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1,167 results for “Real-world”
Predicting human complexity perception of real-world scenes
Perceptual load is a well-established determinant of attentional engagement in a task. So far, perceptual load has typically been manipulated by increasing either the number of task-relevant items or the perceptual processing demand (e.g. conjunction vs. feature tasks). The tasks used often involved rather simple visual displays (e.g. letters or single objects). How can perceptual load be operationalised for richer, real-world images? A promising proxy is the visual complexity of an image. However, current predictive models for visual complexity have limited applicability to diverse real-world images. Here we modelled visual complexity using a deep convolutional neural network trained to learn perceived ratings of visual complexity. We presented 53 observers with 4000 images from the PASCAL VOC dataset, obtaining 75,020 2AFC paired comparisons across observers. Image visual complexity scores were obtained using the TrueSkill algorithm. A CNN with weights pre-trained on an object recognition task predicted complexity ratings with r=0.83. In contrast, feature-based models as used in the literature, working on image statistics such as entropy, edge density and JPEG compression ratio, only achieved r = 0.70. Thus, our model offers a promising method to quantify the perceptual load of real-world scenes through visual complexity.
Dataset for 'Assessing Golang Static Analysis Tools on Real-World Issues'
<p>Go Linter Evaluation Dataset</p> <p>This is a publicly available dataset for 'An empirical evaluation of Golang static code analysis tools for real-world issues.' Please refer to the data according to the names of the spreadsheets.</p> <p>Authors: Jianwei Wu, James Clause</p> <p>Collected Survey Data:<br>- This Excel file contains the collected survey data for the empirical study in details.</p> <p>R Scripts and Raw Data:<br>- These scripts are used for data analysis and processing.<br>- This is the initial data collected from surveys or other sources before any processing or analysis.</p> <p>Surveys for External Participants:<br>- This Excel file contains survey data collected for the evaluation of Go linters.<br>- This folder contains the surveys sent to external participants for collecting their feedback or data.</p> <p>Recruitment Letter.pdf:<br>- This PDF contains an example of the recruitment letter sent to potential survey participants, inviting them to take part in the study.</p> <p>Outputs from Existing Go Linters and Summarized Categories.xlsx:<br>- This Excel file contains outputs from various Go linters and categorized summaries of these outputs. It helps in comparing the performance and features of different linters.</p> <p>Selection of Go Linters.xlsx:<br>- This Excel file lists the Go linters selected for evaluation, along with criteria or reasons for their selection.</p> <p>UD IRB Exempt Letter.pdf:<br>- This PDF contains the Institutional Review Board (IRB) exemption letter from the University of Delaware (UD), indicating that the study involving human participants was exempt from full review.</p> <p>Survey Template.pdf:<br>- This PDF contains an example of the survey sent to the participants.</p> <p>govet issues.pdf:<br>- This PDF contains a list of reported issues about govet. Collected from various pull requests.</p> <p>Approved linters:<br>- staticcheck gofmt govet revive gosec deadcode errcheck.</p> <p>Table 2.jpg:<br>- A detailed figure to show the technical data in Table 2 of the paper.</p> <p>PR-summary-standalone:<br>- This is the AI tool we used to summarize the content of pull requests. Readme file is avaliable in this folder.</p>
Real-World Gait Speed Estimation: Laboratory and Real-Life Patient Data from TOLIFE Wearables
<div><strong>TOLIFE Wearable Dataset Collection</strong></div> <p>This data collection was carried out in the context of the TOLIFE Horizon Europe project, which aims to develop and validate artificial intelligence (AI) solutions for processing patient data collected through non-intrusive wearable sensors. The ultimate goal is to enable personalized treatment, monitor health outcomes, and improve the quality of life of patients with Chronic Obstructive Pulmonary Disease (COPD).</p> <p>The dataset is composed of four distinct subsets, each acquired under different conditions and with specific goals:</p> <div><strong>1. </strong>Gait Speed Dataset</div> <div><strong>2.</strong> Activity Recognition Dataset</div> <div><strong>3. </strong>Validation Dataset</div> <div><strong>4. </strong>Daily Life Dataset</div> <div> </div> <p><strong>1. Gait Speed Dataset:</strong></p> <div><strong>Objective:</strong> To collect reference gait speed data under controlled conditions using wearable sensors, with a focus on validating gait speed estimation algorithms.</div> <div> </div> <div><strong>Materials:</strong></div> <div>Smartphone: Samsung Galaxy A14</div> <div>Smartwatch: Samsung Galaxy Watch 5</div> <div>Smart shoes (custom prototype)</div> <p><strong>Acquisition Protocol:</strong></p> <div>Participants: 25 healthy individuals (10 M, 15 F), age: 28.9 ± 4.8 years</div> <div>Test: Six Minute Walking Test (6MWT) along a 10-meter path</div> <div>Paces: Slow, Medium, Fast (self-selected)</div> <div> </div> <div>Device Placement:</div> <div>- Watch: left </div> <div>- Phone: left front pocket (screen facing thigh, Y-axis upward)</div> <div>- Reference System: Xsens Awinda (17 wireless IMUs) with MVN Analyze software</div> <div>- Reference gait speed = horizontal velocity of Center of Mass (CoM)</div> <p><strong>Folder Content:</strong></p> <div>Wearable folder: individual .csv files for each sensor (phone, watch, shoes)</div> <div>Reference folder: CoM speed from Xsens + walked distances in smwd.csv</div> <div> </div> <p><strong>2. Activity Recognition Dataset:</strong></p> <div><strong>Objective:</strong></div> <div>To build a dataset for training AI models to recognize different daily activities (e.g., resting, walking, stair climbing) using wearable sensor data.</div> <p><strong>Materials:</strong></p> <div>Same TOLIFE wearable platform as above (phone, watch, smart shoes).</div> <p><strong>Acquisition Protocol:</strong></p> <div>Participants: 15 healthy individuals (7 F, 8 M), age: 27.3 ± 3 years</div> <div>Activities:</div> <div>- Resting: lying, sitting, standing (max 5 sec walking allowed)</div> <div>- Walking: 3 sessions (2 x 180 m + 1 x 450 m with directional changes)</div> <div>- Stair climbing: continuous ascent and descent for ~2 minutes</div> <div>Smartwatch placement: user's preferred wrist</div> <p><strong>Folder Content:</strong></p> <div>Data folder: wearable sensor data for each recording session</div> <div>Timestamps folder: .csv files with annotated start/stop times and activity labels</div> <div> </div> <p><strong>3. Validation Dataset:</strong></p> <div><strong>Objective:</strong></div> <div>To test the robustness of trained activity and gait estimation models in less controlled, real-world environments.</div> <p><strong>Materials: </strong>Same TOLIFE wearable platform.</p> <p><strong>Acquisition protocol:<br></strong>Participants: 10 healthy adults (5 M, 5 F), age: 43.8 ± 12.1 years<br>Protocol: free walking outdoors, with mixed activities:<br>- Resting, level walking, stair climbing<br>- 4 walking paths of known length (100–280)<br>- Device placement: user’s preferred side (watch), front pants pocket (phone)</p> <p><strong>Folder Content:<br></strong>Data folder: continuous sensor data recordings<br>Timestamps folder: annotated transitions between activities</p> <p> </p> <div><strong>4. Daily Life Dataset:</strong></div> <div> </div> <div><strong>Objective: </strong>To capture real-world data from COPD patients in daily life conditions for long-term monitoring of mobility and gait speed.</div> <p><strong>Materials: </strong>TOLIFE wearable platform (smartphone, smartwatch, smart shoes)</p> <p><strong>Acquisition Protocol:<br></strong>Participants: 38 COPD patients (23 M, 15 F), age: 63.3 ± 5.8 years<br>Duration: continuous data collection over two weeks<br>Initial Reference Visit (RV):<br>- 2 clinical 6MWTs performed by a pulmonologist, average distance used as a reference value<br>- Instructions: participants used the devices freely (indoors/outdoors) in comfortable conditions</p> <p><strong>Folder Content:<br></strong>Data folder: two weeks of wearable sensor recordings per patient<br>Clinical Six_Minute_Walking_Distance.csv: distances from RV</p> <p> </p> <p>Citation:</p> <p>When using any portion of this dataset collection, please cite:</p> <p>Zanoletti, M.; Bufano, P.; Bossi, F.; Di Rienzo, F.; Marinai, C.; Rho, G.; Vallati, C.; Carbonaro, N.; Greco, A.; Laurino, M.; et al. Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings. Sensors 2024, 24, 3205. https://doi.org/10.3390/s24103205</p>
Real-world vehicular source indicators for exhaust and non-exhaust contribution to PM2.5 during peak and off-peak hours using tunnel measurement
<p>The data are the species concentrations, traffic and meteorological information during the sampling periods in WJL tunnel, and the estimated function of E<sub>N</sub> curve under vehicle electrification.</p>
The Bangladesh Road Traffic Sign Dataset in Real-World Images for Traffic Sign Recognition
Open the record for dataset details and reuse information.
Online Supplement: Search-based Diverse Sampling from Real-world Software Product Lines
<p>This is the online supplement for the following paper: Yi Xiang, Han Huang, Yuren Zhou, Sizhe Li, Chuan Luo, Qingwei Lin, Miqing Li, and Xiaowei Yang. 2022. Search-based Diverse Sampling from Real-world Software Product Lines. In 44th International Conference on Software Engineering (ICSE’22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3510003.3510053</p>
Clinical, technical, and implementation characteristics of real-world health applications using FHIR
<p>Fast Healthcare Interoperability Resources (FHIR) has received vast support globally, with a growing number of use cases implemented throughout various healthcare settings. To assess the use of real-world FHIR applications (apps) implemented in practice, we distributed an electronic survey to FHIR developers and implementers. The data includes characteristics of 112 FHIR apps, including health domain, target audience, terminologies, FHIR specifications, implementation details, and types of organizations developing with FHIR. This repository includes the survey dataset and survey codebook.</p>
AlleNoise - large-scale text classification benchmark dataset with real-world label noise
<div> <div> <div> <div> <p><span>AlleNoise</span><span> is a benchmark dataset for large-scale multi-class text classification with real-world label noise. It consists of e-commerce product titles from Allegro.com with corresponding category labels. The noise distribution comes from actual users of a major e-commerce marketplace, so it realistically reflects the semantics of human mistakes. In addition to the noisy labels, we provide human-verified clean labels and a meaningful, hierarchical taxonomy of categories. Code and data is available at https://github.com/allegro/AlleNoise.<br></span></p> </div> </div> </div> </div>
LLAMAFUZZ real-world programs experiment data
<p>This is the raw experimental data of LLAMAFUZZ and AFL++ on a set of real-world programs. We choose FuzzBench as running framework to ensure the fairness.</p>
Real-World Evidence of Effectiveness and Safety of Pasireotide in the Treatment of Acromegaly: A Systematic Review and Meta-Analysis
<p>Dataset for the study: Real-World Evidence of Effectiveness and Safety of Pasireotide in the Treatment of Acromegaly: A Systematic Review and Meta-Analysis </p>
Ten Real-World Problem Instances (Malta) of the Single-School Bus Routing Problem
<div> <div> <div> <div> <p>Each problem instance is specified in a file with the extension .bus. The format of each file is similar to that of the problem instances used in </p> <div> <div> <div> <div> <p><strong>Lewis, R. and Smith-Miles, K. (2018). A heuristic algorithm for finding cost-effective solutions to real-world school bus routing problems. <em>Journal of Discrete Algorithms</em>, 52-53:2–17</strong></p> <p>and is as follows.</p> <p> </p> </div> </div> </div> </div> <p>The first line in each file gives:</p> <ul> <li>The number of bus stops, including the school;</li> <li>The number of student addresses;</li> <li>The number of listed walks from student addresses to bus stops;</li> <li>The distance unit (K for km, M for miles);</li> <li>The minimum eligibility distance;</li> <li>The maximum walking distance;</li> <li>Further information, which can be ignored. </li> </ul> <div> <div> <div> <p>The second line contains the following details on the school:</p> <ul> <li>An “s” indicating that this line contains information about a stopping location; </li> <li>School's latitude;</li> <li>School's longitude;</li> <li>School's name. </li> </ul> <p>The next lines starting with an "s" contain the following details on the bus stops: </p> <ul> <li>Stop's latitude;</li> <li>Stop's longitude;</li> <li>Stop's name. </li> </ul> <p>The next lines starting with an "a" contain the following details on the student addresses: </p> <ul> <li>Address's latitude;</li> <li>Address's longitude;</li> <li>Number of children requiring transport to the school at this address;</li> <li>Family name at this address.</li> </ul> <p>The next lines starting with a "d" contain the following distance details between pairs of stopping locations:</p> <ul> <li>Index of start location;</li> <li>Index of end location;</li> <li>Driving distance between start and end locations;</li> <li>Driving time (seconds) between start and end locations.</li> </ul> </div> </div> </div> <p>The next lines starting with a "w" contain the following walk details between pairs of one student address and one bus stop: </p> <ul> <li>Index of address;</li> <li>Index of bus stop;</li> <li>Walking distance between address and bus stop;</li> <li>Walking time (seconds) between address and bus stop. </li> </ul> <p><em>Note that all indices start from 0. For stopping locations, 0 corresponds to the school and the rest correspond to the bus stops. </em></p> </div> </div> </div> </div>
Dataset of ICSME-2018 paper: a closer look at real-world patches.
<p>Dataset of ICSME-2018 paper: a closer look at real-world patches.</p> <p>Information:<br> Keywords folder: patch commits are identified by bug-related keywords.<br> Linked folder: patch commits are identified by fixed bug IDs that are reported in issue tracking system.<br> <br> diffs folder: bug fix code diffs.<br> preFiles folder: buggy code files in the commits before fixing.<br> revFiles folder: fixed code files in the commits of submitting fixes.<br> <br> File names are named as previous-commit-id_bug-fix-commit-id_code-file-path.txt/.java</p>
Dataset for paper: Prognostic Stratification by the Meet-URO Score in Real-World Older Patients With Metastatic Renal Cell Carcinoma (mRCC) Receiving Cabozantinib: A Subanalysis of the Prospective ZEBRA Study (Meet-URO 9)
<p>Dataset for paper: Prognostic Stratification by the Meet-URO Score in Real-World Older Patients With Metastatic Renal Cell Carcinoma (mRCC) Receiving Cabozantinib: A Subanalysis of the Prospective ZEBRA Study (Meet-URO 9)</p>
Dataset for the ARON-2 trial, paper Global real-world experiences with pembrolizumab in advanced urothelial carcinoma after platinum-basedchemotherapy: the ARON-2 study.
<p>Dataset for the ARON-2 trial, paper: Global real-world experiences with pembrolizumab in advanced urothelial carcinoma after platinum-basedchemotherapy: the ARON-2 study.</p>
LLAMAFUZZ real-world programs supplementary experiment data
<p>This is the raw experimental data of LLAMAFUZZ and AFL++ on a set of real-world programs. We choose FuzzBench as running framework to ensure the fairness.</p>
Programmable optoelectronic Ising machine for optimization of real-world problems
<p>The data and code provided here are used to generate the figures in the manuscript titled "Programmable optoelectronic Ising machine for optimization of real-world problems" and its supplementary materials.</p>
The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling -- Supplementary Data
Open the record for dataset details and reuse information.
Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.
<p>Raw-data related to a manuscript submitted to "Cancers" journal - MDPI - https://www.mdpi.com/journal/cancers</p> <p><strong>Manuscript Title</strong>: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.</p> <p><strong>Authors:</strong> Gabriele Madonna1,#, Giuseppe V. Masucci2,3,#, Mariaelena Capone1, Domenico Mallardo1, Antonio Maria Grimaldi1, Ester Simeone1, Vito Vanella1, Lucia Festino1, Marco Palla1, Luigi Scarpato1, Marilena Tuffanelli1, Grazia D’angelo1, Lisa Villabona2, Isabelle Krakowski2,4, Hanna Eriksson2,3, Felipe Simao5, Rolf Lewensohn2,3, Paolo Antonio Ascierto1,+</p> <p><strong>Affiliations</strong>:</p> <p>1 Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale", Napoli, Italy</p> <p>2 Theme Cancer, Karolinska University Hospital, Stockholm, Sweden</p> <p>3 Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden</p> <p>4 Theme Inflammation, Karolinska University Hospital Stockholm, Sweden</p> <p>5 Genevia technologies OY, Tampere, Finland</p> <p># these authors equally contributed</p> <p>+ Corresponding author</p> <p><strong>Abstract of submitted Manuscript:</strong> The real-life application of immune checkpoint inhibitors (ICI) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICI at Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli Italy (INT-NA). To compare patients’ clinical variables (age, Lactate Dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive non-hierarchical way, a Clinical Categorization Algorithm (CLICAL) was defined and validated by the application of machine learning, Survival Random Forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms convened into predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with a 95% accuracy.</p> <p><strong>Funding: </strong>This research was funded by Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2-2. Additional funding [N#184093) from the Stockholm Cancer Society and King Gustav V’s Jubilee foundation Stockholm.</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
Associated data underlying the publication "Introducing Open Data Concepts to STEM Students Using Real-World Open Datasets"
<p>While open data concepts become more important in our society, education about its benefits and technical issues is still behind the practice. Students of STEM disciplines should be introduced to open data during their education. The Open Computing course, completely redesigned in the new Computing curriculum, introduces open data concepts, providing both the basics and advanced topics, from technical to social and legal viewpoints. Among the several educational activities, one was particularly useful for understanding the needs and implications of using open data: a synchronous group activity where students had to choose a societal issue, find and analyze two open datasets that would help gaining insight into this issue, assess interdisciplinarity approaches and stakeholders, and finally propose the added value emerging from the solution. In a short amount of time needed, this activity – which tackled multiple aspects of the problem - brought a clearer insight into the topic, building upon the conventional lectures. Students highly graded such an approach to their education, where they had to construct their knowledge by the group experience. A similar group activity appeared to be useful in the context of open data PhD training and might also be used in other disciplines and domains.</p>
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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.