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1,855 results for “framework”
Photoinduced Electron Transfer in Multicomponent Truxene- Quinoxaline Metal−Organic Frameworks
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>ARACAT_WP4_20200825_ULEI_03_60min_MUF77_OME_100K </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>MUF7_OME – </strong>NC-MUF-7_dbc-dpq-OMe MOF, <strong>MUF7_OME – </strong>MUF-7_dbc-dpq-OMe MOF, <strong>MUF7_dpq – </strong>MUF-7_dbc-dpq MOF<strong> MUF77_paq – </strong>MUF-7_dbc-paq MOF</li> <li>_100K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Synthesis of Phenol-Tagged Ruthenium Alkylidene Olefin Metathesis Catalysts for Robust Immobilisation Inside Met-al-Organic Framework Support
<p>Data confirming the structure of the new compounds obtained within the project, published in <em>Catalysts</em> <strong>2023</strong>, <em>13</em>(2), 297; <a href="https://doi.org/10.3390/catal13020297">https://doi.org/10.3390/catal13020297</a></p> <p>The research was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860322 for the ITN-EJD “Coordination Chemistry Inspires Molecular Catalysis” (CCIMC) and by the National Science Centre, Poland (OPUS grant 2017/27/B/ST5/00941).</p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
A Modular Quantum Compilation Framework for Distributed Quantum Computing
<p>This repository contains the data used for the plots in "<em>A Modular Quantum Compilation Framework for Distributed Quantum Computing</em>" by D. Ferrari, S. Carretta and M. Amoretti.</p> <p>Data is located in the <em>'data'</em> directory in <em>.csv</em> format, a python script to generate the plots can be found in the main directory. The script was tested with <strong>python3.10</strong> and needs <strong>matplotlib</strong>, <strong>pandas</strong> and <strong>seaborn</strong> packages. Plots are saved as <em>.pdf</em> files in the <em>'figures'</em> directory.</p>
The CodeSparks Framework - Augmenting Source Code with Glyph-based Visualizations
<p>Supplemental material to: The CodeSparks Framework - Augmenting Source Code with Glyph-based Visualizations<br> Revision: Replaced the survey (quantitative) with focus group interviews (qualitative).</p>
CALIPR Framework Myelin Water Imaging Supporting Data
<p>Data supporting the publication:</p> <p><em><strong>The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity</strong></em></p> <p> </p>
Extensions to Mining Framework Annotation Rules
<p>Framework usage is challenging because the requirements for the correctness are often implicit. We focus on making</p> <p>such requirements more explicit by association rule mining on the data from client projects that use a framework.</p> <p>We present an extension to an existing baseline method that does this. In particular, we examine alternative rule</p> <p>quality measures used in the ranking of association rules mined, and alternatives in the selection of client projects.</p> <p>Such alternatives are novel and have not been explored in the context of the baseline method. We evaluate the alternatives</p> <p>by comparing their results to those produced by the baseline method. More concretely, we base the comparison on their</p> <p>ranking of incorrect rules, and on their measurements for the Area Under Curve metric. We conclude that some of</p> <p>the evaluated quality measures outperform the baseline for the ranking and selection of rules. We also show that the</p> <p>selection of secondary client projects, adding some clients that do not directly use the framework of interest, matters.</p>
Example data for working with the ASpecD framework
<p>ASpecD is a Python framework for handling spectroscopic data focussing on reproducibility. In short: Each and every processing step applied to your data will be recorded and can be traced back. Additionally, for each representation of your data (e.g., figures, tables) you can easily follow how the data shown have been processed and where they originate from.</p> <p>To provide readers of the publication describing the ASpecD framework with a concrete example of data analysis making use of recipe-driven data analysis, this repository contains both, a recipe as well as the data that are analysed, as shown in the publication describing the ASpecD framework:</p> <ul> <li>Jara Popp, Till Biskup: ASpecD: A Modular Framework for the Analysis of Spectroscopic Data Focussing on Reproducibility and Good Scientific Practice. Chemistry--Methods 2:e202100097, 2022. doi:10.1002/cmtd.202100097</li> </ul>
Research Excellence Framework (REF) 2021 enhanced submissions dataset
<p>An enhanced version of the public <a href="https://results2021.ref.ac.uk/">REF 2021 submissions dataset</a>, produced by Jisc, which contains metadata for all outputs submitted to the exercise. Metadata has been cleaned and new fields have been added to increase the potential for analytical purposes, e.g. by identifying where the publisher exists as an imprint of a larger parent company.</p> <p>For its own analytical purposes, Jisc focused on long-form output types (books and parts of books), but cleaning measures were performed on the entire dataset, uploaded here.</p>
A scalable, accurate, and universal analysis framework using individual-level allele frequency for large-scale genetic association studies in an admixed population
<p>Inclusion of individuals with diverse or admixed genetic ancestries is crucial to discover novel findings that may be missed by genomics analyses rooted solely in Caucasian population. Here, we present an analysis framework, SPAmix, which is scalable to a large-scale biobank data analysis including hundreds of thousands of admixed individuals and is universally applicable to various types of complex traits including binary trait, quantitative trait, time-to-event trait, longitudinal traits, etc. For each genetic variant, SPAmix uses genotype data and genetic principal components (PCs) to estimate individual-level allele frequency, which is subsequently used to calibrate p values via a retrospective analysis. A hybrid strategy including saddlepoint approximation (SPA) can greatly increase the accuracy to analyze rare genetic variants, especially if the phenotypic distribution is unbalanced or extremely unbalanced. Compared to Tractor, SPAmix does not require local ancestry information and can be straightforwardly applicable to a multi-way admixed population. Meanwhile, SPAmix can also be extended to SPAmix<sub>local</sub> in which the local ancestry can be incorporated if available. In addition, we propose SPAmix<sub>CCT</sub> to combine the p values of SPAmix and SPAmix<sub>local</sub> via Cauchy combination (CCT). SPAmix<sub>local</sub> performs close to Tractor when analyzing quantitative traits and is more accurate when analyzing binary traits with an unbalanced case-control ratio. And SPAmix<sub>CCT </sub>is an optimal unified approach for various cross-ancestry genetic architectures. Extensive simulation studies and real data analyses of 369,314 UK Biobank individuals from multiple ancestries demonstrated that SPAmix is scalable and can discover novel hits while controlling type I error rates well.</p>
London Dispersion Governs the Interaction Mechanism of Small Polar and Non-Polar Molecules in Metal-Organic Frameworks
<p>Raw data set relating to publication.</p>
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
Scripts from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms
<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various <i>symptoms</i> (macroinvertebrate metrics) and probabilistically link them to various potential <i>causes</i> of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> <i>Synthesis and applications.</i> Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28 July 2020</li> </ol>
Data from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms
<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various <i>symptoms</i> (macroinvertebrate metrics) and probabilistically link them to various potential <i>causes</i> of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> <i>Synthesis and applications.</i> Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28-Jul-2020</li> </ol>
The Emergency Conceptual Framework Big Picture
<p>The Emergency System Conceptual Framework Big Picture gives an insight to better manage and plan for different emergencies (Accidents, Incidents, Crisis, Disasters, Catastrophes). </p>
Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework
<p>Dataset for "Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework"</p>
Dataset for the paper "Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943
<p>This repository contains the data needed to reproduce the results in: </p> <p>Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943</p> <p>The dataset was also used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of data are specified in the dataset (under tab "References")</p>
SCRUM framework adaptations - dataset of systematic mapping study, EclipseIDE project
<p>Dataset from the SCRUM framework adaptations systematic mapping study</p>
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense
<p>This is the dataset associated with <a href="https://softsys4ai.github.io/athena/">ATHENA</a>, a framework based on Diverse Weak Defenses for building Adversarial Defense.</p>
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.