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239 results for “Data-driven”
Measured BSDF and data-driven model of a laser cut panel (LCP001)
<p>Model generated from the measured Bidirectional Scattering Distribution Function (BSDF) of a LCP. The RADIANCE tool-chain pabopto2bsdf, bsdf2ttree was employed. Initial tensor resolution set to 128x128 incident, and equal number of outgoing, scattered directions (corresponding to approx. 1.4 degree). Subsequent adaptive data reduction by approx. 98%. Photometric BSDF.</p> <p>The measured data is included as Differential Scattering Function (DSF): DSF = BSDF x cos(theta_s), where theta_s is the off-normal angle of the outgoing, scattered direction of light (the first column in the measured data). The data was measured on a scanning gonio-photometer (pab advanced technologies pgII) at the optical laboratory of CC Building Envelopes. A halogen lamp was employed, focud on the detector plane for maximum resolution, with a hot-mirror installed in the illuminator to block near infrared emission. The Si photocell of the detector was equipped with a weighing filter to match photometric response v(lambda). The profiles through the unobstructed beam are given for the phi=0°,180°, and phi=90°,270° planes.</p> <p>The angular coordinates theta=90, phi=0 correspond to the intended up direction when installed vertically, e.g. in a window.</p> <p>When publishing any work making use of this data-set, please reference either this data-set, including its Digital Object Identifier (DOI:<a href="https://doi.org/10.5281/zenodo.3375294">10.5281/zenodo.3375294</a>), or (preferred) by this article which describes sample, model and its exemplary application:</p> <p>Lars Oliver Grobe. Photon mapping in image-based visual comfort assessments with BSDF models of high resolution. Journal of Building Performance Simulation. DOI:10.1080/19401493.2019.1653994</p>
Data-driven physics-based modeling of pedestrian dynamics
<p>Python package to create physics-based pedestrian models from crowd measurements</p> <p>Github: <a href="https://github.com/c-pouw/physics-based-pedestrian-modeling">https://github.com/c-pouw/physics-based-pedestrian-modeling</a></p>
Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures
<p>This dataset comes from the following paper:</p> <p>Chiara Pasini, Oscar Ramponi, Stefano Pandini, Luciana Sartore, Giulia Scalet, Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures, J. of Materi Eng and Perform, 2024. <a href="https://doi.org/10.1007/s11665-024-10199-x">https://doi.org/10.1007/s11665-024-10199-x</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. m of the neural network</li> <li>. inp of the Abaqus finite element simulations</li> </ul>
A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation
<p>This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Supporting data for: "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides"
<p>This repository contains supporting data and code for the paper titled "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides" by Kirill Shmilovich, Sayak Subhra Panda, Anna Stouffer, John D. Tovar, and Andrew L. Ferguson.</p>
Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
<p>20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.</p>
Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews
<p>6000 French user reviews from three applications on Google Play (Garmin Connect, Huawei Health, Samsung Health) are labelled manually. We selected four labels: rating, bug report, feature request and user experience.</p> <ul> <li><strong>Ratings</strong> are simple text which express the overall evaluation to that app, including praise, criticism, or dissuasion.</li> <li><strong>Bug reports</strong> show the problems that users have met while using the app, like loss of data, crash of app, connection error, etc.</li> <li><strong>Feature requests</strong> reflect the demande of users on new function, new content, new interface, etc.</li> <li>In <strong>user experience</strong>, users describe their experience in relation to the functionality of the app, how does certain functions be helpful.</li> </ul> <p>As we can observe from the following table, that shows examples of labelled user reviews, each review belongs to one or more categories.</p> <table> <tbody> <tr> <th>App</th> <th>Total</th> <th>Rating</th> <th>Bug report</th> <th>Feature request</th> <th>User experience</th> </tr> </tbody> <tbody> <tr> <td>Garmin Connect</td> <td>2000</td> <td>1260</td> <td>757</td> <td>170</td> <td>493</td> </tr> <tr> <td>Huawei Health</td> <td>2000</td> <td>1068</td> <td>819</td> <td>384</td> <td>289</td> </tr> <tr> <td>Samsung Health</td> <td>2000</td> <td>1324</td> <td>491</td> <td>486</td> <td>349</td> </tr> </tbody> </table> <p> </p> <h2>New Dataset</h2> <p>Based on this dataset, we developed a labeled dataset containing 6,000 English and 6,000 French reviews for classification, as well as 1,200 bilingual reviews for clustering. The new dataset has been made publicly available on Zenodo at the following link: <a href="../records/11066414">https://zenodo.org/records/11066414</a></p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
Cover Your Basis: Comprehensive Data-Driven Characterization of the Binary Black Hole Population
<p>The accompanying data and code release for the analyses presented in "Cover Your Basis: Comprehensive Data-Driven Characterization of the Binary Black Hole Population". See the github paper repository at https://github.com/bruce-edelman/CoveryingYourBasis and the arxiv release of the paper at: https://arxiv.org/abs/2210.12834</p>
name lists for 'Detecting intersectionality in NER models: A data-driven approach'
<p>Name lists used for data augmentation for testing biases (in terms of error disparities) of Name Entity Recognition in Danish NLP pipelines.</p> <p>The following lists are from <a href="https://www.dst.dk/da/Statistik/emner/borgere/navne/navne-i-hele-befolkningen">Statistics Denmark</a>:</p> <ul> <li>majority_first_names_2023_men.csv</li> <li>majority_first_names_2023_women.csv</li> <li>majority_last_names_2023.csv</li> </ul> <p>The following lists are from Eva Villarsen Meldgaard. 2005. <a href="https://nors.ku.dk/publikationer/webpublikationer/muslimske_fornavne/">Muslimske fornavne i danmark</a>. Publisher: Københavns Universitet</p> <ul> <li>minority_first_names_men.csv</li> <li>minority_first_names_men.csv</li> </ul> <p>The list majority_unisex_names.csv is retrieved from <a href="https://familieretshuset.dk/navne/navne/godkendte-fornavne">The Agency of Family Law</a> in Denmark, and the numbers are retrieved from the above lists from Statistics Denmark.</p> <p>The list minority_last_names.csv is retrieved from <a href="https://www.familyeducation.com/baby-names/surname/origin/muslim">FamilyEducation</a>.</p> <p>The list overlapping_names.csv contains first names, which both occur on the list of majority names and the list of minority names.</p>
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
<p>Selection of the data in the following paper:<br> Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32.</p> <p>This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar. </p> <p>Cite this as:<br> B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395</p>
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography
<p>These are the data sets used for the publication https://arxiv.org/abs/2203.15413.</p> <p>In more detail, the DataFile.pkl files in the training_data and testing_data folders are the actual data, including diffraction patterns, the corresponding reconstructions and the used illumination probe. The folders dwcnt_training_data and dwcnt_testing_data include the data sets of the simulated double-walled carbon nanotube.</p> <p>The reconstruction folder includes the source code of the reconstruction algorithm ROP and the used parameter files.</p> <p>The results folder includes the updated weights of the network model and the reconstructed potentials for the 25 test data sets used in the comparison of the publication.</p> <p>The sequence_grid.npy file stores the sparse grid scanning sequence used for initialization and comparison as described in the publication. The dwcnt_sequence_grid.npy file is the corresponding file for the double-walled carbon nanotube data.</p> <p>If you want more information, please contact the corresponding author of the publication, at schlozma@hu-berlin.de. </p>
Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
<p>This is the dataset accompanying Andrae et al (2023), "Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra".</p> <p>Table 1 (174,922,161 rows) contains XGBoost parameters (temperature, surface gravity, and metallicity) for all stars in the sample. </p> <p>Table 2 (17,558,141 rows) contains Gaia DR3 parameters and XGBoost parameters for a vetted sample of RGB stars with reliable measurements. </p> <p>The tables are provided in compressed CSV and FITS format, and the full data model is described in the accompanying paper. </p>
Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks
<p>This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. </p> <p>The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.</p> <p>The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.</p>
Supplementary Material to 'Exploring the power of data-driven models for groundwater system conceptualization: A case study of the Grazer Feld Aquifer, Austria'
<p>This folder contains the supplementary materials to reproduce the results, tables, and figures from the following publication submitted to the Hydrogeology Journal: </p> <p>Kokimova A., Collenteur, R.A. & Birk, S. Exploring the power of data-driven models for groundwater system conceptualization: A case study of the Grazer Feld Aquifer, Austria.</p>
Data from: Data-driven bioregionalization: A seascape-scale study of macrobenthic communities in the Eurasian Arctic
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A data-driven supervised machine learning approach to estimating global ambient air pollution concentrations with associated prediction intervals
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POLAR-Sim: Augmenting NASA's POLAR dataset for data-driven lunar perception and rover simulation
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Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model
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Data-driven quasiconformal morphodynamic flows
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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.