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Dataset results
67 results for “Lightweight”
Benefits of Lightweight Ambulatory Oxygen Systems for Individuals With Chronic Obstructive Pulmonary Disease
ClinicalTrials.gov study NCT00325754. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: The avian lightweights: trans-Saharan migrants show lower lean body mass than short-/medium-distance migrants
Open the record for dataset details and reuse information.
Leveraging basecaller's move table to generate a lightweight k-mer model
<p>The ONT RNA004 dataset that is used to create a 5-mer model using the basecaller's movetable.</p> <p>The dataset is a sub-sample extracted from a dataset that sequenced Universal Human Reference RNA.</p> <p>The specification of the bio sample is here</p> <p>https://www.agilent.com/cs/library/usermanuals/public/740000.pdf</p>
Revisiting Lightweight Compiler Provenance Recovery on ARM Binaries
<p>This dataset is for Section 5.4 of the paper. The 32-bit ARM binaries and the objdumps we scraped from the package repositories of three Linux distributions were too large to upload to GitHub, unlike the rest of our data on our submission.</p> <p><strong>Size warning </strong>- when decompressed, this dataset is large. The following are the sizes of each subset before compression:</p> <ul> <li>centos-scrape: 2.2 GB</li> <li>centos-scrape-objdumps: 11.0 GB</li> <li>raspbian-scrape: 10.0 GB</li> <li>raspbian-scrape-objdumps: 52.1 GB</li> <li>ubuntu-scrape: 5.6 GB</li> <li>ubuntu-scrape-objdumps: 34.5 GB</li> </ul> <p>(Total size: 115.4 GB)</p> <p> </p>
MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network
<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>
Brief Communication: Monitoring active layer dynamic using a lightweight nimble Ground-Penetrating Radar system. A laboratory analog test case
<p>GPR dataset from Leger et al., 2023 (https://tc.copernicus.org/preprints/tc-2022-214/)</p> <p> </p>
Our processed LoveDA dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed LoveDA dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>"</p>
Our processed CITY_OSM dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed CITY_OSM dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
A dataset of aerial images taken by UAV that we collected for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our private dataset of UAV aerial imagery for paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
Dataset from: A lightweight framework for chromatin loop detection at the single-cell level
<p>This dataset includes the following components:</p> <ul> <li>Loops identified by scGSLoop at the single-cell level on the mES dataset and the hPFC dataset</li> <li>Consensus loops of each cell type. These consensus loops are derived from the single-cell results and represent the common loops shared across the cells of a particular cell type.</li> </ul> <p> </p>
The surgical instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>In the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention, we collected and annotated the surgical instrument dataset.</p>
Lightweight usable cryptography: a usability evaluation of the Ascon 1.2 family
<p>We present a usability study of the Ascon 1.2 family of cryptographic algorithms. As far as we know, this is the first published experimental evaluation aimed at a cryptographic design (i.e. not a specific API) with the purpose of informing which aspects to standardise. While the results show the general difficulty of choosing and applying cryptographic algorithms, there are some more specific insights. These include the possibility of confusing multiple variants, the relevance of small interfaces, and the desire for higher-level wrapper functions (e.g. for protocols). Overall, many questions are still open, including how usability could be integrated into the design and evaluation of cryptographic algorithms. Our main takeaway is that lightweight usable cryptography is an open research area that deserves greater focus. For the review of NISTIR 7977, the standardisation process of Ascon as a FIPS, and when exploring potential future SPs, the key criterion of usability should be based on realistic user testing and on triangulation from other methods.</p>
Our processed EndoVis2017 dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed EndoVis2017 dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Our processed Kvasir-Instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed Kvasir-Instrument dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Comparison of 2 Lightweight Y-meshes After Laparoscopic Sacrocolpopexy
ClinicalTrials.gov study NCT02248935. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Laparoscopic Lightweight Mesh Repair of Large Hiatal Hernias
ClinicalTrials.gov study NCT01408108. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Laparoscopic Repair of Giant Hiatal Hernias With Nitinol-framed Lightweight Polytetrafluoroethylene Mesh
ClinicalTrials.gov study NCT01780285. IPD Sharing: Not stated. Countries: 1. Publications: 13.
A Prospective Observational Study on the Use of a Self Fixating Lightweight Polyester Mesh in Open Inguinal Hernia
ClinicalTrials.gov study NCT01421602. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effectiveness of a Lightweight Mesh in the Laparoscopic Prevention of Parastomal Hernia
ClinicalTrials.gov study NCT00908661. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effects of Lightweight Meshes in Laparoscopic Inguinal Hernia Repair on Quality of Life and Male Fertility Aspects
ClinicalTrials.gov study NCT00925067. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.