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37 results for “Process-based models”
Nearshore high-frequency temporal water quality observations and process-based modeling of aquatic ecosystem metabolism in Lake Tahoe completed by members of the Blaszczak Lab at the University of Nevada Reno, 2021-2023
The overarching goal of this project was to develop a process-based understanding of how watershed-to-lake connections drive nearshore productivity dynamics in a large oligotrophic mountain lake (Lake Tahoe). We addressed this goal through a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, laboratory incubations, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project and the ecosystem metabolism estimates we generated demonstrate how variable ecosystem productivity is in time and space in the nearshore of Lake Tahoe. Although maintenance of the sensor arrays during the exceptional winter of 2023 was challenging, we were able to capture the data necessary to estimate a complete time series of metabolic activity across two years with very different hydroclimatic conditions. Throughout this project we accomplished the following: 1. We generated over two years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from multiple locations on both the east and west shores of the lake and from areas in close proximity to and far away from stream water inflows. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water samples from both Glenbrook and Blackwood creeks and the nearshore of Lake Tahoe for over two years. 3. We quantified rates of NH4+ and NO3- uptake in benthic samples of the dominant substrate type collected during peak streamflow, the receding limb, and baseflow conditions in 2023 from multiple locations in the nearshore using established laboratory incubation methods. 4. Finally, we used a combination of time series models and structural equation modeling to integrate our results and improve understanding of the direct and indirect effects of hydroclimatic variability on observed patterns in ecosystem metabolism in the nearshore. See this git code repository
Dataset: An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine
<p><i><strong>"An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a><i> </i></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el proceso de toma de decisión multicriterio para la selección del software y del MCU de una maquina CNC. </p><p>En el repositorio podrán encontrar los datos referentes a los criterios, subcriterios, indicadores, datos, fuentes de los datos extraídos, política de decisión, cálculos de las evaluaciones de los modelos AHP aplicados y el análisis de sensibilidad de estos. Además, podrán encontrar las gráficas utilizadas en el estudio en la mejor calidad posible. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Potential distribution of invasive boxwood blight pathogen (Calonectria pseudonaviculata) as predicted by process-based and correlative models
<p>R project, R scripts, and data files for reproducing most of the analyses presented in a climatic suitability study for boxwood blight. The README. md file describes how to run the scripts and provides details on data inputs.</p> <p><strong>Abstract: </strong>Boxwood blight caused by <em>Cps</em> is an emerging disease that has had devastating impacts on <em>Buxus</em> spp. in the horticultural sector, landscapes, and native ecosystems. In this study, we produced a process-based climatic suitability model in the CLIMEX program and combined outputs of four different correlative modeling algorithms to generate an ensemble correlative model. All models were fit and validated using a presence record dataset comprised of <em>Cps</em> detections across its entire known invaded range. Evaluations of model performance provided validation of good model fit for all models. A consensus map of CLIMEX and ensemble correlative model predictions indicated that not-yet-invaded areas in eastern and southern Europe and in the southeastern, midwestern, and Pacific coast regions of North America are climatically suitable for <em>Cps</em> establishment. Most regions of the world where<em> Buxus</em> and its congeners are native are also at risk of establishment. These findings provide the first insights into <em>Cps</em> global invasion threat, suggesting that this invasive pathogen has the potential to significantly expand its range.</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)</p>
Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models
<p>1. Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate.</p> <p>2. We selected the most advanced process-based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data-driven approach and one where we allow the expert opinion estimates to inform the calibration process.</p> <p>3. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores.</p> <p>4. Our results highlight a key universal challenge of calibrating spatially-explicit, process-based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that is often noisy, biased, asynchronous and sometimes inaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data-driven calibration and expert opinion are integrated into an iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data.</p>
Learning stochastic process-based models of dynamical systems from knowledge and data - Libraries, incomplete models and data
<p>The archive contains all libraries of domain knowledge, the incomplete models and the data used in the experiments described in the manuscript titled "Learning stochastic process-based models of dynamical systems from knowledge and data" pubilshed in BMC Systems Biology</p>
Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"
<p>This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.</p>
Process-based Modeling of Ecosystem-Level Monoterpene from a Japanese Larch (Larix Kaempferi) Forest
<p>Title ''Process-based Modeling of Ecosystem-Level Monoterpene from a Japanese Larch (Larix Kaempferi) Forest''<br>Zhanzhuo Chen 1,2, Tomomichi Kato 3, Akihiko Ito 4,5, Tatsuya Miyauchi 3, Yoshiyuki Takahashi 4, and Jing Tang 2</p> <p>1 Graduate School of Global Food Resources, Hokkaido University, Sapporo, Hokkaido, 060-0809, Japan<br>2 Center for Volatile Interactions (VOLT), Department of Biology, University of Copenhagen, DK-2100, Copenhagen, Denmark<br>3 Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan<br>4 Earth System Division, National Institute for Environmental Studies (NIES), Onogawa, Tsukuba, Ibaraki, 305-8506, Japan<br>5 Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan<br>Correspondence to: Tomomichi Kato (tkato@agr.hokudai.ac.jp)</p>
Data from: Process-based modelling of nonharmonic internal tides using adjoint, statistical, and stochastic approaches. Part II: adjoint frequency response analysis, stochastic models, and synthesis
<p>Meta data updated after publication.</p> <p> </p>
Data from: Process-based modelling of nonharmonic internal tides using adjoint, statistical, and stochastic approaches. Part I: statistical model and analysis of observational data
<p>Meta data updated after publication.</p> <p> </p>
Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset
<p>This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.</p>
A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Falling Creek Reservoir multi-model ensemble (MME) forecasting work using the FLARE (Forecasting Lake And Reservoir Ecosystems) system and baseline models: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations, forecasts.zip contains forecast parquet files generated from the MME workflow (FLARE & baseline models), and scores.zip contains forecast skill metrics required for analysis.</p>
Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models
Open the record for dataset details and reuse information.
Confronting assumptions about prey selection by lunge-feeding whales using a process-based model
<ol> <li class="CH3AbstractCxSpFirst"><span>The relative energetic benefits of foraging on one type of prey rather than another are not easily measured, particularly for large free-ranging predators. Nonetheless, assumptions about preferred and alternative prey are frequently made when predicting how a predator may impact its environment, adapt to environmental change, or interact with human activities.</span></li> <li class="CH3AbstractCxSpMiddle"><span>We developed and implemented a process-based model to investigate the potential energetic benefit (PEB) of <i>in situ</i> foraging opportunities in rorqual whales. The model integrates and evaluates the energetic importance of measured prey patch characteristics (prey distribution, energy content and predator avoidance) and predator characteristics (morphometrics, foraging tactics and feeding rates). We applied the model to test the assumption that hatchery-released juvenile salmon are an "easy meal" for humpback whales compared to more common prey, herring and krill. </span></li> <li class="CH3AbstractCxSpMiddle"><span>In eleven out of the thirteen foraging situations considered, whales were found to be feeding in a manner where net energy gain was greater than the energetic costs of non-foraging swimming. Humpback whale PEB for hatchery-released juvenile salmon fell within the range of the PEB for krill and herring but varied by species, from relatively high PEB for chum salmon to relatively low for coho salmon. Our model provides behavioral insight as well, indicating that shallow feeding may be more important for reducing energy expenditure through slower lunge speeds than for increasing prey capture. The model also provides a means of identifying prey patch characteristics, with prey aggregation playing the largest role in determining PEB despite being a poor overall proxy for PEB, supporting the use of the complex model framework. </span></li> <li class="CH3AbstractCxSpLast"><span>Modeling approaches are especially valuable where they can use reasonable assumptions to substitute for lack of reliable observations, thereby integrating a range of interacting factors into a single framework. Additionally, because process-based models can make predictions outside the range of previously observed conditions, they will be increasingly useful in a changing climate.</span></li> </ol>
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.