Data in order to reproduce the benchmark of the associated publication "Validation of a Uniaxial Structure-Borne Sound Benchmark with Emphasis on Power and Phase Accuracy". The dataset contains:
1. Geometry data
The geometry of the single parts of the benchmark structure provided as STEP-files.
2. FE data
ASCII FE representation for the benchmark (SIMULIA Abaqus input file syntax)
3. Measurement data
Data files containing the results of the measurements, which were used for generating the Figures contained in the publication
Exposure capture factors which could be manifested in the magnitude and intensity of long-term changes in climate (Intergovernmental Panel on Climate Change, 2007) and in this context factors with impact on agricultural production. Temperature and rainfall were used to capture the extent to which Maize is exposed to climate change. Data was sourced from the Centre for Environmental Data Analysis (CRU TS release 4), with data extracted for 1941 - 2015. The data were processed within R (Version 3.4.2), within this environment the mean (temperature and rainfall) for northern and southern parts of the country were computed. The growing season for Maize in the north spans from May to September while in the south it starts from March and ends in August (FAO, 2018). Furthermore, long (1941 – 2015) and short (1961 – 2015) term averages for the respective growing season were computed for each of the regions. Following the computation of the long and short-term averages, exposure was computed as the ratio of the long-term to the short-term averages. With exposure index for rainfall and temperature computed separately, the two were added to get the combined exposure index. A high value indicates high exposure to climate variability.
In this dataset, the exposure index is presented in raster format (Geotiff) to allow for easy processing across GIS software. In addition, the boundaries of the northern and the southern regions were also included as shapefiles.
Are Individual Differences Quantitative Or Qualitative? An Integrated Behavioral And Fmri Mimic Approach.
Authors: Jacqueline N. Zadelaar, Wouter D. Weeda, Lourens J. Waldorp, Anna C. K. Van Duijvenvoordee, N. E. Blankenstein, Hilde M. Huizenga
In cognitive neuroscience there is a growing interest in individual differences. We propose the Multiple Indicators Multiple Causes (MIMIC) model of combined behavioral and fMRI data to determine whether such differences are quantitative or qualitative in nature. A simulation study revealed the MIMIC model to have adequate power for this goal, and parameter recovery to be satisfactory. The MIMIC model was illustrated with a re-analysis of Van Duijvenvoorde et al. (2016) and Blankenstein et al. (2018) decision making data. This showed individual differences in Van Duijvenvoorde et al. (2016) to originate in qualitative differences in decision strategies. Parameters indicated some individuals to use an expected value decision strategy, while others used a loss minimizing strategy, distinguished by individual differences in vmPFC activity. Individual differences in Blankenstein et al. (2018) were explained by quantitative differences in risk aversion. Parameters showed that more risk averse individuals preferred safe over risky choices, as predicted by heightened vmPFC activity. We advocate using the MIMIC model to empirically determine, rather than assume, the nature of individual differences in combined behavioral and fMRI datasets.
This dataset provides sample data demonstrating the capacities of the OBIA4RTM tool. OBIA4RTM combines radiative transfer modelling (RTM) of vegetation with object-based image analysis (OBIA). Its main purpose is to provide vegetation parameters such as Leaf Area Index (LAI) or leaf Chlorophyll a+b content (CAB) on a per-object rather than per pixel base.
In this dataset, the OBIA4RTM tool was applied to two Sentinel-2 scenes covering an agricultural area in Southern Germany. Field parcels were used as image objects that were delineated from high-resolution ortho-photography and classified into vegetated and non-vegetated parcels using a Support Vector Machine trained on manually selected samples. For each of the two scenes - dating back on the 6th and 18th of July 2017 - the canopy RTM ProSAIL was run in forward mode and the synthetic spectra stored in a Lookup-Table (LUT). For parameter retrieval, the 5 closest matches between spectra in the LUT and a given observed satellite spectrum averaged per parcel were used. Matches were found in terms of the lowest Root Mean Squared Error (RMSE). The utilized vegetation parameterisation is provided additionally.
The results include the Leaf Area Index (LAI), the Chlorophyll a+b content (CAB) of leaves and the fraction of brown leaves (Cbrown). In addition, the retrieval error in terms of RMSE is provided together with the average of the 5 best matching synthetic spectra in the LUT to a given object-based spectrum. This allows for evaluating the quality of the inversion results and enables user to further improve the results by applying a more appropiate vegetation parameterisation.
The structure of the dataset (see below) is straightforward:
- The "Field Parcels" folder contains an ESRI shapefile with the field parcels as well as the classification results for the two image acquisition dates
- The "ProSAIL Parametersisation" directory provides the vegetation parameters used to run the ProSAIL model.
- The actual results are stored as ESRI-shapefiles in "Retrieved Vegetation Parameters" folder containing the LAI, CAB, Fraction of brown leaves and the RMSE as well as inverted Sentinel-2 spectra
- "Sentinel-2 data" contains the utilized Sentinel-2 data as GeoTiff clipped to the study area in Level-2A
This information should allow for reproducing the results using the freely available base version of OBIA4RTM (for research and education) or within other software packages.
All geodata is projected in UTM-Zone 32N, WGS-84.
Contributors:Szabó Szilárd, Balázs Boglárka, Kovács Zoltán, Deák Balázs, Kertész Ádám
The dataset is derived from the Hungarian part of the CarpatClim database (https://doi.org/10.1002/joc.4059) and the MODIS MOD13Q1 16 days 250 m (https://doi.org/10.5067/MODIS/MOD13Q1.006) between 2000-2010, using bivariate linear regression on monthly data. The 1038 points represent 1038 R-squared (R2) values of the regressions. R2 values reflect the strength of relationship between aridity, precipitation, potential evapotranspiration, maximum temperature and the normalized vegetation index (NDVI). For spatial analysis, we provided the codes of Hungarian macro regions, land cover and topography data (terrain height, slope and aspect).
Column name Description
CC_ID: CarpatClim identifier
Country: Country code of CarpatClim /1=Hungary/
UTM_X: X UTM Coordinate
UTM_Y: Y UTM Coordinate
ARIvsNDVI_R2: R2 of Aridification Index and NDVI 2000–2010
PRECvsNDVI_R2: R2 of Precipitation and NDVI 2000–2010
PETvsNDVI_R2: R2 of Potential Evapotranspiration and NDVI 2000–2010
TMAXvsNDVI_R2: R2 of Maximum Temperature and NDVI 2000–2010
DEM_slope: SRTM slope value (degree)
DEM_aspect: SRTM aspect value (azimuth)
DEM: SRTM elevation (m)
CLC_code: CORINE Land Cover code /arable lands (211, 213,221,222, 242,243), grasslands (231, 321), forests (311, 312, 313, 324), wetlands (411, 412), water bodies (511, 512) and artificial surfaces (112, 121, 122, 131, 142)
Macro_reg_code: Hunrarian Macro Region code /Great Hungarian Plain=1, Kisalföld=2, Alpokalja=3, Transdanubian Hills=4, Transdanubian Mountains=5, North-Hungarian Mountains=6/
Microregion_code: Hungarian Micro Region code (Dövényi, Z. 2010)
Dövényi, Z. ed. 2010. Inventory of Natural Micro-regions of Hungary, Hungarian Academy of Sciences Geographical Institute, Budapest
Abaqus input file of a thermal-mechanical-metallurgical directly coupling finite element model of grinding under grinding parameters, vw=10mm/s, ap=0.2mm (model_of_grinding.inp). The temperature history and evolution history of each phase of several elements IP:3(
SDV1_IP3.xlsx for temperature, SDV4_IP3.xlsx for austenite, SDV24_IP3.xlsx for martensite, SDV25_IP3.xlsx for ferrite+pearlite, SDV42_IP3.xlsx for J2, SDV43_IP3.xlsx for A1, SDV66_IP3.xlsx for Hydrostatic Stress, SDV67_IP3.xlsx for Ms).
Abaqus 2D and 3D UEL and UMAT subroutines for the phase-field modeling for fracture of elasto-plastic solids. The code consists of the 2-layered system of user elements and user material subroutine producing a staggered algorithm. The codes along with input files for three benchmark examples from the associated journal article are given. A tutorial is provided in the associated journal article.