Contributors: Olanrewaju Lawal
... 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.
Contributors: Jacqueline Zadelaar
... 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.
Contributors: Szilárd Szabó, Boglárka Balázs, Zoltán Kovács, Balázs Deák, Ádám Kertész
... 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
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Contributors: Alex Waldron, Filippo Pecci, Ivan Stoianov
... This dataset is supplementary data to "Parameter Estimation for Water Distribution Networks with Multiple Head Loss Formulae" in ASCE Journal of Water Resources and Planning Management (under review). The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence. Any use of this dataset must credit the authors. BWFLnet is an operational network in Bristol, UK, operated by Bristol Water. The data provided is a the product of a long term research partnership between Bristol Water and Infrasense Labs at Imperial College London on dynamically adaptive networks. We acknowledge the financial support of EPSRC (EP/P004229/1, Dynamically Adaptive and Resilient Water Supply Networks for a Sustainable Future) for the acquisition of this data set. All data provided is recorded hydraulic data with locations and names anonymised. The authors hope that the publication of this dataset will facilitate the reproducibility of research in hydraulic model calibration as well as broader research in the water distribution sector.
Contributors: Jessica Noviello, Zachary Torrano, Kelsi Singer, Alyssa Rhoden
... These are the ArcMap files created and reported on in Noviello et al. (submitted here)
Characterization of the Corals and Sponges of the Eastern Scotian Slope from a Benthic Imagery Survey
Contributors: Lindsay Beazley, Camille Lirette, Javier Guijarro
... A benthic imagery survey was conducted along the Eastern Scotian Slope in June 2018 to collect data in support of a Strategic Program for Ecosystem-Based Research and Advice project to evaluate the effectiveness of the Lophelia Coral Conservation Area and identify new areas of importance for benthic species that may qualify for protection under Fisheries and Oceans Canada’s 2009 Policy for Managing the Impact of Fishing on Sensitive Benthic Areas. Linear video and photographic transects from ~200 to 1000 m depth were collected at 10 stations between the Gully Marine Protected Area and the Lophelia Coral Conservation Area using the video and photographic camera system Campod and the ‘4K Camera’ drop camera system. Here we present a quantitative assessment of the corals and sponges observed at each of these 10 stations. Patterns in distribution by transect and depth are presented, as well as the relationship between coral distribution and groundfish fishing effort. We highlight the importance of the slope outside the canyons for the distribution of corals and sponges, where nearly 25 taxa were recorded between 167 – 970 m depth. Diversity and abundance appeared to show a west-to-east gradient across the study area, being highest on those stations adjacent to the Lophelia Coral Conservation Area. Groundfish fishing activity overlapped the distribution of corals and sponges in some parts of the study area, particularly between 200 and 500 m where the large branching corals Paragorgia arborea and Primnoa resedaeformis were observed, and also suggested that fishing may have taken place within the boundaries of the Lophelia Coral Conservation Area since its implementation in 2004. An extension of the boundaries of this closure may ensure its continued effectiveness and provide protection for the diverse and abundant coral and sponge communities that reside beyond its boundaries.
Abaqus Code for a Residual Control Staggered Solution Scheme for the Phase-Field Modeling of Brittle Fracture
Contributors: Karlo Seleš
... Abaqus UEL and UMAT subroutines for the phase-field modeling of brittle fracture. The code consists of the 3-layered system of user elements and user material subroutine producing a staggered algorithm with a residual norm based stopping criterion. The elements are 4-node full integration 2D and 8-node full integration 3D linear elements. The implementation files (source code and input files) for some examples published in the associated journal article are given. The files contain detailed explanations and instructions for users. This is an updated version of the dataset. See more info in Version_3-ChangeLog.txt For additional information, suggestions or comments, please contacts us at email@example.com
Data for: Performance Assessment of Supersonic and Hypersonic Intake Systems with Nano-Particle Injection
Contributors: Rangesh Jagannathan, Craig Johansen, William Hinman
... Boundary conditions, coarse grid files and solver setup data for gas-particle flow simulations for a mixed compression supersonic intake, in OpenFOAM
Contributors: Shin Sugiyama, Masahiro Minowa, Marius Schaefer
... This is a data set published in the paper below. Sugiyama, S., Minowa, M., & Schaefer, M. (2019). Underwater ice terrace observed at the front of Glaciar Grey, a freshwater calving glacier in Patagonia. Geophysical Research Letters, 46. https://doi.org/10.1029/2018GL081441
Contributors: Emily Schworer, Lisa A. Daunhauer, Mark Prince, Amy Needham, Elizabeth A. Will, Deborah J. Fidler
... This dataset represents one timepoint of data collection for a sample of infants with Down syndrome. Complete Bayley-III information is provided, as well as demographic information and child medical/health history information. In addition, infants were administered a short exploration task that involved presenting the infant with a teether in their line of vision, and then placing it on the table before them. Exploration trials were coded for the percentage of time the infant spent visually, manually, and orally exploring the object.