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33,754 results for “Enhancers”
Enhanced localized Temperature and Dewpoint forecast for Thessaloniki region
<p>2D Dataset of enhanced localized forcast for 2m Air Temperature and Dewpoint for the Thessaloniki region. The forecast has an hourly resolution and is is up to 72 hours. The compressed region is located at the city Thessaloniki with a little surrounding suburbs. The spacial resolution is 4x4km and comes from the SuperHD wheatehr forecast model at Meteologix.com. <br>The DMO model output was enhanced with statistical information of up to 10 weather stations located in Thessaloniki. Individual MOS-model forecasts for these 10 measurement stations where merge into the DMO model output. </p>
Dataset for "Nanoparticle doping as a way to enhance holmium fiber lasers efficiency"
<p>This dataset contains the specific numerical values of fiber properties used in Figures 2-4.</p>
UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow
<p>UniSpec is a comprehensive DL spectrum predictor that can predict the intensity of the entire HCD MS/MS fragment ion series, going beyond existing tools limited to b/y ion series. </p> <p>All datasets developed for UniSpec model are shared on Zenodo as part of the UniSpec publication, "UniSpec: Deep Learning for Predicting Comprehensive Peptide Fragment Ion Series to Improve Peptide-Spectrum Matches from Shotgun Proteomics Experiments".</p> <p>This includes UniSpec datasets, downstream evaluation and analysis, and application case studies.</p> <p>1. pre-processed training, evaluation and testing data for machine learning;</p> <p> UniSpec-Datasets.7z, Readme_UniSpecDatasets.txt</p> <p>2. Streamlined input datasets based on the fragmentation dictionary;</p> <p> Streamlined_inputdatasets.7z, Readme_Streamlined_inputdatasets.txt</p> <p>3. Predictions on the validation and test sets;</p> <p> UniSpecPred_Validation-Test.7z, Readme_Predictons_ValidationTest.txt</p> <p>4. Evaluation by comparison with Prosit;</p> <p> a. Predictions: prosit_and_unispec_predictions.7z, Readme_prosit_and_unispec_predictions.txt</p> <p> b. Cosine similarity scores: prosit_vs_unispec_CS.7z, Readme_prosit_vs_unispec_CS.txt</p> <p>5. CSS for Different HCD Fragment Ion Series;</p> <p> CS_for_ion_splits.tsv</p> <p>6. Application 1: PSM rescoring;</p> <p> PSM rescoring_zipfiles.7z, PSM rescoring_readme.txt</p> <p>7. Application 2: In-silico spectral library search </p> <p> in-silico_librarysearch.7z, in-silico_librarysearch_readme.txt</p> <p> </p>
Secondary Data for: Enhanced Modeling of Back-Mixing in Chemical Reactor Networks
<p>Secondary data for the results presented in the preprint "Enhanced Modeling of Back-Mixing in Chemical Reactor Networks" by L. Gossel, M. Fricke and D. Bothe (2023). </p> <p>https://arxiv.org/abs/2305.11591</p> <p>Tables containing the secondary data of the results presented in Figure 5, a-d are provided. </p> <p>The used code is confidential and thus not included in the repository. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Exploring the Synergy of Enhanced Weathering and Bacillus subtilis: A Promising Strategy for Sustainable Agriculture
<p>Data: Exploring the Synergy of Enhanced Weathering and Bacillus subtilis: A Promising Strategy for Sustainable Agriculture</p>
Delphi Study Data: Rounds 1 and 2 for Enhancing Efficiency and Transparency in Ghana's Rental Housing Market
<p><span>The dataset includes responses from two rounds of a Delphi study conducted among experts in the real estate sector, policymakers, and government officials in Ghana. The data captures both quantitative and qualitative insights on factors influencing rental housing decisions.</span></p> <ul> <li><span>Round 1</span><span>: Initial ratings of factors such as rental cost, proximity to work, landlord responsiveness, and availability of amenities.</span></li> <li><span>Round 2</span><span>: Pairwise comparisons of the top factors identified in Round 1, used for AHP analysis to determine their relative importance.</span></li> </ul>
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
Saline groundwater irrigation affects the date palm bulk soil associated fungal guild community and enhances the pathotroph abundance
<p><strong><em> Exploring the Influence of Saline Groundwater Irrigation on Soil Fungal Biodiversity in Date Palm (Phoenix dactylifera) Bulk Soil</em></strong></p>
Dataset and Data Dictionary for "Enhancing Consumer Satisfaction in Live Commerce: A Study of Middle-Aged Women's Cosmetics Purchases Using TAM, PVT, and SIT Models
<p>This dataset is part of a study investigating the underexplored factors driving middle-aged Chinese women’s purchasing behavior in live commerce, particularly in the context of their decision-making amidst the rapid expansion of e-commerce. The study employs a comprehensive theoretical framework based on the Technology Acceptance Model (TAM), Perceived Value Theory (PVT), and Social Influence Theory (SIT).</p> <p>Data were collected through a structured survey administered to 653 women aged 40 to 59. The dataset captures key variables including ease of use, pricing, consumer trust, platform interactivity, and purchase satisfaction. These variables are essential for understanding the complex relationships that influence purchasing decisions in live-stream shopping environments.</p> <p>The dataset has been analyzed using Structural Equation Modeling (SEM), revealing that factors such as ease of use, perceived value from competitive pricing, consumer trust, and real-time platform interactivity significantly enhance purchase satisfaction. Moreover, the results demonstrate that perceived value moderates these relationships, amplifying their effects under conditions of high perceived value.</p> <p>This dataset provides valuable insights into the psychological and social factors that shape e-commerce behavior, offering implications for optimizing platform design to promote consumer trust and long-term engagement.</p> <p>Keywords: Live commerce, Middle-aged women, Purchasing behavior, Technology Acceptance Model (TAM), Perceived Value Theory (PVT), Social Influence Theory (SIT), Structural Equation Modeling (SEM), Consumer trust, E-commerce.</p>
NYC Bike Sharing Network: Time-Series Enhanced Nodes and Edges Dataset
<p>This dataset presents a comprehensive graph representation of the New York City Bike Sharing system, structured with nodes representing stations and edges delineating trips between these stations. The dataset is distinctive in integrating dynamic properties as time series data, which are meticulously updated using historical records (csv files) and live data feeds (gbfs files) provided by<a href="https://citibikenyc.com/system-data" target="_blank" rel="noopener"> NYC Bike sharing system</a>. </p> <ul> <li> <p><strong>Nodes</strong>:</p> <ul> <li><strong>Source</strong>: Data is collected from the New York City Bike Station Information API.</li> <li><strong>Attributes</strong>: <ul> <li><strong>ID</strong>: Unique identifier for each station.</li> <li><strong>Name</strong>: Name of the station.</li> <li><strong>Capacity</strong>: Number of bikes the station can accommodate.</li> <li><strong>Short ID</strong>: A condensed identifier used internally.</li> </ul> </li> <li><strong>Time Series Data</strong>: <ul> <li>Updated every 5 minutes from the Station Status API.</li> <li>Captures changes in bike availability, recording values only when they differ from previous data points.</li> </ul> </li> </ul> </li> <li> <p><strong>Edges</strong>:</p> <ul> <li><strong>Source</strong>: Compiled from trip data provided in CSV format specific to NYC Bike Sharing.</li> <li><strong>Attributes</strong>: <ul> <li><strong>Trip Counter</strong>: Total number of trips recorded.</li> <li><strong>Bike Type Counter</strong>: Counts trips made with electric versus classic bikes.</li> <li><strong>Trip Type Counter</strong>: Separates trips made by members versus casual riders.</li> <li><strong>Active Trips Tracker</strong>: Tracks the number of active trips at any given moment.</li> </ul> </li> <li><strong>Aggregation</strong>: Trip data between identical start and end points, in the same direction, are aggregated into a single edge, with time-series tracking the frequency of these trips.</li> </ul> </li> </ul>
Enhancing Motivation in Software Engineering Education through Gamified Agile Project-based Learning
<p>Project-based learning (PBL), e.g., student software development projects, is an essential part of today's Software Engineering (SE) education. They allow students to work on real-world projects and gain practical experience as a team. However, several challenges arise in such projects, including learning new technologies and dealing with communication and coordination issues within the team. These factors can lead to a lack of motivation to contribute to the project and a decrease in productivity, potentially resulting in an insufficient project outcome. This paper aims to promote student motivation in PBL and increase team productivity by applying gamification. We conducted a user and requirements analysis to identify the needs of students and supervisors of such projects. Based on the insights, we designed and implemented DinoDev, a gamified project management tool that combines project management features with gamification elements. The DinoDev concept was evaluated in a student project, indicating increased motivation and team productivity. The findings are valuable for advancing research on using gamification in PBL and for lecturers to improve their students' motivation and team productivity in SE education.</p>
On-chip phonon-enhanced IR near-field detection of molecular vibrations
<p>This dataset contains the source data and raw interferograms associated with our manuscript, <em>'On-chip phonon-enhanced IR near-field detection of molecular vibrations</em>' by A. Bylinkin et al. The source data, provided in an .xlsx file, includes the datasets used to generate the figures in both the main text and the Supplementary Information. The raw interferogram dataset, located in the <em>'Interferograms</em>' folder, was used to calculate the experimental spectra shown in Fig. 3b, f, and Suppl. Fig. 13. This dataset was acquired using a NeaSNOM microscope (attocube AG).</p> <p>The data processing procedure for calculating spectra from the interferograms is described in the Methods section of our manuscript.</p>
A combined NMR and Deep Neural Network approach for enhancing the spectral resolution of aromatic side chains in proteins
<p><span><span>Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the </span><span>structure, </span><span>dynamics</span> <span>and interactions </span><span>of </span><span>macromolecules. Wh</span><span>ile</span><span> a plethora of </span><span>NMR </span><span>methods are </span><span>now </span><span>available to inform on backbone and methyl-bearing </span><span>side-chains</span><span> of proteins, </span><span>a </span><span>characterisation</span><span> of</span><span> aromatic side chains is more challenging and often require</span><span>s</span><span> specific labelling or </span></span><span><span>13</span></span><span><span>C-detection. Here we </span><span>present</span><span> a deep neural network (DNN)</span> <span>named FID-Net-2</span><span>, which transforms NMR spectra recorded on simple uniformly </span></span><span><span>13</span></span><span><span>C labelled samples to yield high-quality </span></span><span><span>1</span></span><span><span>H</span><span>-</span></span><span><span>13</span></span><span><span>C correlation spectra of the aromatic side chains. </span><span>Key to the success of the DNN is the design of a </span><span>complementary</span><span> set of</span> <span>NMR experiment</span><span>s</span><span> that </span><span>produce</span> <span>spectra with </span><span>unique </span><span>features</span><span> to aid the</span> <span>DNN </span><span>prod</span><span>uce</span><span> high-resolution aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation spectra with </span><span>accurate</span><span> intensities. </span><span>The </span><span>reconstructed spectra can be used for quantitative </span><span>purposes as FID-Net-2</span><span> predicts uncertainties </span><span>in </span><span>the </span><span>resulting</span> <span>spectra</span><span>. </span><span>We </span><span>have </span><span>validated</span> <span>the new </span><span>methodology</span><span> experimentally</span> <span>on protein</span><span> samples</span><span> ranging from 7 to 40 </span><span>kDa</span><span> in size. We </span><span>demonstrate</span><span> that the method can</span> <span>accurately reconstruct</span> <span>high resolution </span><span>two-dimensional </span><span>aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation </span><span>maps</span><span>,</span> <span>high resolution </span><span>three-dimensional aromatic</span><span>-</span><span>methyl NOESY spectra to </span><span>facilitate</span><span> aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C assignments</span><span>,</span><span> and </span><span>that the intensities of peaks from the reconstructed</span> <span>aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation maps </span><span>can be used to </span><span>quantitatively </span><span>characterise</span><span> the kinetics of protein folding</span><span>. </span><span>More generally, w</span><span>e believe that this strategy of</span><span> devising new</span><span> NMR</span><span> experiments</span><span> specifically</span> <span>for analysis</span> <span>using customised </span><span>DNN</span><span>s </span><span>represents</span><span> a </span><span>substantial</span><span> advance </span><span>that</span> <span>will</span><span> have a major impact on the study of molecules </span><span>using NMR </span><span>in the years to come.</span></span><span> </span></p>
Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behaviour
<p>This work presents an analysis based on training AI models directly on devices to make personalized predictions tailored to individual usage patterns, ensuring that each user benefits from a personalized approach to battery management. By integrating these AI-based insights, mobile devices can proactively manage power consumption, improving battery performance and user satisfaction. This personalized, intelligent approach to battery management represents a significant advance in optimizing device efficiency and addresses the growing demand for longer-lasting mobile technology.</p>
Transfer learning and DNA language models enhance transcription factor binding predictions
<p>This is the dataset for replicating the results of the paper called "Transfer learning and DNA language models enhance transcription factor binding predictions" by Ekin Deniz Aksu and Martin Vingron.</p> <p>See https://github.com/ekinda/tfbs_prediction_paper</p>
Raw data for the manuscript "Enhanced Photonic Maxwell's Demon with Correlated Baths"
<p>This folder contains the raw data and analysis coded need to reproduce all of the major results in the manuscript "Enhanced Photonic Maxwell’s Demon with Correlated Baths". </p> <p>The folder "demonPower" contains the data and code related to the photon number difference that the demon can generate. There are four analysis scripts, written in MATLAB, one for each type of bath used. Each script loads the data, and plots the final results in Fig. 4 of the paper. </p> <p>There is one data file for each reflectivity. The different columns in the data files correspond to different count rates measured with our tagger. Only the first two columns are relvant, they are the counts per second measured at detector A and B. Each line is one second worth of data.</p> <p><br> The folder "g2data" contains the data and code used to characterize the thermal and split thermal sources. The two txt data files contain lists of the the channel that registered an event together with the clock cycle of the time tagger at which the event was registerd. The data are described and analyzed in the MATLAB script "thermalLightG2_withSave.m".</p>
Dataset: Quantitative Evaluation of Enhanced multi-plane clinical fetal diffusion MRI with a crossing-fiber phantom
<p>This dataset provides MRI acquisitions of a customized crossing phantom for fetal brain. It contains:<br> <br> 1) High Resolution acquisitions of 1.5 mm<sup>3</sup> isotropic and 61 directions (with b-vectors/b-values)</p> <p>2) Six low resolution acquisitions of 1x1x4 mm<sup>3</sup> and 9-16-25 directions (with respective b-vectors/b-values)</p> <p>3) A structural T2-w acquisition</p>
(open) data literacy as barrier and enabler of open government data enhancement. A systematic review of the literature.
<p>This systematic review of the literatu was conducted with the PRISMA method, to explore the contexts in which the use of open government data germinates, identifying barriers to its use and identifying, the role of data literacy among those barriers to use; and the role of open data in promoting informal learning that supports the development of critical data literacy. This file includes a codebook of the main characteristics that were studied in a systematic literature review, where data from 66 articles related to Open Data Usage were identified and coded. Also, the file includes an analysis of Cohen's Kappa, a concordance statistic used to measure the level of agreement among researchers in classifying articles on the characteristics defined in the Codebook. Finally, it includes main tables of the results' analysis.</p>
TEST DATA for Enhanced protein isoform characterization through long-read proteogenomics
<p>Test data for The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.</p> <p>Companion Repositories:</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> <p>Companion Datasets</p> <ol> <li><a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a></li> <li><a href="http://10.5281/zenodo.5920920">Long-Read-Proteogenomics Workflow Results using Jurkat Sample data</a></li> </ol> <p>This Repository contains the test data, specifically:</p> <p><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></p>
Jumping the green wall: the use of PNA-DNA clamps to enhance microbiome sampling depth in wildlife microbiome research
As microbiome research moves away from model organisms to wildlife, new challenges for microbiome high throughput sequencing arise caused by the variety of wildlife diets. High levels of contamination are commonly observed emanating from the host (mitochondria) or diet (chloroplast). Such high contamination levels affect the overall sequencing depth of wildlife samples thus decreasing statistical power and leading to poor performance in downstream analysis. We developed an amplification protocol utilizing PNA-DNA clamps to maximize the use of resources and to increase the sampling depth of true microbiome sequences in samples with high levels of plastid contamination. We chose two study organisms, a bat (Leptonyteris yerbabuenae) and a bird (Mimus parvulus), both relying on heavy plant-based diets that sometimes lead to traces of plant-based faecal material producing high contamination signals from chloroplasts and mitochondria. On average, our protocol yielded a 13-fold increase in bacterial sequence amplification compared with the standard protocol (Earth Microbiome Protocol) used in wildlife research. For both focal species, we were able significantly to increase the percentage of sequences available for downstream analyses after the filtering of plastids and mitochondria. Our study presents the first results obtained by using PNA-DNA clamps to block the PCR amplification of chloroplast and mitochondrial DNA from the diet in the gut microbiome of wildlife. The method involves a cost-effective molecular technique instead of the filtering out of unwanted sequencing reads. As 33% and 26% of birds and bats, respectively, have a plant-based diet, the tool that we present here will optimize the sequencing and analysis of wild microbiomes.
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.