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1,855 results for “Framework”
Figure 3 from: Evans N, van Hoof M, Hartman L, Marusic A, Gordijn B, Dierickx K, Bouter L, Widdershoven G (2021) EnTIRE: Mapping Normative Frameworks for EThics and Integrity of REsearch. Research Ideas and Outcomes 7: e76240. https://doi.org/10.3897/rio.7.e76240
Figure 3 Content for and by the community.
Figure 2 from: Evans N, van Hoof M, Hartman L, Marusic A, Gordijn B, Dierickx K, Bouter L, Widdershoven G (2021) EnTIRE: Mapping Normative Frameworks for EThics and Integrity of REsearch. Research Ideas and Outcomes 7: e76240. https://doi.org/10.3897/rio.7.e76240
Figure 2 PERT Description of the work packages and their interrelations.
Figure 1 from: Evans N, van Hoof M, Hartman L, Marusic A, Gordijn B, Dierickx K, Bouter L, Widdershoven G (2021) EnTIRE: Mapping Normative Frameworks for EThics and Integrity of REsearch. Research Ideas and Outcomes 7: e76240. https://doi.org/10.3897/rio.7.e76240
Figure 1 Bottom-up participatory approach.
Figure 5 from: Evans N, van Hoof M, Hartman L, Marusic A, Gordijn B, Dierickx K, Bouter L, Widdershoven G (2021) EnTIRE: Mapping Normative Frameworks for EThics and Integrity of REsearch. Research Ideas and Outcomes 7: e76240. https://doi.org/10.3897/rio.7.e76240
Figure 5 Geographic spread of participants.
An explainable deep machine vision framework for plant stress phenotyping
<p>This is a companion dataset to the paper 'An explainable deep machine vision framework for plant stress phenotyping' by Ghosal et al., published in 2018 in the Proceedings of the National Academy of Sciences. The full citation is:</p> <p>Ghosal, Sambuddha, David Blystone, Asheesh K. Singh, Baskar Ganapathysubramanian, Arti Singh, and Soumik Sarkar. 'An explainable deep machine vision framework for plant stress phenotyping.' Proceedings of the National Academy of Sciences 115, no. 18 (2018): 4613-4618.</p>
Supplementary Material to the Manuscript "On How to Transform Refueling Station Networks: Planning Framework, Model, and Exact Solution Approach"
<p>This dataset consists of two .xlsx files containing the supplementary material to the manuscript "On How to Transform Refueling Station Networks: Planning Framework, Model, and Exact Solution Approach".</p> <p>The file "supplementary_parameter.xlsx" contains the parameters used in the experiments. </p> <p>The file "supplementary_random_networks.xlsx" contains the random networks used in the numerical experiments.</p>
Toward Understanding Drug Incorporation and Delivery from Biocompatible Metal-Organic Frameworks in View of Cutaneous Administration
<p>Although metal−organic frameworks (MOFs) have widely<br> demonstrated their convenient performances as drug-delivery systems, there is<br> still work to do to fully understand the drug incorporation/delivery processes<br> from these materials. In this work, a combined experimental and<br> computational investigation of the main structural and physicochemical<br> parameters driving drug adsorption/desorption kinetics was carried out. Two<br> model drugs (aspirin and ibuprofen) and three water-stable, biocompatible<br> MOFs (MIL-100(Fe), UiO-66(Zr), and MIL-127(Fe)) have been selected to<br> obtain a variety of drug−matrix couples with different structural and<br> physicochemical characteristics. This study evidenced that the drug-loading<br> and drug-delivery processes are mainly governed by structural parameters<br> (accessibility of the framework and drug volume) as well as the MOF/drug<br> hydrophobic/hydrophilic balance. As a result, the delivery of the drug under<br> simulated cutaneous conditions (aqueous media at 37 °C) demonstrated that<br> these systems fulfill the requirements to be used as topical drug-delivery systems, such as released payload between 1 and 7 days.<br> These results highlight the importance of the rational selection of MOFs, evidencing the effect of geometrical and chemical<br> parameters of both the MOF and the drug on the drug adsorption and release.</p>
Molecular diffusion enhanced performance evaluation of metal-organic frameworks for carbon dixoide capture
<p>This data set contains the process-level performance ranking of 982 metal-organic frameworks (MOF) which were down-selected from 10,143 structures contained in the puplic CoRE MOF 2019 data set. To rankorder MOFs for application in post-combustion carbon dioxide capture, we have used a computational workflow that combines active-learning based structure selection, molecular-level modeling, and process-level optimization. A detailed description of the repository content is provided in the README file which is included in the zip archive “Diffusion-MOF-Screening.zip”.</p>
Dataset for the paper 'Debris-flow return times and pre-event terrains: a novel framework using copula functions and quantile-based approaches'
<p>here attached you can find the dataset we used for our paper. While in the first two columns, you have X, and Y coordinates of the domain's pixels, the other five columns are DEMs' elevations (2006, 2010, 2011, 2015, and 2021). See in the paper's 'Methodology' Section how to employ these data. For any questions, please reach us at massimiliano.schiavo@unipd.it. Thanks!</p>
Evaluation Framework tests on RDF2Vec
<p>The ZIP file contains the results of the tests run through the evaluation framework available at https://git.rwth-aachen.de/KGEmbedding/evaluationFramework executed on vectors produce by RDF2Vec combined with 11 different weighting techniques, described in <br> <br> Cochez, M., Ristoski, P., Ponzetto, S.P., Paulheim, H.:<br> Biased graph walks for RDF graph embeddings.<br> In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (2017).<br> <br> The framework tested the vectors upon Machine Learning tasks - classification, regression, and clustering - and semantic tasks - document modeling, semantic analogies, and entity relatedness. <br> <br> At the top level, there is a summary of the results, detailed in the inner folder. </p>
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach: Workflow and Data
<p>This repository contains input files, workflow scripts, and output datasets and interatomic potentials for a diverse set of metal-organic frameworks, as discussed in this <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6363dbf718a8ccae675d2ac8">preprint paper</a>. In addition, we provide the scripts to compute the extended Hessian and subsequently the elastic constants using automatic differentiation.</p>
Sentiment analysis of media's political bias in micro-blogging - A computational framework for optimized recommendation systems
<p>This dataset contains tweets from four Pakistani news channels, namely DAWN, GEO, ARY, and 24News, collected using the twarc command line tool with a Twitter academic researcher account. The tweets were collected between Nov, 2015, and April 4, 2022, and relate to three major political parties in Pakistan, PTI, PMLN, and PPP through their official names in the relevant news channel page tweets. </p>
A generalizable TCAD framework for silicon FinFET spin qubit devices with electrical control
<p>supporting data for figures in manuscript "A generalizable TCAD framework for silicon FinFET spin qubit devices with electrical control".</p>
Smart Waste Management System Development and Evaluation Decision Support Framework
<p><strong>Smart Waste Management System Development and Evaluation Decision Support Framework</strong></p> <p><strong>Framework concept and goal</strong></p> <ul> <li> <p>For various cities with various infrastructure, financial capabilities, needs and goals of implementing a waste management (WM) system, a various set of WM services and technologies is needed</p> </li> <li> <p>There is a large number of studies describing individual aspects of WM systems in various specific context to solve various local problems</p> </li> <li> <p>We developed this improvement and evaluation decision support framework to provide recommendations about WM in the city, based on current goals, challenges, the characteristics of the city, city context and context of the city WM system</p> </li> <li> <p>This version of the framework is intended for the mayor of the city / city authorities</p> </li> </ul> <p> </p> <p><strong>Primary data for the framework operation and decision support</strong></p> <p>Primary data summarizes:</p> <ul> <li> <p>scientific research (173 primary studies, period 2014-2022) <br> <a href="https://ieeexplore.ieee.org/document/9815071">I. Sosunova and J. Porras, “IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review,” IEEE Access, vol. 10, no. July, pp. 73326–73363, 2022, doi: 10.1109/ACCESS.2022.3188308</a></p> </li> <li> <p>Surveys for residents, authorities and companies (Lappeenranta, Helsinki, Saint-Petersburg)</p> </li> <li> <p>practical research (2 international projects, hackathons, experience of leading Finnish companies)</p> </li> </ul> <p> </p> <p>Based on this data we build a Smart Waste Management (SWM) Model, that includes:</p> <ul> <li> <p>physical infrastructure</p> </li> <li> <p>services</p> </li> <li> <p>stakeholders</p> </li> <li> <p>data, shared between stakeholders </p> </li> </ul> <p> </p> <p><strong>Basic steps for using the framework</strong></p> <p>To get <strong>recommendations (5) </strong>about Improvement and Evaluation of the Waste Management System in your city, you will need read these <strong>Guide (1</strong>, we don't provide it in the figure<strong>)</strong> to define:</p> <ul> <li> <p><strong>Context (2)</strong> of your city and your city waste management system</p> </li> <li> <p><strong>Goals (3) </strong>of WM system Improvement and Evaluation and KPIs to measure the achievement of each goal</p> </li> <li> <p><strong>Challenges (4) </strong>for each goal and KPIs to some of the challenges (this will allow to measure the achievement of this challenges more accurately)</p> </li> </ul>
Prosthetic Complications in Fixed All-On-Four Prosthesis Reinforced with Poly Ether Ether Ketone (PEEK) Versus Cobalt Chromium (Co-Cr) Framework
ClinicalTrials.gov study NCT06631573. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Titanium Versus Soft Metal CAD/CAM Frameworks for All-on-4 Implant Supported Prosthesis
ClinicalTrials.gov study NCT06114966. IPD Sharing: NO. Countries: 1. Publications: 0.
(PEKK) Framework for Mandibular Implant-supported Complete Fixed Prostheses
ClinicalTrials.gov study NCT06164990. IPD Sharing: NO. Countries: 1. Publications: 0.
Internal vs. External Connection in Screw-retained Multi-units Bridges With Cad/Cam Milled Co/Cr Framework (ISUS-Atlantis)
ClinicalTrials.gov study NCT02789956. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Towards Life-Long Healthy Lungs: A Multidisciplinary Follow-up Framework for Preterm Infants
ClinicalTrials.gov study NCT05618769. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
PEEK Versus Metallic Framework for Extra Coronal Attachment
ClinicalTrials.gov study NCT04990453. IPD Sharing: YES. Countries: 1. Publications: 0.
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.