Mendeley Data Showcase

This depository is the result of a calibrated microsimulation that generated the first individual-level dataset for Ghana’s Basic National Pension Scheme (Tier 1) for the period 2015 to 2024. The dataset combines publicly available annual aggregates from the Social Security and National Insurance Trust (SSNIT), National Pensions Regulatory Authority (NPRA) and the Ministry of Labour, Jobs and Employment (MLJE) with demographic, labour and mortality inputs from the Ghana Statistical Service (GSS) and the United Nations World Population Prospects. Using stratified sampling, membership transition modeling and poststratification calibration, the dataset recreates contributors, retirees and non-members at fine age-sex resolution while exactly reproducing official totals on contributions and benefits/pensions. The resulting microdata support distributional analysis of earnings, contributions, pensions and replacement rates, as well as actuarial projections, policy simulations and research on pension adequacy, coverage expansion and long-term social protection financing in Ghana.

This data is for the U.S. Corn Belt (US-CB), a major agricultural region characterized by intensive corn cultivation. The data includes 210 counties consistently dominated by corn production, selected based on their suitability for sub-pixel SIF disaggregation and inclusion in the USDA’s annual yield reporting. The data include five growing seasons: 2015, 2016, 2018, 2019, and 2020. The year 2017 was excluded from the data due to incomplete satellite data caused by sensor malfunction. Annual corn yield records were obtained from the U.S. Department of Agriculture’s National Agricultural Statistics Service (USDA-NASS). County-level yield data, measured in tons per hectare, are generated using a combination of farmer-reported surveys, field assessments, and calibrated simulation models. This integration ensures their reliability as ground-truth references for both training and validating predictive models. Their consistent spatial resolution and broad temporal span across the U.S. Corn Belt make them a solid basis for model development and performance testing. Standard satellite-derived SIF products are provided at coarse spatial resolutions, complicating their use in heterogeneous agricultural landscapes. To overcome this limitation, I applied a sub-pixel extraction method developed by Kira and Sun (2020), which uses high-resolution land cover and crop maps to isolate the contribution of corn within each SIF pixel. This technique allowed for the derivation of corn-specific SIF values at the county level, minimizing contamination from other crops or land covers.

This dataset supports the manuscript “Renewable curtailment under China’s mechanism-settled electricity reform: A three-party evolutionary game analysis of green certificate pricing”, submitted to Energy Policy. The dataset contains the processed inputs and simulation materials used to examine whether green electricity certificate (GEC) prices can contribute to renewable curtailment reduction after China’s Notice No. 136. It includes author-compiled renewable utilization data for Heilongjiang Province in 2025, official monthly GEC price anchors, policy-stage classification, scenario-calibrated model parameters, GEC-price perturbation settings, and simulation outputs for the August transition-stage and October post-implementation experiments. The renewable utilization data are derived from publicly available official and sectoral sources and are processed into monthly wind, photovoltaic, and composite renewable utilization indicators. The GEC price anchors are based on official monthly green certificate market bulletins. Other model parameters, including mechanism-settlement proxies, market-price proxies, behavioral parameters, and sensitivity ranges, are scenario-calibrated values used for mechanism-testing simulation rather than directly observed market data. The dataset is intended to improve transparency and reproducibility of the perturbation experiments reported in the manuscript. The accompanying simulation code or pseudocode reproduces the main scenario-calibrated results, including the identification of stage-specific effective GEC-price windows and the comparison between August and October policy environments.

Calibrated dataset created for set-theoretic analysis of the relations between Democracy, Rule of Law, Internationalisation, Corporate Governance, and GNIpc in producing the outcome of CSR adoption.

This dataset contains all raw and processed experimental data, SEM images, and finite element simulation files supporting the findings of the related research article. Includes DMA data, shape memory test results, recovery stress measurements, calibrated Prony series parameters, WLF constants, Abaqus input files, and validation plots.

This file contains simulation model input files for the simulations carried out including a windows executable and the source code for the simulation software OGS v5. details: - exe/ windows executables for the software ogs5 - ogs5/ source code of ogs5 - calibration/ recalibrated process models, model calibrated on data calibration by Boog (2019) - validation/ validated process model, model validated on data cross-validation by Boog (2019) - scenario_analysis/ process model used for scenario_analysis - how_to_run_simulations.pdf/ guideline how to run simulations on win and linux OS - file_orga.csv details of model input files

Calibrated pXRF data for sourcing hornfels artefacts in eastern Tasmania

This package accompanies the manuscript on an integrated SysML–Modelica workflow for propulsion-system modeling, co-simulation, and validation. The purpose is to: (1) demonstrate the end-to-end workflow; and (2) allow readers to execute the supplied Modelica/co-simulation project and inspect the system structure and interface mapping. The model framework and workflow artifacts are shared in full. The parameter sets and datasets included in this package are synthetic or modified such that the resulting simulation outputs resemble the trends shown in the paper, but they do not correspond to the confidential bench-calibrated values used in the actual experiments. They are provided solely to verify workflow consistency and to allow users to experience the complete simulation and validation process. These files therefore enable reproduction of the modeling and validation workflow, but not reproduction of the quantitative experimental results. Users wishing to obtain quantitative fidelity on their own hardware should perform parameter identification/calibration using their own experimental data and controller–actuator configuration.

Daily precipitation, runoff, and sediment yield data in a 'deadwood + grass' covered plot in the Eastern Italian Alps, together with climate, soil, slope, and plant/management input files for uncalibrated, partially calibrated, and fully calibrated WEPP simulations.

This dataset contains the underlying simulation models, raw data exports, and comparative analysis worksheets for the research paper "Validation of Two-Box Spacer Modeling Across Spacer Widths for Fenestration Thermal Analysis," submitted to Energy and Buildings. The study evaluates the accuracy and limitations of two simplified two-box spacer modeling approaches across a range of spacer widths (8-20 mm) and three spacer families, using detailed THERM simulations as the baseline. The data was gathered using an automated simulation framework that interfaced with LBNL THERM 7.8.71.0 and WINDOW 7.8.71.0. 25 discrete widths were modelled for three generic spacer families using detailed geometries. Corresponding two-box models were generated, one using width-specific calibration and one using nominal-width calibration (calibrated at 8 mm only). A total of 900 full-frame simulations were performed to evaluate the total window thermal U-factor and Condensation Index. The data demonstrates that width-calibrated two-box models are stable across all widths and spacer families. However, for the intermediate conductivity spacer family (metal, double seal), the data shows a linear increase in error as the width deviates from the calibration point. Specifically using an 8 mm calibration for a 20 mm metal double seal spacer resulted in errors exceeding industry-accepted tolerances for U-factor (0.01 W/m²·K). Files are organized into folders mapping to the figures in the associated manuscript. The dataset includes: .xlsx: Calculation spreadsheets for U-factor aggregation and error plotting. .thm: THERM simulation files (serialized binary) .o THERM simulation output file (node data, used for CI Tool) .thmx THERM simulation output file (XML format .thm file, used for CI Tool) .txt Text log file .csv Result CSV file (result data exports) .mdb Access database files (used by WINDOW) Researchers can use this data to: Benchmark the accuracy of other FEA-based thermal modeling tools. Validate alternative simplified spacer modeling approaches. Test how different calibration tolerances affect full-window results. Development of standards and acceptance criteria for two-box spacer models in certification workflows.