of_practices_before_final_exam.csv</p>
The official dataset of the paper "Valorisation of Blackcurrant Pomace by Extraction of Pectin-Rich Fractions: Structural Characterization and Evaluation as Multifunctional Cosmetic Ingredient"
<p>This is the official repository of the paper "Valorisation of Blackcurrant Pomace by Extraction of Pectin-Rich Fractions: Structural Characterization and Evaluation as Multifunctional Cosmetic Ingredient" (<a href="https://doi.org/10.3390/polym16192779">https://doi.org/10.3390/polym16192779</a>).</p> <p>DISCLAIMER</p> <p>The repository contains experimental data and is published for the sole purpose of giving additional background details on the respective publication, "Valorisation of Blackcurrant Pomace by Extraction of Pectin-Rich Fractions: Structural Characterization and Evaluation as Multifunctional Cosmetic Ingredient" (<a href="https://doi.org/10.3390/polym16192779">https://doi.org/10.3390/polym16192779</a>).<strong> </strong>See the README.txt file for more details.</p>
Evaluating an instrument of the research software related to software use and disclosure - Dimension 1 - Dataset of Focus Groups
<p>Artifacts used for data collection and analysis of the focus groups sessions during the evaluation of an instrument for research software related to software use and disclosure - dimension 1.</p>
ADEPT: A Dataset for Evaluating Prosody Transfer
<p>The ADEPT dataset consists of prosodically-varied natural speech samples for evaluating prosody transfer in english text-to-speech models. The samples include global variations reflecting emotion and interpersonal attitude, and local variations reflecting topical emphasis, propositional attitude, syntactic phrasing and marked tonicity.</p> <p>Txt and wav files are organised according to the folder structure {speech_class}/{subcategory_or_interpretation}/{filename}, where filename follows the naming convention {speaker}_{utterance_id}. Speakers comprise 'ad00' (female voice) and 'ad01' (male voice). For classes with multiple interpretations, we provide the interpretations used in the disambiguation tasks in 'adept_prompts.json'.</p> <p>The corpus only includes prosodic variations that listeners are able to distinguish with reasonable accuracy, and we report these figures as a benchmark against which text-to-speech prosody transfer can be compared. More details can be found in our pre-print about the dataset (https://arxiv.org/abs/2106.08321).</p>
Research project on field data collection for honey bee colony model evaluation - datasets
<p><strong>Description of the datasets</strong></p> <p>The file 00_MUSTB_field_data_model.docx contains the data model according to which the data collected in the context of the MUSTB field data collection were reported to EFSA. The current data model description includes some modifications with respect to the specifications published before the beginning of the project (EFSA, 2017, https://doi.org/10.2903/sp.efsa.2017.EN-1234). All the tables included in the data model are published here in csv format. The underlying schemas are also published in xsd format.</p> <p>Sites: General information about the sites where the data collection took place;</p> <p>Polygons: General information about the polygons where the botanical survey took place.</p> <p>Table I: Pesticide application, reporting data on experimental spraying events;</p> <p>Table II: Resource providing unit and landscape fitness, reporting data on abundance of flowering plants in polygons mostly within 1.5 km, but in some cases up to 3 km of the experimental colony;</p> <p>Table III: Master list of all hives included in the study;</p> <p>Table IV: Colony management, reporting the log of the beekeeper regarding input (if material was added to the hive: e.g. empty frames, chemicals for varroa treatment, sugar), output (if material was removed from the hive, e.g. honey combs, supers), queen loss, swarming, or clinical signs observed in the experimental hives;</p> <p>Table V: Hive inspection, reporting data on in-hive measurements in the experimental colonies. This table contained several types of data, including:</p> <ul> <li>Data on brood development and food provision (“cell utilization”) obtained from image analysis of combs;</li> <li>Data on forager activity obtained from automatic video recordings and image analysis by a bee counter;</li> <li>Data on hive weight obtained from automatic logging by a hive scale;</li> <li>Data on adult bee strength, obtained by weight assessment of combs with and without adult bees (“bees per comb data”);</li> </ul> <p>Table VI: SSD2, reporting data on results of laboratory analyses of pollen, pesticide residues and parasites/pathogens. These four types of laboratory analyses involved different methods, and were reported according to different standards. Therefore, a number of the fields in the technical specifications for the SSD2 table (EFSA, 2017) were not applicable for records reporting results of some analyses, in particular palynological, parasite and pathogen analyses. These fields were left empty;</p> <p>Table VII: Colony observation, reporting observations of honey bee waggle dances from observation hives. Orientation denotes the angle of the waggling phase relative to the vertical axis on the comb. Direction denotes the actual direction in the landscape, as calculated from the orientation of the waggle dance.</p> <p>In all the csv files, columns with the suffix "_desc" have been included, where relevant, to include the name corresponding to the EFSA controlled terminology used in the previous column (e.g. resUnit contains EFSA term codes while resUnit_desc contains the term names).</p> <p><strong>Data storage</strong></p> <p>All data collected during the project was stored in a relational database. The database was developed in .NET Entity Framework Core, ran on a PostgreSQL, and was hosted by Amazon Web Service during the whole duration of the project development. Data could be imported or entered manually in the database through a web form. Administrators could create new users and administrators, new sites, and new colonies, i.e., administrators were allowed to enter or change data of all tables. Users were allowed to enter data, and could view, retrieve, and modify their own data of all tables, except for Table III (description of experimental colonies). Administrators could view and retrieve all data. Data was retrieved in CSV and XML formats, and were structured to secure a smooth transmission of data to the Data Collection Framework of EFSA. Furthermore, data flow from the field data collection to the development of ApisRAM was secured by direct communication between the field and modelling teams.</p> <p> </p> <p><strong>Version 2</strong> contains the UTM coordinates in tables Sites, Polygons and Resource providing unit.</p>
Dataset: Quantitative Evaluation of Enhanced multi-plane clinical fetal diffusion MRI with a crossing-fiber phantom
<p>This dataset provides MRI acquisitions of a customized crossing phantom for fetal brain. It contains:<br> <br> 1) High Resolution acquisitions of 1.5 mm<sup>3</sup> isotropic and 61 directions (with b-vectors/b-values)</p> <p>2) Six low resolution acquisitions of 1x1x4 mm<sup>3</sup> and 9-16-25 directions (with respective b-vectors/b-values)</p> <p>3) A structural T2-w acquisition</p>
Throwbot - Dataset and evaluation of a thrown rubber egg 1
<p>Dataset, videos and MATLAB live script for the evaluation of the recoded throw of a rubber egg.</p>
Throwbot - Dataset and evaluation of a thrown rubber egg 5
<p>Dataset, videos and MATLAB live script for the evaluation of the trajectory of a thrown rubber egg</p>
Throwbot - Dataset and evaluation of a thrown rubber egg 4
<p>Dataset, videos and MATLAB live script for the evaluation of the trajectory of a thrown rubber egg</p>
Throwbot - Dataset and evaluation of a thrown rubber egg 3
<p>Dataset, videos and MATLAB live script for the evaluation of the trajectory of a thrown rubber egg</p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Evaluation dataset
<p>The dataset is a derivative of the SINS dataset and is meant to be used as an evaluation set for the <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">DCASE2018 Task 5 challenge</a>. The development set to be used can be found <a href="https://zenodo.org/record/1247102#.WzIF_NUzZhE">here</a>. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 evaluation dataset' consists of data collected by 7 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files. In total 72972 segments are made available, leading to approximately 200 hours of data with annotations.</p> <p>More information about the challenge and the specific dataset can be found here. Information solely related to the content of the dataset is available in 'DCASE2018-task5-eval.doc.zip'.</p> <p>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-eval.doc.zip).</p>
Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations
<p>This dataset provides data on typical pollutant removal efficiencies, evaluation critera and cost estimation for 37 common wastewater treatment unit processes. The data is based on literature research, expert workshops and estimations.</p> <p>The following files are available:</p> <ul> <li>Pollutant removal efficiencies, evaluation criteria and cost estimations for wastewater treatment unit processes (dataset) - Microsoft Excel:<br> Database of pollutant removal efficiencies for 11 paramameters, cost estimation and evaluation criteria.</li> <li>Pollutant Removal Efficiencies for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Evaluation Criteria for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Cost Estimation for Wastewater Treatment Unit Processes (Dataset) - pdf</li> </ul> <p> </p> <p> </p>
Numerical methodology for design and evaluation of natural circulation systems for MSR applications - Dataset
<p>Dataset associated to the 2022 ANS Annual meeting conference paper "Numerical methodology for design and evaluation of natural circulation systems for MSR applications".</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.