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
Data for: MAPPING CHARACTERISTICS OF AT-RISK POPULATION TO DISASTERS IN THE CONTEXT OF BRAZILIAN EARLY WARNING SYSTEM
Contributors: Regina Célia dos Santos Alvalá, Mariane Assis Dias, Silvia Saito, Claudio Stenrer, Cayo Franco, Pilar Amadeu, Julia Ribeiro, Rodrigo Santana, Carlos Nobre
... This dataset includes 6.437 polygons of BATER from 825 brazilian municipalites with landslides and hydrological risk areas that was used to characterize the at-risk population in this present article. Also is available the data dictionary that describes the variables about the residents and households. This datased was produced in 2018 by CEMADEN and IBGE, as detailed in the article. It is available for everyone in the link: https://www.ibge.gov.br/apps/populacaoareasderisco/
Data for: Landscape structural analysis of the Lençóis Maranhenses National Park: Implications for conservation
Contributors: Yuri Teixeira Amaral, Larissa Barreto, Edyane Moraes dos Santos, MILTON RIBEIRO
... Matadata of final mapping and landscape analysis of Lençóis Maranhenses National Park.
Top results from Data Repository sources. Show only results like these.
Contributors: Tongtong Wang, Yuankun Luo, Zhilin Tao, Weijie Chen, Xin Gu
... The zip file contains project files, screenshots of research results, chart data, experimental data, simulation data, and grid independence verification data.
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
Contributors: Tanika Chakraborty, Rajshri (Raji) 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: Alexander 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)