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6,467 results for “process”
Processed dataset used for developing tsunami inundation emulators(Part-III Simulation and Emulation Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed datasets covering tsunami inundation depths using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effectwith project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <br> <div>The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz):</div> <br> <div>Processed inundation depth files from simulation and emulation typically stored in model/CT/ptha or model/SR/ptha</div> <div> -<strong>pthaSR.tar.gz </strong>- max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))</div> <div> -<strong>pthaCT.tar.gz</strong> - max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))</div> <br> <div>The filename follows the nomenclature with:</div> -true - simulation results</div> <div>-pred - emulator with pretraining</div> <div>-pred_direct - emulator without pretraining</div> <div>-pred_nodeform - emulator without pertaining and deformation input</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> </div> </div> <p> </p>
Processed files used for OQ based tsunami loss modelling using HPC based inundation and emulators(Part-IV OQ Simulation and Emulation Hazard Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed OpenQuake files covering tsunami inundation depth hazard using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <div> </div> <div>The risk calculation and procedure is available in the main repo: <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk">https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk </a></div> <div> </div> <br> <div>The processed files for the test locations of Catania(CT) are provided in compressed gzip files(.gz):</div> <br> <div>Processed hazard information used to prepare and run OQ event based risk analysis are as below,</div> <div> -<strong>hazard.tar.gz </strong>- preliminary numpy files with sitcol, eventid and hazard magnitude info</div> <div> -<strong>loss.tar.gz</strong> -final hdf5 files with both event hazard and site info</div> <br> <div>The filename follows the nomenclature with:</div> </div> <div>ML4SicilyTsunami/risk/loss/tsunami_prob_892.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_1658.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_3454.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_7071.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_true.hdf5</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk</strong></div> <div><strong>https://github.com/naveenragur/OQ-Tsunami</strong></div> </div> </div>
Processed datasets and codes for differential expression analysis on polulation-level RNA-seq data
<p>This version includes codes and data necessary to reproduce all results in our response to the correspondences ("Response to 'Neglecting normalization impact in semi‑synthetic RNA‑seq data simulation generates artificial false positives' and 'Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples'") (<a href="https://doi.org/10.1186/s13059-024-03232-8">https://doi.org/10.1186/s13059-024-03232-8</a>).</p> <p>It also includes a README file to guide the reproduction of the results in our original publication and resources for the goodness of fit test in the original publication, "Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples" (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4</a>).</p>
Novel Macrophage Subpopulation Linked to CAD: raw and processed data files
<div> <p>This directory contains all single-cell datasets analyzed in the "Partitioning heritability using single-cell multi-omics identifies a novel macrophage subpopulation conveying increased genetic risk of coronary artery disease". Associated scripts are available at https://github.com/jhjiang2020/multiome_paper. </p> <p><strong>Jan 2025 updates</strong>: include the 10x Cell Ranger ARC output for the scATAC-seq assay. <em>Please note that 10x peaks were not used for downstream analyses. Instead, we trimmed the atac-seq fragments to retain the 9bp Tn5 cut sites at both ends and aggregated into one consensus bed file. We then recalled peaks using `MACS3 --qvalue 1e-5 --nomodel --shift -50 --extsize 100 --broad` (see discussion in https://github.com/stuart-lab/signac/issues/682). </em></p> <p> </p> </div>
Raw data and supporting files for "Modular comparison of untargeted metabolomics processing steps"
<p>Raw data and supporting files for the Paper titled "Modular comparison of untargeted metabolomics processing steps". The dataset encompasses 42 samples, with 3 solvent blanks, 7 QC samples, and 32 biological samples (4 biological replicates: Banane, Bergrose, Narbe, Ricky) spiked with 42 compounds in different concentrations (0 ngmL, 30 ngmL, 100 ngmL, 300 ngmL). The files were uploaded in the vendor format (.raw) and in the open format (.mzML). Also, further supporting data for the processing results was uploaded as well.</p>
Geoclaw inputs and processed outputs used in building tsunami ML surrogates for nearshore and onshore approximation
<p>This dataset contains some input files needed for GeoClaw tsunami simulation and post-processed outputs used for training the nearshore and onshore surrogates discussed in the article - Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates available as preprint (https://doi.org/10.5194/nhess-2024-72) and project repo - https://github.com/naveenragur/tsunami-surrogates</p> <p>The GeoClaw simulation requires the following files: </p> <p><strong><a href="../api/records/10817116/draft/files/geoclaw_dtopo_files.tar.gz/content" target="_blank" rel="noopener noreferrer">geoclaw_dtopo_files.tar.gz</a></strong><strong>: </strong>input bathymetry and topography elevation in asc format from multiple sources of datasets.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_his.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_his.tar.gz</a></strong>: tsunami displacement(dtopo) input files in tt3 format for historic earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeA.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeA.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeB.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeB.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p>The machine learning training and testing requires:</p> <p><strong><a href="../api/records/10817116/draft/files/procesed_tsunami_data.tar/content" target="_blank" rel="noopener noreferrer">procesed_tsunami_data.tar</a>: </strong>the post-processed outputs for the three test location i.e. (1) the waveforms( time series recorded for the water level at the offshore and the nearshore gauge locations and (2) the maximum inundation depth recorded at the fixed grids onshore.</p> <p><a href="https://zenodo.org/uploads/14902165"><strong>tsunami-surrogates-nhess-2024-72.tar.gz</strong></a><a href="https://zenodo.org/uploads/14902165"> </a>: Model code, scripts and notebooks used in the study</p>
Combining datasets after pre-processing
<p>These are downsampled datasets for the single cell case study in Galaxy</p> <p>https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.html</p>
A Comprehensive Review of Techniques, Methods, Processes, Frameworks, and Tools for Privacy Requirements
<p> Context: Requirements Engineering (RE) relies on the collaboration of various roles—such as requirements engineers, stakeholders, and developers—and various techniques, methods, processes, frameworks, and tools. This makes RE a highly human-dependent process that benefits greatly from tool support. Understanding how these techniques, methods, processes, frameworks, and tools are applied across RE phases could provide valuable insights into ways to enhance the RE process, contributing to more successful outcomes. Objective: The primary objective of this study is to identify the techniques, methods, processes, frameworks, and tools applied across different requirements engineering phases—such as elicitation, analysis, specification, validation, and management—to address privacy requirements. Method: We conducted a systematic literature review (SLR) and identified 125 primary studies, and we also conducted a survey with 37 practitioners. Results: Our review identified a range of techniques, methods, processes, frameworks, and tools for addressing privacy requirements. Most studies were conducted in academic contexts, with the most frequently used tools being: PriS Method, Secure Tropos, LINDDUN, i* (i-star), STRAP (Structured Analysis for Privacy), Privacy by Design (PbD), and SQUARE. Additionally, over 75% of the studies applied these tools in the privacy requirements elicitation phase. In the industry, most of the techniques identified in the literature are not known or used by practitioners. Conclusion: This study provides a comprehensive analysis of techniques and tools for privacy requirements in RE, revealing a strong focus on academic contexts with limited industry application. Future research should explore the scalability and effectiveness of these tools in real-world environments, as well as the reasons why practitioners do not use them.</p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
<p>Diet analysis integrates a wide variety of visual, chemical and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, e.g., highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package <i>mvtweedie</i> that extends capabilities of widely used packages in R (<i>mgcv</i> and <i>ggplot2</i>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted puffins (<i>Fratercula cirrhata</i>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (<i>Canis lupus ligoni</i>) in Southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. </p>
Study Data: Obtaining Semi-Formal Models from Qualitative Data: From Interviews into BPMN Models in User-Centered Design Processes
<p>This dataset (Data.zip) contains the raw data of a user study on the investigation of transforming think aloud interviews into BPMN models. All information on how to use the data are provide in the SPSS files and as a readme file. This transformation is executed following a manual additionally provided in Documents.zip. For the training phase, a website was used provided in Website.zip including Screenshots for simpler re-use. Further information are also included as readme file in the zip container.</p> <p>Main research question answered is in how far the manual reduces interpretation and variance in the created models. </p>
Global biogeography and diversification of a group of brown seaweeds (Phaeophyceae) driven by clade-specific evolutionary processes
<p class="BodyA"><span><b>Aim:</b> Historical processes that shaped current diversity patterns of seaweeds remain poorly understood. Using Dictyotales, a globally distributed order of brown seaweeds as a model, we test if historical biogeographical and diversification patterns are comparable across clades. Dictyotales contain some 22 genera, three of which, <i>Dictyota</i>, <i>Lobophora</i> and <i>Padina</i>, are exceptionally diverse. Specifically we test if the evolutionary processes in these clades that shaped their latitudinal diversity patterns are in line with the tropical conservatism, the out-of-the-tropics, and diversification rate hypotheses.</span></p> <p class="BodyA"><span><b>Location:</b> Global coastal benthic marine environments.</span></p> <p class="BodyA"><span><b>Taxon:</b> Dictyotales (Phaeophyceae)</span></p> <p class="BodyA"><span><b>Methods:</b> Species diversity was inferred using DNA-based species delineation, addressing cryptic diversity and circumventing taxonomic problems. A six-gene time-calibrated phylogeny, distribution data of 3,755 specimens, and probabilistic modeling of geographic range evolution was used to infer historical biogeographical patterns. The phylogeny was tested against different trait dependent models in order to compare diversification rates for different geographical units as well as different thermal affinities.</span></p> <p class="BodyA"><span><b>Results:</b> The Dictyotales originated in the Middle Jurassic and reach a current peak of species diversity in the Central Indo-Pacific. Ancestral range estimation points to a southern hemisphere origin of Dictyotales corresponding to the tropical southern Tethys Sea. Our results demonstrate that diversification rates were generally higher in tropical regions, but increased diversification rates in different clades are driven by different processes. Three major clades underwent a major diversification burst in the early Cenozoic, with <i>Dictyota </i>and <i>Padina </i>expanding their distribution into temperate regions, while <i>Lobophora</i> retained a predominantly tropical niche.</span></p> <p><b>Main conclusions:</b> Our results are consistent with both the tropical conservatism hypothesis, in which clades originate and remain in the tropics (<i>Lobophora</i>), and the out-of-the-tropics scenario, where taxa originate and expand towards the temperate regions while preserving their presence in the tropics (<i>Dictyota</i>, <i>Padina</i>).</p>
Enhancing understanding of the hydrological cycle via pairing of process‐oriented and isotope ratio tracers
<p>This dataset contains monthly average output files from the iCAM6 simulations used in the manuscript "Enhancing understanding of the hydrological cycle via pairing of process-oriented and isotope ratio tracers," in review at the Journal of Advances in Modeling Earth Systems. A file corresponding to each of the tagged and isotopic variables used in this manuscript is included. Files are at 0.9° latitude x 1.25° longitude, and are in NetCDF format. Data from two simulations are included: 1) a simulation where the atmospheric model was "nudged" to ERA5 wind and surface pressure fields, by adding an additional tendency (see section 3.1 of associated manuscript), and 2) a simulation where the atmospheric state was allowed to freely evolve, using only boundary conditions imposed at the surface and top of atmosphere.</p> <p>Specific information about each of the variables provided is located in the "usage notes" section below.</p> <p>Associated article abstract:</p> <p>The hydrologic cycle couples the Earth's energy and carbon budgets through evaporation, moisture transport, and precipitation. Despite a wealth of observations and models, fundamental limitations remain in our capacity to deduce even the most basic properties of the hydrological cycle, including the spatial pattern of the residence time (RT) of water in the atmosphere and the mean distance traveled from evaporation sources to precipitation sinks. Meanwhile, geochemical tracers such as stable water isotope ratios provide a tool to probe hydrological processes, yet their interpretation remains equivocal despite several decades of use. As a result, there is a need for new mechanistic tools that link variations in water isotope ratios to underlying hydrological processes. Here we present a new suite of "process-oriented tags," which we use to explicitly trace hydrological processes within the isotopically enabled Community Atmosphere Model, version 6 (iCAM6). Using these tags, we test the hypotheses that precipitation isotope ratios respond to parcel rainout, variations in atmospheric RT, and preserve information regarding meteorological conditions during evaporation. We present results for a historical simulation from 1980 to 2004, forced with winds from the ERA5 reanalysis. We find strong evidence that precipitation isotope ratios record information about atmospheric rainout and meteorological conditions during evaporation, but little evidence that precipitation isotope ratios vary with water vapor RT. These new tracer methods will enable more robust linkages between observations of isotope ratios in the modern hydrologic cycle or proxies of past terrestrial environments and the environmental processes underlying these observations.<br> </p>
Community metabarcoding reveals the relative role of environmental filtering and spatial processes in metacommunity dynamics of soil microarthropods across a mosaic of montane forests
<p><span><span><span><span><span><span><span><span><span><span><span>Disentangling the relative role of environmental filtering and spatial processes in driving metacommunity structure across mountainous regions remains challenging, as the way we quantify spatial connectivity in topographically and environmentally heterogeneous landscapes can influence our perception of which process predominates. More empirical datasets are required to account for taxon- and context-dependency but relevant research in understudied areas is often compromised by the taxonomic impediment<span><span>. </span></span>We here employed haplotype-level community DNA metabarcoding, enabled by stringent filtering of Amplicon Sequence Variants (ASVs), to characterize metacommunity structure of soil microarthropod assemblages across a mosaic of five forest habitats on the Troodos mountain range in Cyprus. We found similar β diversity patterns at ASV and species (OTU, Operational Taxonomic Unit) levels, which pointed to a primary role of habitat filtering resulting in the existence of largely distinct metacommunities linked to different forest types. Within-habitat turnover was correlated to topoclimatic heterogeneity, again emphasizing the role of environmental filtering. However, when integrating landscape matrix information for the highly fragmented <i>Quercus alnifolia</i> habitat, we also detected a major role of spatial isolation determined by patch connectivity, indicating that stochastic and niche-based processes synergistically govern community assembly. Alpha diversity patterns varied between ASV and OTU levels, with OTU richness decreasing with elevation and ASV richness following a longitudinal gradient, potentially reflecting a decline of genetic diversity eastwards due to historical pressures. Our study demonstrates the utility of haplotype-level community metabarcoding for characterising metacommunity structure of complex assemblages and improving our understanding of biodiversity dynamics across mountainous landscapes worldwide.</span></span></span></span></span></span></span></span></span></span></span></p>
Runtime over 100 iterations with varying number of MPI processes
<p>Runtime over 100 iterations with varying number of MPI processes (np) and fixed block size <span class="math-tex">\(k=192\)</span> for matrix M5.</p>
Master Thesis Replication Package for: Empirical Scalability Evaluation of Hopping Window Aggregation Methods in Distributed Stream Processing
<p>Master Thesis Replication Package for: Empirical Scalability Evaluation of Hopping Window Aggregation Methods in Distributed Stream Processing</p> <p>A detailed description can be found in the <em>README.md</em>.</p>
Present-day surface deformation of Sicily: Insights from Sentinel-1 data processed by a PS-InSAR approach
<p>The directory DATASET.zip provides PS-InSAR data used in Henriquet et al., (2022). The data set contains for each Sentinel-1 track (44, 117, 22, 124) the mean PS velocities along the LOS, before (ps_mean_v.xy.v-dos) and after (ps_mean_v-dos_adjusted2GPS.xy) their adjustment to the 3D-GNSS velocity field, as well as the disparities of the PS velocities (ps_mean_disp.xy). The data set also includes the East- and Up-component of the reconstructed mean PS velocity field (East.grd and Up.grd) used in the Figures 7 to 12 in the paper.</p>
A Nuclear Equation of State Inferred from Stellar r-Process Abundances: Data
<p>This repository contains the raw MCMC posteriors as H5 files for the three cases presented in "A Nuclear Equation of State Inferred from Stellar <em>r</em>-Process Abundances" (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p> <p>Also included are Python scripts with a variety of functions to read the H5 files and interpret the data with <a href="https://git.ligo.org/lscsoft/lalsuite">LALSuite</a>. These include:</p> <ul> <li>reading the data contained in the H5 file (likelihood, acceptance, and values for each MCMC step)</li> <li>generating a corner plot of the data</li> <li>finding the maximum likelihood in the posterior distribution</li> <li>calculating a neutron star mass-radius curve for a posterior EOS</li> <li>calculating pressure and density for a posterior EOS</li> <li>calculating observables (M_TOV, R_1.4, and L) associated with an EOS</li> </ul> <p>The scripts are written for Python3 compatibility and depend on:</p> <ul> <li><a href="https://pypi.org/project/lalsuite/">lalsuite</a></li> <li><a href="https://docs.h5py.org/en/stable/">h5py</a></li> <li><a href="https://numpy.org/">numpy</a></li> <li><a href="https://scipy.org/">scipy</a></li> <li><a href="https://matplotlib.org/">matplotlib</a></li> <li><a href="https://corner.readthedocs.io/en/latest/">corner</a></li> </ul> <p>For more information and how to use these scripts, see the comments in `example.py` or contact Erika Holmbeck.</p> <p>If any of our posterior samples are used in your work, we ask that you appropriately cite this repository and the original paper (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p>
Integrating dynamic processes into waterfowl conservation prioritization tools
<p><b>Aim: </b> Traditional approaches for including species' distributions in conservation planning have presented them as long-term averages of variation. Like these approaches, the main waterfowl conservation targeting tool in the United States Prairie Pothole Region (US PPR) is based primarily on long-term averaged distributions of breeding pairs. While this tool has supported valuable conservation, it does not explicitly consider spatiotemporal changes in spring wetland availability and does not assess wetland availability during the brood rearing period. We sought to develop a modeling approach and targeting tool that incorporated these types of dynamics for breeding waterfowl pairs and broods. This goal also presented an opportunity for us to compare predictions from a traditional targeting tool based on long-term averages to predictions from spatiotemporal models. Such a comparison facilitated tests of the underlying assumption that this traditional targeting tool could provide an effective surrogate measure for conservation objectives such as brood abundance and climate refugia.</p> <p><b>Location: </b>US PPR</p> <p><b>Methods:</b> We developed spatiotemporal models of waterfowl pair and brood abundance within the PPR of the US. We compared the distributions predicted by these models and assessed similarity with the averaged pair data that is used to develop the current waterfowl targeting tool.</p> <p><b>Results:</b> Results demonstrated low similarity and correlation between the averaged pair data and spatiotemporal brood and pair models. The spatiotemporal pair model distributions served as better surrogates for brood abundance than the averaged pair data.</p> <p><b>Main conclusions:</b> Our study underscored the contributions that the current targeting tool has made to waterfowl conservation but also suggested that conservation plans in the region would benefit from the consideration of inter- and intra-annual dynamics. We suggested that using only the averaged pair data and derived products might result in the omission of 46-98% of important pair and brood habitat, respectively, from conservation plans.</p>
Data archive for paper "Machine Learning Emulation of Urban Land Surface Processes"
<p>This archive contains models, data* (Overview), as well as the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002744">Machine Learning Emulation of Urban Land Surface Processes</a>".</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/">Singularity</a> (tested with version 3.6.3-1.el8)</li> </ul> <p>Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p><strong>Overview</strong></p> <p>A general overview of the repository structure is given below. Due to licensing restrictions analysis and forcing data (*) cannot be included and need to be requested separately (see Initialization). Data derivatives (**) from either analysis or forcing, as well as intermediary data (***), are not included as they can be generated by rerunning experiments (see Usage).</p> <pre><code>. ├── data │ ├── analysis* │ ├── forcing* │ ├── teb │ ├── utils │ ├── wps │ └── wrf ├── hpc ├── models │ ├── teb │ ├── unn │ ├── wps │ └── wrf-unn ├── notebooks ├── outputs │ ├── analysis** │ ├── benchmark*** │ ├── forcing** │ ├── kerastuner*** │ ├── notebooks │ ├── tabular │ ├── teb** │ ├── unn** │ ├── wps*** │ └── wrf ├── paper │ └── figures ├── singularity └── tools </code></pre> <p><strong>Initialization</strong></p> <p>Forcing and analysis data need to be requested separately. The following directories should map to their respective data archives:</p> <ul> <li><code>./data/analysis</code> -> <a href="http://doi.org/10.5281/zenodo.4678387">Grimmond et al. (2013)</a></li> <li><code>./data/forcing</code> -> <a href="http://doi.org/10.5281/zenodo.4679279">Grimmond et al. (2021)</a></li> </ul> <p><strong>Usage</strong></p> <p>To rerun all experiments and reproduce results, run <code>tools/run_all.sh</code> from your command prompt. After completion, all results are saved in the <code>outputs</code> directory. Note that WRF simulations require high CPU time and may take hours or days to complete.</p> <p>Alternatively, if <a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System (PBS)</a> is available on your system, the following helpers may be used instead:</p> <pre><code>qsub hpc/submit_init.pbs qsub hpc/submit_tuner.pbs qsub hpc/submit_unn.pbs qsub hpc/submit_find_median_unn.pbs qsub hpc/submit_wrf.pbs qsub hpc/submit_postprocess.pbs qsub hpc/submit_benchmark.pbs </code></pre> <p>Note that you may need to modify PBS helper scripts to suit your specific environment.</p> <p><strong>Development notes</strong></p> <p>See DEVELOP.md.</p> <p><strong>License</strong></p> <p>The source code developed for this work is licensed under MIT (<code>LICENSE_CODE.txt</code>). For licensing information of third-party software see licenses under the <code>models</code> directory. Data files in this archive, including the initial and boundary condition data from the European Centre for Medium-Range Weather Forecasts (<code>data/wps/ungrib</code>), are licensed under CC BY-NC 4.0 (<code>LICENSE_DATA.txt</code>).</p>
Impairment of aversive episodic memories during Covid-19 pandemic: The impact of emotional context on memory processes
<p>The threatening context of the COVID-19 pandemic provided a unique setting to study the effects of negative psychological symptoms on memory processes. Episodic memory is an essential function of the human being related to the ability to store and remember experiences and anticipate possible events in the future. Studying this function in this context is crucial to understand what effects the pandemic will have on the formation of episodic memories. To study this, the formation of episodic memories was evaluated by free recall, recognition, and episode order tasks for an aversive and neutral content. The results indicated that aversive episodic memory is impaired both in the free recall task and in the recognition task. Even the beneficial effect that emotional memory usually has for the episodic order was undermined as there were no differences between the neutral and aversive condition. The present work adds to the evidence that indicates that the level of activation does not modify memory processes in a linear way, which also depends on the type of recall and the characteristics of the content to be encoded.</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.