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2,863 results for “Challenge”
Fig. 5 in Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)
Fig. 5. Spectrograms illustrating differences between two different Molurina species tapping. Both plots were generated based on videos of beetles kept in captivity: Tuberocnodes synhimboides (Supp Movie S1 [online only]) and Toktokkus vialis (Kamiński et al. 2021a). Sex of the beetles in both cases remain unknown. Contrary to To. vialis, the recorded individual of T. synhimboides is producing sound in a series of four to five hits.
Fig. 4 in Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)
Fig. 4. Examples of morphological characters used by Koch (1955) to separate Molurina and Phanerotomeina. Margination of the fifth ventrite (A: Distretus amplipennis; B: Ocnodes similis, with margination on only one side of the ventrite); protibia (C: O. similis, D: Toktokkus vialis); differences in facial structure (O. M. Gearner and M. J. Kamiński, personal observations; note: not used by Koch, 1955) (E: Psammodes pinguis, F: O. similis); apex of metatibia (G: P. pinguis; H: O. similis); setation on tibiae (I: P. pinguis, J: O. similis); metatarsi (K: O. similis; L: P. pinguis).
Trojan Detection Challenge - Analysis Track (Trigger Synthesis) Dataset
<p>This contains the dataset for the trigger synthesis subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This contains the same files as the trigger_synthesis folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the trigger synthesis track can be found <a href="https://zenodo.org/record/7217600">here</a>.</p>
Trojan Detection Challenge - Evasive Trojans Track Dataset
<p>This contains the dataset for the evasive trojans track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the evasive_trojans folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the evasive trojans track can be found <a href="https://zenodo.org/record/7217616">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Dataset
<p>This contains the dataset for the target label prediction subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This contains the same files as the target_label_prediction folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the target label prediction track can be found <a href="https://zenodo.org/record/7217508">here</a>.</p>
Trojan Detection Challenge - Detection Track Dataset
<p>This contains the dataset for the detection track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This contains the same files as the detection folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the detection track can be found <a href="https://zenodo.org/record/7217508">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Trigger Synthesis) Test Set
<p>This contains the test set for the trigger synthesis track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full trigger synthesis track Zenodo record, which can be found <a href="https://zenodo.org/record/7213751">here</a>.</p>
Trojan Detection Challenge - Evasive Trojans Track Test Set
<p>This contains the test set for the evasive trojans track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full evasive trojans track Zenodo record, which can be found <a href="https://zenodo.org/record/7213753">here</a>.</p>
Trojan Detection Challenge - Detection Track Test Set
<p>This contains the test set for the detection track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full detection track Zenodo record, which can be found <a href="https://zenodo.org/record/7213750">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Test Set
<p>This contains the test set for the target label prediction track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full target label prediction track Zenodo record, which can be found <a href="https://zenodo.org/record/7213751">here</a>.</p>
Privacy Challenges and Solutions in Smart Health: A Systematic Literature Review Extraction Forms
<p>Data extraction forms for a Systematic Literature Review on Privacy Challenges and Solutions in Smart Health</p>
Supplementary material 1 from: Neylon C, Chan L (2016) Exploring the opportunities and challenges of implementing open research strategies within development institutions. Research Ideas and Outcomes 2: e8880. https://doi.org/10.3897/rio.2.e8880
A translation of the proposal into French. IDRC operates in both English and French as a Canadian Crown Corporation.
THE SECOND INTERNATIONAL SCIENTIFIC-PRACTICAL VIRTUAL CONFERENCE IN HEALTH INNOVATIONS & RESEARCH: PROGNOSIS, ACHIEVEMENT, AND CHALLENGES.
<p>THE SECOND INTERNATIONAL SCIENTIFIC-PRACTICAL VIRTUAL CONFERENCE IN HEALTH INNOVATIONS & RESEARCH: <br>PROGNOSIS, ACHIEVEMENT, AND CHALLENGES.</p>
Dams for hydropower and irrigation: Trends, challenges, and alternatives
<p>Data associated with the paper "Dams for Hydropower and Irrigation: Trends, Challenges, and Alternatives" by R. J. P. Schmitt and L. Rosa (2024) in Renewable and Sustainable Energy Reviews</p> <p>Notably, global dataset of hydropower installed in major dams and reservoirs (based on Lehner B, Liermann CR, Revenga C, Vörösmarty C, Fekete B, Crouzet P, et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front Ecol Environ 2011;9:494–502.)</p>
E-Commerce Price Prediction Challenge
<h2>Problem Statement</h2> <p>Akshat is new to the market and is unaware of the prices of multiple products. he wants to be assured of the price before making any purchase! Help Akshat out in predicting the prices for Products and derive some conclusive evidences!</p> <h2>What's in there!?</h2> <p>In the dataset, there are products/images/dimensions/prices/ratings and much more for Feature Engineering.</p> <h2>What is the Inspiration from this!?</h2> <p>This type of business problem is typical for any new products being launched into the market and sites like Trivago/Policy Bazaar compare same product over multiple sites to reach a conclusive rate. Can you achieve the same!?</p>
Challenges in submarine fiber-optic earthquake monitoring
<p>This dataset contains three subsets of data from a Distributed Acoustic Sensing (DAS) experiment near Santorini, Greece. The file named microseisms* contains 10 minutes of data during a period of strong microseismic activity. The two files named earthquake* contain the data for two seismic events underneath the Kolumbo volcanic chain. </p>
DCASE 2024 Challenge Task 2 Additional Training Dataset
<p><strong><Important ! Notes on releasing Version v2 (23 May, 2024)></strong><br><strong>Due to some data issues, data files for 3DPrinter and RoboticArm have been updated. The new versions of these files are renamed as "eval_data_3DPrinter_train_r2.zip" and "eval_data_RoboticArm_train_r2.zip". Please use these files for the DCASE 2024 Challenge Task 2. (Other files have not been changed from Version v1) We apologize for your inconvenience.</strong></p> <p><strong>Description</strong></p> <p>This dataset is the "additional training 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><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 training data. A set of test data corresponding to this training data will be provided in another seperate zenodo page as an "evaluation dataset" for the DCASE 2024 Challenge task 2. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training and (ii) ten clips of normal sounds in the target domain for training. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.</p> <p> </p> <p><strong>File names and attribute csv files</strong></p> <p>File names and attribute csv files provide reference labels for each clip. The given reference labels for each training clip include machine type, section index, normal/anomaly information, and attributes regarding the condition other than normal/anomaly. The machine type is given by the directory name. The section index is given by their respective file names. For the datasets other than the evaluation dataset, the normal/anomaly information and the attributes are given by their respective file names. Note that for machine types that has its attribute information hidden, the attribute information in each file names are only labeled as "noAttributes". Attribute csv files are for easy access to attributes that cause domain shifts. In these files, the file names, name of parameters that cause domain shifts (domain shift parameter, dp), and the value or type of these parameters (domain shift value, dv) are listed. Each row takes the following format:</p> <p> [filename (string)], [d1p (string)], [d1v (int | float | string)], [d2p], [d2v]...</p> <p>For machine types that have their attribute information hidden, all columns except the filename column are left blank for each row.</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> - /train (only normal clips) <br> - /section_00_source_train_normal_0001_<attribute>.wav <br> - ... <br> - /section_00_source_train_normal_0990_<attribute>.wav <br> - /section_00_target_train_normal_0001_<attribute>.wav <br> - ... <br> - /section_00_target_train_normal_0010_<attribute>.wav <br> - attributes_00.csv (attribute csv for section 00)<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> </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>
Male victims of intimate partner violence: a systematic review of challenges, barriers and facilitators for help-seeking
<p>The data identified by the full database search through Scopus and APA PsycINFO 29th of February 2024.</p>
Experimental data from long-term elevated pCO2 exposure in Pacific herring. Datasets include measurements of enviromental treatment conditions, growth and developmental traits, and mortality data from a disease challenge.
<p>This study evaluated the effects of ocean acidification on the health, survival, fitness, and disease susceptibility of early life stage Pacific herring. Wild larvae were reared from hatching under three pCO2 treatments [low (~650 μatm), intermediate (~1,500 μatm), and high (~3,000 μatm)] to determine the effects of elevated pCO2 on larval growth, yolk consumption, and foraging capabilities and to evaluate how chronic exposure to elevated pCO2 affects long-term growth and maximum swimming speed. After prolonged exposure (98 d), we tested how elevated pCO2 experienced during development altered the susceptibility of juvenile Pacific herring to VHS following water-borne exposure to VHSV. This directory includes datasets for: 1) daily pH and temperature measurements from replicate rearing tanks; 2) carbon chemistry measurements from discrete seawater samples; 3) morphometric and feeding data for larval herring (1 - 16 days post hatch); 4) standard length measurements from larval and juvenile samples (1 - 98 days post hatch); 5) All dry mass measurements from larval and juvenile samples (1 - 98 days post hatch); 6) survival statistics for whole experiment ( 1- 98 days post hatch); 7) maximum swim speed measurements on juvenile herring; 8) disease challenge mortality and viral titer data; 8) a README file containing information on the dataset. </p>
Pharmaceutical Logistics Supply Chain: Challenges and Possible Solutions
<p>Explore the complexities of pharmaceutical supply chain management in this comprehensive article. Discover the challenges faced, such as regulatory compliance and counterfeit drugs, and delve into innovative solutions like blockchain technology and smart packaging. Learn how these advancements enhance efficiency, ensure product safety, and improve patient outcomes in the global pharmaceutical industry</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.