Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
33,754
datasets available to search
ShareScore release 0.9.0
Dataset results
33,754 results for “Enhancers”
Atomistic Structures discussed in "Segregation-enhanced grain boundary embrittlement of recrystallised tungsten evidenced by site-specific microcantilever fracture"
<p>The tar file Sigma7_GB.tar contains all data to reproduce the results shown and discussed in the Publication "Segregation-enhanced grain boundary embrittlement of recrystallised tungsten evidenced by site-specific microcantilever fracture", DOI: <a href="https://doi.org/10.1016/j.actamat.2023.119256">10.1016/j.actamat.2023.119256</a></p><p>It contains three folders for the grain boundary creation, decoration with P atoms, and fracture simulations.<br>The naming conventions and additional information are provided in README.txt files in the directories.</p>
Enhanced Biosafety of the Sleeping Beauty Transposon System by Using mRNA as Source of Transposase to Efficiently and Stably Transfect Retinal Pigment Epithelial Cells
<p>Raw data of the publication "Enhanced Biosafety of the Sleeping Beauty Transposon System by Using mRNA as Source of Transposase to Efficiently and Stably Transfect Retinal Pigment Epithelial Cells".</p> <p>Abstract: Neovascular age-related macular degeneration (nvAMD) is characterized by choroidal<br> neovascularization (CNV), which leads to retinal pigment epithelial (RPE) cell and photoreceptor<br> degeneration and blindness if untreated. Since blood vessel growth is mediated by endothelial cell<br> growth factors, including vascular endothelial growth factor (VEGF), treatment consists of repeated,<br> often monthly, intravitreal injections of anti-angiogenic biopharmaceuticals. Frequent injections are<br> costly and present logistic difficulties; therefore, our laboratories are developing a cell-based gene<br> therapy based on autologous RPE cells transfected ex vivo with the pigment epithelium derived factor<br> (PEDF), which is the most potent natural antagonist of VEGF. Gene delivery and long-term expression<br> of the transgene are enabled by the use of the non-viral Sleeping Beauty (SB100X) transposon system<br> that is introduced into the cells by electroporation. The transposase may have a cytotoxic effect and a<br> low risk of remobilization of the transposon if supplied in the form of DNA. Here, we investigated<br> the use of the SB100X transposase delivered as mRNA and showed that ARPE-19 cells as well as<br> primary human RPE cells were successfully transfected with the Venus or the PEDF gene, followed<br> by stable transgene expression. In human RPE cells, secretion of recombinant PEDF could be detected<br> in cell culture up to one year. Non-viral ex vivo transfection using SB100X-mRNA in combination<br> with electroporation increases the biosafety of our gene therapeutic approach to treat nvAMD while<br> ensuring high transfection efficiency and long-term transgene expression in RPE cells.</p>
Curlie Enhanced with LLM Annotations: Two Datasets for Advancing Homepage2Vec's Multilingual Website Classification
<h3>Advancing Homepage2Vec with LLM-Generated Datasets for Multilingual Website Classification</h3> <p>This dataset contains two subsets of labeled website data, specifically created to enhance the performance of Homepage2Vec, a multi-label model for website classification. The datasets were generated using Large Language Models (LLMs) to provide more accurate and diverse topic annotations for websites, addressing a limitation of existing Homepage2Vec training data.</p> <p><strong>Key Features:</strong></p> <ul> <li><strong>LLM-generated annotations:</strong> Both datasets feature website topic labels generated using LLMs, a novel approach to creating high-quality training data for website classification models.</li> <li><strong>Improved multi-label classification:</strong> Fine-tuning Homepage2Vec with these datasets has been shown to improve its macro F1 score from 38% to 43% evaluated on a human-labeled dataset, demonstrating their effectiveness in capturing a broader range of website topics.</li> <li><strong>Multilingual applicability:</strong> The datasets facilitate classification of websites in multiple languages, reflecting the inherent multilingual nature of Homepage2Vec.</li> </ul> <p><strong>Dataset Composition:</strong></p> <ul> <li><strong>curlie-gpt3.5-10k:</strong> 10,000 websites labeled using GPT-3.5, context 2 and 1-shot</li> <li><strong>curlie-gpt4-10k:</strong> 10,000 websites labeled using GPT-4, context 2 and zero-shot</li> </ul> <p><strong>Intended Use:</strong></p> <ul> <li>Fine-tuning and advancing Homepage2Vec or similar website classification models</li> <li>Research on LLM-generated datasets for text classification tasks</li> <li>Exploration of multilingual website classification</li> </ul> <p><strong>Additional Information:</strong></p> <ul> <li><strong>Project and report repository:</strong> https://github.com/CS-433/ml-project-2-mlp</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>This dataset was created as part of a project at EPFL's Data Science Lab (DLab) in collaboration with <a href="https://people.epfl.ch/robert.west">Prof. Robert West</a> and <a href="https://tizianopiccardi.github.io/" rel="nofollow">Tiziano Piccardi.</a></p>
Evaluation datasets and results for the paper "Enhancing Business Process Simulation Models with Extraneous Activity Delays"
<p>Event-logs and Business Process Simulation Models used in the experimentation of the paper "Enhancing Business Process Simulation Models with Extraneous Activity Delays", where the '<em>inputs</em>' folder contains all the files used as input, and the '<em>output</em>' folder the results of the evaluation.</p> <p> </p> <p><em><strong>Inputs</strong></em>: event-logs, BPS models, and simulation parameters used as input in the experimentation.</p> <ul> <li><em><strong>Real-life</strong></em>: real-life event logs, corresponding to two disjoint subsets of traces from an Academic Credentials' process, and the BPIC 2012 and BPIC 2017 event logs (filtered as explained in the paper), and the BPS model (plus simulation parameters) used as input for each dataset in the presented approach.</li> <li><em><strong>Synthetic</strong></em>: simulated event-logs and corresponding BPS models (plus simulation parameters) for four different processes with 0, 1, 3 and 5 timer events.</li> </ul> <p><em><strong>Outputs</strong></em>: results of the experimentation.</p> <ul> <li><em><strong>Real-life</strong></em>: results corresponding to the evaluation with real-life event logs. Each of the folders is composed by the original and the enhanced BPS models, 10 event logs simulated with each of them, two folders with the best iteration of the two hyperparameter optimization processes, and the values for the injected timers in each case. In addition, a CSV file with the EMD metrics (cycle time and absolute hour event distribution) for each dataset is provided.</li> <li><em><strong>Synthetic</strong></em>: results corresponding to the simulated event-logs. <ul> <li>Before-After: BPS models and discovered timer events for the four synthetic processes, with five timers placed before and after different activity instances.</li> <li>Complete: BPS models and quality measures (precision, recall, and SMAPE of the discovered timers) for the four synthetic processes with zero, one, three, and five timer events.</li> <li>Individual: event logs enhanced with the discovered extraneous delay for each activity instance, for the four synthetic processes with zero, one, three, and five timer events; and SMAPE of the estimations.</li> </ul> </li> </ul>
Enhancing the ReaxFF DFT database
<h1>Enhancing the ReaxFF DFT database</h1> <p>This repository contains the database used to re-parametrize the ReaxFF force field for LiF, an inorganic compound. The purpose of the database is to improve the accuracy and reliability of ReaxFF calculations for LiF. The results and method used were published in the article <a href="https://doi.org/10.1038/s41598-023-50978-5">Enhancing ReaxFF for Molecular Dynamics Simulations of Lithium-Ion Batteries: An interactive reparameterization protocol</a>.</p> <p>This database was made using the simulation obtained using the protocol published in <a href="https://github.com/paolodeangelis/Enhancing_ReaxFF">Enhancing ReaxFF repository</a>.</p> <h2>Installation</h2> <p>To use the database and interact with it, ensure that you have the following Python requirements installed:</p> <p><strong>Minimum Requirements:</strong></p> <ul> <li>Python 3.9 or above</li> <li>Atomic Simulation Environment (ASE) library</li> <li>Jupyter Lab</li> </ul> <p><strong>Requirements for Re-running or Performing New Simulations:</strong></p> <ul> <li>SCM (Software for Chemistry & Materials) Amsterdam Modeling Suite</li> <li>PLAMS (Python Library for Automating Molecular Simulation) library</li> </ul> <p>You can install the required Python packages using pip:</p> <pre><code>pip install -r requirements.txt</code></pre> <blockquote> <p><strong>Warning</strong></p> <p>Make sure to have the appropriate licenses and installations of SCM Amsterdam Modeling Suite and any other necessary software for running simulations.</p> </blockquote> <h2>Folder Structure</h2> <p>The repository has the following folder structure:</p> <pre><code>. ├── CONTRIBUTING.md ├── CREDITS.md ├── LICENSE ├── README.md ├── requirements.txt ├── assets ├── data │ ├── LiF.db │ ├── LiF.json │ └── LiF.yaml ├── notebooks │ ├── browsing_db.ipynb │ └── running_simulation.ipynb └── tools ├── db ├── plams_experimental └── scripts</code></pre> <ul> <li><code>CONTRIBUTING.md</code>: This file provides guidelines and instructions for contributing to the repository. It outlines the contribution process, coding conventions, and other relevant information for potential contributors.</li> <li><code>CREDITS.md</code>: This file acknowledges and credits the individuals or organizations that have contributed to the repository.</li> <li><code>LICENSE</code>: This file contains the license information for the repository (CC BY 4.0). It specifies the terms and conditions under which the repository's contents are distributed and used.</li> <li><code>README.md</code>: This file.</li> <li><code>requirements.txt</code>: This file lists the required Python packages and their versions. (see <a href="#installation">installation section</a>)</li> <li><code>assets</code>: This folder contains any additional assets, such as images or documentation, related to the repository.</li> <li><code>data</code>: This folder contains the data files used in the repository. <ul> <li><code>LiF.db</code>: This file is the SQLite database file that includes the DFT data used for the ReaxFF force field. Specifically, it contains data related to the inorganic compound LiF.</li> <li><code>LiF.json</code>: This file provides the database metadata in a human-readable format using JSON.</li> <li><code>LiF.yaml</code>: This file also contains the database metadata in a more human-readable format, still using YAML.</li> </ul> </li> <li><code>notebooks</code>: This folder contains Jupyter notebooks that provide demonstrations and examples of how to use and analyze the database. <ul> <li><code>browsing_db.ipynb</code>: This notebook demonstrates how to handle, select, read, and understand the data points in the <code>LiF.db</code> database using the ASE database Python interface. It serves as a guide for exploring and navigating the database effectively.</li> <li><code>running_simulation.ipynb</code>: In this notebook, you will find an example of how to get a data point from the <code>LiF.db</code> database and use it to perform a new simulation. The notebook showcases how to utilize either the <a href="https://www.scm.com/doc/plams/index.html">PLAMS</a> library or the <a href="https://www.scm.com/doc/plams/interfaces/amscalculator.html">AMSCalculator</a> and ASE Python library to conduct simulations based on the retrieved data and then store it as a new data point in the <code>LiF.db</code> database. It provides step-by-step instructions and code snippets for a seamless simulation workflow.</li> </ul> </li> <li><code>tools</code>: This directory contains a collection of Python modules and scripts that are useful for reading, analyzing, and re-running simulations stored in the database. These tools are indispensable for ensuring that this repository adheres to the principles of <strong>I</strong>nteroperability and <strong>R</strong>eusability, as outlined by the <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a>. <ul> <li><code>db</code>: This Python module provides functionalities for handling, reading, and storing data in the database.</li> <li><code>plasm_experimental</code>: This Python module includes the necessary components for using the <code>AMSCalculator</code> with PLASM and the SCM software package, utilizing the ASE API. It facilitates running simulations, and performing calculations.</li> <li><code>scripts</code>: This directory contains additional scripts for advanced usage scenarios of this repository.</li> </ul> </li> </ul> <h2>Interacting with the Database</h2> <p>There are three ways to interact with the database: using the ASE db command line, the web interface, and the ASE Python interface.</p> <h3>ASE db Command-line</h3> <p>To interact with the database using the ASE db terminal command, follow these steps:</p> <ol> <li> <p>Open a terminal and navigate to the directory containing the <code>LiF.db</code> file.</p> </li> <li> <p>Run the following command to start the ASE db terminal:</p> <pre><code>ase db LiF.db</code></pre> </li> <li> <p>You can now use the available commands in the terminal to query and manipulate the database. More information can be found in the <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE database documentation</a>.</p> </li> </ol> <h3>Web Interface</h3> <p>To interact with the database using the web interface, follow these steps:</p> <ol> <li> <p>Open a terminal and navigate to the directory containing the <code>LiF.db</code> file.</p> </li> <li> <p>Run the following command to start the ASE db terminal:</p> <pre><code>ase db -w LiF.db</code></pre> </li> <li> <p>Open your web browser and connect to the local server at <a href="http://127.0.0.1:5000">http://127.0.0.1:5000</a>.</p> </li> </ol> <blockquote> <p><strong>Warning</strong></p> <p>To visualize the 3D structure of the system, you need to install the <a href="https://jmol.sourceforge.net/">JMOL extension</a>. You can use the script <code>tools/scripts/install_jmol.py</code> to automatically download and install it:</p> <pre><code>cd tools/scripts/ python install_jmol.py</code></pre> </blockquote> <h3>ASE Python Interface</h3> <p>To interact with the database using the ASE Python interface, you can use the following example code:</p> <pre><code>from ase.db import connect # Connect to the database db = connect("LiF.db") # Query the database results = db.select("success=True") # Iterate over the results for row in results: print(f"ID: {row.id}, Energy: {row.energy}")</code></pre> <div> <pre>For a more detailed example, refer to the notebook <code>notebooks/browsing_db.ipynb</code>. To learn how to perform a simulation, check the notebook <code>notebooks/running_simulation.ipynb</code>.</pre> </div> <h2>Contributing</h2> <p>If you would like to contribute to the Enhancing ReaxFF DFT Database by performing new simulations and expanding the database, please follow the guidelines outlined in the <a href="CONTRIBUTING.md">Contribution Guidelines</a>. You are welcome to submit pull requests or open issues in the repository. Your contributions are greatly appreciated!</p> <h2>How to Cite</h2> <p>If you use the database or the tools provided in this repository for your work, please cite it using the following BibTeX entries:</p> <pre><code>@article{deangelis2023enhancing, title={Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol}, author={De Angelis, Paolo and Cappabianca, Roberta and Fasano, Matteo and Asinari, Pietro and Chiavazzo, Eliodoro}, journal={Scientific Reports}, volume={14}, number={1}, pages={978}, year={2024}, publisher={Nature Publishing Group UK London} }</code></pre> <pre><code>@dataset{EnhReaxFFdatabase, author = {De Angelis, Paolo and Cappabianca, Roberta and Fasano, Matteo and Asinari, Pietro and Chiavazzo, Eliodoro}, title = {{Enhancing the ReaxFF DFT database}}, month = may, year = 2023, publisher = {Zenodo}, version = {1.0.0-beta}, doi = {10.5072/zenodo.1204707}, url = {https://doi.org/10.5281/zenodo.7959121} }</code></pre> <div> <h2>License</h2> </div> <p>The contents of this repository are licensed under the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <h2>Acknowledgments</h2> <p>This project has received funding from the European Union's <a href="https://ec.europa.eu/programmes/horizon2020/en">Horizon 2020 research and innovation programme</a> under grant agreement <a href="https://cordis.europa.eu/project/id/957189">No 957189</a>. The project is part of <a href="https://battery2030.eu/">BATTERY 2030+</a>, the large-scale European research initiative for inventing the sustainable batteries of the future.</p> <p>The authors also acknowledge that the simulation results of this database have been achieved using the <a href="https://prace-ri.eu/hpc-access/deci-access/">DECI</a> resource <a href="https://www.archer2.ac.uk/">ARCHER2</a> based in UK at <a href="https://www.epcc.ed.ac.uk/">EPCC</a> with support from the <a href="https://prace-ri.eu/">PRACE</a> aisbl.</p>
Gnomadv4.1 Enhanced Allele Frequencies (EAF) for use in PhyloFrame
<p>Source data to accompany manuscript: Equitable machine learning counteracts ancestral bias in precision medicine.</p> <pre><br><br><br></pre>
Sensitivity enhancement using chemically reactive gas cluster ion beams in secondary ion mass spectrometry (SIMS)
<p>We report for the first time on significant molecular secondary ion yield increases by modifying the chemistry of a water cluster primary ion beam. This was demonstrated using 70 keV ion beams of 0.15 eV/amu. For the neutral drug Bezafibrate, secondary ion yield enhancements ×5-10 were observed when replacing the Ar carrier gas in a water gas cluster ion beam (GCIB) source with a mixture containing 12% CO2 and 2% O2 in Ar. For the cationic drug Ranitidine the ion yield enhancements using the CO2-containing carrier gas were up to ×20-50 in positive mode and ×2-4 in negative mode. The extent of molecular fragmentation was very similar from both cluster beams. We conclude that additional chemically reactive species are present in the impact zone using the (H2O/CO2)n projectile which promote the formation of secondary ions of both polarity through projectile impact-induced chemical reactions. This methodology can be applied to further extend the capabilities of high-resolution 3-dimensional mass spectral imaging using reactive GCIB-SIMS.</p>
Data from: Functional structure of European forest beetle communities is enhanced by rare species
<p>From article abstract:</p> <p><a href="https://doi.org/10.1016/j.biocon.2022.109491">https://doi.org/10.1016/j.biocon.2022.109491</a></p> <p><strong>ABSTRACT</strong></p> <p>Biodiverse communities have been shown to sustain high levels of multifunctionality and thus a loss of species likely negatively impacts ecosystem functions. For most taxa, however, the roles of individual species are poorly known. Rare species, often the most likely to go extinct, may have unique traits leading to unique functional roles. Alternatively, rare species may be functionally redundant, such that their loss would not disrupt ecosystem functions. We quantified the functional role of rare species by using capture records of wood-living (saproxylic) beetle species, combined with recent databases of their morphological and ecological traits, from three regions in central and northern Europe. Using a rarity index based on species’ local abundance, geographic range, and habitat breadth, we used local and regional species removal simulations to examine the contributions of both the rarest and the most common beetle species to three measures of community functional structure: functional richness, functional specialization, and functional originality. In both regional species pools and local communities, all three of these measures declined more rapidly when rare species were removed than under common (or random) species removal scenarios. These consistent patterns across scales and among several forest types give evidence that rare species provide unique functional contributions, and that their loss may disproportionately impact ecosystem functions. This implies that conservation measures targeting rare and endangered species, such as preserving intact forests with dead wood and mature trees, can provide broader ecosystem-level benefits. Experimental research linking functional structure to ecosystem processes should be prioritized to increase our understanding of the functional consequences of species loss and to develop more effective conservation strategies.</p> <p> </p> <p><strong>DATASET DESCRIPTION</strong></p> <p>This dataset includes a) beetle capture information and b) beetle trait information from three countries: 1) Norway, 2) Finland, and 3) Germany. </p> <p> </p> <p><strong>FILES</strong></p> <p><strong>readme.txt</strong> -- this has the information from this description section</p> <p><strong>Norway_traits.csv</strong>, <strong>Finland_traits.csv</strong>, <strong>Germany_traits.csv</strong> -- these are the trait files, including all species</p> <p><strong>Norway_sites.species.csv</strong>, <strong>Finland_sites.species.csv</strong>, <strong>Germany_sites.species.csv</strong> -- this has species (rows) by sites (columns); values are the number of beetles caught (for number of traps, dates, and other site covariates, see related dataset: <a href="https://doi.org/10.5061/dryad.tmpg4f50b">https://doi.org/10.5061/dryad.tmpg4f50b</a> and manuscript: <a href="https://doi.org/10.1111/jbi.14272">https://doi.org/10.1111/jbi.14272</a>). Species names follow GBIF taxonomic backbone.</p> <p><strong>Traits_METADATA.csv</strong> -- this has information on all the fields in the trait data</p> <p> </p>
Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Dataset for "Fast and efficient demultiplexing of single photons from a GaAs quantum dot with resonantly enhanced electro-optic modulators"
<p><strong>Dataset for "Fast and efficient demultiplexing of single photons from a quantum dot with resonantly enhanced electro-optic modulators"</strong></p> <p>A description of the dataset is found in the <strong>readme.md</strong> file (markdown markup language).</p>
Language-enhanced cognitive skills model
<p>This is a model of cognitive skills required in the workplace which enhance previous models by including a more detailed measurement of linguistic skills. Linguistic skills are defined as the set of abilities, competencies and knowledge which principally involve the use of linguistic code. More specifically, the linguistic items used are reading and writing competencies, ability to speak, listen or communicate, as well as knowledge of second languages (as a whole). These variables were factorialised together with a list of competencies from previous models of cognitive skills. Principal component analysis (PCA) with equamax rotation was applied to reduce the dimensionality of all items to a few interpretable dimensions according to the correlations between them. The result is nine factors with similar variances among at least three express linguistic-related skills: The first factor expresses the demand for scientific and engineering knowledge. The second refers to a collection of competencies which could be called verbal-reasoning. These include deductive and inductive reasoning skills or those of identifying and solving complex problems. Some linguistic competencies relating to the level of oral and written comprehension and expression are also relevant in this factor. The third factor expresses numerical or quantitative competencies. The fourth expresses the demand for communicative competencies, composed of variables related to efficient communication goals such as clarity of speech, active listening or speaking. The fifth factor expresses creative abilities. The sixth, competencies and knowledge linked to electronics and computers. The seventh expresses managerial competencies. The eighth expresses nurturing competencies and the ninth factor basically expresses knowledge of foreign languages.</p>
Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection
<p>Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. <a href="https://doi.org/10.1002/admt.202101726">https://doi.org/10.1002/admt.202101726</a></p> <p> </p> <p> </p> <p>Abstract:</p> <p>Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B<sub>20</sub>, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B<sub>20</sub>, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B<sub>20</sub> by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.</p>
Source code and simulation results for nanoantennas supporting an enhanced Purcell factor due to interfering resonances
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "<a href="https://doi.org/10.1103/PhysRevResearch.4.023189">Enhanced Purcell factor for nanoantennas supporting interfering resonances</a>" [1], whose subject are the effects of coupled resonances and quasibound states in the continuum on the Purcell factor in dielectric resonant nanoantennas. The provided scripts reproduce the analysis of interfering resonances in a nanodisk coupled to an enclosed emitter and can be easily adapted for further investigations. </p> <p><strong>Structure</strong></p> <p>The cases refer to different aspect ratios of the nanodisk with (a and b) and without (c and d) substrate. The scans reproduce the data used to find the aspect ratios (a and c) supporting the maximal Purcell enhancement. </p> <p><a href="https://doi.org/10.1016/j.softx.2021.100763">RPExpand</a> [2] is used for Riesz projection expansions, which quantify the interactions of the resonances.</p> <p>The directories <strong>resonance</strong> and <strong>scattering </strong>contain input files for the commercial software JCMsuite, which rigorously solves Maxwell's equations with the finite-element method (FEM). In order to switch to a custom setup, you must adapt these input files. If you want to recalculate all results, make sure that you remove the directories containing resultbags. These are stored in the directory <strong>results</strong>, e.g., results/case_a/resultbags.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (tested with version: 4.6.3)</li> <li>Matlab (tested with version: R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p>[1] Rémi Colom, Felix Binkowski, Fridtjof Betz, Yuri Kivshar, Sven Burger, Enhanced Purcell factor for nanoantennas supporting interfering resonances, Physical Review Research <strong>4</strong>, 023189 (2022), https://doi.org/10.1103/PhysRevResearch.4.023189</p> <p>[2] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p>
Experimental study dataset: "Enhancing Touch Sensibility by Sensory Retraining in a Sensory Discrimination Task via Haptic Rendering"
<p>Experimental study dataset: "Enhancing Touch Sensibility by Sensory Retraining in a Sensory Discrimination Task via Haptic Rendering."</p> <p>This research was supported by the Swiss National Science Foundation through the grant PP00P2 163800. This work was also supported by SENACYT and IFARHU, the Panamanian Government.</p> <p>Files and data to upload:</p> <ol> <li>Description of variables </li> <li>Continue robot data </li> <li>Discontinue robot data </li> <li>Questionnaire data </li> </ol> <p> </p>
Simultaneously Enhanced Tenacity, Rupture Work, and Thermal Conductivity of Carbon Nanotubes Fibers by Raising Effective Tube Portion
<p>Although individual carbon nanotubes (CNTs) are superior as constituents to polymer chains, the mechanical and thermal properties of CNT fibers (CNTFs) remain inferior to synthetic fibers due to the failure of embedding CNTs effectively in superstructures. Conventional techniques resulted in a mild improvement of target properties while achieving parity at best on others. Here, a Double-Drawing technique is developed to rearrange the constituent CNTs in both mesoscale and nanoscale morphology. Consequently, the mechanical and thermal properties of the resulting CNTFs can simultaneously reach their highest performances with specific strength ~3.30 N/tex, work of rupture ~70 J/g, and thermal conductivity ~354 W/m/K, despite starting from low-crystallinity materials (<em>I</em><sub>G</sub>:<em>I</em><sub>D</sub>~5). The processed CNTFs are more versatile than comparable carbon fiber, Zylon and Dyneema. Based on evidence of load transfer efficiency on individual CNTs measured with In-Situ-Stretching-Raman, we find the main contributors to property enhancements are the increasing of the effective tube contribution, in addition to the known optimization on CNTs alignment and stacking.</p>
Data: DEAE-Dextran enhances the lentiviral transduction of primary human mesenchymal stromal cells from all major tissue sources without affecting their proliferation and phenotype
<p>This data set includes all the raw data collected for the following article: "DEAE-Dextran enhances the lentiviral transduction of primary human mesenchymal stromal cells from all major tissue sources without affecting their proliferation and phenotype"</p>
5D-NP-FABTECH_ALD - Open Dataset for: "Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films"
<p>This is the open dataset for the paper: "Demelius, L. <em>et al.</em> Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films. <em>Applied Surface Science</em> <strong>604</strong>, (2022)."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
Supporting Material for "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review"
<p>This dataset contains all supporting material for the paper "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review", published in the journal Swiss Psychology Open:</p> <p><em>Mack, M., Scarampi, C., Joly-Burra, E., Zuber, S., de Freitas, C., Teixeira, R. and Kliegel, M. (2025) ‘Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review’, Swiss Psychology Open, 5(1), p. 2. Available at: <a href="https://doi.org/10.5334/spo.81.">https://doi.org/10.5334/spo.81</a>.</em></p> <p>It includes the following documents and files:</p> <p><strong>S1. Protocol:</strong> ADVANCE Protocol for desk reviews</p> <p><strong>S2. Search strategy</strong></p> <p><strong>S3. Guidelines for title and abstract screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S4. Guidelines full-text screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S5. Guidelines data extraction:</strong> ADVANCE Guidelines/codebook data extraction</p> <p><strong>data extraction_desk review_switzerland.xlsx</strong></p> <p>This desk review was conducted as part of the ADVANCE project, which aims to enhance our understanding of mental health promotion and prevention. This desk review evaluates the current state of interventions for mental health and cognitive functioning among older adults in Switzerland focusing on the features of these interventions as well as on Swiss-specific contextual factors that contribute to vulnerability and stigma. This results of the desk review has been submitted for publication to 'LIVES Working Papers' and 'Swiss Psychology Open' . The two versions of the desk review differ slightly. The version for LIVES Working Papers, included the results of the Delphi survey and the resulting intervention scenarios. The version for Swiss Psychology Open, did not include the Delphi survey results and the resulting intervention scenarios, but included a more detailed discussion of the review results.</p>
Dataset from the Survey on Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice
<p>This database contains all the responses from the participants in the survey: Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice.</p> <p>The main purpose of this survey was to explore how the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C)<br>industry can adapt and better prepare to embrace the innovative principles and enabling technologies of Industry 5.0. This could ultimately result in<br>enhanced conservation practices for built cultural heritage.</p> <p> </p>
Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning
<p>README<br>Title<br>Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning</p> <p>Authors<br>Mays Abukeshek, School of Computer Science, Faculty of Technology, University of Sunderland, University of Huddersfield, UK<br>Email: mays.abukeshek@sunderland.ac.uk, Mays.abukeshek@hud.ac.uk<br>Basel Barakat, School of Computer Science, Faculty of Technology, University of Sunderland, UK<br>Email: basel.barakat@sunderland.ac.uk<br>Bamidele Ajayi, School of Computer Science, Faculty of Technology, University of Sunderland, UK<br>Email: bamidele.ajayi@research.sunderland.ac.uk<br>Abstract<br>This dataset supports the paper "Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning," which presents a comprehensive implementation of a cybersecurity solution for smart grid network containers. The methodology utilizes:</p> <p>Qualys API-based vulnerability scanning and reporting system for vulnerability identification<br>Docker deployment for security and isolation<br>Advanced load balancing techniques for resource optimization<br>Machine learning-powered anomaly detection for threat identification and vulnerability prioritization.<br>The dataset contains details of several simulated attacks enabling effective training and evaluation of a robust machine-learning model.</p> <p>Data Description<br>The dataset includes logs from conducted attacks on containerized nodes, generated to reflect real-world scenarios. The simulated attacks include:</p> <p>Denial of Service (DoS)<br>Remote-to-Local (R2L)<br>User-to-Root (U2R)<br>Probes<br>Contents<br>Csv_file.csv: This file contains the dataset used for training and evaluating the machine learning models. The columns in the dataset represent various features and results of the simulated attacks.<br>Data Columns and Rows<br>Timestamp:</p> <p>Description: The exact date and time when the data was recorded.<br>time: 2023-06-01 12:00:00</p> <p>Attack_Type:</p> <p>Description: The type of cyber-attack conducted.<br>Possible Values: DoS, R2L, U2R, Probe<br>Example: DoS<br>Notes: Categorizes the type of attack, crucial for training classification models.<br>CPU_Utilization (%):</p> <p>Description: The percentage of CPU resources used during the attack.<br>Example: 52.3<br>Notes: Indicates the load on the CPU during the attack, useful for assessing the impact of attacks on system performance.<br>Memory_Utilization (%):</p> <p>Description: The percentage of memory resources used during the attack.<br>Example: 63.4<br>Notes: Shows memory usage which can be a critical factor in understanding system performance under attack conditions.<br>Network_Bandwidth (Mbps):</p> <p>Description: The bandwidth of the network in Megabits per second.<br>Example: 100<br>Notes: Reflects the network load and is essential for analyzing the impact on network performance.<br>Vulnerabilities_Detected:</p> <p>Description: The number of vulnerabilities detected during the attack.<br>Example: 289<br>Notes: Indicates the effectiveness of the vulnerability scanning process and the system's exposure to threats.<br>Mean_Response_Time (ms):</p> <p>Description: The average response time in milliseconds during the attack.<br>Example: 87<br>Notes: Important for evaluating the responsiveness of the system under attack conditions.<br>Throughput (requests/second):</p> <p>Description: The number of requests the system can handle per second during the attack.<br>Example: 1068<br>Notes: Measures the capacity and efficiency of the system under load.<br>Example Row<br>Timestamp Attack_Type CPU_Utilization (%) Memory_Utilization (%) Network_Bandwidth (Mbps) Vulnerabilities_Detected Mean_Response_Time (ms) Throughput (requests/second)<br>2023-06-01 12:00:00 DoS 52.3 63.4 100 289 87 1068<br>Usage<br>This dataset can be used to:</p> <p>Train and evaluate machine learning models for cybersecurity applications in smart grid systems.<br>Analyze the performance of different machine learning models in detecting and prioritizing vulnerabilities.<br>Understand the impact of various types of cyber-attacks on containerized environments.<br>Methodology<br>The dataset was created using a combination of Qualys API-based vulnerability scanning and Docker containerization. Multiple container clusters were subjected to various simulated attacks, and the performance of machine learning models was evaluated based on accuracy, precision, recall, and F1-scores.</p> <p>Acknowledgments<br>This research was supported by the University of Sunderland and the University of Huddersfield.</p> <p>References<br>Please refer to the full paper for detailed methodology, implementation, and analysis:<br>IEEE</p>
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.