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Dataset results
256 results for “submissions”
Grocery Store Tour Videos - Diertetics Submission
<p>Supplementary videos for manuscript The use of virtual worlds to provide a dietitian led grocery store tour</p>
Submissions_dcase_2021_task4
<p>Researcher(s)</p> <p>Turpault, Nicola; Salamon, Justin; Wisdom, Scott; Erdogan, Hakan; Hershey, John; Seetharaman, Prem; Ellis, Daniel P. W; Cornell, Samuele; Fonseca, Eduardo. </p> <p>Predictions and technical reports of the systems submitted to DCASE task 4 2021.</p> <p>Mapping files and ground-truth related to the different versions of the synthetic evaluation datasets used in [1] are also available in the ICASSP_folder.zip file.</p> <p><br> [1] Ronchini F., Serizel R., “A benchmark of state-of-the-art sound event detection systems<br> evaluated on synthetic soundscapes”, in ICASSP 2022 IEEE International Conference on<br> Acoustics, Speech and Signal Processing (ICASSP)</p> <p> </p>
Data set for FSE 2022 Submission Program Merge Conflict Resolution via Neural Transformers
<p>Data set for FSE 2022 Submission Program Merge Conflict Resolution via Neural Transformers</p>
Supporting data for the submission to the WRR journal
<p>It is supporting data for the submission entitled "Uncertainty Quantification of Transient-based Leakage Identification: A Frequency Domain Approach" to the WRR journal.</p> <p>1. All figures in fig format.</p> <p>2. Experimental data of the Perugia Test [1], and the Shahid Chamran Test [2].</p> <p>[1] Keramat, A., Louati, M., Wang, X., Meniconi, S., Brunone, B., Ghidaoui, M.S. (2019). Objective Functions for Transient-Based Pipeline Leakage Detection in a Noisy Environment: Least Square and Matched-Filter. Journal of Water Resources Planning and Management, ASCE, 145(10), 04019042.</p> <p>[2] Rezapour, Shafai Bejestan, M., & Aminnejad, B. (2021). Case study of leak detection based on Gaussian function in experimental viscoelastic water pipeline. Water Science & Technology. Water Supply. https://doi.org/10.2166/ws.2021.145</p>
Supplementnary file to a submission
<p>This is additional material including Appendix A, B and C.</p>
Artifacts for ASE 2022 Paper Submission # 1095
<p><strong>This data set is for ASE 2022 Paper Submission #1095</strong></p>
Supporting data for paper submission to the WRR
<p>These are the figures for supporting the data used in the paper "Efficient pipe burst detection in tree-shape water distribution networks using forward-backward transient analysis".</p> <p>The data including the Figs. in the paper, and experimental data from the University of Perugia, and the numerical data for burst and leaky cases.</p>
Supporting data for paper submission to the WRR
<p>These are the figures for supporting the data used in the paper "Efficient pipe burst detection in tree-shape water distribution networks using forward-backward transient analysis".</p> <p>The data including the Figs. in the paper, and experimental data from the University of Perugia, and the numerical data for burst and leaky cases.</p>
The Artifact of the TSE Submission #TSE-2022-12-0520
<p>In this online repository, we open source the source code of each standalone technique as well as the fusion approaches implemented by ourselves (stored in the <em>Fusion_approach.zip</em> file).</p> <p> </p> <p>Our evaluation is based on the large-scale CodeSearchNet dataset, which can be accessed through its <a href="https://github.com/github/CodeSearchNet">official webpage</a>.</p> <p> </p> <p>We also provide the experimental results we obtained in our study, including the rank and score results from each standalone techniques (stored in the <em>Standalone_Java.zip</em> and <em>Standalone_Python.zip</em> files) and the fusion results (stored in the <em>Fusion_results.zip</em> file).</p>
Datasets to support ICSE 2023 submission
<p>Datasets used in experiments.</p>
Dataset for paper submission: "Revisiting the Long-Run Relationship Between Inward/Outward FDI and Income Inequality: New Evidence from the OECD"
<p>The relatively small panel cointegration literature on the dynamics between FDI and income inequality predominantly finds that FDI will reduce income inequality in the long-run in developed countries. However, we point out an important technical oversight in the literature. Not accounting for cross-section dependence in panel data methodologies may yield unreliable results. Expanding on the work of @herzer/nunnenkamp:13, who pioneered the use of panel cointegration in the European context, we obtain different results when we account for cross-section dependence and employ economic procedures robust to it. Using a panel containing 16 OECD countries (1979-2017), 2 income inequality measures, and 4 FDI measures, we begin by showing strong evidence for the existence of cross-section dependence. Then, using second-generation econometric procedures, we do not find any evidence for a cointegrating relationship between inward FDI and income inequality. We do find evidence that outward FDI is cointegrated with income inequality; however, contrary to the main results of the literature, we find that it widens the income gap in the long-run. Additionally, our results support the view that fiscal policy is an important tool to reduce income inequality.</p>
Data for GRL submission
<p>Continuous active source seismic monitoring data, which is a high-frequency time-lapse cross-well dataset with fixed source-receiver geometry for monitoring fractures.</p>
External Source for Anonymous Submission TETHICS 2024
Open the record for dataset details and reuse information.
Dataset supporting the submission to the journal "Ocean Dynamic" and titled "Hybrid covariance super-resolution data assimilation"
Open the record for dataset details and reuse information.
Testing script and data needed to run it for NeurIPS 2024 submission 18634
<p>Dependencies:<br>pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 (get specific command for your environment from Pytorch website. If you can't use CUDA, you must edit the code to use the CPU.)<br>pip install timm<br>pip install opencv-python<br>pip install pillow<br>pip install joblib<br>pip install scipy<br>pip install scikit-learn</p> <p>Please make the following edits to run the script. Also, either move the models folder (https://zenodo.org/records/11245477) or change the directory.<br>Also, add a folder to the directory with the testing script called "Results"</p> <p>The testing script also includes the models for directionality, another statistic we attempted to predict. We did not report the values for directionality in our paper, so feel free to comment lines 2175-2253 and lines 2296-2308 to speed up the computation for the statistics presented in the paper. Due to time constraints, this version also doesn't include the class-modifying arguments for Brownian and straight-trajectory motion. These will be included in the testing script on the official release of DeepTrakStat on Github. <br>line 485: sim_directory='ground_truth_trajectories/'<br>line 486: tmate_directory='tmate_trajectories/'</p> <p>lines 509-525:<br>directories = [<br>'simulated_imagery/1000part_16xspeed_heterogeneous/','simulated_imagery/1000part_32xspeed_heterogeneous/',<br>'simulated_imagery/1000part_4xspeed_heterogeneous/',<br>'simulated_imagery/2000part_16xspeed_heterogeneous/','simulated_imagery/2000part_32xspeed_heterogeneous/', ## Group 1<br>'simulated_imagery/500part_16xspeed_heterogeneous/','simulated_imagery/500part_32xspeed_heterogeneous/',<br>'simulated_imagery/500part_4xspeed_heterogeneous/',<br>'simulated_imagery/sim111_brown/', 'simulated_imagery/sim112_brown/',<br>'simulated_imagery/sim113_brown/','simulated_imagery/sim114_brown/', # Group 2<br>'simulated_imagery/sim115_brown/','simulated_imagery/sim119_brown/','simulated_imagery/sim120_brown/',<br>'simulated_imagery/sim2201/', 'simulated_imagery/sim2210/', 'simulated_imagery/sim2215/',<br>'simulated_imagery/sim2220/','simulated_imagery/sim2230/','simulated_imagery/sim2235/','simulated_imagery/sim2240/', # Group 3<br>'simulated_imagery/sim2241/','simulated_imagery/sim2242/','simulated_imagery/sim2243/','simulated_imagery/sim2244/', #Group 4<br>'simulated_imagery/sim2251/','simulated_imagery/sim2252/','simulated_imagery/sim2253/',<br>'simulated_imagery/sim2254/','simulated_imagery/sim2255/','simulated_imagery/sim2256/',<br>'simulated_imagery/sim2257/','simulated_imagery/sim2258/','simulated_imagery/sim2259/','simulated_imagery/sim2260/', # Group 5<br>'simulated_imagery/test1/','simulated_imagery/test2/','simulated_imagery/test5/','simulated_imagery/test8/' #Group 6<br>]</p> <p>line 606:<br> sorted_frames = sorted(files, key=lambda x: int(x[4:-4]))</p>
PRICE: Anonymous Submission
<p>These are the anonymous artifacts for the paper "A Study of Privacy-Related Data Collected by Android Apps".</p> <p><strong>1) IdentifierDatasets:</strong> Contains the <em>Identifier Keywords Dataset</em> (for UI data analysis) and <em>Identifier API dataset</em> (for System API analysis)</p> <p><strong>2) PRICE_SystemAPIAnalysis</strong><em><strong>:</strong> </em>Contains the code and output for the system API analysis component of PRICE. More details in PRICE_SystemAPIAnalysis/README.md.</p> <p><strong>3) PRICE_UIAnalysis: </strong>Contains the code and output for the UI analysis component of PRICE. More details in PRICE_UIAnalysis/readme.md.</p> <p><strong>4) RQStats: </strong>All files contain tables and observations from the experiments we conducted to answer the research questions. </p> <p><strong>5) RQ3Apps: </strong>Contains the apps used for the case study conducted to answer RQ3.</p> <p> </p>
Evaluation set DCASE 2024 task 4 (for submissions)
<p>This repo contains the evaluation dataset to download and submit system outputs for when participating in task 4 of the DCASE 2024 Challenge.</p>
Figures with raw prediction samples from submissions to ICDAR'24 MapText Competition
<h2>Raw predictions sample from MapText submissions</h2> <p><em>Generated on June 6th, 2024</em></p> <p>Files structure: <code>{TASK}/{SUBSET}/{SELECTION}/{IMAGEID}.pdf</code> where</p> <ul> <li><code>{TASK}</code> is "task1", "task2", "task3" or "task4"</li> <li><code>{SUBSET}</code> is "rumsey", or "ign" (for French land registers dataset)</li> <li><code>{SELECTION}</code> is "random", "easy" or "hard"</li> <li><code>{IMAGEID}</code> is the image id in the dataset</li> </ul> <p>Example: <code>20-raw-predictions/task2/ign/hard/000016.pdf</code></p> <p>For each image, the PDF contains a comparison of the raw predictions of each submission, with the ground truth.</p> <p>For each task, the random images are the same, but the easy and hard images are different: their selection is based on the mean performance of all submissions regarding the main evaluation metric of the task.</p> <p>Sorting strategies for each task:</p> <ul> <li>Task 1: by detection quality (Panoptic Quality) for isolated words</li> <li>Task 2: by detection quality (Panoptic Quality) for word groups</li> <li>Task 3: by detection quality (Panoptic Quality) for isolated words while constraining matches between the ground truth and predictions to have exactly the same transcription</li> <li>Task 4: by character quality (Panoptic Character Quality) for word groups</li> </ul> <p>Task details and legend for each task:</p> <ul> <li><code>task1/</code>: detections for isolated words. Red means cropped or ignored regions, blue means prediction, green is ground truth.</li> <li><code>task2/</code>: detections for word groups. Groups are colored with different colors, and a link is drawn between successive group members. Ground truth is displayed separately.</li> <li><code>task3/</code>: detections and transcriptions of isolated words. Groups are colored with different colors, and the transcription for each isolated work is overlaid as black text. Ground truth is displayed separately.</li> <li><code>task4/</code>: detection and transcription of word groups. Same visualization as task3, but different sorting for easy and hard images.</li> </ul> <p><br>An additional directory <code>extrafigs/</code> contains a couple of figures with a different layout for inclusion in the report.</p> <p><em>These figures were produced by MapText'24 organizers.</em></p>
Evaluations of public submissions to ICDAR'24 MapText Competition
<p>Evaluations of submissions to the <a href="https://rrc.cvc.uab.es/?ch=28">ICDAR'24 Competition on Historical Map Text Detection, Recognition, and Linking</a>.</p> <p>Files in the archive (<code>evaluations.tar.bz2</code>) are stored in <code>ch28/tY/fZ/W.json</code> where <code>Y</code> is the task number (1–4), <code>Z</code> is the file number (1–2), and <code>W</code> is the submission ID.</p> <p>Tasks are:</p> <ul> <li><code>1</code>: Word Detection</li> <li><code>2</code>: Phrase Detection (Word Detection and Grouping)</li> <li><code>3</code>: Word Detection and Recognition</li> <li><code>4</code>: Phrase Detection and Recognition</li> </ul> <p>Files are:</p> <ul> <li><code>1</code>: Rumsey data set</li> <li><code>2</code>: IGN (French Land Register) data set</li> </ul> <p>The JSON output format results from the <a href="https://github.com/icdar-maptext/evaluation">official competition evaluation script</a> (commit-id: <code>816da23d6d4e44f7fd89ee07278ebbcd12d50d4d</code>).</p> <p>Version 2 updates results to run in an environment consistent with the RRC server.</p>
data for submission to GRL by Tu et al.
<p>Turbulence, salinity, temperature, and velocity data used in Figures 2, 3, and 4 were presented in turbulence_binned1.mat, ST_binned.mat, and velocity1.mat. The echosounder images used in Figure 3 were presented in echo_sounder.zip.</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.