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470 results for “Evaluation dataset”
Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset
<p>This is a replication data package for a paper titled "<em>Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset</em>".</p> <p>Paper abstract: Even though Static Code Analysis (SCA) tools are integrated into many modern software building and testing pipelines, their practical impact is still seriously hindered by the excessive number of false positive warnings they usually produce. To cope with this problem, researchers have proposed several post-processing methods that aim to filter out false hits (or equivalently identify ``actionable'' warnings) after the SCA tool produced its results. However, we found that most of these approaches are targeted (i.e., deal with only a few SCA warning types) and evaluated on synthetic benchmarks or small-scale manually collected data sets (i.e., with typical sample sizes of several hundred).</p> <p>In this paper, we present a code embedding-based approach for filtering false positive warnings produced by 160 different SonarQube rule checks, one of the most widely adopted SCA tools today. We evaluate the method on a dataset containing 224,484 real-world warning samples fixed (true positive samples) or ignored (false positive samples) by the developers, which we collected from 9,958 different open-source Java projects from GitHub using a data mining approach. It is the most extensive real-world study and public dataset we know of in this area. Our method works with an accuracy of 91% (best F1-score of 81.3% and AUC of 95.3%) for the classification of SonarQube warnings.</p>
Dataset of article: Evaluation of the Effects of Submarine Groundwater on Nutrient Concentration and Primary Production in a Deep Bay of the Japan Sea
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DCASE 2024 Challenge Task 2 Evaluation Dataset
<p><strong><Important ! Version v2 of "DCASE 2024 Challenge Task 2 Additional Training dataset" has been published on 23 May, 2024></strong><br><strong>Version v2 of the "DCASE 2024 Challenge Task 2 Additional Training dataset", which corresponds to this "DCASE 2024 Challenge Task 2 Evaluation Dataset (Version v1)", has been published on <a href="../records/11259435">https://zenodo.org/records/11259435</a> (23 May, 2024). In the "DCASE 2024 Challenge Task 2 Additional Training dataset", data files for 3DPrinter and RoboticArm are updated from Version v1 and are renamed as "eval_data_3DPrinter_train_r2.zip" and "eval_data_RoboticArm_train_r2.zip". Please use those files for the "Additional Training dataset" in DCASE 2024 Challenge Task 2. (Other files have not been changed from Version v1.) We apologize for your inconvenience.</strong></p> <p> </p> <p><strong><The data files will be made publicly accessible on June 1st, 2024></strong></p> <p><strong>Description</strong></p> <p>This dataset is the "evaluation dataset" for the <a href="https://dcase.community/challenge2024/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">DCASE 2024 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring"</a>.</p> <p>The data consists of the normal/anomalous operating sounds of nine types of real/toy machines. Each recording is a single-channel audio that includes both a machine's operating sound and environmental noise. The duration of recordings varies from 6 to 10 seconds. The following nine types of real/toy machines are used in this task:</p> <ul> <li>3DPrinter</li> <li>AirCompressor</li> <li>BrushlessMotor</li> <li>HairDryer</li> <li>HoveringDrone</li> <li>RoboticArm</li> <li>Scanner</li> <li>ToothBrush</li> <li>ToyCircuit</li> </ul> <p> </p> <p><strong>Overview of the task</strong></p> <p><strong>Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. </strong>Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial-intelligence-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines. </p> <p>This task is the follow-up from DCASE 2020 Task 2 to DCASE 2023 Task 2. The task this year is to develop an ASD system that meets the following five requirements.</p> <p><strong>1. Train a model using only normal sound (unsupervised learning scenario)</strong> <br>Because anomalies rarely occur and are highly diverse in real-world factories, it can be difficult to collect exhaustive patterns of anomalous sounds. Therefore, the system must detect unknown types of anomalous sounds that are not provided in the training data. This is the same requirement as in the previous tasks.</p> <p><strong>2. Detect anomalies regardless of domain shifts (domain generalization task) </strong><br>In real-world cases, the operational states of a machine or the environmental noise can change to cause domain shifts. Domain-generalization techniques can be useful for handling domain shifts that occur frequently or are hard-to-notice. In this task, the system is required to use domain-generalization techniques for handling these domain shifts. This requirement is the same as in DCASE 2022 Task 2 and DCASE 2023 Task 2.</p> <p><strong>3. Train a model for a completely new machine type</strong><br>For a completely new machine type, hyperparameters of the trained model cannot be tuned. Therefore, the system should have the ability to train models without additional hyperparameter tuning. This requirement is the same as in DCASE 2023 Task 2.</p> <p><strong>4. Train a model using a limited number of machines from its machine type</strong><br>While sounds from multiple machines of the same machine type can be used to enhance the detection performance, it is often the case that only a limited number of machines are available for a machine type. In such a case, the system should be able to train models using a few machines from a machine type. This requirement is the same as in DCASE 2023 Task 2.</p> <p><strong>5 . Train a model both with or without attribute information</strong><br>While additional attribute information can help enhance the detection performance, we cannot always obtain such information. Therefore, the system must work well both when attribute information is available and when it is not.</p> <p>The last requirement is newly introduced in DCASE 2024 Task2.</p> <p> </p> <p><strong>Definition</strong></p> <p>We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.".</p> <ul> <li>"Machine type" indicates the type of machine, which in the additional training dataset is one of nine: 3D-printer, air compressor, brushless motor, hair dryer, hovering drone, robotic arm, document scanner (scanner), toothbrush, and Toy circuit.</li> <li>A section is defined as a subset of the dataset for calculating performance metrics.</li> <li>The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, signal-to-noise ratio, etc.</li> <li>Attributes are parameters that define states of machines or types of noise. For several machine types, the attributes are hidden.</li> </ul> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset consists of nine machine types. For each machine type, one section is provided, and the section is a complete set of test data. A set of training data corresponding to this test data is provided in another seperate zenodo page as an "additional training dataset" for the DCASE 2024 Challenge task 2 (<a href="../records/11259435">https://zenodo.org/records/11259435</a>). For each section, this dataset provides 200 clips of test data.</p> <p> </p> <p><strong>Recording procedure</strong></p> <p>Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.</p> <p> </p> <p><strong>Directory structure</strong></p> <p>- /eval_data </p> <p> - /raw<br> - /3DPrinter<br> - /test <br> - /section_00_0001.wav <br> - ... <br> - /section_00_0200.wav <br> - /AirCompressor (The other machine types have the same directory structure as 3DPrinter.) <br> - /BrushlessMotor<br> - /HairDryer<br> - /HoveringDrone<br> - /RoboticArm<br> - /Scanner<br> - /ToothBrush<br> - /ToyCircuit</p> <p> </p> <p><strong>Baseline system</strong></p> <p>The baseline system is available on the Github repository <<a href="https://github.com/nttcslab/dcase2023_task2_baseline_ae">https://github.com/nttcslab/dcase2023_task2_baseline_ae</a>>. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Condition of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd.,</strong> <strong>NTT Corporation and STMicroelectronics</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>Citation</strong></p> <p><TBD></p> <p> </p> <p><strong>Contact</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Tomoya Nishida, <a href="mailto:kota.dohi.gr@hitachi.com">tomoya.nishida.ax@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> <li>Noboru Harada, <a href="mailto:noboru@ieee.org">noboru@ieee.org</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>
Dataset of the Article Evaluating a Framework of Conceptual Modelling Research
<p>Excel files with the raw data of the article Evaluating a Framework of Conceptual Modelling Research</p>
Dataset for "Ammonia water as a biobased fertiliser: Evaluating agronomic and environmental performance for Lactuca sativa compared to synthetic fertilisers"
<p>This datasent was uploaded as an addition to the works published in "Ammonia water as a biobased fertiliser: Evaluating agronomic and environmental performance for <em>Lactuca sativa</em> compared to synthetic fertilisers". DOI: https://doi.org/10.1016/j.bcab.2023.102907</p> <p>1) The 1st attached file contains the raw and calculated dataset for the greenhouse gas emission monitering from the lettuce pot trials for the different bio-based fertilisers under Belgian pilot plant.</p> <p>2) The 2nd attached file contains the raw and calculated dataset for biomass, nitrogen uptake, nitrogen fertiliser replacement value, nitrogen use efficiency and other agronomic data related to the lettuce pot trials from the different bio-based fertilisers under Belgian pilot plant.</p> <p> </p>
Dataset to - "Evaluation of agronomic efficiency and stress resistance on Swiss chard via use of biostimulants"
<p>This is the datasent to the published manuscript titled - "Evaluation of agronomic efficiency and stress resistance on Swiss chard via use of biostimulants". DOI: https://doi.org/10.1016/j.scienta.2024.113053</p> <p>1) The attached file contains the raw and calculated dataset for biomass, nitrogen uptake, nitrogen fertiliser replacement value, nitrogen use efficiency, chlorophyll, proline, MDA and other agronomic data related to the swiss chard pot trials from the different bio-based fertilisers under Belgian pilot plant.</p> <p> </p>
Dataset for Evaluating geopolitical gas supply chain security in the EU: A literature-based index and a clustering analysis
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Raw dataset for evaluating the effects of M sand and fly ash on concrete strength using experimental and machine learning approaches
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Dataset Characterizing Architectural Evaluations and Identifying Quality Attributes addressed in Systems-of-Systems: A Systematic Mapping Study
<p>Dataset</p>
Model Code and Various Datasets for Evaluation
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Dataset for article "Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"
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A multi-center MRI dataset for bladder cancer and baseline evaluations of federated learning in its clinical application
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MassSpectrometry Dataset to "Mitochondrial Creatine Kinase 2 (Ckmt2) as a Plasma-Based Biomarker for Evaluating Reperfusion Injury in Acute Myocar-dial Infarction"
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Loss Cone Offset Method for Evaluating the Effect of Magnetic Field Line Curvature Scattering [Dataset]
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Dataset for SLR paper - Identification and Evaluation of Pitfalls in the Migration from IEC 61131-3 to IEC 61499: A Review
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Dataset for "Using Peer Review to Evaluate the Societal Relevance of Humanities Research"
<p>Data for the study on using peer review for evaluating the societal relevance of humanities research. In addition, the study exploratorily uses peer review to measure the societal relevance of humanities research.</p> <p>The raw data for studies 1 and 2 was published earlier as a previous version of this dataset. This updated version again includes the raw data of studies 1 and 2, but now also the raw data of study 3 as well as the combined, processed data of all three studies. This is the dataset that was used for the analyses described in the paper that we wrote about this study.</p>
Dataset for "The Politics of AI – An Evaluation of Political Preferences in Large Language Models from a European Perspective"
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Training and Evaluation Datasets for Readseeker Model
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Entity based dataset retrieval - Evaluation results
<p>This repository provides the TREC evaluation results for various entity based retrieval models with the [Befchina](https://doi.org/10.3897/rio.7.e67887) and [BioCADDIE](https://dx.doi.org/10.1093%2Fdatabase%2Fbax061) test collections</p> <p><br> All metrics were computed with the latest <a href="https://trec.nist.gov/trec_eval/">TREC scripts</a>. For BioCADDIE, in addition, we computed the <a href="https://trec.nist.gov/data/clinical/sample_eval.pl">inferred measures</a>.</p> <p>The code for the experiments is available at GitHub: <a href="https://github.com/fusion-jena/entity-retrieval-evaluation">https://github.com/fusion-jena/entity-retrieval-evaluation</a></p> <p> </p>
Evaluating Deep Learning-based Vulnerability Detection Models on Realistic Datasets
<p>This package contains all the data we used for our experiments.<br> </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.