The algorithms in this data release implement a State-Space Model (SSM) of vertical infiltration through the unsaturated zone and recharge to the water table. These algorithms build on previous investigations available at https://doi.org/10.1029/2020WR029110 and https://doi.org/10.1111/gwat.13206. The SSM is defined by observed states (i.e., the water-table altitude) and unobserved states (i.e., fluxes through the unsaturated zone and recharge to the water table)and interprets time-series data for observations of water-table altitude and meteorological inputs (i.e., the liquid precipitation rate and the Potential Evapotranspiration (PET) rate). The algorithms first perform the estimation of the SSM parameters from the time-series data over a Parameter-Estimation Window (PEW). The estimated model parameters are then used in a subsequent State-Estimation Window (SEW) to estimate the observed and unobserved systems states of the SSM using the Kalman Filter (KF). The application of the KF to the SSM facilitates the assimilation of the recently available observations of the water-table altitude in the estimation of the observed and unobserved system states over the SEW. An additional outcome of applying the KF is the calculation of the time-varying error covariance of the system states over the SEW. The algorithms are used to demonstrate a comparison of the model outcomes for forecasting, filtering, and fixed-lag smoothing (FLS) using data for water-table altitude and meteorological inputs available from the Masser Recharge Site, which was operated by the U.S. Department of Agriculture, Agricultural Research Service. The algorithms were prepared and executed using the computational software MATLAB to meet the needs of the investigation presented in https://doi.org/10.1111/gwat.13349. MATLAB is a proprietary software, and thus, an executable version of the software cannot be supplied with this data release. The MATLAB files comprising the algorithms are included in this data release. The interested user would need to secure the appropriate versions of MATLAB and the associated MATLAB toolboxes. This USGS data release contains all of the input and output files for the simulations described in the associated journal article (https://doi.org/10.1111/gwat.13349).

This repository contains the Matlab code and generated data for the manuscript "Data-driven geometric parameter optimization for PD-GMRES" which uses a quadtree approach to optimize parameters for the iterative solver PD-GMRES. It includes hardware specific data to allow for reproducibity of our results. Our calculations were performed using MATLAB R2019a and should be reproducible up to and including version R2022a. A change in version R2022b leads to different numerical behavior. However, the code does run on newer Matlab versions. Further information is contained in the README.

Parameter descriptions and values from [8].

This is a new, open, and transparent database of toxicokinetic data supporting EPA decision making. The database has already become the basis of research efforts within EPA to improve HTTK modeling using generic TK models and has facilitated the creation and validation of models for new exposure routes. Publishing the database supports open, transparent science and this database (the largest public database for this domain) will spur improvement and development of TK models by external experts in the field. Future efforts to improving the accessibility of this database (with a graphical user interface) and encouraging crowdsourcing to expand the size and scope of the database will lead to larger validation sets for our modeling efforts and likely lower uncertainties when estimating TK. This dataset is associated with the following publication: Sayre, R., J. Wambaugh, and C. Grulke. Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals. Scientific Data. Springer Nature Group, New York, NY, 7: 122, (2020).

The insitu.calibrated level 2 science.data bundle contains selected fully calibrated (L2) data from the Particles and Fields package and NGIMS, together with ephemeris information. These data are in physical units and are averaged/sampled at a uniform cadence. In situ instrument data is derived directly from Level 2 data. Ephemeris information is derived using SPICE libraries and kernels provided by MAVEN/NAV team and Lockheed-Martin.

The Budget Notices set out the key auction parameters for the Allocation Round, namely the available spend on applicants for the round and the parameters against which applicant bids will be assessed. BEIS publishes a Draft Budget Notice in advance of a Final Budget Notice. Administrative Strike Price, Maxima, and Minima parameter values can be found in the "Administrative Strike Price, Maxima, and Minima" dataset: https://www.lowcarboncontracts.uk/data-portal/dataset/administrative-strike-prices This dataset takes the Budget and Capacity Cap of each Technology Group per year from these publications:- https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/336101/draft_cfd_budget_notice.pdf https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/360129/CFD_Budget_Notice.pdf https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/398665/150127_Budget_Revision_Notice_for_CfD_Round_One.pdf https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/598824/Budget_Notice.pdf https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/798885/Final_Budget_Notice_AR3.pdf https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1016774/cfd-ar4-draft-budget-notice.pdf

Result of PRJ3132 FY23 SSIF Rat Database. This project aimed to compile a list of potential data sources for parameterising a rat population model. The focus was on density estimates (to be able to calculate rates of increase) and vital rates (breeding state, number off offspring per litter, survival/mortality), although a few trap-catch datasets snuck in because they were monitored over a longer period. Also did the same process for mice since rat data were so few.

To aid in parameterization of mechanistic, statistical, and machine learning models of hydrologic systems in the contiguous United States (CONUS), flow-conditioned parameter grids (FCPGs) have been generated describing upstream basin mean elevation, slope, land cover class, latitude, and 30-year climatologies of mean total annual precipitation, minimum daily air temperature, and maximum daily air temperature. Additional datasets of upstream basin area and binary stream presence-absence are provided to help validate queries against the flow-conditioned data. These data are provided as virtual raster tile (vrt) mosaics of cloud optimized GeoTIFFs to allow point queries of the data (see Distribution Information) without requiring downloading the whole dataset.

Different from the output-input representation based identification methods of two-block Hammerstein systems, this paper concerns a separate block based parameter estimation method for each block of a two-block Hammerstein CARMA system, without combining the parameters of two parts together. The idea is to consider each block as a subsystem and to estimate the parameters of the nonlinear block and the linear block separately (interactively), by using two least squares algorithms in one recursive step. The internal variable between the two blocks (the output of the nonlinear block, and also the input of the linear block) is replaced by different estimates: when estimating the parameters of the nonlinear part, the internal variable between the two blocks is computed by the linear function; when estimating the parameters of the linear part, the internal variable is computed by the nonlinear function.

The proposed parameter estimation method possesses property of the higher computational efficiency compared with the previous over-parameterization method in which many redundant parameters need to be computed. The simulation results show the effectiveness of the proposed algorithm.

This dataset contains key characteristics about the data described in the Data Descriptor Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

aThis probability of transmission gives rise to a basic reproduction number of R0 = 1.5.

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter’s relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data. We prioritized 13 out of 38 parameters for developing ML-based approaches, while flagging another nine with critical data gaps. For all prioritized parameters, we performed a chemical space analysis to assess further the potential for ML-based approaches to predict data for diverse chemicals considering the structural diversity of available measured data, showing that ML-based approaches can potentially predict 8–46% of marketed chemicals based on 1–10% with available measured data. Our results can systematically inform future ML model development efforts to address data gaps in chemical toxicity characterization.

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The data are measurements of test scattering parameter calibration data for on-wafer measurements from 10 MHz to 110 GHz. Data includes S-parameter data (Ref_Cs_g25_HF_0.s2p, Ref_Rs_g25_HF_0.s2p, Ref_L1_g25_HF_0.s2p, Ref_S1_g25_HF_0.s2p, Ref_Rg_g25_HF_0.s2p, Ref_Og_g25_HF_0.s2p, Ref_L2_g25_HF_0.s2p, Ref_L3_g25_HF_0.s2p, Ref_L4_g25_HF_0.s2p, Ref_L5_g25_HF_0.s2p, Ref_L6_g25_HF_0.s2p, Ref_L7_g25_HF_0.s2p, Ref_L8_g25_HF_0.s2p, Ref_L9_g25_HF_0.s2p). Data is in an *.s2p format. The format is "# GHZ S RI R 50". Frequency is GHz, data is S-parameters as real and imaginary pairs to a reference impedance of 50 Ohms. The effective permittivity data is a frequency real part of permittivity and imaginary part of permittivity. The effective permittivity data is a frequency real part of characteristic impedance and imaginary part of characteristic impedance.

Title of program: SEARCH Catalogue Id: ABOD_v1_0

Nature of problem Many computational problems require several parameters to be adjusted so that agreement with certain data is obtained. The subroutine SEARCH does this job automatically and can be adapted for use with any program with a minimum of difficulty.

ADAPTATION SUMMARY: Vol:Year:Page 1:1970:198 "0001 ADAPT SEARCH TO ELASTIC" "Adaptation of subroutine SEARCH for use with program ELASTIC." W.R. Smith

Note: adaptation instructions are contained in source code

Versions of this program held in the CPC repository in Mendeley Data ABOD_v1_0; SEARCH; 10.1016/0010-4655(69)90006-X

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

No description is available. Visit https://dataone.org/datasets/ess-dive-768e318197f5497-20241028T152052192925 for complete metadata about this dataset.

This data release contains input data for hydrologic simulations of the Alaska Domain application of the U.S. Geological Survey (USGS) Precipitation Runoff Modelling System (PRMS) as implemented in the National Hydrologic Model (NHM) infrastructure (Regan and others, 2018). The NHM Alaska Domain parameter database consists of 114 parameter files in ASCII format (CSV), two files needed to run the Alaska Domain PRMS (control.fy19deliverable and fy19_deliv.param), two xml files (dimensions.xml and parameters.xml) containing descriptive information about the parameters, and a table that defines each parameter (AK_paramDB_datadictionary.csv). The Entity and Attribute element of this metadata record describe the data dictionary (AK_paramDB_datadictionary.csv). Please refer to the Supplemental Information element of this metadata record for references cited.

This metadata record documents a set of 116 comma delimited files and a data dictionary describing the inputs for the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS) which is used to drive the National Hydrologic Model (NHM) for the Hawaiian _domain. The National Hydrologic Model database contains parameters for hydrologic response units (HRUs) and stream segments needed to run the NHM. These parameters are generated using python scripts to process input datasets such as digital elevation models, soil maps, and land cover classifications. Many of the parameters were left at their default model value as they would need to be calibrated as part of the PRMS model development process. Please refer to the Supplemental Information and the Process Description elements of this metadata record for more details on the source datasets and scripts used to generate these parameters.

Scientists at the National Center for Atmospheric Research have recently carried out several experiments to better understand the uncertainties associated with future climate projections. In particular, the NCAR Climate and Global Dynamics Lab (CGDL) working group has completed a large Parameter Perturbation Experiment (PPE) utilizing the Community Land Model (CLM), testing the effects of 32 parameters over thousands of simulations over a range of 250 years. The CLM model experiment is focused on understanding uncertainty around biogeophysical parameters that influence the balance of chemical cycling and sequestration variables. The current website for displaying model results is not intuitive or informative to the broader scientific audience or the general public. The goal of this project is to develop an improved data visualization dashboard for communicating the results of the CLM PPE. The interactive dashboard would provide an interface where new or experienced users can query the experiment database to ask which environmental processes are affected by a given model parameter, or vice versa. Improving the accessibility of the data will allow professionals to use the most recent land parameter data when evaluating the impact of a policy or action on climate change. Methods Data Source:

University of California, Santa Barbara – Climate and Global Dynamics Lab, National Center for Atmospheric Research: Parameter Perturbation Experiment (CGD NCAR PPE-5). https://webext.cgd.ucar.edu/I2000/PPEn11_OAAT/ (Only public version of the data currently accessible. Data leveraged in this project is currently stored on the NCAR server and is not publicly available), https://www.cgd.ucar.edu/events/seminar/2023/katie-dagon-and-daniel-kennedy-132940 (Learn more about this complex data via this amazing presentation by Katie Dragon & Daniel Kennedy ^) The Parameter Perturbation Experiment data leveraged by our project was generated utilizing the Community Land Model v5 (CLM5) predictions. https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.CLM_LAND_ONLY.html

Data Processing: We were working inside of NCAR’s CASPER cluster HPC server, this enabled us direct access to the raw data files. We created a script to read in 500 LHC PPE simulations as a data set with inputs for a climate variable and time range. When reading in the cluster of simulations, there is a preprocess function that performs dimensional reduction to simplify the data set for wrangling later. Once the data sets of interest were loaded, they were then ready for some dimensional corrections – some quirks that come with using CESM data. Our friend’s at NCAR CGDL actually provided us with the correct time-paring bug. The other functions to weigh each grid cell by land area, properly weigh each month according to their contribution to the number of days in a year, and to calculate the global average of each simulation were generated by our team to wrangle the data so it is suitable for emulation. These files were saved so they could be leveraged later using a built-in if-else statement within the read_n_wrangle() function. The preprocessed data is then used in the GPR ML Emulator to make 100 predictions for a climate variable of interest and 32 individual parameters. To summarize briefly without getting too into the nitty gritty, our GPR emulator does 3 things:

Simplifies the LHC data so it can look at 1 parameter at a time and assess its relationship with a climate variable. Applies Fourier Amplitude Sensitivity Analysis to identify relationships between parameters and climate variables. It helps us see what the key influencers are. In the full chaotic LHC, it can assess the covariance of the parameter-parameter predictions simultaneously (this is the R^2 value you’ll see on your accuracy inset plot later)

Additionally, it ‘pickles’ and saves the predictions and trained gpr_model so they can be utilized for further analysis, exploration, and visualizations. Attributes and structures defined in this notebook outlines the workflow utilized to generate the data in this repo. It pulls functions from this utils.py to execute the desired commands. Below we will look at the utils.py functions that are not explicitly defined in the notebook. – General side note: if you decide to explore that Attributes and structures defined in this notebook explaining how the data was made, you’ll notice you’ll be transported to another repo in this Organization: GaiaFuture. That’s our prototype playground! It’s a little messy because that’s where we spent the second half of this project tinkering. The official repository is https://github.com/GaiaFuture/CLM5_PPE_Emulator.

The NAME Aircraft: P-3 Meteorology Navigation and State Parameters dataset in NOAA/AOC Standard Tape format is one of several datasets archived by the National Center for Atmospheric Research/Earth Observing Laboratory (NCAR/EOL) in support of the North American Monsoon Experiment (NAME). The data were collected by the National Oceanic and Atmospheric Administration/Aircraft Operation Center P-3 Aircraft (NOAA/AOC).