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470 results for “Evaluation dataset”
FIND: A Function Interpretation Dataset and Benchmark for Evaluating Interpretability Methods
<p><strong>FIND</strong> is an interactive dataset for evaluating AI interpretability methods on black box functions. </p> <p>This dataset contains all function files for the <strong>FIND</strong> benchmark and JSON files with associated metadata. The utilities provided in the associated <strong>FIND</strong> <a href="https://github.com/multimodal-interpretability/FIND">GitHub Repository</a> support running and evaluating interpretation of the functions with user-defined interpreters.</p>
Evaluation dataset for Relation Extraction of relationships between Organisms and Natural-Products
<p>A curated evaluation dataset for end-to-end Relation Extraction of relationships between organisms and natural-products.</p> <p>Details about the manual annotation:</p> <ul> <li>For Chemicals: <ul> <li>The chemical labels are annotated as they appear in the abstract.</li> <li>In abstracts, singular chemicals and classes of chemicals produced by a specific organism were distinguished.</li> <li>The "type" attribute {“chemical”, “class”} is used to indicate the nature of the mentioned name.</li> <li>A "class" attribute for chemical entities has also been included if class information is present in the abstract.</li> <li>A Wikidata and PubChem identifiers were assigned to chemicals and classes when available.</li> </ul> </li> <li>For Organisms: <ul> <li>The organism labels are annotated as they appear in the abstract.</li> <li>If in an abstract, the genus name was mention first, e.g. "Plakinastrella sp." and then the specie name e.g "Plakinastrella clathrata" is precise, then only the specie name is used.</li> <li>A Wikidata identifier was assigned to all organisms.</li> <li>In some abstracts, only the genus name is mentioned.</li> </ul> </li> <li>For Relations: <ul> <li>Only the relations explicitly mentioned in the abstract are reported in the output labels.</li> <li>Relations are reported in their order of appearance in the abstract.</li> </ul> </li> </ul>
Experimental evaluation of louver guidance efficiency for green sturgeon (Acipenser medirostris), Primary Datasets, 2016-2017
Throughout the world, louver-bypass systems are a common method for fish protection at water diversion sites. This study used controlled laboratory experiments to quantify louver efficiency for juvenile green sturgeon under a range of conditions. Green sturgeon juveniles used in the study were spawned from the University of California, Davis (hereafter UC Davis) captive broodstock program. Experimental trials were conducted within an indoor flume at the J. Amorocho Hydraulics Laboratory (JAHL) at UC Davis. The flume had a zero degree bed slop, and was equipped with a louver placed at a 15-degree angle to the streamwise flow. Louver slats had 25-mm clear spacing. The louver terminated at a bypass channel 0.3 m wide, and was operated to maintain water velocity in the bypass that was 1.2 times greater than the velocity in the flume. Full factorial experimental treatments were designed to address the influence of sturgeon size, water velocity, diel period, and water temperature on louver performance and behavior. Sturgeon were tested within three predefined size classes (range: 6 – 34 cm TL). At each size class, fish were tested under combinations of water velocity, water temperature, and photophase. During each experimental trial, 60 (+/-2) juvenile sturgeon were released from an acclimation chamber to enter the test area at the bottom of the flume. Fish were allowed to freely navigate throughout the test area until they were transported through the louver slats or bypass channel, at which point they were removed and the time was recorded. Trials ended when all fish were collected at a downstream endpoint, or after 90 minutes of exposure time, and remaining fish were removed from the flume. Trials were completed across two study years within seven months after hatch, following the same protocols with a few minor adjustments in year two. During daytime trials in both study years the louver face was monitored with video recordings to observe contact between fish and the
DCASE 2020 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This dataset is the "evaluation dataset" 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 "evaluation dataset". This evaluation dataset was the last of the three released. This dataset includes around 400 samples for each Machine Type and Machine ID used in the evaluation dataset, none of which have a condition label (i.e., normal or anomaly).</p> <p>The recording procedure and data format are the same as the <a href="http://zenodo.org/record/3678171">development dataset</a> and <a href="https://zenodo.org/record/3727685">additional training dataset</a>. The Machine IDs in this dataset are the same as those in the <a href="https://zenodo.org/record/3727685">additional training dataset</a>. For more information, please see the pages of the <a href="http://zenodo.org/record/3678171">development dataset</a> and the <a href="http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds">task description</a>. </p> <p>After the DCASE 2020 Challenge, we released the <a href="https://zenodo.org/record/3951620">ground truth for this evaluation dataset</a>.</p> <p> </p> <p><strong>Directory structure</strong></p> <p>Once you unzip the downloaded files from Zenodo, you can see the following directory structure. Machine Type information is given by directory name, and Machine ID and condition information are given by file name, as:</p> <ul> </ul> <p>/eval_data</p> <ul> <li>/ToyCar <ul> <li>/test (Normal and anomaly data for all Machine IDs are included, but they do not have a condition label.) <ul> <li>/id_05_00000000.wav</li> <li>...</li> <li>/id_05_00000514.wav</li> <li>/id_06_00000000.wav</li> <li>...</li> <li>/id_07_00000514.wav</li> </ul> </li> </ul> </li> <li>/ToyConveyor (The other Machine Types have the same directory structure as ToyCar.)</li> <li>/fan</li> <li>/pump</li> <li>/slider</li> <li>/valve</li> </ul> <p> </p> <p>The paths of audio files are:</p> <ul> <li>"/eval_data/<Machine_Type>/test/id_<Machine_ID>_[0-9]+.wav"</li> </ul> <p>For example, the Machine Type and Machine ID of "/ToyCar/test/id_05_00000000.wav" are "ToyCar" and "05", respectively. Unlike the <a href="http://zenodo.org/record/3678171">development dataset</a> and <a href="https://zenodo.org/record/3727685">additional training dataset</a>, its condition label is hidden. </p> <p> </p> <p><strong>Baseline system</strong></p> <p>A simple baseline system is available on the Github repository <a href="https://github.com/y-kawagu/dcase2020_task2_baseline">[URL]</a>. The baseline system provides a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. It is a good starting point, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</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>
RTPTorrent: An Open-source Dataset for Evaluating Regression Test Prioritization
<p>This dataset is designed to be used in evaluation studies of regression test prioritization techniques. It includes 20 open-source Java projects from GitHub and over 100,000 logs of real-world build logs from TravisCI. The projects span a wide range with regard to size, number of contributors, and maturity of open-source Java projects available on GitHub.</p> <p>Futher, the dataset includes the results of baseline approaches to ease the comparison of new techniques applied to the dataset.</p> <p>A readme file with a more detailed description of the structure of the dataset is included. For even more information see the corresponding MSR 2020 publication.</p> <p>Versions:</p> <ul> <li> 2020-09-23 (version 1.1) <ul> <li>Updated archived `deeplearning4j` repository with a fork that contains all of the original commits. Repository at the original GitHub location had been replaced. Defect identified by Daniel Elsner (Technische Universität München).</li> <li>Renamed root folder from MSR2 to rtp-torrent</li> </ul> </li> <li>2020-05-25 (version 1.0) <ul> <li>Initial release</li> </ul> </li> </ul> <p> </p>
Soil Dataset for Land evaluation in southwestern of Egyptian Delta
<p>Soil Dataset for Land evaluation in southwestern of Egyptian Delta</p>
Evaluation of Android Malware Detection Based on System Calls - Dataset
<p>This is a data set accompanying a paper "Evaluation of Android Malware Detection Based on System Calls" published in the proceedings of the International Workshop on Security and Privacy Analytics, New Orleans, USA, 2016.</p> <p>See README.md for details of the data set.</p>
Model and observational datasets used for evaluating CHASER simulated formaldehyde (HCHO) abundances in 2019 and 2020.
<p>The dataset entails the model simulation results and the observational data (satellite, aircraft, and ground-based MAX-DOAS) used for the study titled " Evaluating CHASER V.40 global formaldehyde (HCHO) simulations using satellite aircraft and ground-based remote sensing observations", submitted for peer-review in JGR: Atmospheres. </p>
Dataset for article "Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"
<p>Dataset for article entitled "<strong>Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"</strong></p>
Early Irish Analogy Dataset for Word Embedding Evaluation
<p>An embedding evaluation dataset for Early Irish described in the paper "<a href="https://aclanthology.org/2023.insights-1.10.pdf">Do not Trust the Experts: How the Lack of Standard Complicates <span>NLP</span> for Historical <span>I</span>rish</a>".</p> <p>Traditionally, analogy datasets are based on pairwise semantic proportion, and therefore every question has a single correct answer. Given the high level of variation in historical languages, such a strict definition of a correct answer seems unjustified. Therefore, Early Irish Analogy Dataset follows the <a href="https://vecto.space/projects/BATS/">Bigger Analogy Test Set (BATS)</a> and provides several correct answers to each analogy question. </p> <p>Morphological and spelling variation data are extracted from the <a href="https://dil.ie/">eDIL</a>, a historical dictionary of medieval Irish. Unlike BATS, no distinction is made between inflection types due to eDIL's structure. The raw data amounted to 2,370 spelling variation and 9,690 morphological variation questions, from which 150 examples were randomly selected for each of the subsets to be comparable in size with the synonym and antonym subsets. The synonym and antonym subsets are translations of the correspondent BATS parts obtained by reverse-searching the eDIL and proofread by four expert evaluators. The dataset includes 98 entries in the synonym subset and 109 entries in the antonym subset, upon which three or more experts agreed.</p>
IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift Evaluation Dataset
<p>The Chinese Acoustic Scene (CAS) 2023 dataset is a large-scale dataset that serves as a foundation for research related to environmental acoustic scenes. The dataset includes 10 common acoustic scenes, with a total duration of over 130 hours. Each audio clip is 10 seconds long with metadata about the recording location and timestamp. The dataset was collected by members of the <em>Joint Laboratory of Environmental Sound Sensing at the School of Marine Science and Technology, Northwestern Polytechnical University</em>. The data collection period spanned from April 2023 to September 2023, covering 22 different cities across China. The CAS 2023 dataset was collected using the XS-SN-2BE1 manufactured by <em>Xi'an Lianfeng Acoustic Technologies Co., Ltd</em> (https://www.lfxstek.com/). </p> <p>The ICME 2024 <em>Semi-supervised Acoustic Scene Classification under Domain Shift</em> challenge (https://2024.ieeeicme.org/grand-challenge-proposals/, https://ascchallenge.xshengyun.com/) dataset consists of development (https://zenodo.org/records/10616533) and evaluation datasets, all derived from the CAS 2023 dataset. The evaluation dataset includes 1,100 recordings, where data are selected from 12 cities, with 5 unseen cities specifically chosen to provide a more comprehensive evaluation of submissions under domain shift.</p> <p>Baseline: https://github.com/JishengBai/ICME2024ASC</p> <p>Acoustic scenes (10): Bus, Airport, Metro, Restaurant, Shopping mall, Public square, Urban park, Traffic street, Construction site, Bar</p>
Dataset for Evaluating habitat-specific interference in automated radio telemetry systems: implications for animal movement studies
<h1>Abstract </h1> <p>Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of one week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats.</p> <p> </p>
Dataset_Experts_Evaluation_for_Integrated_MCDM_Approach_for_PGT_Selection_in_SES
<p>Template and Dataset containing experts' evaluation for the case study of the research paper entitled "Evaluation_for_Integrated_MCDM_Approach_for_PGT_Selection_in_SES"</p>
Evaluation of Spatiotemporal Fusion Methods Using Sentinel-2 And Sentinel-3: A New Benchmark Dataset And Comparison
<p>In Earth observation, data fusion is important to generate high temporal and spatial resolution images. Nevertheless, existing research on data fusion primarily concentrates on merging two sources of data (mostly MODIS and Landsat). Therefore, we offer the community a new benchmark dataset for evaluating data fusion using new European sensors (Sentinel-2 and Sentinel-3).</p> <p>The dataset is composed of three different sites located in different parts of the world to ensure the diversity of the ecosystem. The two components of the dataset are collected from operating missions ( Sentinel-2 and Sentinel-3). We also provide 10 bands for Sentinel-2 ranging from blue to SWIR, 4 bands at 10m resolution and 6 at 20m resolution. For Sentinel-3 16 bands are provided with a spatial resolution of 300m. The multiple bands allow for different applications for this dataset such as testing data fusion methods, etc.</p>
LCC - Datasets for PlastiCircle Economic Evaluation of the Light Packaging Waste Management
<p>These datasets are related to deliverable 7.4 which presents the results from the economic assessment for the waste management systems evaluation of the city pilots and converter industries.</p> <p>PlastiCircle technologies allowed higher collection of plastic packaging and better input quality which might result in an increased market demand achieving the European recycling objectives. The higher volumes collected cause higher management costs which are compensated by higher remuneration from the PRO. The higher recovery resulted in higher recycling costs which are offset by higher revenues from sales. The evaluation of the industrial case studies indicates that the profitability of the production of industrial products using recycled post-consumer plastics is dependent of the market prices and the stability of the supply.</p>
S-LCA - Datasets for PlastiCircle Social Evaluation of the Light Packaging Waste Management (stakeholder perpectives)
<p>This deliverable (7.5) presents S-LCA results for the waste management systems in Valencia (Spain), Utrecht (Netherland) and Alba Iulia (Romania). Moreover, an extrapolation of the results has been calculated to show the results on the European level.</p> <p>Data is collected from groups of citizens, citizens associations, waste managers and municipalities (city hall workers).</p> <p>Questionnaires have been done in order to ask this groups of people questions to assess the social impact of the waste management systems in each city.</p> <p>Questions have been prepared according to the guidelines for social life cycle assessment of products - UNEP DTIE.</p>
LCA - Datasets for PlastiCircle Environmental Evaluation of the Light Packaging Waste Management
<p><strong>Datasets presented are related to Delievrable 7.3 - LCA.</strong></p> <p>To structure the environmental assessment, cities and industries are treated separately. The three pilot cities evaluated in PlastiCircle are:<br> 1. Valencia 2. Utrecht 3. Alba Iulia</p> <p><br> Within the project several technologies and strategies have been developed to improve the waste management of the pilot cities and other aspects in the value chain. Main developments spin around (a) citizens identification through a labelling system integrated on the container on a PAYT basis; (b) route optimization based on filling level sensors integrated on the container and corresponding algorithms for the optimization; (c) Eco-driving app to monitor the transport parameters and suggest efficient driving to the drivers. Moreover, on the sorting stage (d) PICVISA developed different improvement on their sorting equipment to enhance yield and purity on extracted fractions, and as well, a mechanical module to improve film recovery was developed. All these conditions have been evaluated on this report, focusing on the following sections:<br> 1. Collection – Impact of PAYT system on the pilot and quality segregation and IoT infrastructure.<br> 2. Transport – Impact of route optimization and efficient driving<br> 3. Sorting – Impact from yield and purity improvements and development of turbosorter</p> <p><br> For the sorting stage a model has been designed for each pilot waste recycling plant, focusing on the energy used per tonne, the yield of each fraction on the plant and the input waste as main parameters, inter alia.</p> <p>As a bridge between the cities and the final converters, there is a need of a proper pre-treatment/ conditioning of the post-consumer polymer to provide quality standard feedstock to the industries. PlastiCircle, lacks a partner on the consortium undertaking these tasks on an industrial level, however, different lab scale trials and some trials with Sorema have been assessed during the project, also considering broken down processes to evaluate impact on these intermediate stages taking into consideration processes such as the ones used by European industries as Sorema or Herbold, and other processes extracted from literature and commercial solutions:<br> 4. Washing/Pre-treatment – Impact on the pre-conditioning for plastic packaging bales.</p> <p><br> Once plastic is ready as flakes or pellets forms, industries within the project use those recycled plastic as raw materials to manufacture their products. Their different processes have been defined analysed, performing specific LCA for each one of their results considering the trials and impacts from the developments done during the project.</p> <p>Attached datasets used for the LCA models in PlastiCircle can be found.</p>
Dataset: Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises
<p>This repository contains supplementary materials for the following conference paper:</p> <p>V. Švábenský, R. Weiss, J. Cook, J. Vykopal, P. Čeleda, J. Mache, R. Chudovský, A. Chattopadhyay.<br> <em>Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises.</em><br> In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE 2022).<br> <a href="https://doi.org/10.1145/3478431.3499414">https://doi.org/10.1145/3478431.3499414</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2112.02053">https://arxiv.org/abs/2112.02053</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@inproceedings{Svabensky2022evaluating, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Weiss, Richard and Cook, Jack and Vykopal, Jan and \v{C}eleda, Pavel and Mache, Jens and Chudovský, Radoslav and Chattopadhyay, Ankur}, title = {{Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises}}, booktitle = {Proceedings of the 53rd ACM Technical Symposium on Computer Science Education}, series = {SIGCSE '22}, location = {Providence, RI, USA}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, month = {03}, year = {2022}, pages = {787--793}, numpages = {7}, isbn = {978-1-4503-9070-5}, url = {https://doi.org/10.1145/3478431.3499414}, doi = {10.1145/3478431.3499414}, }</code></pre> <p><strong>Attached content</strong></p> <p>The materials include the research dataset, source code, and graphs. See the README.md file inside the attached ZIP file for more details.</p>
DATASET: Evaluation of sorbents for high temperature removal of tars, hydrogen sulphide, hydrogen chloride and ammonia from biomass-derived syngas by using Aspen Plus
<p>Effect of S/B ratio on syngas; Effect of temperature on syngas; sensitivity analysis of H2S concentration respect to steam content in syngas; sensitivity analysis of H2S concentration respect to temperature.</p>
Dataset for "Evaluation of Publicly Available Information on Sex-related Differences in the Efficacy and Safety of New Molecular Entities and Therapeutic Biological Products"
<p>Contains our extraction sheets with additional documents/notes on methods used in our study.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.