br Acknowledgments This work was supported

Acknowledgments This work was supported by National Natural Science Fund of China (Grant no. 61375079).
Data The subsequent 52 files are for the specific environmental conditions, under which the six layer configurations are examined. The 52 files are the production of 13 climates and four occupancy schedule profiles. The file naming convention is as follows [Serial Number_Climate Representative City_Occupancy Profile.xls], and the naming abbreviations are provided in Table 1. Each file of these 52 files has 8 tabs; six are the primary PTI graphs for the 6 layer configurations. The remaining two tabs contain; first, a tab that comprises the raw cooling and heating loads simulation results, and second, a tab that displays the layer configurations performance ranking.
Experimental design, materials and methods The EnergyPlus standalone version (8.4) was employed as a simulation software. The “IdealLoadsAirSystem” object was used to calculate the annual cooling and heating loads, where It calculates the purchase diltiazem hcl that is being consumed to maintain the desired set-points. A window-less single-zone room of 6×6×3m served as a case study, and It was assumed that all its surfaces (walls, roof and floor) have the same construction. The six construction configurations were produced by rearranging brick and insulation board layers. The total brick width was fixed at 20cm across the configurations, whereas the insulation varied to generate the PTI graphs. For each of the 1248 cases, the PTI values were obtained by plotting the cooling load of 25 permutations, i.e. five insulation thicknesses and five cooling set-points, while having the 20cm bare-brick cases as the energy saving/loss benchmarks. Eventually, the PTI values are sorted and statistically processed, then transferred into the spreadsheets that are supplied herewith in this article. Further information on the adopted methodology is presented in [1].
Acknowledgements This work was supported by Mae Laboratory, Department of Architecture, Graduate School of Engineering, The University of Tokyo, Japan.
Data Table 1 provides the description and encoding values for the variables (note that variable descriptions are also available in the spreadsheet). The first line provides the full name, the second line a short name. Encoding values are represented by integer numbers (where appropriate, such as sample sizes, or number of individual estimations performed) or by categorical coding.
Experimental design, materials and methods Original Scopus query:
Acknowledgements The dataset creation was supported by Mendel University׳s IGA project PEF_TP_2016009. “The use of meta-analysis in the empirical causal relationship literature in energy and environmental economics”. The research team responsible for the dataset creation: Vladimír Hajko (project lead), Agáta Kociánová and Martina Buličková.
Data These mapping products were derived through terrain analysis and a technique of pattern classification performed on DEMs obtained from HydroSHEDS – the void-filled DEM, and the hydrologically conditioned DEM (http://hydrosheds.cr.usgs.gov/overview.php) – with a 3 arc-second resolution (0.00083333°, approximatively 90m at the equator). Specifically, the flood-prone areas were identified by applying a linear binary classifier based on a morphologic descriptor named Geomorphic Flood Index (GFI) [4,7,8]. The raster maps have a 90m resolution and are geo-referenced. The coordinate system of the maps is UTM (Universal Transverse Mercator) Zone 17N, the projection is Transverse Mercator, and the geodetic system is NAD (North American Datum) 1983. To simplify the management and the use of the data, the continental U.S. was divided into 18 major water resources regions, considering the hydrologic units identified by the United States Geological Survey (USGS) (see Fig. 1, panel 1).
Experimental design, materials and methods