Data for: Validation of a Uniaxial Structure-Borne Sound Benchmark With Emphasis on Power and Phase Accuracy
Contributors: Rupert Ullmann
... 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
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: 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: Tanika Chakraborty, Rajshri Jayaraman
... 1. Data Use: Data were obtained and used by request from ASER. You are kindly requested to respect this and also obtain the authorization from ASER before using these data for a different purpose. Contact details are available here: http://www.asercentre.org/ 2. Software: The analysis was conducted in STATA, v14.2 3. Data files: Following are raw data files: a. Cross-sectional household surveys for the years 2005-2012: aser_2005_hh.dta aser_2006_hh.dta aser_2006_hh.dta aser_2007_hh.dta aser_2008_hh.dta aser_2009_hh.dta aser_2010_hh.dta aser_2011_hh.dta aser_2012_hh.dta b. Cross-section school surveys for the years 2007, and 2009-12: sch_2007.dta sch_2009.dta sch_2010.dta sch_2011.dta sch_2012.dta c. State-level data for the state-level regression results: states.dta d. Geographic data base of Indian administrative boundaries, obtained from http://www.gadm.org: IND*.* The .shp files could not be uploaded to Mendeley Data. Hence we have provides 2 .shp files along with the manuscript under program files.
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.
Contributors: Sly Wongchuig, Rodrigo Paiva
... We make available the daily discharge dataset of the Hydrological Reanalysis across the 20th Century for the Amazon Basin in its v1.0 version, which correspond to the HRXX_Amz.nc file. The matrix of daily discharge contains 36890 columns (one per day, starting at 01Jan/1910) and 12466 rows (one per catchment). Authors: Sly Wongchuig Correa Rodrigo Cauduro Dias de Paiva Vinícius Siqueira Walter Collischonn Institute of Hydraulic Research - (IPH/UFRGS) Brazil - November 2018 Citation: When using these data, please refer to the following paper: Wongchuig, C.S.; Paiva, R.C.D.; Siqueira, V.; Collischonn, W. 2019. Hydrological Reanalysis Across the 20th Century: A Case Study of the Amazon Basin. Journal of Hydrology, vol. 570, p. 755-773. https://doi.org/10.1016/j.jhydrol.2019.01.025
Species Distribution Modelling of Corals and Sponges in the Maritimes Region for Use in the Identification of Significant Benthic Areas
Contributors: Lindsay Beazley, Ellen Kenchington, Javier Murillo-Perez, Camille Lirette, Javier Guijarro-Sabaniel, Andrew McMillan, Anders Knudby
... Effective fisheries and habitat management processes require knowledge of the distribution of areas of high ecological or biological significance. On the Scotian Shelf and Slope, a number of benthic ecologically or biologically significant areas consisting of habitat-forming species such as sponges and deep-water corals have been identified. However, knowledge of their spatial distribution is largely based on targeted surveys that are limited in their spatial extent. We used a species distribution modelling approach called random forest (RF) to predict the probability of occurrence and biomass of sponges, sea pens, and large and small gorgonian corals across the entire spatial extent of Fisheries and Oceans Canada’s (DFO) Maritimes Region. We also modelled the rare sponge Vazella pourtalesi, which forms the largest known aggregation of its kind on the Scotian Shelf. We utilized a number of data sources including DFO multispecies trawl catch data and in situ benthic imagery observations. Most models had excellent predictive capacity with cross-validated Area Under the Receiver Operating Characteristic Curve (AUC) values ranging from 0.760 to 0.977. Areas of suitable habitat were identified for each taxon and were contrasted against their known distribution and when applicable, the location of closure areas designated for their protection. Generalized additive models (GAMs) were developed to predict the biomass distribution of each taxonomic group and serve as a comparison to the RF models. The RF and GAM models provided comparable results, although GAMs provided superior predictions of biomass along the continental slope for some taxonomic groups. In the absence of data observations, the results of this study could be used to identify the potential distribution of sensitive benthic taxa for use in fisheries and habitat management applications. These results could also be used to refine significant concentrations of these taxa as identified through the kernel density analyses.
Distribution Modelling of Sea Pens, Sponges, Stalked Tunicates and Soft Corals from Research Vessel Survey Data in the Gulf of St. Lawrence for Use in the Identification of Significant Benthic Areas
Contributors: Javier Murillo-Perez, Ellen Kenchington, Lindsay Beazley, Camille Lirette, Anders Knudby, Javier Guijarro-Sabaniel, Hugues Benoit, Hugo Bourdages, Bernard Sainte-Marie
... Models of probability of occurrence and predicted biomass distribution have been created using random forest (RF) machine learning techniques for different invertebrate taxa in the Gulf of St. Lawrence. Response data were derived from by-catch data collected from DFO research vessel trawl surveys following a stratified random design based on depth and geographic region. Predictors were drawn from 78 environmental data layers. Occurrence models performed very well for sea pens and stalked tunicates and better than those for soft corals and sponges, with cross-validated AUC (area under the receiver operating characteristic curve) values ranging from 0.71 to 0.91. For the models based on biomass, soft corals and sea pens had the highest R2 values (0.42 and 0.37, respectively) in the southern Gulf of St. Lawrence and stalked tunicates and sea pens in the north (0.41 and 0.27, respectively). Sponges had R2 values less than 0.1 in both areas indicating poor model performance. Biomass models from RF were compared with Generalized Additive Models (GAM). In most of the cases RF and GAM models provided similar results and were both good options, although the fewer assumptions required for RF makes this method more convenient. These results could be used to identify the potential distribution of some vulnerable marine ecosystems indicator taxa and help to refine the borders of the significant benthic area polygons defining significant concentrations of these taxa as identified through the kernel density analyses. In particular these models can be used to extrapolate to areas not covered by the research vessel surveys.