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.
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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.
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
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 firstname.lastname@example.org