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62 results for “human system modeling”
Representing Socio-Economic Uncertainty in Human System Models
<p>This data repository is associated with the paper:<br> Morris,J., J. Reilly, S. Paltsev, A. Sokolov and K. Cox (2022): Representing socio-economic uncertainty in human system models. <em>Earth's Future</em>, In press.</p>
Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems
<p>This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future.</p> <p>Abstract:</p> <p>Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.</p>
Data and models for "Modelling human behaviour in cognitive tasks with latent dynamical systems"
<p>Ebb and Flow gameplay data and trained model parameters for:</p> <p>Jaffe, P.I., Poldrack, R.A., Schafer, R.J. & Bissett, P.G.<em> </em>Modelling human behaviour in cognitive tasks with latent dynamical systems. <em>Nat Hum Behav</em> (2023). https://doi.org/10.1038/s41562-022-01510-8 </p> <p>Ebb and Flow is a task-switching game offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The data and model parameters are organized by participant/model in individual archived directories (140 participants; 245 models). Within each model directory, “data_pre_split.pickle” contains the raw Ebb and Flow data. The processed model inputs for the training, validation, and holdout/test splits are contained in the files "train_model_inputs.pt", "val_model_inputs.pt", and "test_model_inputs.pt", respectively. Other metadata associated with each split is contained in "train_other_data.pkl", "val_other_data.pkl", and "test_other_data.pkl". The parameters from the trained model are stored in “model_params.pth”. Some intermediate analysis products are contained in the subfolder “model_analysis”.</p> <p>Metadata for all models can be found in “model_metadata.csv”. The metadata field “switch_cost_type” identifies models that were trained on data with (sc+) or without (sc-) a switch cost (note that models marked “NA”, except for the optimal models, were also trained on data with a switch cost but were not included in the paired comparison of the sc+ and sc- models; see manuscript for details). The "exgauss" field identifies models that were trained with an exGaussian response template (coded as "exgauss+"); models identified as "exgauss-" were trained with a Gaussian kernel and were used in paired comparisons with the exgauss+ models. The "early" field identifies models that were trained with early-stage practice data if set to TRUE. The "optimal" field identifies models that were trained to perform the task optimally if set to TRUE. The other metadata fields are self-explanatory.</p> <h2><strong>Fast command line download instructions (macOS/linux) </strong></h2> <p>For help downloading on Windows, see <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a>.<strong><br></strong></p> <p>1) Copy and save the complete list of files below to a text file, e.g. "files.txt". Save it to the same directory you would like to save the data to. </p> <p>2) Install parallel if it's not already installed:</p> <pre><code>sudo apt-get install parallel</code></pre> <p>3) Run the following from the directory with files.txt (all data will be saved here). The flag -jN will create N parallel wget instances to download the files, e.g.:</p> <pre><code>cat files_test.txt | parallel -j8 wget {}</code></pre> <p>4) Unzip the files and cleanup:</p> <pre><code>unzip "*.zip" rm *.zip files.txt</code></pre> <h2><strong>List of files</strong></h2> 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<p> </p>
Dataset part one to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>This study was supported in parts by research funding from the Swiss National Science Foundation (grant number 310030_189144), the Thurgauische Stiftung für Wissenschaft und Forschung, and the State Secretariat for Education, Research and Innovation to DFL.</p>
Dataset accompanying paper submission for "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"
<p>This data set accompanies code archived at DOI: <a href="https://doi.org/10.5281/zenodo.3833186">10.5281/zenodo.3833186</a>, which was used in the experiments for the paper submission "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"</p>
Systemic inflammation accelerates neurodegeneration in a rat model of Parkinson's disease overexpressing human alpha synuclein
<p><span>Parkinson’s disease (PD) involves genetic and<span> </span>environmental risk factors. Increasing research efforts have been made to understand how they interact<span> </span>to<span> </span>impair<span> </span>homeostasis<span> </span>and<span> </span>elevate<span> </span>risk. Inflammation could be one unifying factor. In this study, <em>wild-type</em> (WT) and overexpressing human </span><span>α</span><span>-synuclein (<em>Snca</em><sup>+/+</sup>) rats <span>were intraperitoneally injected with a single dose of </span>lipopolysaccharide<span> </span>(LPS) or with saline (SAL). In these animals we assessed </span><span>the development of PD-like symptoms by immunohistology, high-dimensional flow cytometry, electrophysiology, and behavioral analyses. A single injection of LPS to both WT and <em>Snca<sup>+/+</sup> </em>rats triggered long-lasting increased activation of pro-inflammatory microglial markers, infiltrating monocytes and T-lymphocytes. However, only LPS <em>Snca</em><sup>+/+</sup> rats displayed dopaminergic neuronal loss in the <em>substantia<span> </span>nigra pars compacta<span> </span></em>(SNpc), associated with a reduction of evoked dopamine<span> release </span>in the striatum. No significant<span> </span>changes were observed in the behavioral domain. </span></p> <p><span> </span></p>
Dataset part two to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>Additional dataset to dataset part one (doi: 10.5281/zenodo.4719596) to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"</p>
Multimodal mucosal and systemic immune characterization of a non-human primate trachoma model highlights the critical role of local immunity during acute phase disease
Open the record for dataset details and reuse information.
Systematic Evaluation of Human Explant Model Systems Engineering
ClinicalTrials.gov study NCT04671654. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A systems biology approach to investigate glucocorticoid response in a cellular model of human bronchial epithelium - Supplementary material
<p>Electronic appendices of PhD thesis: "A systems biology approach to investigate glucocorticoid response in a cellular model of human bronchial epithelium".</p>
Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated human-Earth System Models
<p>Soil and vegetation carbon densities play a critical role in global and regional human-Earth system models. These densities affect variables such as land use change emissions and also influence land use change pathways under climate mitigation scenarios where terrestrial carbon is assigned a carbon price. Recently, more spatially explicit, fine resolution data have become available for both soil and vegetation carbon. However, for models to effectively use these data the fine resolution data need to be reharmonized to initial land use and land cover conditions represented by these models. Without such reharmonization the carbon values may be very inaccurate for particular land types and places where the source data and the model disagree on the land use/cover type. Here we present reharmonized soil and vegetation carbon densities both at the grid cell level at 5 arcmin resolution and also aggregated to 235 water sheds for 4 different land use and 15 land cover types. These data are particularly useful as initial land carbon conditions for global Multisectoral Dynamic Models (MSD). Moreover, these data include six different statistical states calculated using distinct resampling methods for each of the land use, land cover types. These statistical states are used to define a range of possible carbon values for each land classification, and any state can be used for defining initial conditions of soil and vegetation carbon in MSD models. We make use of these statistical states to calculate spatially distinct uncertainties in the carbon densities by land type. We have implemented these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses in the model, especially when terrestrial carbon is assigned a carbon price. The statistical states in our data are validated against similar estimates in the literature both at a grid cell level and at a regional level. </p> <p>This is a data record which corresponds to the paper "Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated Multisectoral Models" (Narayan et al. 2023, under review)</p> <p>We have now also added a tabular version of the dataset aggregated to GTAP's AEZ definitions as opposed to GCAM's GLUs</p> <p> </p>
Anatomical models and scripts for conducting simulations of conduction delays in the ventricular conduction system in human post myocardial infarction using MonoAlg3D
<p>The repository contains:<br><br>1. Video example (example_LBBB.mp4). Simulation of accelerating sinus rhythm (120 to 171 bpm) in myocardial infarction (MI) and left bundle branch block (LBBB) conditions leading to arrhythmia.</p> <p>2. Collection of anatomical models (ventricles and conduction system) used to create the population of models in the study. It includes variability in the infarct size (none, small, large) and multiple conduction delay conditions (LBBB, RBBB and left ventricle) in different sizes, locations and combinations. Models can be visualised in Paraview using the script paraview-alg-plugin.py. The file simulation_plan.xlsx provides further details on the population of models.</p> <p>3. Simulation scripts, defining the scenario and model conditions of the simulations are described in these files, to be used in MonoAlg3D (<span><a href="https://github.com/LLRiebel/MonoAlg3D_C-2023">https://github.com/LLRiebel/MonoAlg3D_C-2023</a></span>). </p>
Bulk RNA-seq of neuromuscular system models generated from human induced pluripotent stem cells (hiPSCs)
GEO Series GSE226477. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Fully defined human PSC-derived microglia and tri-culture system reveals cell type specific potentiation of complement C3 production in a model of Alzheimer’s disease [smarter-seq]
GEO Series GSE139549. Homo sapiens. 23 samples. Type: Expression profiling by high throughput sequencing.
Fully defined human PSC-derived microglia and tri-culture system reveals cell type specific potentiation of complement C3 production in a model of Alzheimer’s disease [scRNA-seq]
GEO Series GSE139550. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Brain region-specific and systemic transcriptomic dysregulation in a human alpha-synuclein overexpressing rat model
GEO Series GSE281984. Rattus norvegicus. 60 samples. Type: Expression profiling by high throughput sequencing.
A Novel Human Gastric Primary Cell Culture System for Modeling Helicobacter Pylori Infection In Vitro
GEO Series GSE58473. Homo sapiens. 14 samples. Type: Expression profiling by array.
Human iPSC-derived retinal pigment epithelium: a model system for identifying and functionally characterizing causal variants at AMD risk loci
GEO Series GSE126847. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Integration of human stem cell-derived in vitro systems and mouse preclinical models identifies complex pathophysiologic mechanisms in retinal dystrophy [human_retinal_organoid]
GEO Series GSE220624. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
In vitro Modeling of the Human Dopaminergic System using spatially arranged ventral Midbrain-Striatum-Cortex Assembloids [scRNAseq]
GEO Series GSE219245. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
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.