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91,407 results for “Effects With / Effects Of”
Short-term effects of biochar on soil CO2 efflux in boreal Scots pine forests
<p>This dataset includes all the data we collected at the first summer after biochar application in boreal forests. Our paper“ the effect of biochar on soil CO<sub>2</sub> efflux in boreal forests“ now is under review in Annals of Forest Science. Biochar prepared at two reaction temperatures was applied at three rates (including non-amended controls). During the first year after treatment, efflux increased with higher rates of biochar, but the reaction temperature had no effect. o explain char effects on efflux, soil moisture and temperature were also added to the model testing treatment effects. These environmental variables explained more of the variation in efflux and caused treatment to no longer have a significant effect. Based on this result, we concluded that soil temperature explains the effect of char on efflux.</p>
An elaborate data set on human gait and the effect of mechanical perturbations
<p>This data set includes measurements during trials of human walking on a treadmill with a movable base intended to provide data rich in content for the identification of the human's control system. The primary experiments were designed to longitudinally perturb the subject at the ground contact by randomly accelerating the belt. The marker locations (treadmill and human), treadmill accelerations, treadmill belt speeds, and the forces and moments from the dual force plates were measured during the trials.</p> <p>PeerJ Article: https://peerj.com/articles/918</p> <p>PeerJ Preprint: https://peerj.com/preprints/700/</p> <p>Paper source repository: https://github.com/csu-hmc/perturbed-data-paper</p>
Model, Data, and Analysis Scripts for The Evolution of Cooperation by the Hankshaw Effect
<p>Model, Data, and Analysis Scripts for The Evolution of Cooperation by the Hankshaw Effect as submitted</p>
Dexmedetomidine Effects On Shivering and Core Temperature in Awake Normal Subjects
<p>Ten normal adult human subjects received a rapid intravenous infusion of two liters of cold (4ºC) isotonic saline on two separate test days, and we measured their core body temperature, shivering, hemodynamics and sedation for two hours. On one test day, fluid infusion was preceded by placebo infusion. On the other test day, fluid infusion was preceded by 1.0 µg/kg bolus of dexmedetomidine over 10 minutes.</p>
Input data for 'forest_carbon_edge_effects'
<p>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.<br /> 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.<br /> 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.<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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<br /> 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.<br /> 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.<br /> 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.<br /> 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.<br /> 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<br /> 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).<br /> 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).<br /> 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).<br /> 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).<br /> 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<br /> 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<br /> 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..<br /> 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..<br /> 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. <br /> 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/<br /> 50: lighted_area_luminosity.tif: NASA Earth Observation Satellite.</p>
Input dataset for carbon forest edge effects analysis
<p>Dataset that is used for the calculation of forest edge biomass effect from the following github project: [DOI forest_carbon_edge_effect] (http://dx.doi.org/10.5281/zenodo.15697)</p> <p>Contains global biomass, landcover data, anthrome, soil, elevation, water capacity, fire, luminosityr, cattle, goat, sheep, human population, and anthrome data.</p>
Reproducibility in science: calculated kinetic isotope effects for cyclopropyl carbonyl radical.
<p>Calculated kinetic isotope effects, without tunnelling corrections, for the ring opening of cyclopropylcarbinyl radical using a variety of different Hamiltonians and basis sets.</p>
Easement_cost-effectiveness: Effects of disputes and easement violations on the cost-effectiveness of land conservation
<p>This repository includes the data and files for the example analysis of the following paper published in PeerJ:</p> <p>Schuster and Arcese (2015) Effects of disputes and easement violations on the cost-effectiveness of land conservation. PeerJ x:xxxx</p>
The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity
<p>This upload comprises supplementary material and data for the paper "The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity" Albrecht et al. (2015), Human Brain Mapping DOI: 10.1002/hbm.23052</p> <p>1) The cleaned and group ICA resting state data in EEGLAB format.</p> <p>2) Basic demographics for the participants. Drug order 1 = placebo first, then dexamphetamine second. Drug order 2 = dexamphetamine first, then placebo second. Gender 1 = Female, Gender 2 = Male.</p> <p>3) Bayesian hierarchical modelling functions for R and Stan (through rstan). See paper for more details.</p>
The Quadratic Zeeman effect used for state-radius determination in neutral donors and donor bound excitons in Si:P.
<p>Raw experimental data of Photoluminescence as a function of magnetic field for phosphorus impurity in silicon at 4.2K. First column is energy in meV, the other columns are the photo-luminescence intensities in arbitrary units measured at different magnetic fields. The first row indicates the values of the magnetic fields presented in each column. The photo-luminescence measured at 10T (and presented in this dataset as column 11) is shown in the paper as Fig.2. </p>
NIMROD growth rates (1/s) by finite element poly_degree (columns) and case from "The Impact of Collisionality, FLR and Parallel Closure Effects on Instabilities in the Tomakak Pedestal: Numerical Studies with the NIMROD code”
<p>NIMROD growth rates (1/s) by finite element poly_degree (columns) and case from "The Impact of Collisionality, FLR and Parallel Closure Effects on Instabilities in the Tomakak Pedestal: Numerical Studies with the NIMROD code” as submitted to Physics of Plasmas</p>
Strain effects on oxygen migration in perovskites
<p>This data is cross-posted here:</p> <p>https://materialsdata.nist.gov/dspace/xmlui/handle/11256/701</p> <p>Please download from the link above instead.</p> <p> </p> <p>Fast oxygen transport materials are necessary for a range of technologies, including efficient and cost-effective solid oxide fuel cells, gas separation membranes, oxygen sensors, chemical looping devices, and memristors. Strain is often proposed as a method to enhance the performance of oxygen transport materials, but the magnitude of its effect and its underlying mechanisms are not well-understood, particularly in the widely-used perovskite-structured oxygen conductors. This work reports on an ab initio prediction of strain effects on migration energetics for nine perovskite systems of the form LaBO3, where B = [Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Ga]. Biaxial strain, as might be easily produced in epitaxial systems, is predicted to lead to approximately linear changes in migration energy. We find that tensile biaxial strain reduces the oxygen vacancy migration barrier across the systems studied by an average of 66 meV per percent strain for a single selected hop, with a low of 36 and a high of 89 meV decrease in migration barrier per percent strain across all systems. The estimated range for the change in migration barrier within each system is +/- 25 meV per percent strain when considering all hops. These results suggest that strain can significantly impact transport in these materials, e. g., a 2% tensile strain can increase the diffusion coefficient by about three orders of magnitude at 300 K (one order of magnitude at 500 degrees C or 773 K) for one of the most strain-responsive materials calculated here (LaCrO3). We show that a simple elasticity model, which assumes only dilative or compressive strain in a cubic environment and a fixed migration volume, can qualitatively but not quantitatively model the strain dependence of the migration energy, suggesting that factors not captured by continuum elasticity play a significant role in the strain response.</p>
Standard Model effective field theory global fit using electroweak data
<p>These are files that can be used to reproduce the fit results in 10.1007/JHEP09(2016)157 and arXiv:1610.01783<strong>.</strong></p> <p> </p>
Equation of State Effects on Gravitational Waves from Rotating Core Collapse
<p>Gravitational waveforms from 1824 fiducial and detailed electron capture simulations, sampled at 65535 Hz. The file is in HDF5 format, using the flags {dtype="f4",compression="gzip",shuffle=True,fletcher32=True}. Each group is contained in the "waveforms" top-level group and is named with the "A" and "omega_0" values from Equation 5 and the EOS. In each sub-group is a dataset containing timestamps in seconds (t=0 is core bounce) and a dataset containing the strain multiplied by the distance in centimeters. The values of A in kilometers, omega_0 in radians/s, and the EOS are stored as attributes of each group.</p> <p>In addition, the Ye(rho) profiles are stored in the "yeofrho" top-level group. Each sub-group is labeled by the EOS used to generate the profile.</p> <p>Finally, select reduced data is stored in the "reduced_data" top-level group. The following quantities are each stored as a 1824-element array, where elements of the same index from different datasets correspond to the same 2D simulation.</p> <p>A(km) -- differential rotation parameter in Equation 5<br> D*bounce_amplitude_1(cm) -- The minimum of the first (negative) GW strain peak, multiplied by distance.<br> D*bounce_amplitude_2(cm) -- The maximum of the second (positive) GW strain peak, multiplied by distance.<br> EOS -- the equation of state used in the simulation<br> MbarICgrav(Msun) -- gravitational mass of the inner core, averaged over time after core bounce<br> Mgrav1_IC_b(Msun) -- gravitational mass of the inner core at bounce<br> Mrest_IC_b(Msun) -- rest mass of the inner core at bounce<br> SNR(aLIGOfrom10kpc) -- signal to noise ratio of the GW signal, assuming a distance of 10kpc and aLIGO sensitivity<br> T_c_b(MeV) -- central temperature at bounce<br> Ye_c_b -- central electron fraction at bounce<br> alpha_c_b -- central lapse at bounce<br> beta1_IC_b -- ratio of rotational kinetic to gravitational potential energy of the inner core at bounce<br> fpeak(Hz) -- frequency of the post-bounce GW oscillations<br> j_IC_b() -- angular momentum of the inner core at bounce<br> omega_0(rad|s) -- initial (pre-collapse) rotation rate used in Equation 5<br> omega_max(rad|s) -- maximum rotation rate achieved outside of 5km<br> rPNSequator_b(km) -- radius of the rho=10^11 g/ccm contour along the equator at bounce<br> rPNSpole_b(km) -- radius of the rho=10^11 g/ccm contour along the pole at bounce<br> r_omega_max(km) -- radius where omega_max occurs<br> rho_c_b(g|ccm) -- central density at bounce (not time averaged)<br> rhobar_c_postbounce(g|ccm) -- central density time averaged after bounce<br> s_c_b(kB|baryon) -- central entropy at bounce<br> t_postbounce_end(s) -- time of the end of the postbounce signal (t=0 is core bounce)<br> tbounce(s) -- time of core bounce (t=0 is the beginning of the simulation)<br> </p>
Disease Spread in Age Structured Populations with Maternal Age Effects
<p>Fundamental ecological processes, such as extrinsic mortality, determine population age structure. This influences disease spread when individuals of different ages differ in susceptibility or when maternal age determines offspring susceptibility. We show that Daphnia magna offspring born to young mothers are more susceptible than those born to older mothers, and consider this alongside previous observations that susceptibility declines with age in this system. We used a susceptible- infected compartmental model to investigate how age-specific susceptibility and maternal age effects on offspring susceptibility interact with demographic factors affecting disease spread. Our results show a scenario where an increase in extrinsic mortality drives an increase in transmission potential. Thus, we identify a realistic context in which age effects and maternal effects produce conditions favouring disease transmission. </p> <p>epi model R script.R</p> <p>This is the script for the SIR model as well as the associated script for life history data. </p> <p>main.body size.csv</p> <p>This is the data for the body size data collected in the main experiment. This was measured using imageJ, was recorded in pixels and converted into millimetres. </p> <p>main.exposed.csv</p> <p>This is the proportion of infected/not infected individuals from an exposed treatment group. This was a subset of individuals from the entire experiment. This was the result of the exposures from the main experiment. </p> <p>main.reproduction.csv</p> <p>This document records reproduction for individuals from old or young mothers. It is a count of the offspring born at each reproductive event, which occurs generally every three days, though variation in interclutch interval increases with age. This was from the main experiment. Only those who were unexposed to the parasite, where used for this portion of the experimental work. </p> <p>sm.body size.csv</p> <p>This records body size similarly to above, and was an independent replication of the main experiment. </p> <p>sm.infection status.csv</p> <p>This is infection outcomes of exposures carried out as above, in an independent replication of the main experiment. </p> <p>sm.total babies.csv</p> <p>This is the reproductive output, carried out similarly to above, but in an independent replication of the main experiment. </p>
Dataset supplementing Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology. DOI: 10.1111/gcb.13714</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
Skyrmions at the edge: Confinement effects in Fe/Ir(111)
<p>We have employed spin-polarized scanning tunneling microscopy and Monte-Carlo simulations to investigate the effect of lateral confinement onto the nanoskyrmion lattice in Fe/Ir(111). We find a strong coupling of one diagonal of the square magnetic unit cell to the close-packed edges of Fe nanostructures. In triangular islands this coupling in combination with the mismatching symmetries of the islands and of the square nanoskyrmion lattice leads to frustration and triple-domain states. In direct vicinity to ferromagnetic NiFe islands, the surrounding skyrmion lattice forms additional domains. In this case a side of the square magnetic unit cell prefers a parallel orientation to the ferromagnetic edge. These experimental findings can be reproduced and explained by Monte-Carlo simulations. Here, the single-domain state of a triangular island is lower in energy, but nevertheless multi-domain states occur due to the combined effect of entropy and an intrinsic domain wall pinning arising from the skyrmionic character of the spin texture.</p>
An Easy Way to Show Memory Color Effects
<p>This dataset supplements the following article:</p> <p>Witzel, C. (2016). An Easy Way to Show Memory Color Effects. i-Perception, 7(5), 1-11. doi:10.1177/2041669516663751; http://journals.sagepub.com/doi/10.1177/2041669516663751</p> <p>The Excell file includes three sheets. In all sheets, rows correspond to participants, the three first columns provide sex, age, and colour deficiency (0 = colour deficient, 1 = non-deficient). The columns "memcol" provide the main data, i.e. the choice between the grey and the bluish version of the respective stimulus.</p> <p><strong>Sheet 1: Study1</strong></p> <p>Data corresponds to Figure 3: memcol1 = disk, memcol2 = banana.</p> <p><strong>Sheet 2: Study2a</strong></p> <p>Data corresponds to Figure 4: memcol1 = disk, memcol2 = banana, memcol3-6 = Mix1-4.</p> <p><strong>Sheet 3: Study2b</strong></p> <p>Data corresponds to Figure 5: memcol1-4 = Mix1-4.</p>
Effect of variable thresholds on calculating linkage disequilibrium and population structure, using Plink 1.9
<p>The figure presented here shows how the number of LD-independent SNPs and the apparent population structure can change drastically, depending on what input thresholds are used for the calculations. The population sample consists of 220 fruit-fly (Drosophila melanogaster). Most of the population structure plots indicate four subpopulations, which on further investigation using Fst indicate that this is caused by defined trans-centromeric regions, without evidence for genotyping error, and probably reflective of historic admixture. In the plots of population structure, the number in each box indicates the number of independent SNPs which were used in the IBD calculatations. Points are coloured by order in which each fly was sequenced, and some error is noticable for the beige points in the top-right plots.</p> <p>The Plink program provides a useful method for selecting single-nucleotide polymorphisms (SNPs) which are independent of linkage disequilibrium (LD), and also of visualising the genetic relatedness between individuals in a population sample, using identity-by-descent analysis (IBD). The selection of LD-independent SNPs requires three user-specfied paramaters, alongside the genotype data: i. Window-size, in kilobases (Kb) within which all pairwise comparisons between SNPs will be made, ii. Step-size, in number of SNPs, iii. r2 threshold between any two SNPs, below which they are considered to be independent (fixed here at 0.5).</p>
Data from Dowiasch et al. (2015) Effects of aging on eye movements in the real world
<p>The effects of aging on eye movements are well studied in the laboratory. Increased saccade latencies or decreased smooth-pursuit gain are well established findings. The question remains whether these findings are influenced by the rather untypical environment of a laboratory; that is, whether or not they transfer to the real world. We measured 34 healthy participants between the age of 25 and 85 during two everyday tasks in the real world: (I) walking down a hallway with free gaze, (II) visual tracking of an earth-fixed object while walking straight-ahead. Eye movements were recorded with a mobile light-weight eye tracker, the EyeSeeCam (ESC). We find that age significantly influences saccade parameters. With increasing age, saccade frequency, amplitude, peak velocity, and mean velocity are reduced and the velocity/amplitude distribution as well as the velocity profile become less skewed. In contrast to laboratory results on smooth pursuit, we did not find a significant effect of age on tracking eye-movements in the real world. Taken together, age-related eye-movement changes as measured in the laboratory only partly resemble those in the real world. It is well-conceivable that in the real world additional sensory cues, such as head-movement or vestibular signals, may partially compensate for age-related effects, which, according to this view, would be specific to early motion processing. In any case, our results highlight the importance of validity for natural situations when studying the impact of aging on real-life performance.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.