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MUSTEC Deliverable 9.1 Inputs and results. MUSTEC project.
<p>This dataset contains the main inputs needed to perform sustainability assessment of concentrated solar power (CSP) plants through an input-output analysis. Results are also included. Additional information is available in the document <em>Deliverable 9.1. Sustainability assessment of future CSP cooperation projects in Europe.</em></p> <p>For information on the project see: https://www.mustec.eu/</p>
Input Data for "Molecular Lignin Solubility and Structure in Organic Solvents"
<p>Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis. In order to make this fit into the 50GB Zenodo limit, it was constructed with the following tar command: `tar -zcvf ligninsolvationstudy.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*log" --exclude="*[0-9].out" --exclude="*npz" --exclude="*pkl" --exclude="*npy" --exclude="*png" --exclude="*bmim*" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*txt" --exclude="*state_prev.cpt" LigninSolvation`, which intentionally excludes large files. The full dataset is available upon request.</p> <p><strong>Directory Descriptions</strong></p> <p><strong>BuildSolventBoxes</strong> contains the scripts and inputs needed to make the solvent boxes suitable for use with the VMD solvate plugin.<br> <strong>BuildSystems</strong> assembles the lignin polymers and solvates them into a complete simulation system. Depends on the outputs from [LigninBuilder](https://github.com/jvermaas/LigninBuilder).<br> <strong>Equilibrium</strong> has all the equilibrium trajectories and the scripts needed to set them up.<br> <strong>FEP</strong> has the free energy perturbation calculation key outputs (the fepout files) and the scripts needed to set up the calculation and analyze them.</p> <p>The scripts are <em>mostly</em> python scripts, but some are also in tcl, and have the appropriate file endings. GROMACS run input files (.tpr) and namd configuration files (.namd) may also be of general interest.</p>
Vortex input files -- McHugh et al. Staying Alive: Long-term success of small cetacean interventions in southwest Florida
<p>Vortex input file used to generate population level impacts in the manuscript "Staying Alive: Long-term success of small cetacean interventions in southwest Florida", by K. A. McHugh, et al. </p>
Vortex input files -- Ashe et al., "Minding the data-gap trap: predicting the dynamics of abundant dolphin species under uncertainty"
<p>Vortex input file used for analyses presented in:<br> "Minding the data-gap trap: predicting the dynamics of abundant dolphin species under uncertainty", <br> by Erin Ashe, Rob Williams, Christopher Clark, Christine Erbe, Leah Gerber, Ailsa Hall, Philip Hammond, Robert C. Lacy, Randall Reeves, & Nicole Vollmer<br> </p>
Input Files for Peptide Translocation Across Phospholipid Membranes Using Various Collective Variables and Martini Coarse-Grained Simulations
<p>Input files for publication: Ivo Kabelka, Radim Brožek, and Robert Vácha: Selecting Collective Variables and Free Energy Methods for Peptide Translocation Across Membranes, Journal of Chemical Information and Modeling, submitted</p>
r-process abundances in neutron-rich merger ejecta given different theoretical nuclear physics inputs
<p>This data release contains nucleosynthesis predictions for the r-process abundances presented in Côté, Eichler, Yagüe, Vassh et al. (2021) for compact object merger ejecta based on the publicly available simulation trajectories of Rosswog et al. (2013). All ejecta for the merger scenarios considered here are very neutron-rich (Ye ~ 0.016-0.11). Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating (here with a reheating efficiency of 50%). Results are reported for several different theoretical nuclear physics inputs but all calculations make use of the GEF fission yield prescription (see Vassh et al. 2019). All abundances are given at 1 Myr (10^6 years) post-merger. Please see the README file for more details and references.</p> <p>When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh et al. 2021), and refer to Vassh et al. (2019) and Côté, Eichler, Yagüe, Vassh et al. (2021) for further details on the nuclear data applied as well as Rosswog et al. (2013), Piran et al. (2013), and Korobkin et al. (2012) for further details on the merger ejecta trajectories.</p>
A low-cost contactless overhead micrometer surface scanner (input data)
<p>This is an open dataset and also the input data to create the supplementary material for the paper "A low-cost contactless overhead micrometer surface scanner (supplementary material)", in the Applied Sciences journal, by the same authors.</p> <p>The design and implementation of a contactless scanner and its software are proposed. The scanner regards the photographic digitisation of planar and approximately planar surfaces and is proposed as a cost-efficient alternative to off-the-shelf solutions. The result is 19.8 Kppi, micrometer scans, in the service of several applications. Accurate surface mosaics are obtained based on a novel image acquisition and image registration approach that actively seeks registration cues by acquiring auxiliary images and fusing proprioceptive data in the correspondence and registration tasks. The device and operating software are explained, provided as an open prototype, and evaluated qualitatively and quantitatively.</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>
IMPACT OF US BROWN SWISS GENETICS ON MILK QUALITY FROM LOW-INPUT HERDS IN SWITZERLAND: INTERACTIONS WITH SEASON
<p>This study aimed to investigate the effect of, and interactions between, US Brown Swiss genetics and season on milk yield, basic composition and fatty acid profiles, from cows on low-input farms in Switzerland. Milk samples (n=1,976) were collected from 1,220 crossbreed cows with differing proportions of BS, Braunvieh and Original Braunvieh genetics on 40 farms during winter-indoor and summer-grazing seasons. Cows with more Brown Swiss genetics produced more milk in winter but not in summer, possibly because of underfeeding high-yielding cows on low-input pasture-based diets. Cows with more Original Braunvieh genetics produced milk with higher concentrations of (i) nutritionally desirable <em>trans</em>-9 palmitoleic, eicosapentaenoic and docosapentaenoic acids, throughout the year, and (ii) vaccenic and α-linolenic acids, total omega-3 fatty acids concentrations and a higher omega-3/omega-6 ratio during the summer-grazing period only. This suggests that overall milk quality could be improved by re-focusing breeding strategies on the cows’ ability to respond to local dietary environments and seasonal changes in feeding regimes.</p>
Gilby et al PLoS ONE Bayesian Belief Network input data
<p>Gilby et al PLoS ONE Bayesian Belief Network input data. Data used to educate relationships between nodes in Bayesian Belief Network for coral reef condition relative to management intervenations on coral reefs in Moreton Bay, Queensland, Australia.</p>
Analyzing the sensitivity of a flood risk assessment model towards its input data, twelve damage scenarios
<p>This dataset contains the output shapefiles of twelve different risk assessment scenarios for the case study of Annotto Bay, Jamaica. These assessments were performed in the context of the research 'Analyzing the sensitivity of a flood risk assessment model towards its input data', published in the journal Natural Hazards and Earth System Sciences. More information on the input data and methodology can be found in this paper.</p>
Scattering parameter (input port voltage reflection coefficient) of transmit network of NQR probehead from 40 MHz to 140 MHz
<p>Data to figure 7 in the related publication:</p> <p>Scattering parameter (input port voltage reflection coefficient) of transmit network of NQR probehead from 40 MHz to 140 MHz</p>
Data used in "Fast metabolite identification with Input Output Kernel Regression"
<p>This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets.</p> <p>Please mention and cite GNPS when using these data.</p> <p>The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR</p> <p><strong>Files description:</strong></p> <ul> <li><em>spectra.txt</em>: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier)</li> <li><em>data_GNPS.mat</em>: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra</li> <li><em>cv_ind.txt</em>: indices of the cross-validation folds</li> <li><em>ind_eval.txt</em>: indices of the examples used for evaluation</li> <li><em>candidates</em>: fingerprints and INCHI for the different candidate sets</li> <li><em>input_kernels</em>: contains 24 input kernel matrices</li> </ul> <p><strong>References:</strong></p> <p>[1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246</p> <p>[2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112</p> <p> </p> <p> </p>
GRIDCERF: Geospatial Raster Input Data for Capacity Expansion Regional Feasibility
<p><strong>Abstract:</strong></p> <p>Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 265 suitability layers for use with 56 power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions.</p> <p><strong>Accompanying GitHub repository:</strong></p> <p>The following GitHub repository contains the code used to generate the data in this archive: https://github.com/IMMM-SFA/vernon-etal_2023_scidata</p> <p><strong>Contents:</strong></p> <p><strong>Note:</strong></p> <p>GRIDCERF does not provide the source data directly due to some license restrictions related for direct redistribution of the unaltered source data. However, the included file "gridcerf_source_data_description.csv" details the provenance associated with each source dataset and notes their individual licenses/disclaimers.</p> <p><strong>Common Rasters:</strong></p> <p><strong>Suitability Layer Type and Source</strong></p> <p><strong>GRIDCERF Raster Name</strong></p> <p><strong>Bureau of Land Management (BLM) Surface Management Agency Areas</strong><strong><sup>33</sup></strong></p> <p>gridcerf_blm_surface_management_agency_areas.tif</p> <p><strong>BLM National Landscape Conservation System (NLCS) - National Monuments</strong><strong><sup>34</sup></strong></p> <p>gridcerf_blm_nlcs_national_monument_conus.tif</p> <p><strong>BLM NLCS - Outstanding Natural Areas</strong><strong><sup>35</sup></strong></p> <p>gridcerf_blm_nlcs_outstanding_natural_areas_conus.tif</p> <p><strong>BLM NLCS - Wilderness</strong><strong><sup>36</sup></strong></p> <p>gridcerf_blm_nlcs_wilderness_conus.tif</p> <p><strong>BLM NLCS - Wilderness Study Areas</strong><strong><sup>37</sup></strong></p> <p>gridcerf_blm_nlcs_wilderness_study_areas_conus.tif</p> <p><strong>National Park Service (NPS) Class 1 airsheds</strong><strong><sup>38</sup></strong></p> <p>gridcerf_class1_airsheds_conus.tif</p> <p><strong>NPS Administrative Boundaries</strong><strong><sup>39</sup></strong></p> <p>gridcerf_nps_administrative_boundaries_conus.tif</p> <p><strong>NPS Historic Trails</strong><strong><sup>40</sup></strong></p> <p>gridcerf_nps_historic_trails_conus.tif</p> <p><strong>NPS Scenic Trails</strong><strong><sup>41</sup></strong></p> <p>gridcerf_nps_scenic_trails_conus.tif</p> <p><strong>U.S. Fish and Wildlife Service (USFWS) - Critical Habitat</strong><strong><sup>42</sup></strong></p> <p>gridcerf_usfws_critical_habitat_conus.tif</p> <p><strong>USFWS - Special Designation</strong><strong><sup>43</sup></strong></p> <p>gridcerf_usfws_special_designation_conus.tif</p> <p><strong>USFWS - Wild and Scenic River System</strong><strong><sup>44</sup></strong></p> <p>gridcerf_usfws_national_wild_scenic_river_system_conus.tif</p> <p><strong>USFWS - National Realty Tracts</strong><strong><sup>45</sup></strong></p> <p>gridcerf_usfws_national_realty_tracts_conus.tif</p> <p><strong>National Land Cover Dataset (NLCD) Wetlands</strong><strong><sup>46</sup></strong></p> <p>gridcerf_nlcd_wetlands_conus.tif</p> <p><strong>U.S. Forest Service (USFS) Administrative Boundaries</strong><strong><sup>47</sup></strong></p> <p>gridcerf_usfs_administrative_boundaries_conus.tif</p> <p><strong>USFS Wilderness Areas</strong><strong><sup>48</sup></strong></p> <p>gridcerf_usfs_wilderness_areas_conus.tif</p> <p><strong>U.S. Geological Survey (USGS) National Wilderness Lands</strong><strong><sup>49</sup></strong></p> <p>gridcerf_usgs_wilderness_areas_conus.tif</p> <p><strong>USGS Protected Areas of the U.S - Class 1&2</strong><strong><sup>50</sup></strong></p> <p>gridcerf_usgs_padus_class_1_to_2_conus.tif</p> <p><strong>U.S. State Protected Lands</strong><strong><sup>51</sup></strong></p> <p>gridcerf_wdpa_state_protected_lands_conus.tif</p> <p><strong>Nature Conservancy lands</strong><strong><sup>52</sup></strong></p> <p>gridcerf_wdpa_tnc_managed_lands_conus.tif</p> <p> </p> <p><strong>Technology-specific Rasters:</strong></p> <p><strong>Suitability Layer Type and Source</strong></p> <p><strong>GRIDCERF Raster Name</strong></p> <p><strong>Bureau of Indian Affairs (BIA) Land Area Representations Dataset</strong><strong><sup>53</sup></strong></p> <p>gridcerf_bia_land_area_representations_conus.tif</p> <p><strong>Slope 5% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_5pct_or_less.tif</p> <p><strong>Slope 10% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_10pct_or_less.tif</p> <p><strong>Slope 12% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_12pct_or_less.tif</p> <p><strong>Slope 20% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_20pct_or_less.tif</p> <p><strong>Airports (10-mile buffer)</strong><strong><sup>54</sup></strong></p> <p>gridcerf_airports_10mi_buffer_conus.tif</p> <p><strong>Airports (3-mile buffer)</strong><strong><sup>54</sup></strong></p> <p>gridcerf_airports_3mi_buffer_conus.tif</p> <p><strong>Proximity to Railroad and Navigable Waters (< 5 km)</strong> <strong><sup>55,56</sup></strong></p> <p>gridcerf_usdot_railnodes_navwaters_within5km.tif</p> <p><strong>Coal Supply</strong><strong><sup>55–57</sup></strong></p> <p>gridcerf_coalmines20km_railnodes5km_navwaters5km_conus.tif</p> <p><strong>United States Environmental Protection Agency (EPA) CO Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_co_conus.tif</p> <p><strong>EPA NOx Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_no2_conus.tif</p> <p><strong>EPA Ozone Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_ozone_conus.tif</p> <p><strong>EPA Lead Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_lead_conus.tif</p> <p><strong>EPA PM10 Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_pm10_conus.tif</p> <p><strong>EPA PM2.5 Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_pm2p5_conus.tif</p> <p><strong>EPA SOx Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_so2_conus.tif</p> <p><strong>Earthquake Potential</strong><strong><sup>59</sup></strong></p> <p>gridcerf_usgs_earthquake_pga_0.3_at_2pct_in_50yrs_conus.tif</p> <p><strong>Densely population areas</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year].tif</p> <p><strong>Densely population areas buffered by 25 miles</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year]_buff25mi.tif</p> <p><strong>Densely population areas – nuclear</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year]_nuclear.tif</p> <p><strong>National Hydrography Dataset (version 2; NHDv2)</strong><strong><sup>32</sup></strong></p> <p>gridcerf_nhd2plus_surfaceflow_greaterthan[bin]mgd_buffer20km.tif</p> <p><strong>National Renewable Energy Laboratory (NREL) concentrating solar direct normal potential</strong><strong><sup>26</sup></strong></p> <p>gridcerf_nrel_solar_csp_centralized_potential.tif</p> <p><strong>NREL photovoltaic potential</strong><strong><sup>26</sup></strong></p> <p>gridcerf_nrel_solar_pv_centralized_potential.tif</p> <p><strong>NREL Wind Integration National Dataset (WIND) toolkit</strong><strong><sup>22</sup></strong></p> <p>gridcerf_nrel_wind_development_potential_hubheight[080,110,140]_cf35.tif</p> <p> </p> <p><strong>Compiled Technology Rasters:</strong></p> <p>The list of layers that make up each compiled technology raster can be found in the "reference/compiled_layer_configuration.txt" file in this data archive.</p> <p>The following technology raster file names are self-descriptive in the format "gridcerf_<technology>_<subtype>_<carbon_capture>_<cooling_type>.tif". Some technologies do not have a carbon capture or cooling type designation and will simply have technology specific considerations listed.</p> <pre>gridcerf_biomass_conventional_ccs_dry.tif gridcerf_biomass_conventional_ccs_oncethrough.tif gridcerf_biomass_conventional_ccs_recirculating.tif gridcerf_biomass_conventional_no-ccs_dry.tif gridcerf_biomass_conventional_no-ccs_oncethrough.tif gridcerf_biomass_conventional_no-ccs_pond.tif gridcerf_biomass_conventional_no-ccs_recirculating.tif gridcerf_biomass_igcc_no-ccs_dry.tif gridcerf_biomass_igcc_no-ccs_oncethrough.tif gridcerf_biomass_igcc_no-ccs_recirculating.tif gridcerf_biomass_igcc_with-ccs_dry.tif gridcerf_biomass_igcc_with-ccs_oncethrough.tif gridcerf_biomass_igcc_with-ccs_recirculating.tif gridcerf_coal_conventional_ccs_dry.tif gridcerf_coal_conventional_ccs_oncethrough.tif gridcerf_coal_conventional_ccs_recirculating.tif gridcerf_coal_conventional_no-ccs_dry.tif gridcerf_coal_conventional_no-ccs_oncethrough.tif gridcerf_coal_conventional_no-ccs_pond.tif gridcerf_coal_conventional_no-ccs_recirculating.tif gridcerf_coal_igcc_no-ccs_dry.tif gridcerf_coal_igcc_no-ccs_oncethrough.tif gridcerf_coal_igcc_no-ccs_recirculating.tif gridcerf_coal_igcc_with-ccs_dry.tif gridcerf_coal_igcc_with-ccs_oncethrough.tif gridcerf_coal_igcc_with-ccs_recirculating.tif gridcerf_gas_cc_ccs_dry.tif gridcerf_gas_cc_ccs_oncethrough.tif gridcerf_gas_cc_ccs_recirculating.tif gridcerf_gas_cc_no-ccs_dry.tif gridcerf_gas_cc_no-ccs_oncethrough.tif gridcerf_gas_cc_no-ccs_pond.tif gridcerf_gas_cc_no-ccs_recirculating.tif gridcerf_gas_turbine_dry.tif gridcerf_gas_turbine_oncethrough.tif gridcerf_gas_turbine_pond.tif gridcerf_gas_turbine_recirculating.tif gridcerf_nuclear_gen3_oncethrough.tif gridcerf_nuclear_gen3_pond.tif gridcerf_nuclear_gen3_recirculating.tif gridcerf_refinedliquids_cc_ccs_dry.tif gridcerf_refinedliquids_cc_ccs_oncethrough.tif gridcerf_refinedliquids_cc_ccs_recirculating.tif gridcerf_refinedliquids_cc_no-ccs_dry.tif gridcerf_refinedliquids_cc_no-ccs_oncethrough.tif gridcerf_refinedliquids_cc_no-ccs_recirculating.tif gridcerf_refinedliquids_ct_dry.tif gridcerf_refinedliquids_ct_oncethrough.tif gridcerf_refinedliquids_ct_pond.tif gridcerf_refinedliquids_ct_recirculating.tif gridcerf_solar_csp_centralized_dry-hybrid.tif gridcerf_solar_csp_centralized_recirculating.tif gridcerf_solar_pv_centralized.tif gridcerf_wind_onshore_hubheight080m.tif gridcerf_wind_onshore_hubheight110m.tif gridcerf_wind_onshore_hubheight140m.tif </pre> <p><strong>Reference Data: </strong>Contains land mask and other useful boundary data. Also contains additional literature review resource and the layers used to build the compiled suitability.</p> <p><strong>References:</strong></p> <p>1. Bureau of Land Management. BLM National Surface Management Agency Area Polygons - National Geospatial Data Asset (NGDA). <em>Landscape Approach Data Portal</em> https://gbp-blm-egis.hub.arcgis.com/datasets/blm-national-sma-surface-management-agency-area-polygons/about (2023).</p> <p>2. Bureau of Land Management. BLM National NLCS National Monuments, National Conservation Areas and Similar Designations Polygons. <em>U.S.Department of Interior Bureau of Land Management Geospatial Business Plaform</em> https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-natl-nlcs-national-monuments-national-conservation-areas-polygons/about (2023).</p> <p>3. Hartger, P. NLCS Outstanding Natural Areas. <em>ArcGIS Hub</em> https://hub.arcgis.com/datasets/Wilderness::nlcs-outstanding-natural-areas/about (2017).</p> <p>4. Bureau of Land Management. BLM National NLCS Wilderness Areas Polygons. <em>U.S.Department of Interior Bureau of Land Management Geospatial Business Plaform</em> https://arcg.is/a01uC (2023).</p> <p>5. Bureau of Land Management. BLM National NLCS Wilderness Study Areas Polygons. <em>U.S.Department of Interior</em> https://arcg.is/14XPiC (2023).</p> <p>6. United States Environmental Protection Agency. Mandatory Class 1 Federal Areas Web Service. <em>Mandatory Class 1 Federal Areas Web Service</em> https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BF234E37B-E7CE-4A47-89D6-68399D540576%7D (2015).</p> <p>7. National Park Service. National Park Service Boundary. <em>National Park Service</em> https://public-nps.opendata.arcgis.com/datasets/nps::nps-boundary-4/about (2023).</p> <p>8. National Park Service. National Historic Trails. <em>Geospatial Energy Mapper</em> https://gem.anl.gov/tool (2019).</p> <p>9. National Park Service. National Scenic Trails. <em>Geospatial Energy Mapper</em> https://gem.anl.gov/tool (2019).</p> <p>10. U.S. Fish and Wildlife Service. USFWS Threatened & Endangered Species Active Critical Habitat Report. <em>ECOS Environmental Conservation Online System</em> https://ecos.fws.gov/ecp/report/table/critical-habitat.html (2023).</p> <p>11. U.S. Fish and Wildlife. U.S. Fish and Wildlife Special Designation. <em>U.S. Fish and Wildlife</em> https://gis-fws.opendata.arcgis.com/datasets/fws::fws-special-designation/about (2023).</p> <p>12. U.S. Fish and Wildlife Service. National Wild and Scenic Rivers System. <em>Geospatial Energy Mapper</em> https://data.fs.usda.gov/geodata/edw/edw_resources/shp/S_USA.WildScenicRiver_LN.zip (2014).</p> <p>13. U.S. Fish and WIldlife Service. USFWS National Realty Tracts. <em>ServCat Fish and Wildlife Service Catalog</em> https://ecos.fws.gov/ServCat/Reference/Profile/154057 (2023).</p> <p>14. Dewitz, J. & U.S. Geological Survey. National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021). <em>Multi-Resoltuion Land Characteristics Consortium</em> https://www.mrlc.gov/data/nlcd-2019-land-cover-conus (2021).</p> <p>15. USDA Forest Service. Administrative Forest Boundaries. <em>Download National Datasets</em> https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundaries (2015).</p> <p>16. U.S. Forest Service. National Wilderness Areas. <em>Download National Datasets</em> https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Wilderness+areas (2023).</p> <p>17. U.S. Geological Survey. Wilderness Areas in the United States. <em>ScienceBase-Catalog</em> https://www.sciencebase.gov/catalog/item/4fc8f0e4e4b0bffa8ab259e7 (2000).</p> <p>18. U.S. Geological Survey Gap Analysis Project. Protected Areas Database of the United States (PAD-US) 3.0 (ver. 2.0, March 2023). <em>ScienceBase-Catalog</em> https://doi.org/10.5066/P9Q9LQ4B (2023).</p> <p>19. Protected Planet. Protected Areas (WDPA). <em>Protected Areas (WDPA)</em> https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA (2023).</p> <p>20. The Nature Conservancy. The Nature Conservancy lands. <em>The Nature Conservancy (TNC) Lands</em> https://geospatial.tnc.org/datasets/TNC::tnc-lands-north-america/about?layer=0 (2022).</p> <p>21. Bureau of Indian Affairs. American Indian and Alaskan Native Land Area Representations (LAR). <em>U.S. Department of the Interior Indian Affairs</em> https://biamaps.doi.gov/bogs/datadownload.html (2019).</p> <p>22. Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4. <em>CGIAR Consortium for Spatial Information</em> https://research.utwente.nl/en/publications/hole-filled-srtm-for-the-globe-version-4-data-grid (2008).</p> <p>23. Bureau of Transportation Statistics. Aviation Facilities. <em>Transportation.gov U.S. Department of Transportation</em> https://geodata.bts.gov/maps/usdot::aviation-facilities (2023).</p> <p>24. U.S. Department of Transportation. North American Rail Network Nodes. <em>U.S. Department of Transportation</em> https://data-usdot.opendata.arcgis.com/datasets/usdot::north-american-rail-network-nodes/explore?location=34.085743%2C-108.310932%2C3.96 (2023).</p> <p>25. U.S. Department of Transportation. Navigable Waterway Network Lines. <em>Bureau of Transportation Statistics</em> https://geodata.bts.gov/datasets/usdot::navigable-waterway-network-lines/about (2023).</p> <p>26. Energy Information Association (EIA). Energy Information Association U.S. Coal Mining Locations. <em>ScienceBase-Catalog</em> https://www.sciencebase.gov/catalog/item/5748a4cbe4b07e28b664dd78 (2017).</p> <p>27. Environmental Protection Agency. Green Book GIS Download. https://www.epa.gov/green-book/green-book-gis-download (2023).</p> <p>28. Shumway, A. <em>Data Release for the 2014 National Seismic Hazard Model for the Conterminous U.S</em>. https://doi.org/10.5066/P9P77LGZ (2019).</p> <p>29. Zoraghein, H. & O’Neill, B. C. U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways. <em>Sustainability</em> <strong>12</strong>, (2020).</p> <p>30. Moore, R. B. <em>et al.</em> <em>User’s guide for the national hydrography dataset plus (NHDPlus) high resolution: U.S. Geological Survey Open-File Report 2019–1096</em>. https://pubs.er.usgs.gov/publication/ofr20191096 (2019).</p> <p>31. Perez, R. <em>et al.</em> A new operational model for satellite-derived irradiances: description and validation. <em>Solar Energy</em> <strong>73</strong>, 307–317 (2002).</p> <p>32. Draxl, C., Clifton, A., Hodge, B.-M. & McCaa, J. The Wind Integration National Dataset (WIND) Toolkit. <em>Applied Energy</em> <strong>151</strong>, 355–366 (2015).</p> <p> </p> <p> </p>
Demonstration of semantic and inter-input constraints on software in OWL 2 and SPARQL for fulfilling the M1 Machine FAIR Use Case
<p>This video demonstrates using hypothetical examples how to (1) find a valid dataset for input into a software using OWL 2 classification inference, (2) validly combine two software using OWL 2 subsumption inference to infer that the output of software 1 is valid input to software 2, and (3) combine OWL 2 inference with a SPARQL query to find two datasets that satisfy a software's inter-input constraints.</p>
Input data for Episim Berlin Corona spreading simulation
<p>This dataset is supplementary material for </p> <ul> <li>Müller, S. A., Balmer, M., Charlton, W., Ewert, R., Neumann, A., Rakow, C., Schlenther, T. &<br>Nagel, K. Predicting the effects of COVID-19 related interventions in urban settings by combining<br>activity-based modelling, agent-based simulation, and mobile phone data. PLOS ONE 16 (ed Benenson, I.) (Oct. 2021) <a href="https://doi.org/10.1371/journal.pone.0259037">https://doi.org/10.1371/journal.pone.0259037</a></li> </ul> <p>The dataset is also used in the <strong>Math+ project EF4-13 "Modeling Infection Spreading and Counter-Measures in a Pandemic Situation Using Coupled Models"</strong> to perform the epidemic simulation studies for Berlin. </p> <p>The open dataset contains a 25 percent sample of the original dataset. The code for running the simulation is also available in this Github repository: <a href="https://github.com/matsim-org/matsim-episim">https://github.com/matsim-org/matsim-episim</a>. </p> <p>For the terms of use, please see the associated LICENSE file.</p> <p>More information can be found on our website: <a href="https://covid-sim.info/">https://covid-sim.info/</a>. If you have questions, please contact <a href="mailto:covid19@vsp.tu-berlin.de">covid19@vsp.tu-berlin.de</a> .</p> <p>Available files:</p> <ul> <li>be_2020-week_snz_entirePopulation_emptyPlans_withDistricts_25pt_split.xml.gz: Population including all persons having activities in one of the events files. The person attributes are homeId, homeCoordinates, age, district of home. The coordinates are in grid accuracy of 500m.</li> <li>be_2020-week_snz_episim_events_sa_25pt_split.xml.gz be_2020-week_snz_episim_events_s_25pt_split.xml.gz be_2020-week_snz_episim_events_wt_25pt_split.xml.gz The episim events files for a weekday, Saturday and Sunday. The events files are filtered for the only necessary types of events (actend, actstart, PersonEntersVehicle, PersonLeavesVehicle).</li> <li>be_2020-vehicles.xml.gz File includes a mapping of vehiclesIds to the vehilce type.</li> <li>be_2020-facilities_assigned_simplified_grid.xml.gz Including the facilities used in the events files. The coordinates are in grid accuracy of 500m.</li> <li>be_2020-mobility_data.csv Daily mobility data for Berlin for the simulated period.</li> </ul>
Input files for paper Insertases Scramble Lipids: Molecular Simulations of MTCH2
<p>Input files for simulations in publication: Ladislav Bartoš, Anant K. Menon, and Robert Vácha: Insertases Scramble Lipids: Molecular Simulations of MTCH2<br> </p>
Preprocessed input files for HyPE model for Upper Indus basin
<p>This archive contains preprocessed input data file setup for running the Hydropower Potential Exploration (HyPE) model for the Upper Indus basin. It contains inputs for running historical or future discharge scenarios. Preprocessed data are based on publicly available datasets described in supporting papers. </p>
Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"
<p>This is a companion dataset to the manuscript: <br><br>Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,</p><p>by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard.<br><br>This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion.<br><br>The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value.<br>The *.csv file contains the geological data: location of the contacts and orientation data.</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.