Contributors: Gaetan Montero, Cécile Tannier
... Contributors: Gaëtan Montero, Cécile Tannier, Isabelle Thomas Date:2019-16-10 Description: This data set can be used to reproduce the analyses made by the authors in their paper “Morphological delineation of cities based on scaling properties of urban patterns: a comparison of three methods”. It contains 12 shapefiles that represent theoretical urban patterns and 4 shapefiles that can be used to delineate the morphological agglomeration of Brussels (Belgium). It also contains a R script to calculate the carrying capacity of a logistic percolation function. Description of each file 2_Figure_1: theoretical street network for testing the Natural Cities method 3_Figure_2: theoretical street network for the comparison of two variants of the Natural Cities method 4_Figure_3: theoretical street network to evaluate the effects of the spatial extent of the study area on the delineation of Natural Cities 5_Figure_5a: theoretical pattern for testing MorphoLim (building footprints) – dense urban core 6_Figure_5b: theoretical pattern for testing MorphoLim (building footprints) – less dense urban core 7_Figure_6: theoretical pattern (building footprints) to evaluate the effects of the geographic extent of the study area on the delineation with MorphoLim 8_Percolation_C_Calculation: R code to calculate the carrying capacity of a logistic function (Hierarchical Percolation) 9_Figure_7: theoretical street network for testing Hierarchical Percolation 10_Figure_8: theoretical polycentric street network for testing Hierarchical Percolation 11_Figure_9ac: theoretical urban pattern crossed by a large non built area (road intersections) 12_Figure_9b: theoretical urban pattern crossed by a large non built area (building footprints) 13_Figure_10ac: theoretical pattern where a built ribbon links two urban centres (roads intersections ) 14_Figure_10b: theoretical pattern where a built ribbon links two urban centres (building footprints) 15_Belgium_buildings: cadastral data of buildings (2D) for Belgium (© 2009 Administration Générale de la Documentation Patrimoniale) 16_Brabant_buildings: cadastral data of buildings (2D) for the province of Brabant (© 2009 Administration Générale de la Documentation Patrimoniale) 17_Belgium_roads: road network data come from the platform Geofabrik of OpenStreetMap (http://download.geofabrik.de, accessed 08/21/2018) for Belgium 18_Brabant_roads: Road network data come from the platform Geofabrik of OpenStreetMap (http://download.geofabrik.de, accessed 08/21/2018) for the province of Brabant.
Contributors: Tetsuji Okada
... DSA files of human (N to Z, by gene name) : UniProt ID is used for a protein to which no gene name is assigned.
Contributors: Tetsuji Okada
... DSA files of human (A to M, by gene name) : UniProt ID is used for a protein to which no gene name is assigned.
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Contributors: Tetsuji Okada
... DSA files of E. coli (A to M, by gene name) UniProt ID is used for a protein to which no gene name is assigned.
Contributors: Juan Gabriel Bayona
... the folder contains input and setup files of the article An Alternative Method to Determine Extreme Hydrodynamic Forces with Data Limitations for Offshore Engineering
Contributors: Chunli Dai
... Here are the results in a paper entitled "Characterization of the 2008 phreatomagmatic eruption of Okmok from ArcticDEM and InSAR: deposition, erosion, and deformation" submitted to JGR Solid Earth in 2019. It includes the 2-m resolution surface elevation change of the 2008 Okmok eruption (Fig. 2a in the paper) and the 2-m resolution post-eruptive elevation change rate map (Fig. 3), as well as the corresponding uncertainties (Fig. S3). It also includes the boundary of the proximal deposit field classified using a minimum elevation increase of 2 m, the boundary of large slope failure, and the shorelines of two lakes (Figs. 2a, S5, and S6) at different acquisition times. The GeoTIFF files can be viewed in free and open-source software QGIS, in Google Earth, or by Matlab using code https://github.com/ihowat/setsm_postprocessing/blob/master/readGeotiff.m. The shapefiles can be viewed in QGIS and Google Earth.
Contributors: Nikhil Kaza
... This curated dataset is derived from public sources. The aim was to link urban form measurements to energy consumption as proxied by sales in gas stations in the United States. The geographic resolution is a county.
Contributors: Qiankun Liu, Jingang Jiang, Changwei Jing, Zhong Liu, Jiaguo Qi
... In this paper, a new, alternative, multi-scale, multi-pollution source waste load allocation (WLA) system was developed, with a goal to produce optimal, fair quota allocations at multiple scales. The new WLA system integrates multi-constrained environmental Gini coefficients (EGCs) and Delphi-analytic hierarchy process (Delphi-AHP) optimization models to achieve the stated goal. This dataset consists of the raw data and the source code of models (The multi-constrained environmental Gini coefficients and Delphi-analytic hierarchy process optimization models). The source code of the multi-constrained EGCs and Delphi-AHP models was used to run the program in MATLAB environment to allocate waste load reduction quotas at both the regional scale and the site-specific scale with multiple pollution sources. The raw data mainly consists of the following two parts: (1) The shp files of various geographic information data, which was used to depicture the administrative divisions, pollution source distribution, geographical characteristics and patterns of Xian-jiang watershed; (2) The basic data includes the statistical yearbook data of villages and towns in Ningbo city, the various indicator data using to calculate the weights at criteria level and decision-making level, the contribution coefficients, and the EGC values of the three pollutants. On the basis of these data, a new, alternative, multi-scale, multi-sector optimal WLA framework was developed. The new scheme provides decision-makers critical information (i.e., the best compromise solutions of WLA) and practical guidance as they address the related water pollution control. The results, in comparison with existing practices by the local governments, suggested that the pollution discharge quota at regional scale is much fairer than the existing WLA and, even have some environmental economic benefits at pollutant source scale after optimal WLA. Some important conclusions had been found: 1) Reductions and proportions of pollutants at regional scale are significantly associated with the region’s actual socioeconomic development modes. 2）There are certain characteristics that high-reduced pollution sources tend to share (which are listed in the article). The sources with the above features should be the top priorities in the reduction of removals. 3）Most previous studies reported primarily on the WLA of removals among point sources pollution. Conversely, we found that the industrial pollution source should be the last option for reduction from an environmental-economic benefit perspective. Instead, the often overlooked types, such as agricultural non-point source and domestic sources, deserve more attention, especially in extensive rural areas.
Contributors: Ryan Watkins
... Version 1.0, September 9, 2019 Purpose: Created as part of a project funded by NASA’S Lunar Data Analysis Program (LDAP), the purpose of this dataset is to provide locations and diameters of boulders around small, young impact craters on the Moon. These boulder counts were conducted as part of a study aimed at determining regolith production rates and assessing landing site hazards, as discussed in the associated publications. Researchers are encouraged to read the publications and data description document to understand how the data was acquired and used. This database contains boulder distributions around small (< 1 km), young (< 200 Ma) lunar impact craters located near spacecraft landing sites. The most up-to-date database contains boulder diameters and coordinates for counts around Surveyor (Apollo 12), Cone (Apollo 14), North Ray (Apollo 16), South Ray (Apollo 16), Camelot (Apollo 17), and Zi Wei (Chang’e-3) craters. Boulders were manually identified and measured on Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images (Robinson et al., 2010) at scales of ~0.5-2 m/pixel. LROC NAC images allow for boulders ~1-2m in size and larger to be identified and measured. The tools for measuring boulders were CraterTools (Kneissl et al., 2011) and Crater Helper Tools (Nava, 2011), both developed for the ArcMap GIS platform. These boulder distributions are being used to understand boulder degradation rates on the lunar surface, and to assess landing site hazards for future surface missions to the Moon. This dataset is being archived in Mendeley Data and at the Planetary Data System (PDS) Cartography and Imaging Node for use in future boulder distribution and landing hazard studies. Future boulder counts and any refinements to existing measurements will be uploaded into subsequent versions of this dataset here and at the PDS IMG Annex: https://astrogeology.usgs.gov/search/map/Moon/Research/Regolith/lunar_boulder_data_bundle
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.