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2,863 results for “Challenge”
Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change
<p>Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Artic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. We investigated this hypothesis in the little auk (<em>Alle alle</em>), a keystone Artic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. These datasets contain the behavioral, blood Hg and environmental data (SST, SIC) used in our analyses. Details about the datasets are found in the accompanying word document.</p>
LISA Data Challenge Spritz (LDC2b)
<p>The purpose of this challenge is to address for the first time the realistic instrumental and environmental noise. We have two datasets with merging MBHBs. (i) Dataset with a loud (SNR ~2000) GW signal, lasting for about 31 days. The signal is expected to be detectable a few weeks before the merger and, therefore, is suitable for testing low-latency algorithms. We have added three short loud glitches distributed in the inspiral, late inspiral and near merger parts of the signal. (ii) Dataset with a quiet (SNR ~100) GW signal lasting for one week, with a several-hour-long glitch placed near the merger. A third 1-year long dataset contains 36 verification binaries. We have placed glitches according to a Poisson distribution with a rate of 4 glitches per day, whose model is described in the Spritz documentation.</p> <p>See <a href="https://lisa-ldc.lal.in2p3.fr/challenge2a">LDC website</a> for more details.</p>
I-MSV 2022: Indic-Multilingual and Multi-sensor Speaker Verification Challenge
<p><strong>Dear Users,</strong></p> <p><strong>Data is password protected, to get password all you need to do is register using below link. Note that data is free of Cost </strong></p> <p><a href="https://forms.gle/1gsVhJaJYT4mBp83A">Click here for Registration</a></p> <p>Speaker Verification (SV) is a task to verify the claimed identity of the claimant using his/her voice sample. Though there exists an ample amount of research in SV technologies, the development concerning a multilingual conversation is limited. In a country like India, almost all the speakers are polyglot in nature. Consequently, the development of a Multilingual SV (MSV) system on the data collected in the Indian scenario is more challenging. With this motivation, the Indic- Multilingual Speaker Verification (I-MSV) Challenge 2022 has been designed for understanding and comparing the state of-the-art SV techniques. For the challenge, approximately 100 hours of data spoken by 100 speakers has been collected using 5 different sensors in 13 Indian languages. The data is divided into development, training, and testing sets and has been made publicly available for further research. The goal of this challenge is to make the SV system robust to language and sensor variations between enrollment and testing. In the challenge, participants were asked to develop the SV system in two scenarios, viz. constrained and unconstrained. The best system in the constrained and unconstrained scenario achieved a performance of 2.12% and 0.26% in terms of Equal Error Rate (EER), respectively.</p>
Data, scripts, and figures of the article: The effect of oregano essential oils on Feed Passage Syndrome in broilers: 2. Assessment under a challenge model
<p>This data set contains the data, JMP scripts, and figures of the article titled "The effect of oregano essential oil on Feed Passage Syndrome in broilers: 2. Assessment under a challenge model" to be published in the journal Animal - Open Space.</p>
KGCW 2023 Challenge @ ESWC 2023
<h1><strong>Knowledge Graph Construction Workshop 2023: challenge</strong></h1> <p>Knowledge graph construction of heterogeneous data has seen a lot of uptake<br>in the last decade from compliance to performance optimizations with respect<br>to execution time. Besides execution time as a metric for comparing knowledge<br>graph construction, other metrics e.g. CPU or memory usage are not considered.<br>This challenge aims at benchmarking systems to find which RDF graph<br>construction system optimizes for metrics e.g. execution time, CPU,<br>memory usage, or a combination of these metrics.</p> <p><strong>Task description</strong></p> <p>The task is to reduce and report the execution time and computing resources<br>(CPU and memory usage) for the parameters listed in this challenge, compared<br>to the state-of-the-art of the existing tools and the baseline results provided<br>by this challenge. This challenge is not limited to execution times to create<br>the fastest pipeline, but also computing resources to achieve the most efficient<br>pipeline.</p> <p>We provide a tool which can execute such pipelines end-to-end. This tool also<br>collects and aggregates the metrics such as execution time, CPU and memory<br>usage, necessary for this challenge as CSV files. Moreover, the information<br>about the hardware used during the execution of the pipeline is available as<br>well to allow fairly comparing different pipelines. Your pipeline should consist<br>of Docker images which can be executed on Linux to run the tool. The tool is<br>already tested with existing systems, relational databases e.g. MySQL and<br>PostgreSQL, and triplestores e.g. Apache Jena Fuseki and OpenLink Virtuoso<br>which can be combined in any configuration. It is strongly encouraged to use<br>this tool for participating in this challenge. If you prefer to use a different<br>tool or our tool imposes technical requirements you cannot solve, please contact<br>us directly.</p> <p><strong>Part 1: Knowledge Graph Construction Parameters</strong></p> <p>These parameters are evaluated using synthetic generated data to have more<br>insights of their influence on the pipeline.</p> <p><em><strong>Data</strong></em></p> <ul> <li>Number of data records: scaling the data size vertically by the number of records with a fixed number of data properties (10K, 100K, 1M, 10M records).</li> <li>Number of data properties: scaling the data size horizontally by the number of data properties with a fixed number of data records (1, 10, 20, 30 columns).</li> <li>Number of duplicate values: scaling the number of duplicate values in the dataset (0%, 25%, 50%, 75%, 100%).</li> <li>Number of empty values: scaling the number of empty values in the dataset (0%, 25%, 50%, 75%, 100%).</li> <li>Number of input files: scaling the number of datasets (1, 5, 10, 15).</li> </ul> <p><em><strong>Mappings</strong></em></p> <ul> <li>Number of subjects: scaling the number of subjects with a fixed number of predicates and objects (1, 10, 20, 30 TMs).</li> <li>Number of predicates and objects: scaling the number of predicates and objects with a fixed number of subjects (1, 10, 20, 30 POMs).</li> <li>Number of and type of joins: scaling the number of joins and type of joins (1-1, N-1, 1-N, N-M)</li> </ul> <p><strong>Part 2: GTFS-Madrid-Bench</strong></p> <p>The GTFS-Madrid-Bench provides insights in the pipeline with real data from the<br>public transport domain in Madrid.</p> <p><em><strong>Scaling</strong></em></p> <ul> <li>GTFS-1 SQL</li> <li>GTFS-10 SQL</li> <li>GTFS-100 SQL</li> <li>GTFS-1000 SQL</li> </ul> <p><em><strong>Heterogeneity</strong></em></p> <ul> <li>GTFS-100 XML + JSON</li> <li>GTFS-100 CSV + XML</li> <li>GTFS-100 CSV + JSON</li> <li>GTFS-100 SQL + XML + JSON + CSV</li> </ul> <p><strong>Example pipeline</strong></p> <p>The ground truth dataset and baseline results are generated in different steps<br>for each parameter:</p> <ol> <li>The provided CSV files and SQL schema are loaded into a MySQL relational database.</li> <li>Mappings are executed by accessing the MySQL relational database to construct a knowledge graph in N-Triples as RDF format.</li> <li>The constructed knowledge graph is loaded into a Virtuoso triplestore, tuned according to the Virtuoso documentation.</li> <li>The provided SPARQL queries are executed on the SPARQL endpoint exposed by Virtuoso.</li> </ol> <p>The pipeline is executed 5 times from which the median execution time of each<br>step is calculated and reported. Each step with the median execution time is<br>then reported in the baseline results with all its measured metrics.<br>Query timeout is set to 1 hour and knowledge graph construction timeout<br>to 24 hours. The execution is performed with the following tool: <a href="https://github.com/kg-construct/challenge-tool">https://github.com/kg-construct/challenge-tool</a>,<br>you can adapt the execution plans for this example pipeline to your own needs.</p> <p>Each parameter has its own directory in the ground truth dataset with the<br>following files:</p> <ul> <li>Input dataset as CSV.</li> <li>Mapping file as RML.</li> <li>Queries as SPARQL.</li> <li>Execution plan for the pipeline in <code>metadata.json</code>.</li> </ul> <p><strong>Datasets</strong></p> <p><em><strong>Knowledge Graph Construction Parameters</strong></em></p> <p>The dataset consists of:</p> <ul> <li>Input dataset as CSV for each parameter.</li> <li>Mapping file as RML for each parameter.</li> <li>SPARQL queries to retrieve the results for each parameter.</li> <li>Baseline results for each parameter with the example pipeline.</li> <li>Ground truth dataset for each parameter generated with the example pipeline.</li> </ul> <p><em>Format</em></p> <p>All input datasets are provided as CSV, depending on the parameter that is being<br>evaluated, the number of rows and columns may differ. The first row is always<br>the header of the CSV.</p> <p><em><strong>GTFS-Madrid-Bench</strong></em></p> <p>The dataset consists of:</p> <ul> <li>Input dataset as CSV with SQL schema for the scaling and a combination of XML,</li> <li>CSV, and JSON is provided for the heterogeneity.</li> <li>Mapping file as RML for both scaling and heterogeneity.</li> <li>SPARQL queries to retrieve the results.</li> <li>Baseline results with the example pipeline.</li> <li>Ground truth dataset generated with the example pipeline.</li> </ul> <p><em>Format</em></p> <p>CSV datasets always have a header as their first row.<br>JSON and XML datasets have their own schema.</p> <p><strong>Evaluation criteria</strong></p> <p>Submissions must evaluate the following metrics:</p> <ul> <li>Execution time of all the steps in the pipeline. The execution time of a step is the difference between the begin and end time of a step.</li> <li>CPU time as the time spent in the CPU for all steps of the pipeline. The CPU time of a step is the difference between the begin and end CPU time of a step.</li> <li>Minimal and maximal memory consumption for each step of the pipeline. The minimal and maximal memory consumption of a step is the minimum and maximum calculated of the memory consumption during the execution of a step.</li> </ul> <p><strong>Expected output</strong></p> <p><em><strong>Duplicate values</strong></em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody><tbody> <tr> <td>0 percent</td> <td>2000000 triples</td> </tr> <tr> <td>25 percent</td> <td>1500020 triples </td> </tr> <tr> <td>50 percent</td> <td>1000020 triples </td> </tr> <tr> <td>75 percent</td> <td>500020 triples</td> </tr> <tr> <td>100 percent</td> <td>20 triples</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Empty values</strong></em></p> <p> </p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody><tbody> <tr> <td>0 percent</td> <td>2000000 triples</td> </tr> <tr> <td>25 percent</td> <td>1500000 triples </td> </tr> <tr> <td>50 percent</td> <td>1000000 triples </td> </tr> <tr> <td>75 percent</td> <td>500000 triples</td> </tr> <tr> <td>100 percent</td> <td>0 triples</td> </tr> </tbody> </table> <p><em><strong>Mappings</strong></em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody><tbody> <tr> <td>1TM + 15POM</td> <td>1500000 triples</td> </tr> <tr> <td>3TM + 5POM</td> <td>1500000 triples </td> </tr> <tr> <td>5TM + 3POM </td> <td>1500000 triples </td> </tr> <tr> <td>15TM + 1POM</td> <td>1500000 triples</td> </tr> </tbody> </table> <p><em><strong>Properties</strong></em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1M rows 1 column</td> <td>1000000 triples</td> </tr> <tr> <td>1M rows 10 columns</td> <td>10000000 triples </td> </tr> <tr> <td>1M rows 20 columns</td> <td>20000000 triples </td> </tr> <tr> <td>1M rows 30 columns</td> <td>30000000 triples</td> </tr> </tbody></table> <p><em><strong>Records</strong></em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>10K rows 20 columns</td> <td>200000 triples</td> </tr> <tr> <td>100K rows 20 columns</td> <td>2000000 triples </td> </tr> <tr> <td>1M rows 20 columns</td> <td>20000000 triples </td> </tr> <tr> <td>10M rows 20 columns</td> <td>200000000 triples</td> </tr> </tbody></table> <p><em><strong>Joins</strong></em></p> <p><em>1-1 joins</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>0 percent</td> <td>0 triples</td> </tr> <tr> <td>25 percent</td> <td>125000 triples </td> </tr> <tr> <td>50 percent</td> <td>250000 triples </td> </tr> <tr> <td>75 percent</td> <td>375000 triples</td> </tr> <tr> <td>100 percent</td> <td>500000 triples</td> </tr> </tbody></table> <p><em>1-N joins</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1-10 0 percent</td> <td>0 triples</td> </tr> <tr> <td>1-10 25 percent</td> <td>125000 triples </td> </tr> <tr> <td>1-10 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>1-10 75 percent</td> <td>375000 triples</td> </tr> <tr> <td>1-10 100 percent</td> <td>500000 triples</td> </tr> <tr> <td>1-5 50 percent</td> <td>250000 triples</td> </tr> <tr> <td>1-10 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>1-15 50 percent</td> <td>250005 triples </td> </tr> <tr> <td>1-20 50 percent</td> <td>250000 triples</td> </tr> </tbody></table> <p><em>1-N joins</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>10-1 0 percent</td> <td>0 triples</td> </tr> <tr> <td>10-1 25 percent</td> <td>125000 triples </td> </tr> <tr> <td>10-1 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>10-1 75 percent</td> <td>375000 triples</td> </tr> <tr> <td>10-1 100 percent</td> <td>500000 triples</td> </tr> <tr> <td>5-1 50 percent</td> <td>250000 triples</td> </tr> <tr> <td>10-1 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>15-1 50 percent</td> <td>250005 triples </td> </tr> <tr> <td>20-1 50 percent</td> <td>250000 triples</td> </tr> </tbody></table> <p><em>N-M joins</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>5-5 50 percent</td> <td>1374085 triples</td> </tr> <tr> <td>10-5 50 percent</td> <td>1375185 triples</td> </tr> <tr> <td>5-10 50 percent </td> <td>1375290 triples</td> </tr> <tr> <td>5-5 25 percent</td> <td>718785 triples</td> </tr> <tr> <td>5-5 50 percent</td> <td>1374085 triples</td> </tr> <tr> <td>5-5 75 percent </td> <td>1968100 triples</td> </tr> <tr> <td>5-5 100 percent </td> <td>2500000 triples </td> </tr> <tr> <td>5-10 25 percent </td> <td>719310 triples</td> </tr> <tr> <td>5-10 50 percent </td> <td>1375290 triples</td> </tr> <tr> <td>5-10 75 percent </td> <td>1967660 triples</td> </tr> <tr> <td>5-10 100 percent </td> <td>2500000 triples</td> </tr> <tr> <td>10-5 25 percent </td> <td>719370 triples </td> </tr> <tr> <td>10-5 50 percent </td> <td>1375185 triples</td> </tr> <tr> <td>10-5 75 percent </td> <td>1968235 triples</td> </tr> <tr> <td>10-5 100 percent </td> <td>2500000 triples</td> </tr> </tbody></table> <p><strong>GTFS Madrid Bench</strong></p> <p><em>Generated Knowledge Graph</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1</td> <td>395953 triples</td> </tr> <tr> <td>10</td> <td>3959530 triples </td> </tr> <tr> <td>100</td> <td>39595300 triples </td> </tr> <tr> <td>1000</td> <td>395953000 triples</td> </tr> </tbody></table> <p><em>Queries</em></p> <table> <tbody><tr> <th>Query</th> <th>Scale 1</th> <th>Scale 10</th> <th>Scale 100</th> <th>Scale 1000</th> </tr> <tr> <td>Q1</td> <td>58540 results</td> <td>585400 results</td> <td>No results available</td> <td>No results available</td> </tr> <tr> <td>Q2</td> <td>636 results</td> <td>11998 results </td> <td>125565 results</td> <td>1261368 results</td> </tr> <tr> <td>Q3</td> <td>421 results</td> <td>4207 results </td> <td>42067 results</td> <td>420667 results</td> </tr> <tr> <td>Q4</td> <td>13 results</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> </tr> <tr> <td>Q5</td> <td>35 results</td> <td>350 results</td> <td>3500 results</td> <td>35000 results</td> </tr> <tr> <td>Q6</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> </tr> <tr> <td>Q7</td> <td>68 results</td> <td>67 results</td> <td>67 results</td> <td>53 results</td> </tr> <tr> <td>Q8</td> <td>35460 results</td> <td>354600 results</td> <td>No results available</td> <td>No results available</td> </tr> <tr> <td>Q9</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> <td>130000 results</td> </tr> <tr> <td>Q10</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> </tr> <tr> <td>Q11</td> <td>130 results</td> <td>260 results </td> <td>260 results </td> <td>260 results </td> </tr> <tr> <td>Q12</td> <td>13 results</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> </tr> <tr> <td>Q13</td> <td>265 results</td> <td>2650 results</td> <td>26500 results</td> <td>265000 results</td> </tr> <tr> <td>Q14</td> <td>2234 results</td> <td>22340 results</td> <td>223400 results</td> <td>No results available</td> </tr> <tr> <td>Q15</td> <td>592 results</td> <td>8684 results</td> <td>35502 results</td> <td>206628 results</td> </tr> <tr> <td>Q16</td> <td>390 results</td> <td>780 results</td> <td>260 results</td> <td>780 results</td> </tr> <tr> <td>Q17</td> <td>855 results</td> <td>8550 results </td> <td>85500 results </td> <td>855000 results </td> </tr> <tr> <td>Q18</td> <td>104 results</td> <td>1300 results</td> <td>13000 results</td> <td>130000 results</td> </tr> </tbody></table> <p> </p>
The image processing of Milani: challenges after DART impact
<p>This dataset contain data for the paper The image processing of Milani challenges after DART impact, presented at ESA-GNC Sopot, Poland, June 2023 </p> <p>The .zip folder Dataset is made of 4 subfolders <br> -CoefficientsPCE With the PCEFull9.mat file with all the coefficients of the aPC basis and PCE listed in the paper<br> <br> -TestResults With the ResultsAll.mat file that contains the predicted and true values of phase angle over the test set illustrated in the paper and the MakePlot.m script in Matlab. You can use this data to compare your method directly with the results we have obtained in this work. The script generate a simple histogram plot and compute the mean, std, Q67, and Q95 values. </p> <p> -D1_with_sz_1p00s0ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by ESA (old values before impact).</p> <p> -D1_with_sz_0p78s0 ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by JHUAPL (updated values after impact). This directory also contains the pre-processed input-output pairs for the WCOB, NN, and PCE (X_features, Y) and CELM, and CNN (X_images, Y) methods. </p> <p>You can either decide to use the same pre-processed data we have used for training, validation, and testing or you can work with the raw data (the one from the ImagesDataset folder) to generate your own dataset. </p> <p>Feel free to drop a line vie email in case you need clarification at mattia.pugliatti@polimi.it or pugliatti.mattia@gmail.com</p>
Dataset: Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
<p>Direct numerical simulation (DNS) data for 2D forced homogeneous isotropic turbulence (FHIT) and 2D Rayleigh-Benard Convection (RBC). This data is used to discover subgrid-scale (SGS) closures for momentum and heat fluxes.</p> <p>Paper: "<strong>Learning Closed-Form Equations for Subgrid-Scale Closures From High-Fidelity Data: Promises and Challenges</strong>" <br> <a href="https://doi.org/10.1029/2023MS003874">https://doi.org/10.1029/2023MS003874</a></p>
Challenges of cultural heritage adaptive reuse: a stakeholders-based comparative study in three European cities. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807.</p> <ul> <li>Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019</li> <li>Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.</li> <li>Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.</li> <li>Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.</li> <li>Language of the data: English.</li> <li>References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602</li> </ul> <p><br> </p>
Data for "Challenges of variational quantum optimization with measurement shot noise"
<p>In this repository, we save all the data used in the article <em>"Challenges of variational quantum optimization with measurement shot noise"</em> <a href="https://doi.org/10.1103/PhysRevA.109.032408" target="_blank" rel="noopener">Phys. Rev. A 109, 032408</a>.</p> <p>For reproducing the same figure as in the article, we provide an interactive notebook 'paper-figure.ipynb' and its HTML version. </p> <p><strong>Data</strong></p> <p>Unzip with</p> <pre><code>tar -xvf data.tar.gz</code></pre> <p>You will find ten data files:</p> <ul> <li> `dataferro.csv` Ferromagnetic model both for VQE and QAOA with random parameters initialization;</li> <li>`datanoise.csv`Ferromagnetic model for VQE with random parameters initialization and circuit noise;</li> <li>`data_spinglass.csv` Disordered model both for VQE and QAOA with random parameters initialization;</li> <li>`data_linopt.csv` Ferromagnetic model for QAOA with annealing-like parameters initialization;</li> <li>`data_lininit_rand.csv` Disordered model for QAOA with annealing-like parameters initialization;</li> <li>`data_spinglass_iter1.csv` Disordered model for QAOA, no optimization and random parameters initialization;</li> <li>`data_opt16shots.csv` Ferromagnetic model for QAOA, no optimization and annealing-like parameters initialization;</li> <li>`datagrad.csv` Ferromagnetic model for VQE with gradient-based optimization;</li> <li>`datagrad_eps05.csv` Ferromagnetic model for QAOA with gradient-based optimization;</li> <li>`grad_epsilon.npz` Ferromagnetic model for QAOA with gradient-based optimization, varying \(\varepsilon\) in the finite differences formula.</li> </ul> <p>Most of the data are CSV sheets, where you can find the aggregate results of the simulations described in our article. </p> <p><strong>Abstract</strong></p> <p>Quantum enhanced optimization of classical cost functions is a central theme of quantum computing due to its high potential value in science and technology.<br>The variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA) are popular variational approaches that are considered the most viable solutions in the noisy-intermediate scale quantum (NISQ) era.<br>Here, we study the scaling of the quantum resources, defined as the required number of circuit repetitions, to reach a fixed success probability as the problem size increases, focusing on the role played by measurement shot noise, which is unavoidable in realistic implementations.<br>Simple and reproducible problem instances are addressed, namely, the ferromagnetic and disordered Ising chains. <br>Our results show that: </p> <ol> <li>VQE with the standard heuristic ansatz scales comparably to direct brute-force search when energy-based optimizers are employed. The performance improves at most quadratically using a gradient-based optimizer;</li> <li>When the parameters are optimized from random guesses, also the scaling of QAOA implies problematically long absolute runtimes for large problem sizes;</li> <li>QAOA becomes practical when supplemented with a physically-inspired initialization of the parameters.</li> </ol> <p>Our results suggest that hybrid quantum-classical algorithms should possibly avoid a brute force classical outer loop, but focus on smart parameters initialization.</p>
HITS Inc.'s models and data for Dacon challenge, Jump AI 2023
<p>Here deposits model and data files developed during HITS Inc.'s participation in the Dacon challenge, Jump AI 2023:</p> <p><a href="https://dacon.io/competitions/official/236127/overview/description">https://dacon.io/competitions/official/236127/overview/description</a></p> <p>Note that the files here alone are less useful unless appropriate codes are employed.</p> <p><strong>File description:</strong></p> <ul> <li>pred_model_AutoGluon.tar.xz: AutoGluon model parameters for prediction.</li> <li>valid_model_AutoGluon.tar.xz: AutoGluon model parameters for validation.</li> <li>ckpts_original.tar.xz: fine-tuned <a href="https://github.com/yuyangw/MolCLR">MolCLR</a> model parameters.</li> <li>qc_out.tar.xz: molecular electronic structure files (.wfn).</li> <li>sdf_optimized.tar.xz: molecular structure files (.sdf).</li> <li>atomwfn.tar.xz: atomic electronic structure files (.wfn).</li> </ul>
GENEA Challenge 2023 Human-Likeness subjective evaluation data
<p>This repository contains human-likeness user-study response data and associated analyses from the GENEA Challenge 2023. The code was written by Gustav Eje Henter and is released under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>The archive contains:</strong><br> * This README.txt file.<br> * A LICENSE.txt file.<br> * A JSON file containing all human-likeness user-study responses included in the analyses.<br> * MATLAB .m files for reproducing the above analyses (both tables and figures).<br> * A folder called "output" that contains all files written by the analysis script, including .mat files with all the results computed by each analysis, as well as files created when compiling the final, pretty-looking pdf figures.<br> * A file output.txt that contains all output written to the MATLAB Command Window when running the analyses.</p> <p><strong>Requirements:</strong><br> * MATLAB and MATLAB Image Processing Toolbox<br> * distinguishable_colors.m by Timothy E. Holy (https://www.mathworks.com/matlabcentral/fileexchange/29702-generate-maximally-perceptually-distinct-colors)<br> * fragmaster by Augustin Martin Domingo and Tilman Vogel (https://ctan.org/pkg/fragmaster), a LaTeX utility<br> This was last tested on 2023-10-08 on MATLAB version 9.10.0.1739362 (R2021a) Update 5, running on macOS version 13.2 build 22D49.</p> <p><strong>To re-generate/reproduce</strong> the contents in the "output" folder:<br> 0. Ensure that all requirements are met.<br> 1. Run through the entire analysis script "run_analyses.m".<br> 2. Run the fragmaster command in the "output" folder.</p> <p><strong>Attribution:</strong><br> If you use this material in a scientific publication, please cite our latest paper on the GENEA Challenge 2023. At the time of writing (2023-10-08) this is our ACM ICMI 2023 paper. You can use the following BibTeX code to cite that work:<br> @inproceedings{kucherenko2023genea,<br> author={Kucherenko, Taras and Nagy, Rajmund and Yoon, Youngwoo and Woo, Jieyeon and Nikolov, Teodor and Tsakov, Mihail and Henter, Gustav Eje},<br> title={The {GENEA} {C}hallenge 2023: {A} large-scale evaluation of gesture generation models in monadic and dyadic settings},<br> booktitle = {Proceedings of the ACM International Conference on Multimodal Interaction},<br> publisher = {ACM},<br> series = {ICMI '23},<br> year={2023}<br> }</p> <p><strong>To find more</strong> GENEA Challenge 2023 material on the web, please see:<br> * https://svito-zar.github.io/GENEAchallenge2023/<br> * https://genea-workshop.github.io/2023/challenge/</p> <p><strong>If you have any questions</strong> or comments, please contact:<br> * Gustav Eje Henter <ghe@kth.se><br> * The GENEA Challenge organisers <genea-challenge@googlegroups.com></p>
EEG: Visual Working Memory + Cabergoline Challenge
Open the record for dataset details and reuse information.
First Speech Separation Challenge
<p>The first international Speech Separation Challenge took place in 2006, with results disseminated at Interspeech in Pittsburgh and later in a special issue of Computer Speech and Language (volume 24, 2010). The main focus of the challenge was to compare algorithms and human listeners on the task of identifying words in sentences from one talker when mixed with similar utterances from another talker, using a single channel (i.e., operating monaurally). Development and test data was also provided for a stationary noise masking condition. For technical details and a summary of the outcome of the Challenge, see the article by Cooke, Hershey and Rennie (pp 1--15) of the special issue (available as cooke_csl2010.pdf in this dataset). </p> <p>The current dataset consisted of the following zip files:</p> <p>twotalker_dev.zip two-talker development set<br> twotalker_test.zip two-talker test set<br> 1.zip, 2.zip etc training data for each of 34 talkers (500 sentences each)<br> ssn_dev.zip speech-shaped noise (SSN) development set<br> ssn_test.zip SSN test set<br> <br> The Challenge was funded by the EU Network of Excellence PASCAL (Pattern Analysis, Statistical Modeling and Computational Learning) </p>
"Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments" video material
<p>"Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments" video material</p>
FIGURE 1a–e in Challenges for the future of taxonomy: talents, databases and knowledge growth
FIGURE 1a–e: Amphipod pictures from BOLD uploaded as reference with sequences. a) Hyperia Latreille in Desmarest, 1823 sequence (arrow) embedded in the BIN of Gammarus setosus Dementieva, 1931, at first glance a misidentification, but b) the uploaded photo of Hyperia sp. confirms the identification; most likely explanation is cross-contamination or tissue sample mix-up during handling; c) example of a too small photo of a specimen, which does not allow a confirmation of the identification, same applies for d) fragment of amphipod; e) six sequences of the same amphipod species sharing just two photos, these repeatedly used photos do not help to verify the identification.
FIGURE 4 in Three challenges to contemporaneous taxonomy from a licheno-mycological perspective
FIGURE 4. Publications on lichens in Latin America from the 1750s to the present, showing trends in overall production and the relation between exclusively foreign or national and collaborative studies (see Supplementary file for detailed data). The peak during the 2000s (A) is largely due to the 305 individual contributions of authors to the Mexican portion of the Lichen Flora of the Greater Sonoran Desert Region (Nash et al. 2002, 2004, 2007), 303 of which were exclusively authored by foreign taxonomists. Removing these adjusts the proportions (B).
Participant Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Compilation of electronic meeting notes made by attendees at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes.</p> <p>Files are provided for Days 1-3 of the meeting. Day 4 inputs are included in Discussion notes under a separate doi.</p> <p>The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants 936723.02.01.09.14 and 936723.02.01.11.21</p>
DCASE 2020 Challenge Task 2 Additional Training Dataset
<p><strong>Description</strong></p> <p>This dataset is the "additional training 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 or will be released: "<a href="http://zenodo.org/record/3678171">development dataset</a>", "additional training dataset", and "<a href="https://zenodo.org/record/3841772">evaluation dataset</a>". This additional training dataset was released before the "<a href="https://zenodo.org/record/3841772">evaluation dataset</a>". This dataset includes around 1,000 normal samples for each Machine Type and Machine ID used in the <a href="https://zenodo.org/record/3841772">evaluation dataset</a> and can be used for model training in advance.</p> <p>The recording procedure and data format are the same as the <a href="http://zenodo.org/record/3678171">development dataset</a>. The Machine IDs in this dataset are different from those in the <a href="http://zenodo.org/record/3678171">development 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> </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>/train (Only normal data for all Machine IDs are included.) <ul> <li>/normal_id_05_00000000.wav</li> <li>...</li> <li>/normal_id_05_00000999.wav</li> <li>/normal_id_06_00000000.wav</li> <li>...</li> <li>/normal_id_07_00000999.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>/train/normal_id_<Machine_ID>_[0-9]+.wav"</li> </ul> <p>For example, the Machine Type and Machine ID of "/ToyCar/train/normal_id_05_00000000.wav" are "ToyCar" and "05", respectively, and its condition is normal (This dataset includes only normal samples). </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>
OWA Wake Modelling Challenge Dataset
<p>This repository collects input and simulation datasets from the Offshore Wind Accelerator (OWA) Wake Modelling Challenge, whose objective is to improve confidence in wake models in the prediction of array efficiency. The data is meant to be used together with the open-source model evaluation scripts available in the following github repository: <a href="https://github.com/CENER-EPR/OWAbench">https://github.com/CENER-EPR/OWAbench</a></p> <p>The results of the challenge are summarized in the following paper:</p> <p>Sanz Rodrigo J, Borbón Guillén F, Fernandes Correia P M, García Hevia B, Schlez W, Schmidt S, Basu S, Li B, Nielsen P, Cathelain M, Dall’Ozzo C, Grignon L, Pullinger D (2020) Validation of Meso-Wake Models for Array Efficiency Prediction Using Operational Data from Five Offshore Wind Farms. J. Phys.: Conf. Ser., under review</p>
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>
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.