Released in October 2009 by Riot Games, League of Legends is a multiplayer online battle arena game. Typically played in teams of five, each player picks a champion to take on either the role of Top, Middle, Jungle, Attack Damage Carry, or Support. The over 150 champions in the game can be broadly categorized into male, female, and other/unknown (e.g., monsters and robots). In this study, we analyze approximately 1.9 million champion match statistics to determine if there was a systematic gender bias in the way the male and female characters are created and played. We determined that each role has champions that have match statistics that differentiate the genders with roles such as Support and Jungle being the most severe. Further, while there are a few champions that are counter to classic stereotypes (such as the heavily defensive male and the healing support female), many of the champions still fit these stereotypes.

This chapter presents the emerging field of esports analytics by outlining replicable methods for analyzing character roles and team compositions using a data-driven approach. Video game genres such as multiplayer online battle arena, first-person shooter, and real-time strategy have started an era of electronic sports (esports) that has gained further ground over the past decade. Business Insider reports that esports are expected to bring in revenues of 1.5 billion dollars by 2020. The chapter aims to secure data of interest from Heroes of the Storm (HotS) matches. HotsApi provides a set of queries to acquire metadata about the replay files it hosts. Cluster analysis is an unsupervised machine learning method commonly used in exploratory analyses to group "similar" items within a dataset based on certain numeric attributes. The HotsApi documentation lists many different ways to acquire information about the matches hosted on the site.

A new methodology is proposed for clustering datasets in the presence of scattered observations. Scattered observations are defined as unlike any other, so traditional approaches that force them into groups can lead to erroneous conclusions. Our suggested approach is a scheme which, under assumption of homogeneous spherical clusters, iteratively builds cores around their centers and groups points within each core while identifying points outside as scatter. In the absence of scatter, the algorithm reduces to k-means. We also provide methodology to initialize the algorithm and to estimate the number of clusters in the dataset. Results in experimental situations show excellent performance, especially when clusters are elliptically symmetric. The methodology is applied to the analysis of the United States Environmental Protection Agency’s Toxic Release Inventory reports on industrial releases of mercury for the year 2000.

Data set for investigating team compositions in Legend of Legends. See the readme file for file and variable descriptions.

League of Legends is a multiplayer online battle arena game where teams of five players compete against each other. Over the years, players (the crowd) have formed a metagaming strategy, which is widely adopted. This paper questions and answers whether the wisdom of the crowd defined the best strategy. We investigate players' choices of champions (and builds) and their team performance from matches in the North America and Western Europe regions, using the data gathered through the Riot Games official application program interface. We classify team compositions by players' spells and attributes of items, and identify several non-meta strategies that show a consistent advantage over the meta.

Production of both livestock and food crops are central priorities of agriculture; however, food safety concerns arise where these practices intersect. In this study, we investigated the public health risks associated with potential bioaerosol deposition to crops grown in the vicinity of manure application sites. A field sampling campaign at dairy manure application sites supported the emission, transport, and deposition modeling of bioaerosols emitted from these lands following application activities. Results were coupled with a quantitative microbial risk assessment model to estimate the infection risk due to consumption of leafy green vegetable crops grown at various distances downwind from the application area. Inactivation of pathogens ( spp., spp., and O157:H7) on both the manure-amended field and on crops was considered to determine the maximum loading of pathogens to plants with time following application. Overall median one-time infection risks at the time of maximum loading decreased from 1:1300 at 0 m directly downwind from the field to 1:6700 at 100 m and 1:92,000 at 1000 m; peak risks (95th percentiles) were considerably greater (1:18, 1:89, and 1:1200, respectively). Median risk was below 1:10,000 at >160 m downwind. As such, it is recommended that a 160-m setback distance is provided between manure application and nearby leafy green crop production. Additional distance or delay before harvest will provide further protection of public health.

Data set for investigating and measuring illicit bot prevalence in North American and Western Europe League of Legends PvP matches associated with C. S. Lee and I. Ramler, "Rise of the bots: Bot prevalence and its impact on match outcomes in league of Legends," 2015 International Workshop on Network and Systems Support for Games (NetGames), Zagreb, 2015, pp. 1-6.doi: 10.1109/NetGames.2015.7382992 Description of Variables: level: level of summoner matchId: de-identified identification number for match winner: flag indicating whether or not the team won kill: number of kills death: number of deaths assist: number of kills timeCreated: match creation time (UTC-05 for N. Amer, UTC-00 for EUW) duration: match duration (seconds) matchType: match type numOfRunes: number of runes numOfMasteries: number of masteries isBot: Flag indicating whether or not the player is a bot

In this study, we report the human health risk of gastrointestinal infection associated with inhalation exposure to airborne zoonotic pathogens emitted following application of dairy cattle manure to land. Inverse dispersion modeling with the USEPA's AERMOD dispersion model was used to determine bioaerosol emission rates based on edge-of-field bioaerosol and source material samples analyzed by real-time quantitative polymerase chain reaction (qPCR). Bioaerosol emissions and transport simulated with AERMOD, previously reported viable manure pathogen contents, relevant exposure pathways, and pathogen-specific dose-response relationships were then used to estimate potential downwind risks with a quantitative microbial risk assessment (QMRA) approach. Median 8-h infection risks decreased exponentially with distance from a median of 1:2700 at edge-of-field to 1:13 000 at 100 m and 1:200 000 at 1000 m; peak risks were considerably greater (1:33, 1:170, and 1:2500, respectively). These results indicate that bioaerosols emitted from manure application sites following manure application may present significant public health risks to downwind receptors. Manure management practices should consider improved controls for bioaerosols in order to reduce the risk of disease transmission.

1. af.tif: Land-cover from MODIS for the continent of Africa clipped to the tropical regions to match the biomass dataset; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182. 2. af_biov2ct1.tif: Above-ground biomass for the tropical regions of Africa; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185. 3. am.tif: Land-cover from MODIS for the Americas; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182. 4: am_biov2ct1.tif: Above-ground biomass for the tropical regions of the Americas; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185.5: anthrome_0.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(0): No data. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 5: anthrome_11.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(11):Urban. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 6: anthrome_12.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(12):Mixed settlements. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 7: anthrome_21.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(21):Rice villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 8: anthrome_22.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(22):Irrigated villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 9: anthrome_23.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(23):Rainfed villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 10: anthrome_24.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(24):Pastoral villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 11: anthrome_31.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(31):Residential irrigated croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 12: anthrome_32.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(32):Residential rainfed croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 13: anthrome_33.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(33):Populated croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 14: anthrome_34.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(34):Remote croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 15: anthrome_41.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(41):Residential rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 16: anthrome_42.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(42):Populated rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 17: anthrome_43.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(43):Remote rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 18: anthrome_51.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(51):Residential woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 19: anthrome_52.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(52):Populated woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 20: anthrome_53.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(53):Remote woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 21: anthrome_54.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(54):Inhabited treeless and barren lands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 22: anthrome_61.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(61):Wild woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 23: anthrome_62.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(62):Wild treeless and barren lands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 24: as.tif: Land-cover from MODIS for the continent of Asia; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182. 25: as_biov2ct1.tif: Above-ground biomass for the tropical regions of Asia; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185. 26-30: ecoregions_projected.(.dbf/.prj/.qpj/.shp/.shx): Terrestrial Ecoregions of the World is a biogeographic regionalization of the Earth’s terrestrial biodiversity. Units are ecoregions, defined as relatively large units of land or water containing a distinct assemblage of natural communities sharing a large majority of species, dynamics, and environmental conditions. From Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. 31: fi_average.tif: Average fire density 1997-2011. Based on the modified algorithm 1 product of World Fire atlas (WFA, ESA-ESRIN) dataset. UNEP/GRID-Europe compiled the monthly data and processed the global fire density. Unit is expected average number of event per 0.1 decimal degree pixel per year multiplied by 100 (e.g. 64 value means 0.64 events per year) and slightly smoothed. From UNEP, DEWA, GRID -Europe, Collection: Global Estimated Risk Index for Multiple Hazards. Web. 30 Sep 2014,http://preview.grid.unep.ch/index.php?preview=data&events=fires. 32: gl_anthrome.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. All values(see items 5-24). From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x 33: glbctd1t0503m.tif: Gridded Livestock of the World: Cattle. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014). 34: glbgtd1t0503m.tif: Gridded Livestock of the World: Goats. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014). 35: glbpgd1t0503m.tif: Gridded Livestock of the World: Pigs. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014). 36: glbshd1t0503m.tif: Gridded Livestock of the World: Sheep. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014). 37: glds00ag.tif: Gridded Population Density of the World, Version 3: (GPWv3): Population Density Grid. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. From CIESIN, IFPRI, Bank, T. W. & CIAT, Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid. (2011). Web. 26 Sep 2014. http://dx.doi.org/10.7927/H4R20Z93 38: glds00g.tif: Gridded Population Density of the World, Version 3: (GPWv3): Population Density Grid. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. From CIESIN, IFPRI, Bank, T. W. & CIAT, Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid. (2011). Web. 26 Sep 2014. http://dx.doi.org/10.7927/H4R20Z93 39: global_elevation.tiff: GTOPO30 is a global digital elevation model (DEM) with a horizontal grid spacing of 30-arc seconds (0.008333333333333 degrees or approximately 1 kilometer), resulting in a DEM having dimensions of 21,600 rows and 43,200 columns. The horizontal coordinate system is decimal degrees of latitude and longitude referenced to World Geodetic System 84 (WGS84). The vertical units represent elevation in meters above mean sea level. The elevation values range from -407 to 8,752 meters. In the DEM, ocean areas have been masked as no data and have been assigned a value of -9999. Lowland coastal areas have an elevation of at least 1 meter (so in the event that a user reassigns the ocean value from -9999 to 0 the land boundary portrayal will be maintained). Small islands in the ocean less than approximately 1 square kilometer are not represented. GTOPO30 was derived from several raster and vector sources of topographic information. These sources include: Digital Terrain Elevation Data, Digital Chart of the World, USGS 1-degree Digital Elevation Models, Army Map Service 1:1,000,000-scale Maps, International 1:1,000,000-scale Map of the World, Peru 1:1,000,000-scale Map, New Zealand DEM, and Antarctic digital Database. GTOPO30 was developed to meet the needs of the geospatial data user community for regional and continental scale topographic data. The data are suitable for many regional and continental applications, such as climate modeling, continental-scale land cover mapping, extraction ofdrainage features for hydrologic modeling and geometric and atmospheric correction of medium and coarse resolution satellite image data. An example of a recent application derived from GTOPO30 is HYDRO1k, a geographic database (at a resolution of 1 km) developed to provide comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins, and ancillary layers . HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale. The raster data sets are the hydrologically correct DEM, derived flow directions, flow accumulations, slope, aspect, and a compound topographic (wetness) index. The derived streamlines and basins are distributed as vector data sets. GTOPO30 was developed through a collaborative effort led by staff at the U.S. Geological Survey's EROS EDC. The following organizations participated by contributing funding or source data: the National Aeronautics and Space Administration (NASA), the United Nations Environment Programme/Global Resource Information Database (UNEP/GRID), the U.S. Agency for International Development (USAID), the Instituto Nacional de Estadistica Geografica e Informatica (INEGI) of Mexico, the Geographical Survey Institute (GSI) of Japan, Manaaki Whenua Landcare Research of New Zealand, and the Scientific Committee on Antarctic Research (SCAR). From Grenlee S., Gesch, D, available online [http://webmap.ornl.gov/wcsdown/dataset.jsp?ds_id=10003] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A.. 40: global_precip.tiff: The Global Precipitation Climatology Centre (GPCC), which is operated by the Deutscher Wetterdienst (National Meteorological Service of Germany), is a component of the Global Precipitation Climatology Project (GPCP) with the main emphasis on the treatment of the global in-situ observations. The GPCC simultaneously contributes to the Global Climate Observing System (GCOS) and other international research and climate monitoring projects. This rain gauge-only data set was acquired from GPCC and resampled to 0.5 degree grid boxes for use in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II. The GPCC collects precipitation data which are locally observed at rain gauge stations and distributed as CLIMAT and SYNOP reports via the Global Telecommunication System of the World Weather Watch (GTS) of the World Meteorological Organization (WMO). The Centre acquires additional monthly precipitation data from meteorological and hydrological networks which are operated by national services. Meeson B., Los, S, Landis, D., Hall F., Collatz, G., Brown de Colstoun, E. available online [http://webmap.ornl.gov/wcsdown/wcsdown.jsp?dg_id=995_20] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A.. 41: global_soil_types.tiff: A global data set of soil types is available at 1-degree latitude by 1-degree longitude resolution. There are 26 soil units based on Zobler’s assessment of FAO Soil Units (Zobler, 1986). The data set was compiled as part of an effort to improve modeling of the hydrologic cycle portion of global climate models. A more extensive version of these data, including 106 soil units as well as soil texture and slope, is available from NCAR, Scientific Computing Division, Data Support Section; the more extensive data set is entitled "Staub and Rosenweig's GISS Soil & Sfc Slope, 1-Deg" [http://www.dss.ucar.edu/datasets/ds770.0/]. A help file prepared by Matthews and Fung (1987) (soil1x1.help) is provided as a companion file. Image of 26 soil types available at 1-degree by 1-degree resolution. Additional documentation from Zobler’s assessment of FAO soil units is available from the NASA Center for Scientific Information. 42: global_water_capacity: Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. It is often assumed to be spatially invariant in large-scalecomputations of the soil-water balance. Empirical evidence, however, suggests that this assumption is incorrect. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil. A representative soil profile, characterized by horizon (layer) particle size data and thickness, was created for each soil unit mapped by FAO (Food and Agriculture Organization of the United Nations)/Unesco. Soil organic matter was estimated empirically from climate data. Plant rooting depths and ground coverages were obtained from a vegetation characteristic data set. At each 0.5 x 0.5 degree grid cell where vegetation is present, unit available water capacity (cm water per cm soil) was estimated from the sand, clay, and organic content of each profile horizon, and integrated over horizon thickness. Summation of the integrated values over the lesser of profile depth and root depth produced an estimate of the plant-extractable water capacity of soil. The global average of the estimated plant-extractable water capacities of soil is 8.6 cm (Greenland, Antarctica and bare soil areas excluded). Estimates are less than 5, 10 and 15 cm - over approximately 30, 60, and 89 per cent of the area, respectively. Estimates reflect the combined effects of soil texture, soil organic content, and plant root depth or profile depth. The most influential and uncertain parameter is the depth over which the plant-extractable water capacity of soil is computed, which is usually limited by root depth. Soil texture exerts a lesser, but still substantial, influence. Organic content, except where concentrations are very high, has relatively little effect. The file is available in an ascii array format. The format is such that j=1 corresponds to the grid cell bounded by 90.0 and 89.5 degrees south latitude (centered on 89.75) and i=1 corresponds to the grid cell bounded by 0.0 and 0.5 degrees east longitude (centered on 0.25). No data are given for land ice grid cells, most of which occur in Antarctica and Greenland, or for other unvegetated areas. A value of -99.0 indicates either a water grid cell or a land ice grid cell. A value of -1.0 indicates that vegetation is absent (and the plant-extractable water capacity of soil is undefined). Units are cm. The data file may be read as follows: dimension whcdat(720,360) do j=1,360 read(iunit,'(36f5.1)') (whcdat(i,j),i=1,720) enddo Data Citation The data set should be cited as follows: Dunne, K. A., and Cort J. Willmott. 2000. Global Distribution of Plant-extractable Water Capacity of Soil (Dunne). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.43-49: ilf2000_last_proj(.cpg/.dbf/.prj/.qpj/.shp/.shx/.tif): Intact Forest Landscape, 2000 (IFL2000). The world's IFL map is a spatial database (scale 1:1,000,000) that shows the extent of the intact forest landscapes (IFL) for year 2000. IFL is an unbroken expanse of natural ecosystems within the zone of current forest extent, showing no signs of significant human activity, and large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. From Potapov P., Yaroshenko A., Turubanova S., Dubinin M., Laestadius L., Thies C., Aksenov D., Egorov A., Yesipova Y., Glushkov I., Karpachevskiy M., Kostikova A., Manisha A., Tsybikova E., Zhuravleva I. 2008. Mapping the World's Intact Forest Landscapes by Remote Sensing. Ecology and Society, 13 (2) http://www.ecologyandsociety.org/vol13/iss2/art51/ 50: lighted_area_luminosity.tif: NASA Earth Observation Satellite.

League of Legends is a multiplayer online battle arena game that follows a freemium model, and the available in-game transactions do little to impact a player’s performance or ability. Although champions can be purchased with actual (or in-game) money, another aspect of the game is a weekly rotation of ten free champions where players can test new champions before buying them. This project involves scraping champion usage data from online sources where we then analyze what lasting impact the free rotation feature and new and updated content (such as new and updated champions, new skins and official game updates) have on champion usage. Additionally, we have constructed a simple web application (LoLNOVA) that allows users to compare charts of usage statistics, perform simple data analyses, and download data for champions of their choice. Educators can use these data as they are relevant and interesting to many students and help increase students’ interest in quantitative fields.

The agricultural expansion and intensification required to meet growing food and agri-based product demand present important challenges to future levels and management of biodiversity and ecosystem services. Influential actors such as corporations, governments, and multilateral organizations have made commitments to meeting future agricultural demand sustainably and preserving critical ecosystems. Current approaches to predicting the impacts of agricultural expansion involve calculation of total land conversion and assessment of the impacts on biodiversity or ecosystem services on a per-area basis, generally assuming a linear relationship between impact and land area. However, the impacts of continuing land development are often not linear and can vary considerably with spatial configuration. We demonstrate what could be gained by spatially explicit analysis of agricultural expansion at a large scale compared with the simple measure of total area converted, with a focus on the impacts on biodiversity and carbon storage. Using simple modeling approaches for two regions of Brazil, we find that for the same amount of land conversion, the declines in biodiversity and carbon storage can vary two-to fourfold depending on the spatial pattern of conversion. Impacts increase most rapidly in the earliest stages of agricultural expansion and are more pronounced in scenarios where conversion occurs in forest interiors compared with expansion into forests from their edges. This study reveals the importance of spatially explicit information in the assessment of land-use change impacts and for future land management and conservation.

Grid cell based regression coefficients for predicting global biomass in the pantropics. To better account for the variability within a continent, we constructed 100-km grid cells throughout the pantropics. In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider: Asymptotic: \(\mathrm{Biomass} = \theta_1-\theta_2\cdot\exp(-\theta_3\cdot\mathrm{Distance})\), Logarithmic: \(\mathrm{Biomass}=\beta_0+\beta_1\ln\cdot(\mathrm{Distance})\) , or Linear: \(\mathrm{Biomass}=\eta_0+\eta_1\cdot Distance\) Then, for each grid cell, the candidate with the highest R2 is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core (\(\theta_1\)) was estimated as the average biomass density at the largest observed distance in the cell.

distancefromforestedge_pantropics.zip - a zipped geotiff file in WGS84 coordinates whose pixel values indicate the distance in meters to the nearest forest edge as defined by: Baccini, A., Goetz, S.J., Walker, W.S., Laporte, N.T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P.S.A., Dubayah, R., Friedl, M.A., Samanta, S., Houghton, R.A., 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2, 182–185. regression_coefficients_as_shapefile - for calculating biomass storage within 100km grid cells across the pantropics. projected spatially as an ESRI Shapefile where the methods are defined as: method 1: Biomass= θ_1-θ_2⋅exp(-θ_3⋅Distance) method 2: Biomass= β_0+β_1⋅ln(Distance) method 3: Biomass = \eta_0+\eta_1 * Distance

Carbon stock estimates based on land cover type are critical for informing climate change assessment and landscape management, but field and theoretical evidence indicates that forest fragmentation reduces the amount of carbon stored at forest edges. Here, using remotely sensed pantropical biomass and land cover data sets, we estimate that biomass within the first 500 m of the forest edge is on average 25% lower than in forest interiors and that reductions of 10% extend to 1.5 km from the forest edge. These findings suggest that IPCC Tier 1 methods overestimate carbon stocks in tropical forests by nearly 10%. Proper accounting for degradation at forest edges will inform better landscape and forest management and policies, as well as the assessment of carbon stocks at landscape and national levels.

Forty-two percent of Escherichia coli and 58 % of Enterococcus spp. isolated from cattle feedlot runoff and associated infiltration basin and constructed wetland treatment system were resistant to at least one antibiotic of clinical importance; a high level of multidrug resistance (22 % of E. coli and 37 % of Enterococcus spp.) was observed. Hierarchical clustering revealed a closely associated resistance cluster among drug-resistant E. coli isolates that included cephalosporins (ceftiofur, cefoxitin, and ceftriaxone), aminoglycosides (gentamycin, kanamycin, and amikacin), and quinolone nalidixic acid; antibiotics from these classes were used at the study site, and cross-resistance may be associated with transferrable multiple-resistance elements. For Enterococcus spp., co-resistance among vancomycin, linezolid, and daptomycin was common; these antibiotics are reserved for complicated clinical infections and have not been approved for animal use. Vancomycin resistance ( n = 49) only occurred when isolates were resistant to linezolid, daptomycin, and all four of the MLS B (macrolide-lincosamide-streptogramin B) antibiotics tested (tylosin, erythromycin, lincomycin, and quinipristin/dalfopristin). This suggests that developing co-resistance to MLS B antibiotics along with cyclic lipopeptides and oxazolidinones may result in resistance to vancomycin as well. Effects of the treatment system on antibiotic resistance were pronounced during periods of no rainfall and low flow (long residence time). Increased hydraulic loading (short residence time) under the influence of rain caused antibiotic-resistant bacteria to be flushed through the treatment system. This presents concern for environmental discharge of multidrug-resistant organisms relevant to public health.

League of Legends is a multiplayer online battle arena game where features are unlocked as players level up their accounts. Because it takes a significant amount of time to reach the max level, there exist accounts that are leveled automatically by illicit “bots” and then sold on the market at the max level. These bots participate in matches like human players but are incapable of either playing intelligently or cooperatively with teammates. This paper presents an investigation into the prevalence of bots in player-versus-player match types and their impact on match outcomes on the North America and Europe West servers, using the data gathered through the Riot Games official application program interface. We demonstrate that bots are present in all major match modes at various levels and that they negatively influence the balance of matches on both servers.