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.
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.
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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: vahid vzairi
... Supplementary Interactive Plot data for NPR and NAG pH values
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: Yufeng Yang
... rotifer species data; environmental fators data; plots data
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: 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