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1,855 results for “Framework”
A patient-centric modelling framework captures recovery from SARS-CoV-2 infection
<p>The biology driving individual patient responses to SARS-CoV-2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year post-disease onset, from 215 SARS-CoV-2 infected subjects with differing disease severities. Our analyses revealed distinct “systemic recovery” profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter- and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery at the patient level, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool <a href="https://aus01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fshiny.mrc-bsu.cam.ac.uk%2Fapps%2Fcovid-19-systemic-recovery-prediction-app&data=05%7C01%7CJulien.Wist%40murdoch.edu.au%7C117789e168814a13808a08dabcf18a3e%7Cc00d4c1bcf7b4e93b7c710113a9bc230%7C1%7C0%7C638030042858394016%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=z7RJ8%2BGTWr84dYvWSoc%2F9Cj1zYRHV7%2BHfyr9MyCnX6M%3D&reserved=0">http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app</a>, designed to test our findings prospectively.</p>
Role of Counterions in the Structural Stabilisation of Redox-Active Metal-Organic Frameworks
<p>Preliminary dataset for "Role of Counterions in the Structural Stabilisation of Redox-Active Metal-Organic Frameworks"</p>
The role of vegetation optimality in the Budyko framework
<p>In this study, we test how vegetation optimality explains convergence on the Budyko-curve. This is done for five study sites along the North Australian Tropical Transect, five catchments in Australia and a selection of catchments of the CAMELS-dataset.</p> <p>We test the following hypotheses:</p> <p>- Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko curve than model simulations without self-optimized vegetation.<br> - The empirical parameter <em>n</em> stays constant as climate changes, as long as vegetation cover and rooting depths stay constant.<br> - Changes in <em>n</em> values are a result of slowly varying, long-term vegetation properties.</p> <p><strong>How to use this project</strong></p> <p>This project contains all pre- and post-processing scripts for the model runs to test our hypotheses. All final figures are in the notebooks, as well as the supplementary analyses. These scripts can be re-used for other applications.</p> <p>This is also a Renku project. Hence, to use this repository for your own analysis, create a free login for renku, then fork the project and modify it to your liking. The simplest way to start this project is right from the Renku<br> platform - just click on the `Environments` tab and start a new session. This will start an interactive environment right in your browser.</p> <p>To work with the project anywhere outside the Renku platform,<br> click the `Settings` tab where you will find the<br> git repo URLs - use `git` to clone the project on whichever machine you want. Python and conda packages are in `requirements.txt` and `environment.yml`.</p> <p>Please consult the various documentation sources for renkulab(https://renkulab.io/).</p>
Optimization Framework for Temporal Interference Current Tibial Nerve Stimulation in Tibial Nerves based on In-Silico Studies
<p><strong>Online Resource 1:</strong> All MR-based 3D models and cross-sections. n = 29.</p> <p> </p> <p><strong>Online Resource 2:</strong> Comparison of changes in electrode pairs according to anatomical differences (independent sample t-test for Online Resource 2, *p<0.0001)</p> <p>When Electrode opt TI was applied, the model in which the combination of electrodes was changed relative to Parallel opt TI was referred to as the Change group, and the model that was unaltered was called the Non-change group. In the case of the Change group, PR values increased in Electrode opt compared with those in the Parallel opt group. When comparing the ratio of nerve depth to the total length of the model section, we confirmed the Change group exhibited a smaller ratio. Since the sample size of each group was different, the difference of average in the PR value of each group was evaluated using an independent sample t-test. Purple dots present raw data. Mean and standard deviation of the changes are shown in black.</p>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 2
<p>This dataset includes model outputs averaged from day 2 to day 15 using the multiscale modeling framework (MMF, also referred to as ``superparameterization'') for</p> <ul> <li>Low-resolution MMF (LR): SP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_32_x_120z1200m.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 32, crm_ny = 1, crm_dx = 1200 m, crm_dt = 5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>High-resolution MMF (HR): UP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 64, crm_ny = 1, crm_dx = 200 m, crm_dt = 0.5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>Same as HR, but considers hyperviscosity with tau = 30s (HRh30): UPhyperlag30_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HR, but considers hyperviscosity with tau = 150s (HRh15): UPhyperlag15_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs15) with tau = 30s and sigmag = 1.5: HPhyper_sedi15_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs12) with tau = 30s and sigmag = 1.2: HPhyper_sedi12_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> </ul>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 1
<p>This dataset includes 6-month simulations using the ne30pg2 grid. Monthly averaged output files from six experiments are included.</p> <ul> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1 <ul> <li>The config options for this control simulation is <pre>CAM_CONFIG_OPTS = -mach summit -phys default -use_MMF -crm samxx -nlev 60 -crm_nz 50 -crm_dt 10 -crm_dx 2000 -crm_nx 64 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -rad rrtmgp -rrtmgpxx -MMF_microphysics_scheme sam1mom -chem none -nlev 125 -crm_nz 115 -crm_dt 2 -crm_dx 200 -crm_nx 256 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -use_MMF_VT -cppdefs ' -DMMF_ESMT -DMMF_USE_ESMT -DMMF_HYPERVISCOSITY -DMMF_SEDIMENTATION ' </pre> </li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED <ul> <li>The HV.SED control case is the same as the control (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1), but considered both hyperciscosity and sedimentation processes.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_1E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_5E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_5E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_5E-05.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_8E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_8E-05.</li> </ul> </li> </ul>
Speos: An ensemble graph representation framework to predict core genes for complex diseases (Datasets)
<p>the "data.tar.gz" tarball contains the unprocessed or minimally processed data used by Speos. If you intend to use the framework or want to inspect the data, download this part of the dataset.</p> <p>The "final_datasets.tar.gz" tarball contains tsv-formatted, processed data matrices which are directly used as input features for the ensemble models. </p> <p>There are two tsv-files per disease, one labeled "normal", which contains the p input features alongside the gene identifiers and a column which indicates if the gene is labeled as Mendelian or not, and another file labeled "with_n2v_vectors", which also contains the 100-dimensional vectors produced by Node2Vec so the N2V+MLP method can be reproduced with the exact same parameters. </p> <p>All files contain a header row which describes the column and no index column.</p> <p>The "model_parameters.tar.gz" tarball contains the model parameters for all ensemble models used to produce the candidate genes.</p>
Species interactions and diversity: a unified framework using Hill numbers
<p>Biodiversity describes the variety of organisms on planet earth. Ecologists have long hoped for a synthesis between analyses of biodiversity and analyses of biotic interactions among species, such as predation, competition, and mutualism. However, it is often unclear how to connect details of these interactions with complex modern analyses of biodiversity. To resolve this gap, we propose a unification of models of biotic interactions and measurements of diversity. We show that analyses of biodiversity obscure details about biotic interactions. For example, identical changes in biodiversity can arise from predation, competition or mutualism. Our approach indicates that traditional models of community assembly miss key facets of diversity change. Instead, we suggest that analyses of diversity change should focus on partitions, which measure mechanisms that directly shape changes in diversity, notably species level selection and immigration, rather than traditional analyses of biotic interactions.</p>
Explainability in Autoscaling Frameworks Questionnaire and Answers
<p>Questionnaire used in the Masterthesis of Zilch Markus</p> <p>Contains the questionnaire in the form of a PDF and the anonymized answers of the participants.</p>
Foraging preferences and interspecific competition generate multimodal complexity-stability relationships in an adaptive food-web framework
<p><span>Ecological theory predicts that complex ecological networks are unstable and are unlikely to persist, despite many empirical studies of such complexity in nature. To resolve real complexity-stability relationships, coupling population dynamics and trait dynamics is considered to be an important way to understand the long-term stability of ecological community assemblages. However, modelling eco-evolutionary dynamics in ecologically realistic networks is still a challenge. Here, we establish an adaptive food web model to evaluate the complexity-stability debate in a mutualist-exploiters-specialist forager-generalist forager system. Our theoretical model predicts that the connectance-stability relationship may show positive monotonic (/), negative monotonic (\), peaked (∩) and double-peaked (oscillatory) patterns. Moreover, the double-peaked pattern is only obtained when both the adaptation intensity and interspecific competition are high, which may explain no complexity-stability relationships revealed in empirical data. Finally, we deduce that foraging adaptation alters positive and/or negative feedback loops to affect the stability of real food webs.</span></p>
Quasi-Newton methods for partitioned simulation of fluid-structure interaction reviewed in the generalized Broyden framework: code and data
<p>These files accompany the publication</p><p>N. Delaissé, T. Demeester, R. Haelterman and J. Degroote. Quasi-Newton methods for partitioned simulation of fluid-structure interaction reviewed in the generalized Broyden framework.<i> Archives of Computational Methods in Engineering</i>, Vol.<strong> </strong>30, 3271-3300, 2023. doi: <a href="https://doi.org/10.1007/s11831-023-09907-y">10.1007/s11831-023-09907-y</a></p><p>In this work, the performance of multiple quasi-Newton methods are compared in terms of memory requirements and computational time. The results are generated for the well-known flexible tube example case, using the open-source code <a href="http://github.com/pyfsi/coconut">CoCoNuT</a>. This code, developed at Ghent University, is Python-based and has the capability to couple existing solvers, both open-source and commercial solvers.</p><p>This archive consists of the following files.</p><ul><li><strong>coconut.tar.gz: </strong>the specific CoCoNuT version used (sep-2022), including the Python flow and structure solvers for the flexible tube and modifications for monitoring memory requirements</li><li><strong>compare_coupling_algorithms.tar.gz: </strong>the scripts to set up the cases and perform the calculations and post-processing</li><li><strong>results.tar.gz:</strong> the generated result data</li></ul><p>For requirements to run CoCoNuT, refer to the <a href="http://pyfsi.github.io/coconut/">documentation</a>. Additionally, the Python package guppy3 is required for monitoring the memory use. In this work the data were generated with Andaconda3-2022.05 and the package guppy3-3.1.2.</p><p>Before running the provided scripts, make sure the parent directory of the "coconut" folder is added to the PYTHONPATH. The calculations can be started with "python run.py". For the cases which names contain "_m" followed by a number, e.g. "_m100", the number refers to the number of discretization points on the interface. The cases with suffix "_c" are distinct from those without, as they don't perform the time consuming memory monitoring and are therefore used for measuring computational time.</p>
Raw data files for "A Simple, Transition Metal Catalyst-Free Method for the Design of Complex Organic Building Blocks Used to Construct Porous Metal-Organic Frameworks" manuscript
<p>Raw data files for a manuscript "A Simple, Transition Metal Catalyst-Free Method for the Design of Complex Organic Building Blocks Used to Construct Porous Metal-Organic Frameworks".</p> <p>All files are organized by instrumental methods, except Figure 1 in the manuscript and Figure S1, S52 in the SI, plots for which are reported as separate files. The file headers contain the necessary information such as column designations, units, etc.</p> <p> </p>
Metal-organic framework optical thermometer based on Cr3+ ions luminescence - data
<p><strong>Project NCN SONATA 16 2020/39/D/ST5/01289<br><br>Experimental data:</strong> Raman and diffuse reflectance spectra, band gap and crystal field strength, decay profiles and lifetimes, temperature-dependent luminescence and emission maps, thermometric parameters, exemplary system’s luminescent characteristics, unit cell characteristics, powder XRD data, Kubelka-Munk function with band gap estimation, excitation and emission spectra, stability of PL</p>
Data for the publication of the SSI Weakness Identification Framework (SWIF)
<p>This is a supplementary data for an article submitted to a journal. There are several files, including:</p> <ul> <li><em>corpus.xml</em>: This is a dataset file for the weakness entry corpus retrieved from the CWE database.</li> <li><em>queries.xml</em>: This is an input file listing a list of SSI functional requirements used in the experiments.</li> <li><em>cdkg.xml</em>: This is the pre-defined cross-domain transfer knowledge graph (CTKG).</li> <li><em>SSI-Specific Weakness Justification.xlsx</em> - This is the justification for the determination of valid SSI-specific weakness, which is the full results for the evaluation of the validity of the identified SSI-specific weaknesses.</li> <li><em>Truth - Summary.xlsx</em>: This file is a spreadsheet that summarize the collected ground truth from the three participants.</li> <li><em>truth.csv:</em> This is a dataset file of the ground truth observed by an experienced system analyst.</li> </ul>
Dataset1 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"
<p>Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian". </p> <p>Contains atomic structures and Hamiltonian matrices of monolayer graphene, monolayer MoS<sub>2</sub>, bilayer graphene and bilayer bismuthene.</p> <p>Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.</p>
Dataset2 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"
<p>Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian". </p> <p>Contains atomic structures and Hamiltonian matrices of bilayer bismuth selenide. </p> <p>Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.</p>
Dataset3 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"
<p>Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian". </p> <p>Contains atomic structures and Hamiltonian matrices of bilayer bismuth telluride. </p> <p>Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.</p>
Determined Material Properties within the framework of the NCN project (OPUS) No. 2021/41/B/ST8/00148
<p>This file presents information on the determination of material properties, of the composite material undertaken for the study. The file presents both the data from which the material properties were determined and also summarises and averages the extracted data in tabular form. The data relates to the composite material manufactured for the NCN project (OPUS) No. 2021/41/B/ST8/00148.</p>
Data and code for "Representing storylines with causal networks to support decision making: framework and example"
<p>Data and code for the paper "Representing storylines with causal networks to support decision making: framework and example", along with the Shiny webapp code accompanying the paper. The paper has been submitted to the journal of Climate Risk Management and is currently under review.</p>
Image frameworks and archives
<p>Frischknecht, Max: Digital Participatory Knowledge Visualizations</p> <p>Digital Image Archives, Max Frischknecht investigates how visualizations and interfaces in digital cultural collections support participatory use. The research focuses on three aspects: The participation and involvement of researchers at universities and the general public in enhancing the collection through the interface, the use of visualizations in exploring and presenting themes in the collection, and the use of machine learning in communicating the cultural collection on a visual and content level. The results of the study will be incorporated into the development of a new participatory image archive for the Swiss Folklore Society.</p> <p> </p> <p> </p> <p>Julien Raemy: The International Image Interoperability Framework (IIIF) APIs as the backbone of scientific and participatory research</p> <p>Within the multidisciplinary project "Participatory Knowledge Practices in Analogue and Digital Image Archives" funded by the Swiss National Science Foundation (SNSF), we aim to design a visual interface with machine learning-based tools.</p> <p>This interoperable platform will make it easy to annotate, contextualise, organise and link both images and their metadata to deliberately encourage the participatory use of the archives from the Swiss Society for Folklore Studies (Schweizerische Gesellschaft für Volkskunde -SGV).</p> <p>One of the most important components that will support this effort is to leverage the APIs developed by the International Image Interoperability Framework (IIIF), not only to facilitate external exchange but also the interaction between the various in-house software packages that will be deployed.</p> <p>The presentation will be an opportunity to present the data and intertoperability model of the project, currently in the conceptualization phase.</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.