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1,855 results for “Framework”
Exploring Mechanistic Routes for Light Alkane Oxidation with an Iron-Triazolate Metal–Organic Framework
<p>Dataset to accompany "Exploring Mechanistic Routes for Light Alkane Oxidation with an Iron-Triazolate Metal–Organic Framework" by Andrew S. Rosen, Justin M. Notestein, and Randall Q. Snurr</p>
Data Associated with Manuscript Titled "Development of a graphical resilience framework to understand a coupled human-natural system in a remote arid highland of Baja California Sur"
<p>This is a dataset in support of analyses of water chemistry and social networks described and interpreted in the associated manuscript titled "Development of a graphical resilience framework to understand a coupled human-natural system in a remote arid highland of Baja California Sur"</p>
FIGURES 2–14 in Unprecedented interweaving of single-helical and unequal double-helical chains into chiral metal–organic open frameworks with multiwalled tubular structures
FIGURES 2–14. Stipesoculus productus sp. nov. 2, 5–14, ♂; 3, 4, ♀. 2, Habitus, antennae and legs partly removed; 3, 4, 5, abdominal segments V–VIII; 6, 7, pygophore; 8, 9, 10, right paramere; 11, 12, phallus and basal structure; 13, 14, phallosoma and vesica. 2, 7, 10, 12, lateral view; 3, 6, 8, 13, dorsal view; 5, 4, 9, 11, 14, ventral view. Scale bar of 2–5 = 1 mm; of 6–7 = 0.5 mm; of 8–14 = 0.25 mm.
GALA: a computational framework for de novo chromosome-by-chromosome assembly with long reads
<p>C.elegans, O.sativa and Human assemblies produced by GALA software</p>
Isotope-selective pore-opening in a flexible metal-organic framework
<p>This dataset contains raw data underlying the results related to the paper "Isotope-selective pore-opening in a flexible metal-organic framework".</p> <p> </p>
UGS-1m: Fine-grained urban green space mapping of 34 major cities in China based on the deep learning framework
<p>Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. The UGS-1m product provides the fine-grained UGS maps of 34 major cities/areas in China, which is generated based on a deep learning (DL) framework.</p> <p>The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4,454 samples of size 512×512 is provided. Code for the UGSet and the UGSet will be soon available at: https://github.com/liumency/UGS-1m. </p> <p>The main steps to obtain UGS-1m can be summarized as follows: a) Firstly, the UGSNet will be pre-trained on the UGSet in order to get a good starting training point for the generator; b) After pre-training on the UGSet, the discriminator is responsible to adapt the pre-trained UGSNet to different cities/areas through adversarial training; c) Finally, the UGS results of the 34 major cities/areas in China (UGS-1m) are obtained using 2,343 Google Earth images with a data frame of 7'30" in longitude and 5'00" in latitude, and a spatial resolution of nearly 1.1 meters. Evaluating the performance of the proposed approach on samples from Guangzhou city shows the validity of the UGS-1m products, with an overall accuracy of 87.4% and an F1 score of 81.14%. </p>
Sequence diagram of the RIPOSTE framework
<p>Sequence diagram of the RIPOSTE framework</p>
Machine Learning Potentials for Metal-Organic Frameworks with Thermodynamic Transferability: training data
<p>This dataset contains potential energies, forces, and virial stress for a large set of reference configurations for UiO-66(Zr) and MIL-53(Al), computed at the PBE-D3 level using CP2K 7.1. The basis set contained both TZVP Gaussian basis functions as well as plane waves (cutoff energy 800 Ry for UiO-66(Zr) and 900 Ry for MIL-53(Al)). The sampling of the Brillouin zone was restricted to the gamma point.</p>
United States Environmentally-Extended Input-Output (USEEIO) Modeling Framework
<p>This is a snapshot of the <a href="https://github.com/USEPA/useeio">USEEIO modeling framework</a> repository that is compilation of a README, versioning scheme, and a python script supplemented with a to convert a USEEIO model generated from <a href="https://github.com/USEPA/useeior">useeior </a>in the API format into openLCA schema JSON-LD files. This is not source code for generating the USEEIO v2.0 model.</p>
On Reporting Performance and Accuracy Bugs for Deep Learning Frameworks: An Exploratory Study from GitHub
<p>This repository aims to store the dataset of performance and accuracy bug reports, which belongs to <em>"On Reporting Performance and Accuracy Bugs for Deep Learning Frameworks: An Exploratory Study from GitHub"</em></p>
Facile synthesis of magnesium-based metal-organic framework with tailored nanostructure for effective VOCs adsorption
<p class="16">A novel Mg(II) metal–organic framework (Mg-MOF) was synthesised based on the ligand of 2,2'-bipyridine-4,4'dicarboxylic acid (Bpdc). Single-crystal X-ray structural analysis confirmed that 3D-nanostructure Mg-MOFs formed a monoclinic system with a channel size of 15.733 Å × 23.736 Å. The N<sub><span>2</span></sub> adsorption isotherm, Fourier-transform infrared spectroscopy, thermogravimetric analysis and high-resolution transmission electron microscopy were performed to characterise the thermal stability and purity of the Mg-MOFs. The adsorption studies on four typical volatile organic compounds (VOCs) emitted during wood drying showed that Mg-MOFs have noteworthy adsorption capacities, especially for benzene and β-pinene with adsorptions of 182.26 mg/g and 144.42 mg/g, respectively. In addition, the adsorption of Mg-MOFs mainly occurred via natural adsorption, specifically, multi-layer physical adsorption, accompanied by chemical forces, which occurred in the pores where the VOCs molecules combined with active sites. As an adsorbent, Mg-MOFs exhibit versatile behaviour for toxic-gas accumulation.</p>
Data for the manuscript 'Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework'.
<p>This zip file contains the data downloaded from external sources used to produce the results in the manuscript 'Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework'. Note that all of the data contained in this zip file was publicly available at the time of writing.</p> <p>The corresponding Github can be found here:<br> https://github.com/alan-turing-institute/jbc-turing-rss-testdebiasing</p> <p>The publication is available here:<br> https://doi.org/10.1038/s41564-021-01029-0</p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PM</p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PMT</p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PMT</p>
Missing data in sea turtle population monitoring: a Bayesian statistical framework accounting for incomplete sampling
<p>Monitoring how populations respond to sustained conservation measures is essential to detect changes in their population status and determine the effectiveness of any interventions. In the case of sea turtles, their populations are difficult to assess because of their complicated life histories. Ground-derived clutch counts are most often used as an index of population size for sea turtles; however, data are often incomplete with varying sampling intensity within and among sites and seasons. To address these issues, we: (1) develop a Bayesian statistical modelling framework that can be used to account for sampling uncertainties in a robust probabilistic manner within a given site and season; and (2) apply this to a previously unpublished long-term sea turtle dataset (n = 17 years) collated for the Republic of the Congo, which hosts two sympatrically nesting species of sea turtle (leatherback turtle [<em>Dermochelys coriacea</em>] and olive ridley turtle [<em>Lepidochelys olivacea</em>]). The results of this analysis suggest that leatherback turtle nesting levels dropped initially and then settled into quasi-cyclical levels of interannual variability, with an average of 573 (mean, 95% prediction interval: 554–626) clutches laid annually between 2012 and 2017. In contrast, nesting abundance for olive ridley turtles has increased more recently, with an average of 1,087 (mean, 95% prediction interval: 1,057–1,153) clutches laid annually between 2012 and 2017. These findings highlight the regional and global importance of this rookery with the Republic of the Congo, hosting the second largest documented populations of olive ridley and the third largest for leatherback turtles in Central Africa; and the fourth largest non-arribada olive ridley rookery globally. Furthermore, whilst the results show that Congo's single marine and coastal national park provides protection for over half of sea turtle clutches laid in the country, there is scope for further protection along the coast. Although large parts of the African coastline remain to be adequately monitored, the modelling approach used here will be invaluable to inform future status assessments for sea turtles given that most datasets are temporally and spatially fragmented. </p>
FIGURE. Distribution of nomenclatural etymological categories of currently accepted specific and infraspecific cycad taxa over time (taxonomic framework sensu Calonje et al. 2022). in Etymological compendium of cycad names
FIGURE. Distribution of nomenclatural etymological categories of currently accepted specific and infraspecific cycad taxa over time (taxonomic framework sensu Calonje et al. 2022).
Data for Functional Group Pair Distance Based Descriptor for Isomerisation in Porous Molecular Framework Materials
<p>This is a dataset of isomer structure files for pore topology: Tri2Di3, Tri4Di6, Tri4-2Di6,Tri6Di9, Tet2Di4, Tet3Di3, Tet4-4Di8, Tet5Di10, and Tet6Di12. </p> <p>All.tar.bz2 contains all pore topologies, the total disk space after unzipping the bundle is 1.8 Gb.</p> <p>The total disk space for pore Tet6Di12 alone is 1.5Gb.</p>
Porous lanthanide metal–organic frameworks with metallic conductivity
<p>Raw data for figures in the article.</p>
Data from: Testing hypotheses of marsupial brain size variation using phylogenetic multiple imputations and a Bayesian comparative framework
<p>Considerable controversy exists about which hypotheses and variables best explain mammalian brain size variation. We use a new, high-coverage dataset of marsupial brain and body sizes, and the first phylogenetically imputed full datasets of 16 predictor variables, to model the prevalent hypotheses explaining brain size evolution using phylogenetically corrected Bayesian generalised linear mixed-effects modelling. Despite this comprehensive analysis, litter size emerges as the only significant predictor. Marsupials differ from the more frequently studied placentals in displaying much lower diversity of reproductive traits, which are known to interact extensively with many behavioural and ecological predictors of brain size. Our results therefore suggest that studies of relative brain size evolution in placental mammals may require targeted co-analysis or adjustment of reproductive parameters like litter size, weaning age, or gestation length. This supports suggestions that significant associations between behavioural or ecological variables with relative brain size may be due to a confounding influence of the extensive reproductive diversity of placental mammals.</p>
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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.