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544 results for “integrated model”
Dataset from: Transport and water age dynamics in soils: a comparative study of spatially-integrated and spatially-explicit models
<p>This dataset contains high-resolution vegetated lysimeter experimental dataset carried out in EPFL, Lausanne, Switzerland in March-August 2016. The lysimeter is 100 cm long with a diameter of 120 cm. During this experiment, a simultaneous spike injection of five different solutes (2,5-DFBA, 2-TFMBA, 3,4-DFBA, 2,6-DFBA, 3-TFMBA) took place on the 3rd of March 2016 at 14:00 in an hour. The solutes' mass recovery at the bottom of lysimeter was observed for these solutes.</p> <p>This dataset contains three files which are described in the following:</p> <ul> <li>"hydrologic_data.dat" contains the hourly fluxes (precipitation, irrigation, evapotranspiration measured from load cells, and the water draining at the bottom of lysimeter ) in mm/hr between the 19th of Feb-the 1st of Sep 2016.</li> <li>"tracer_data.dat" contains the tracer concentration observed at the bottom of lysimeter in mg/L. These samples are collected at variable frequencies with an approximate average of 1.5 samples per day.</li> <li>"additional_data.dat" informs you on dry mass and soil volume in the lysimeter, vegetation type, and volume of injection per solute.</li> </ul> <p> </p>
An integrated framework for analysing, simulating and testing UML models
<h1>Project Overview</h1> <p>This is the artefact associated with the paper "An integrated framework for analysing, simulating and testing UML models", submitted to the 27th Brazilian Symposium on Formal Methods (SBMF 2024).</p> <h2>Directory Structure</h2> <ul> <li><strong>CSP_Validation</strong>: Contains the validation of our mapping rules from UML diagrams to CNL requirements.</li> <li><strong>NAT2TEST_Projects</strong>: Contains the NAT2TEST projects for two case studies: the classical Dijkstra's dining philosophers problem, and a distributed ring-buffer model.</li> <li><strong>UML2CNL</strong>: Contains the implementation of our mapping rules from UML diagrams to CNL requirements.</li> </ul>
Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks
Open the record for dataset details and reuse information.
The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling
<p>This record pertains to the integrative model of the desmosome ODP based on data from X-ray crystallography, electron cryo-tomography, immuno-electron microscopy, yeast two-hybrid experiments, co-immunoprecipitation, in vitro overlay, in vivo co-localization assays, in-silico sequence-based predictions of transmembrane and disordered regions, homology modeling, and stereochemistry information. The modeling was performed using Bayesian integrative structure determination via IMP (Integrative Modeling Platform).</p> <p>This record contains a) compressed version of Github folder: input data, scripts for modeling and results including bead models and localization probability density maps,and b) the ensemble of major cluster models for the main and supplementary runs reported in the paper.</p>
Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi."
<p>Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi", published in npj Comput. Mater. <strong>10</strong> 272 (2024).</p> <p>Version 2 contains additional results relating to Ni-rich systems.</p> <p>Version 3 contains data relating to vibrational considerations for the A1-L1$_0$ transition in equiatomic FeNi.</p> <p>Version 4 contains date pertaining to the Curie temperatures of the disordered, partially ordered, and fully ordered alloys considered in this work.</p>
Replication files for: Integrative modeling of the spread of serious infectious diseases and corresponding wastewater dynamics
<p>This repository contains the inputs used and outputs produced by the urban water management modelling software ++SYSTEMS for the paper Integrative Modeling of the Spread of Serious Infectious Diseases and Corresponding Wastewater Dynamics. It includes the following files:</p> <ol> <li>simulation_output.zip:</li> <ol> <li>In the subfolder infection_model, .csv and .txt files containing the agent-based model outputs for 250 simulations with homogeneous infection initialisation (2024_09_17) or localised infection initialisation (2024_10_15). These files were used as inputs for ++SYSTEMS.</li> <li>In the subfolder wastewater_model, .txt files containing the flow rates by pipe and the viral concentrations by sampling location for each combination of ABM simulation and rain/decay scenario. These files were the outputs of ++SYSTEMS.</li> </ol> <li>S1_INSIDe_Demonstrator_AreaList.txt: .txt file defining the area types and number of inhabitants for each surface area within the synthetic neighbourhood used in the paper. This file was also used as an input for ++SYSTEMS.<span> </span></li> <li>S2_systems_model_files.zip: .csv files defining the characteristics of the sewage system for the synthetic neighbourhood as well as the rain scenarios used in the paper. These file were also used as inputs for ++SYSTEMS.<span> </span></li> </ol>
Data and code for "Mapping hotspots of zoonotic pathogen emergence: an integrated model- and participatory-based approach"
<p>Code for "Mapping hotspots of zoonotic pathogen emergence: an integrated model- and participatory-based approach". Note shapefiles will need to be downloaded from GADM (https://gadm.org/), or from the gadm package in R (https://rdrr.io/github/rspatial/geodata/man/gadm.html). </p> <p>Data are available in Version 1.0 of this record</p>
Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance
<p>Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using presence-absence and presence-only data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km<sup>2</sup> resolution across the island. Models' predictions were associated to spatial estimates of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.</p>
A Scalable Data Management System Data Model facilitating the integration of any inspection system and the automated data digitalization process.
<p>In the context of the EU-funded project PILOTING (No. 871542), a scalable Data Management System (DMS) was deployed and facilitated the easy integration of nine different robotic systems and various payloads and the storing of all the data observations produced during the inspections. In particular, a three-phase methodology was adopted for the creation of the DMS Data Model (DMS-DM) in order to define the architectural design of the DMS. <br> The first phase was to semantically define the entities of the PILOTING ecosystem, identifying assets and activities that were important to the data providers, and including them in the data model design. The second phase was to identify relationships between the entities, focusing on information that must pre-exist for entities to be semantically accurate, hierarchies between entities, especially physical assets, and the data needed to form the inspection plan. For the third phase, the information collected by the previous two stages of the data model construction was used to create the overall data model, taking into consideration the possible integration with third parties and the needs of the I&M Visualization portal. <br> The constructed document is attempted to present the designed Entity-Relationship-Diagram (ERD) of the DMS-DM. Additional definitions of the existing entities and the relations between them are also depicted. </p>
Case study n°10 : From hazards in Senegal to migrants' integration in the City of Paris, Impact Chain model
<p>The case work led to the development of two correlated impact chains. The <em>first</em> impact chain (“risk sender”) models the components of the decision to migrate for rural Senegalese, accounting for hazard occurrence as well as the exposure and vulnerability. The outcome of the individual arbitrage is migration (internal or international), or immobility (willing or trapped). The <em>second</em> impact chain (“risk receiver”) considers the integration process for international migrants, accounting for the exposure and vulnerability of Paris in multiple dimensions (economic, social, cultural, linguistic, residential).</p>
A big data–model integration approach for predicting epizootics and population recovery in a keystone species
<p>Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex non-linear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a non-parametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, <em>Cynomys ludovicianus</em>) and sylvatic plague as an example. We synthesized colony data between 2001–2020 from eight USDA Forest Service National Grasslands across the range of BTPD in central North America. We then modeled extinctions due to plague and colony recovery of BTPD in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summer/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally > 0.80). Thus, these spatially-explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially-explicit forecasting of disease-induced population fluctuations, for use in natural resource management decision-making.</p>
Integration of a deep-learning-based fire model into a global land surface model
<p>JSBACH4+DL-fire simulation results & their comparison (DL-fire, JSB4-DL-fire, JSB4-simple)</p>
Development of a prognostic model for early breast cancer integrating neutrophil to lymphocyte ratio and clinical-pathological characteristics
<p><strong>Abstract</strong></p> <p>Breast cancer-related inflammation is critical in tumorigenesis, cancer progression, and patient prognosis. Several inflammatory markers derived from peripheral blood cells count, such as the neutrophil-lymphocyte ratio (NLR), derived neutrophil-lymphocyte ratio (dNLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR) and systemic immune-inflammation index (SII) are considered as prognostic markers in several types of malignancy. Here, we investigate and validate a prognostic model in early breast cancer (eBC) patients to predict disease-free survival (DFS) based on readily available baseline clinicopathological prognostic factors and preoperative peripheral blood-derived indexes. We analyzed a training cohort of 710 BC patients and two external validation cohorts of 980 and 157 eBC patients, respectively, with different demographic origins. An elevated preoperative NLR is a better DFS predictor than PLR, MLR, and SII in patients with eBC. The prognostic model generated in this study was able to classify patients into three groups with different risks of relapse based on ECOG-PS, presence of comorbidities, T and N stage, PgR status, and NLR. Prognostic models derived from the combination of clinicopathological features and peripheral blood indices, such as NLR, represent attractive markers mainly because they are easily detectable and applicable in daily clinical practice. More comprehensive prospective studies are needed to unveil their actual effectiveness.</p>
A unified meta-ecosystem dynamics model: Integrating herbivore-plant subwebs with the intermittent upwelling hypothesis
<p>Determining the relative influence of biotic and abiotic processes in structuring communities at local to large spatial scales is best understood using a biogeographic comparative-experimental approach. Using this approach, previous work suggests that intertidal community dynamics (top-down and bottom-up effects) vary unimodally along an upwelling-based productivity gradient, termed the Intermittent Upwelling Hypothesis (IUH). Evidence consistent with the IUH comes from the sessile invertebrate/predator (SIP) subweb in certain rocky intertidal communities, but whether this pattern extends to macrophyte/herbivore (MH) subwebs is unknown. Here we ask: Are MH subwebs also structured as predicted by the IUH? What is the relative importance of herbivory and predation in structuring these communities? Under what conditions do ecological subsidies like nutrients or propagule production drive community dynamics? And are omnivorous interactions important? We hypothesize that MH subwebs are driven by a new construct, the Grazing-Weakening Hypothesis (GWH), which states that MH interactions weaken monotonically with increasing nutrients, with strong (weak) herbivory and low (high) macrophyte productivity at low (high) nutrients. We explored local-to-large spatial scale dynamics of both subwebs using a biogeographic comparative-experimental factorial field experiment testing joint and separate effects of herbivores and predators between two continents. Experiments at ten sites ranging across from persistent upwelling to persistent downwelling regimes ran for 26-29 months in Oregon and California, and New Zealand South Island. For the MH subweb, results were consistent with the GWH: herbivory declined and macrophytes increased with increasing nutrients. As expected, results for the SIP subweb were consistent with the IUH: predator effect size was unimodally related to upwelling. Overall, herbivory explained more variation in community structure than did predation, especially in New Zealand. Omnivory was weak, sessile invertebrates outcompeted macrophytes, and ocean-driven subsidies provided the basic template driving ecosystem dynamics. We propose a unified Meta-Ecosystem Dynamics Model (MEcoDynaMo) combining MH and SIP results: with increased upwelling, sessile invertebrates and underlying dynamics vary unimodally (as in the IUH), while herbivory decreases and macrophytes generally increase. While this model was based on research in temperate ecosystems varying in upwelling regime, its wider applicability remains to be tested.</p>
UAV-Based Height Measurement and Height-Diameter Model integrating Taxonomic Effects: Exploring Vertical Structure of Aboveground Biomass and Species Diversity in a Malaysian Tropical Forest
<p>These Excel files are the dataset used for the analysis in the submitted paper</p> <p>Dataset S1: Data for 6-ha pot in Pasoh Forest Researve</p> <p>Dataset S2: Data for height–diameter (HD) models</p>
Data from: Integrating ecological niche and hydrological connectivity models to assess the impacts of hydropower plants on an endemic and imperiled freshwater turtle
<p>We built this dataset to assess the impacts of hydropower plants on the distribution of an endemic and imperiled freshwater turtle with very unique ecological requirements, the Williams' side-necked turtle (<em>Phrynops</em> <em>williamsi</em>). To prevent and mitigate impacts, we prioritized sites for species conservation by classifying planned HPP locations according to their predicted adverse effects on species distribution. The dataset has two files: i) species occurrence records and ii) hydropower plant data. The first dataset was fully built by the authors and the second was modified from the Brazilian Electricity Regulatory Agency (ANEEL) georeferenced data system.</p>
Dataset of paper "Evaluation of new photochemical systems for water disinfection by the integration of particle tracking into kinetic models for microbial inactivation"
<p>Dataset of paper "Evaluation of new photochemical systems for water disinfection by the integration of particle tracking into kinetic models for microbial inactivation":</p> <ul> <li>Results for the CFD model of the residence time distribution in the tubular reactor and E(t) curve for an ideal laminar.</li> <li>UV dosage histograms at the outlet surface for the tubular reactor illuminated with the four different LED configurations.</li> <li>Microbial inactivation histograms at the outlet surface for the tubular reactor illuminated with the four different LED configurations.</li> <li>Results for the CFD model of the residence time distribution in the annular reactor for different flow rates.</li> <li>UV dosage histograms at the outlet surface for the annular reactor for different flow rates.</li> <li>Microbial inactivation histograms at the outlet surface in the annular reactor for different flow rates.</li> </ul>
Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data
<p>This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling. This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling.</p>
Integrated Community-Clinical Linkage Model to Promote Weight Loss Among South Asians With Pre-Diabetes
ClinicalTrials.gov study NCT03188094. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Implementation of the Care Ecosystem Training Model for Individuals With Dementia in a High-risk, Integrated Care Management
ClinicalTrials.gov study NCT04556097. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
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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.