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139 results for “Experimental evaluation”
Data from: An experimental evaluation of traits that influence the sexual behaviour of pollinators in sexually deceptive orchids
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Data from: An experimental evaluation of the effects of geolocator design and attachment method on between-year survival on whinchats Saxicola rubetra
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Data from: Experimental evaluation of the robustness of the growth-stress tolerance trade-off within the perennial grass Dactylis glomerata
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Inflation test on eye globe for: Experimental evaluation of stiffening effect induced by UVA/riboflavin corneal cross-linking using intact porcine eye globes
<p><span>UVA/riboflavin corneal cross-linking (CXL) is a common used approach to treat progressive keratoconus. This study aims to investigate the alteration of corneal stiffness following CXL by mimicking the inflation of the eye under the <i>in vivo</i> loading conditions. Seven paired porcine eye globes were involved in the inflation test to examine the corneal behaviour. Cornea-only model was constructed using the finite element method, without considering the deformation contribution from sclera and limbus. Inverse analysis was conducted to calibrate the non-linear material behaviours in order to reproduce the inflation test. The corneal stress and strain values were then extracted from the finite element models and tangent modulus was calculated under stress level at 0.03 MPa. UVA/riboflavin cross-linked corneas displayed a significant increase in the material stiffness. At the IOP of 27.25 mmHg, the average displacements of corneal apex were 307 ± 65 μm and 437 ± 63 μm (p = 0.02) in CXL and PBS corneas, respectively. Comparisons performed on tangent modulus ratios at a stress of 0.03 MPa, the tangent modulus measured in the corneas treated with the CXL was 2.48 ± 0.69, with a 43±24% increase comparing to its PBS control. The data supported that<b> </b>corneal material properties can be well-described using this inflation methods following CXL. The inflation test is valuable for investigating the mechanical response of the intact human cornea within physiological IOP ranges, providing benchmarks against which the numerical developments can be translated to clinic. </span></p>
Data from : Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning
<h2>Description</h2> <p>This repository serves as a complementary resource accompanying the academic paper titled "Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning" published in <em>Applied Acoustics </em>(available at: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apacoust.2024.109955" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.apacoust.2024.109955</a>). It comprises a dataset containing the acoustic data, metadata, and perceptual annotations.</p> <p>In addition, we provide the .<em>h5</em> datasets and PyTorch model weights to run the code corresponding to the neural network section discussed in the paper (publicly accessible at: <a href="https://github.com/effajr/predicting_alarm_audibility">https://github.com/effajr/predicting_alarm_audibility</a>).</p> <p>The repository is composed of five .<em>zip</em> files:</p> <ul> <li><strong>source_audio </strong>: contains the source audio files used to generate the alarms and backgrounds present in the <strong>data</strong> file.</li> <li><strong>data</strong> : contains the audio files corresponding to the alarms and backgrounds, along with the perceptual annotations.</li> <li><strong>metadata</strong> : contains the metadata related to the alarms and backgrounds present in the <strong>data</strong> file, and to the source audio files contained in <strong>source_audio</strong>.</li> <li><strong>features </strong>: contains the .<em>h5</em> files representing the development and evaluation subsets (mel-spectrograms and perceptual labels) used in the deep learning approach presented in the paper.</li> <li><strong>trained_models </strong>: contains the PyTorch model weights for the 10 runs of model training mentionned in the paper.</li> </ul> <h2>How to use the data</h2> <p>To run the code present in the GitHub repository, we recommend extracting the files <strong>data</strong>.zip, <strong>features</strong>.zip and <strong>trained_models</strong>.zip in their corresponding folders in the "<strong>application</strong>" folder (see <a href="https://github.com/effajr/predicting_alarm_audibility">https://github.com/effajr/predicting_alarm_audibility</a>).</p> <h2>Content</h2> <p>Content of <strong>data</strong>.zip </p> <pre>annotations/ ├─ dev/ │ ├─ annotation_compilation_dev.csv : Compilation of all the listening conditions and <br>│ │ individual annotator responses for the development data.<br>│ ├─ dev_conditions.csv : Unique listening conditions (extracted from annotation_compilation_dev.csv). │ ├─ dev_labels.csv : All individual annotator responses for each <br>│ │ unique listening condition (extracted from annotation_compilation_dev.csv). │ ├─ dev_train_valid_split.csv : Random 80%/20% training/validation split used for development <br>│ │ in the experiments reported in the paper. ├─ eval/<br>│ ├─ annotation_compilation_eval.csv : Compilation of all the listening conditions and individual annotator <br>│ │ responses for the evaluation data.<br>│ │ The column 'clearly_audible_mean' represents individual annotator <br>│ │ binary responses evaluated for each listening condition.<br>│ │ The column 'clearly_audible_pf' represents individual annotator <br>│ │ psychometric functions evaluated for each listening condition.<br>│ │<br>│ ├─ eval_conditions.csv : Unique listening conditions (extracted from annotation_compilation_eval.csv). │ ├─ eval_labels_apf.csv : All individual annotator psychometric function values for each <br>│ │ unique listening condition (extracted from annotation_compilation_eval.csv). │ ├─ eval_labels_mv.csv : All individual annotator binary responses for each <br>│ │ unique listening condition (extracted from annotation_compilation_eval.csv)<br>│<br>audio/ : <em>.wav</em> files corresponding to the alarms and backgrounds for Development and <br> Evaluation subsets of the dataset. ├─ dev/ │ ├─ alarms/ │ ├─ backgrounds/ ├─ eval/ │ ├─ alarms/ │ ├─ backgrounds/<br><br><br>Content of <strong>metadata</strong>.zip <br><br>├─ audio_metadata.xlsx : Table of the alarms and background files with short descriptions, <br>│ source file names, and temporal information (in seconds). ├─ source_file_metadata.xlsx : Metadata table of the original files used to generate alarms and backgrounds. </pre>
Raw dataset for evaluating the effects of M sand and fly ash on concrete strength using experimental and machine learning approaches
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Comparison of Software Engineering Experiment Quality Evaluation Approaches - Experimental Package
<p>Experimental Package of the Comparison of Experiment Quality Evaluation Approaches</p>
Simulation (static & dynamic) and experimental (dynamic) raw data for evaluating the performance of the developed adaptive MPPT algorithm
<p>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union) along with the research on the developed adaptive maximum power point tracking (MPPT) algorithm to achieve boosting efficiency in heterogeneous power scenarios. As part of the project’s work packages (specifically WP4), the published work presented here introduces the development of a chip and corresponding algorithm designed to efficiently convert energy harvested by the regenerative shock absorber into stable DC electricity.</p> <p>Underlying data for the publication Zhao, W. et al. Adaptive MPPT: Boosting efficiency in heterogeneous power scenarios. Sustainable Energy Technologies and Assessments, 2024, 67, 103843. https://doi.org/10.1016/j.seta.2024.103843. Data related to Figures 1b, 4, 5, and 7 in the article.</p> <p>Contact Person: Dr. Weiwei Zhao (Birmingham Centre for Energy Storage, University of Birmingham).</p> <p>ORCID: https://orcid.org/0000-0001-9708-4252</p>
VMTools-RA Experimental Evaluation Package
<p>VMTools-RA Experimental Evaluation Package</p>
Negative polarization properties of regolith simulants - Systematic experimental evaluation of composition effects
<p>This dataset refers to the paper "Negative polarization properties of regolith simulants - Systematic experimental evaluation of composition effects"<br> DOI: https://doi.org/10.1051/0004-6361/202243844</p> <p>Different dust mixtures were created to simulate the mineralogical complexity of asteroids using different mineral endmembers: silica, magnetite, forsterite, spinel, graphite, fayalite.</p> <p>Binary mixtures and ternary mixtures were created using the end-members, and this dataset collects all their polarimetric curves. Furthermore, aggregate of less than 0.2mm and more than 2mm (only for silica-graphite mixtures) have been created and measured. </p> <p>Each .txt file shows the data of a polarimetric curve. The columns correspond respectively to:</p> <p>#phase_angles[°] #Q/I #U/I #V/I #DOLP #DC #delta_Q/I #delta_U/I #delta_V/I #delta_DOLP #delta_dc</p> <p>"delta" refers to the error associated with the measurement, obtained by the repetition of the measurement for different azimuthal angles of the sample (see paper for more detailed description). </p> <p>The SEM images are the one corresponding to Fig. 2 and 3 of the paper.</p>
Safety Evaluation of an Experimental Treatment, Intradermal Human Fcγ1-Fel d1 Fusion Protein (GFD), for Cat Allergy
ClinicalTrials.gov study NCT01292070. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Efficacy of Experimental Gel to Foam Dentifrices in Dental Erosion
ClinicalTrials.gov study NCT01657903. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluating the Asthmatic Response to an Experimental Infection With Rhinovirus in the Atopic
ClinicalTrials.gov study NCT02111772. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of the Efficacy, Safety and Tolerance of Experimental Morning-only MOVIPREP® Bowel Preparation in Comparison With Split-dose With Nocturnal Pause MOVIPREP® Bowel Preparation
ClinicalTrials.gov study NCT01732692. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Evaluation of a Experimental Denture Adhesive to Prevent Food Ingress
ClinicalTrials.gov study NCT02928380. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluate the Effectiveness of an Experimental Urethane Dimethacrylate Resin Based Dental Composite Material
ClinicalTrials.gov study NCT02018822. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Experimental Evaluation of a Program to Foster Socioemotional Competence of Children in Third Grade and Their Teachers
ClinicalTrials.gov study NCT06515444. IPD Sharing: NO. Countries: 0. Publications: 1.
Evaluation of Prototype Lenses With Experimental UV/HEV Blocker
ClinicalTrials.gov study NCT04885296. IPD Sharing: YES. Countries: 1. Publications: 0.
Clinical Evaluation of an Experimental Remineralization Product
ClinicalTrials.gov study NCT06166849. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study to Evaluate PEMF Therapy on Experimentally Inducted Pain in Subjects With Painful Peripheral Diabetic Neuropathy
ClinicalTrials.gov study NCT02809911. IPD Sharing: NO. Countries: 1. Publications: 0.
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.