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25 results for “network variability”
Data from: Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network
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Benchmark datasets used in "Classification of Periodic Variables with Cyclic-Permutation Invariant Neural Networks"
<p>To aid the reproducibility of the results in the paper “Classification of Periodic Variables with Cyclic-Permutation Invariant Neural Networks,” we make our aggregation of the following data available. Code used to load the data and generate the results can be found at <a href="https://github.com/kmzzhang/periodicnetwork">https://github.com/kmzzhang/periodicnetwork</a>. These datasets have been constructed from publicly available data sources. If you use these datasets, please cite the original papers [1, 2, 3], in addition to ours [TBD]. Others might find this data useful for testing time-series inference techniques.</p> <p>[1] Jayasinghe, T. et al. The ASAS-SN catalogue of variable stars I: The Serendipitous Survey. Monthly Notices of the Royal Astronomical Society 477, 3145–3163 (2018). URL <a href="https://academic.oup.com/mnras/article/477/3/3145/4961151">https://academic.oup.com/mnras/article/477/3/3145/4961151</a>.<br> [2] Alcock, C. et al. The MACHO Project LMC Variable Star Inventory.II.LMC RR Lyrae Stars- Pulsational Characteristics and Indications of a Global Youth of the LMC. The Astronomical Journal 111, 1146 (1996). URL <a href="http://adsabs.harvard.edu/abs/">http://adsabs.harvard.edu/abs/</a>1996AJ....111.1146A.<br> [3] Udalski, A. The Optical Gravitational Lensing Experiment. Real Time Data Analysis Systems in the OGLE-III Survey. Acta Astronomica 53, 291–305 (2003). URL http: <a href="https://bloomlab-berkeley.slack.com//adsabs.harvard.edu/abs/2003AcA....53..291U">//adsabs.harvard.edu/abs/2003AcA....53..291U</a>.</p>
Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks
<p>Abstract</p> <p>Catchment water storage capacity (CWSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the CWSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the CWSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global CWSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. CWSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global CWSC parameter dataset as a benchmark for geoscientific modelling by users.</p> <p>A global terrestrial CWSC dataset with 0.5 spatial resolution is now available. All input factors and the global CWSC data are publicly available as NetCDF files or download from smsc_data.zip at Zenodo. Python codes are available to calculate the basin average CWSC value from grid values in any interested basin on a global scale.</p> <p>The Fortran codes for parameter calibration of semi distributed global monthly water balance models are available at https://github.com/xiekangwhu/CWSC_monthly_water_balance_models. The Python codes of deep residual network we developed for the global reconstruction map of CWSC are available at https://github.com/xiekangwhu/CWSC_deep_residual_network.</p> <p> </p> <p>Major code contributor: Kang Xie (PhD Student, Wuhan University), Liting Zhou (PhD Student, Wuhan University), Shujie Cheng (PhD Student, Wuhan University), and Shuanghong Shen (PhD Student, University of Science and Technology of China)</p> <p> </p> <p>Citations</p> <p>If you find our code to be useful, please cite the following papers:</p> <p>Xie, K. et al. Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy - ScienceDirect. Journal of Hydrology 598, doi:10.1016/j.jhydrol.2020.125772 (2020).</p> <p>Xie, K. et al. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology 603, doi:10.1016/j.jhydrol.2021.127043 (2021).</p> <p>Xie, K. et al. Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models. Journal of Hydrologic Engineering 25, doi:10.1061/(asce)he.1943-5584.0001887 (2020).</p>
Characterization and impact of fiber size variability on the mechanical properties of fiber networks with an application to paper materials
<p>This repository contains data to</p> <p>Characterization and impact of fiber size variability on the mechanical properties of fiber networks with an application to paper materials</p> <p>In prep. 2021.</p> <p> </p> <p>There are a few key files to note:</p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/mainArticleE.m?versionId=e434d0da-db30-4588-a807-de72c4187468">mainArticleE.m </a><br> Generates a number of plots from the manuscript, and also contains calls to the functions that can be used to study how the copula formulation can be used and worked with.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/calculationSequenceForArticle.m?versionId=be372e6e-6eda-4ba5-b78b-2c8966de45f0">calculationSequenceForArticle.m </a><br> Shows the steps performed in the fitting and sampling that are presented in the manuscript.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/data.zip">data.zip </a><br> Contains all of the data. Raw machine outputs, condensed outputs (several runs collected into a single file), the ESI simulations and the result files used to generate the plots. The full solution files are not included as they would have taken way too much space. However, the input files can be found in each repository.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/v1.0.zip?versionId=8134396c-687b-4c31-a220-d27676f391cf">v1.0.zip </a><br> Output from <a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/calculationSequenceForArticle.m?versionId=be372e6e-6eda-4ba5-b78b-2c8966de45f0">calculationSequenceForArticle.m </a>just before submission.</li> </ul> <p> </p> <p>The FEM files are not included (MyPacking.exe - an internal tool I do not own the rights to, and the linked ANSYS executable which I likewise do not own the rights to).</p> <p>Also absent is the "pulpDistr" package (will be a seperate ZENODO submission)-</p> <p> </p> <p> </p> <p>Should something not work, please do get in touch.</p> <p> </p> <p>/A. Brandberg, 2021-08-10</p> <p> </p> <p> </p>
Effects of High-intensity Interval Training and Moderate-intensity Continuous Training on Functioning of Attentional Networks and Heart Rate Variability in Healthy Adults
ClinicalTrials.gov study NCT06602700. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.