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INFORMATION SECURITY IN THE ERA OF THE DIGITAL ECONOMY: CHALLENGES AND STRATEGIES. INNOVATIVE APPROACHES TO SOLVING ECONOMIC CHALLENGES
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What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge: scores and targets
<p>This data publication contains the zipped scores parquet database and the targets used in the analysis for the NEON Challenge aquatics synthesis: scores.zip contains evaluated forecast scores, targets.zip contains in-situ water temperature and dissolved oxygen data generated for the Challenge, and Model_descriptions.csv contains the meta data for the submitted models used in the analysis. </p> <p><strong>scores</strong>: this is the primary dataset used in the manuscript. The database includes the scores (forecasts evaluated against observations).</p> <p><strong>targets</strong>: these are the data used to evaluate the forecast (observations) and were also available to forecast teams in near-real time. The R script included in this folder (aquatics_targets.R) is intended for reference only and cannot be run. </p> <p><strong>Model_descriptions.csv</strong>: table of summarised meta data on forecast model approaches submitted by forecast teams. This includes whether the forecasts include (1) or exclude (0) each source of uncertainty/initial conditions/model updating, which covariates are included, and the reported model class. </p>
THE INCLUSION PROCESS IN EARLY EARLY EDUCATION: POSSIBILITIES AND CHALLENGES OF THIS PRACTICE IN EVERYDAY EDUCATIONAL
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CHALLENGES IN MODERN EDUCATION BY HANNAH ARENDT: RELATIONS WITH BRAZILIAN EDUCATION
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Challenges in the Application of ABNT Standards: A Study on the Transition to Higher Education and Academic Production
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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>
SAT Challenge 2012 Application Track Benchmarks
<div> <div> <p>These are the 600 CNF files in DIMACS format from the application track of the SAT challenge 2012.</p> </div> </div>
INTEGRATING CULTURE INTO LANGUAGE LEARNING: STRATEGIES AND CHALLENGES
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CHALLENGES AND OPPORTUNITIES: A COMPREHENSIVE ANALYSIS OF UNITED STATES–CENTRAL ASIA RELATIONS
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GEORGIAN-AZERBAIJANI RELATIONS IN THE POST-SOVIET PERIOD: CHALLENGES AND PERSPECTIVES
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FIGURE 2 in Seeking for gaps in taxonomic descriptions of endemic fishes: a pathway to challenge the Linnean shortfall in a Neotropical basin
FIGURE 2 | Accumulation curve considering the taxonomic description of endemic fish species made between 1758 and 2023 in the Tocantins-Araguaia River basin, Brazil.
FIGURE 3 in Seeking for gaps in taxonomic descriptions of endemic fishes: a pathway to challenge the Linnean shortfall in a Neotropical basin
FIGURE 3 | Results of cross-site analysis between the number of endemic fish species descriptions and sampling effort (A), and cross-species analysis between the year of description and altitude (B), and fish body size of endemic species (C).
An empirical analysis of challenges of implementing AfCFTA on South Africa's trade in the SADC region: Evidence using the PPML Estimation Technique
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Multistep screening of transition metal based homonuclear double atom catalysts to unravel the electronic origin of their activity and selectivity challenges for nitrogen reduction.
<p><span>Lack of robust catalyst design strategies for tackling the selectivity and activity challenges poses serious limitations in the development of efficient catalysts for nitrogen reduction to ammonia. The synergistic interactions in double-atom catalysts (DACs) have aroused great interest in developing promising catalytic centers for nitrogen-reduction reaction (NRR). Using a multistep screening strategy based on systematic first-principles simulations, we find that Fe<sub>2</sub>, Co<sub>2,</sub> and W<sub>2</sub> dimer species impregnated in <span>tetracyanoquinodimethane</span> based monolayer achieve suitable adsorption behaviour for the various NRR intermediates leading to excellent activity and selectivity among the 27 DACs considered in this study for NRR. Here we have uploaded input and output files of studied catalysts and reaction intermediates of NRR</span></p>
Kludt et al. Cell Reports Medicine - Challenging Subtyping Cases (Biopsy)
<p>Anonymized whole slide images to challenging biopsy cases from the publication:</p> <p>"Next generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms"</p> <p>in Cell Reports Medicine 2024</p> <p>The ground truth information is included.</p> <p><br>The dataset can be used for academic research purposes only.</p> <p> </p> <p>(c) Yuri Tolkach<br> </p>
Kludt et al. Cell Reports Medicine - Challenging Subtyping Cases (Resection)
<p>Anonymized whole slide images to challenging resection cases from the publication:</p> <p>"Next generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms"</p> <p>in Cell Reports Medicine 2024</p> <p>The ground truth information is included.</p> <p> </p> <p>The dataset can be used for academic research purposes only. </p> <p> </p> <p>(c) Yuri Tolkach</p>
Decolonizing archaeology in Brazilian Amazonia: challenges, contradictions, and horizons
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Multistep screening of transition metal based homonuclear double atom catalysts to unravel the electronic origin of their activity and selectivity challenges for nitrogen reduction.
<p>We have uploaded input and output files of studied TM2@TCNQ catalysts and NRR intermediates</p>
Figure 2 from: Luangsuphabool T, Lumbsch HT, Piapukiew J, Sangvichien E (2018) Architrypethelium murisporum (Ascomycota, Trypetheliaceae), a remarkable new lichen species from Thailand challenging ascospore septation as an indicator of phylogenetic relationships. MycoKeys 34: 25-34. https://doi.org/10.3897/mycokeys.34.23836
Figure 2 Morphological characters of Architrypethelium murisporum (holotype): A thallus and pseudostromata with ascomata B ascus with ascospores and C ascospores. Scale bars: 1 mm (A); 10 μm (B–C).
Figure 1 from: Luangsuphabool T, Lumbsch HT, Piapukiew J, Sangvichien E (2018) Architrypethelium murisporum (Ascomycota, Trypetheliaceae), a remarkable new lichen species from Thailand challenging ascospore septation as an indicator of phylogenetic relationships. MycoKeys 34: 25-34. https://doi.org/10.3897/mycokeys.34.23836
Figure 1 Phylogenetic relationships of Architrypethelium and sister genera based on a combined data set of two DNA loci (mtSSU and nuLSU rDNA). Bootstrap values ≥ 70% and posterior probabilities ≥ 0.95 are shown at above and below branches.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.