Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,600
datasets available to search
ShareScore release 0.9.0
Dataset results
1,600 results for “input”
Autumnal Litter Input in DIRT Litter Manipulation Experiment at Harvard Forest 2008
Climate change will alter forest ecosystem productivity, changing the quantity and quality of detrital inputs to soil and altering rates of soil organic matter (SOM) accumulation and stabilization. To examine changes in forest soil SOM pools, we have used the Detritus Input and Removal Treatments (DIRT) Project to alter organic matter input rates and sources (roots, leaves) to soils, allowing us to measure contributions of organic matter sources to long-term SOM storage at five temperate forests (Harvard Forest, HJ Andrews, Bousson (PA) Experimental Forest (BEF), U. Michigan Biological Station (UMBS), Síkfokut ILTER, Hungary). Organic matter inputs are altered by excluding or adding leaf inputs, or by excluding roots from forested plots. Soil respiration partitioning at HF, BEF, and UMBS shows that soil fertility controls the allocation of C to above- and belowground tissue. At UMBS, glacial outwash sandy soils are extremely low in N, and C released from root respiration plus root litter decomposition is 87% of total soil respiration. Conversely, at the N-rich BEF site, total belowground sources of CO2 are only 61% of soil respiration, with 47% attributed to root litter. These data suggest that at BEF, leaf litter, comprising only 39% of soil respiration, would be a more important source of long-term SOM than root litter. However, soil chemistry and radiocarbon data have shown us that long-term soil C storage is complex. The year 2010 represents the 20-year anniversary of the initiation of DIRT treatments at the Harvard Forest (2010), and we are therefore planning to conduct systematic sampling campaigns for a comprehensive study of changes in SOM quality after long-term manipulation of inputs. One objective is to quantify how 20 years of litter input alterations have affected SOM quantity and quality at the surface (0-20 cm) and deeper in the soil profile (20-100 cm). To uderstand these changes, we need to quantify the quantity and quality of aboveground litter inp
North Temperate Lakes LTER Estimated winter inputs of stream water and groundwater to primary study lakes 1982 - 2014
This data set integrates and summarizes daily surface and groundwater inputs to 5 primary study lakes, using model estimates from a data-driven USGS hydrologic model (Hunt et al. 2013; Hunt and Walker 2017), and ice phenology data (number of days since ice-on). The lakes are Allequash, Big Muskellunge, Crystal, Sparkling, and Trout. Powers et al. (2017) used these data to estimate upper and lower bounds for exogenous chemical inputs to the lakes during winter. For a given lake and winter year, cumulative surface water and groundwater inputs were calculated across the ice cover period. For each lake, this data set reports the mean, maximum, and minimum winter water inputs observed across years, in units of water volume, % of average lake volume, and volume per winter day. Sampling Frequency: 1 per lake, with multiple summary values reported (i.e., mean, min, max). Number of sites: 5. Hunt, R.J. et al., 2013. Simulation of Climate - Change effects on streamflow, Lake water budgets, and stream temperature using GSFLOW and SNTEMP, Trout Lake Watershed, Wisconsin. USGS Scientific Investigations Report., pp.2013-5159. Available at: https://www.researchgate.net/publication/258363719_Simulation_of_Climate... Hunt, R.J., and Walker, J.F., 2017, GSFLOW groundwater-surface water model 2016 update for the Trout Lake Watershed, Wisconsin: U.S. Geological Survey data release, https://dx.doi.org/10.5066/F7M32SZ2. Powers SM, Labou SG, Baulch HM, Hunt RJ, Lottig NR, Hampton SE, Stanley EH. In press (expected 2017). Ice duration drives winter nitrate accumulation in north temperate lakes. Limnology and Oceanography Letters.
PIE LTER dissolved nutrient and particulate concentrations of freshwater inputs to the Plum Island estuarine system, Massachusetts, taken approximately monthly.
Multi-year data of water chemistry including nutrient concentrations for various forms of N, P, C, as well as suspended sediments, was determined from monthly grab samples taken at watershed inputs to the Plum Island Sound Estuary. Sampling sites were the Ipswich River (Sylvania Dam, Ipswich, MA), Parker River Dam (Central St, Newbury, MA), Egypt River (Ipswich, MA) Mill River (Newbury, MA), Muddy Run (Ipswich, MA), Little River (Newbury, MA). These nutrient concentrations are then used in conjunction with USGS discharge data (recorded at gages in the Parker River at Byfield, MA and the Ipswich River at Ipswich, MA) to calculate annual nutrient loading to the Plum Island Sound Estuary, coming over each dam. Annual yield is also calculated for both dams. Refer to file WAT-VA-Load for loading data.
Soil water content measurements and rainfall data for plots with experimentally altered precipitation and nutrient inputs at the Jornada Basin LTER site, 2011-ongoing
This dataset contains soil volumetric water content data collected starting in 2011 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Soil sensors are installed at surface and deep soil layers in each plot and collect hourly averages of volumetric water content using a time-domain reflectometry method. This dataset contains daily averages. This is an ongoing study and the dataset will be updated yearly.
Input data for MFAssignR Galaxy workflow tutorial
<p>This is the input dataset for the MFAssignR Galaxy training workflow. The input dataset corresponds to the model data of MFAssignR (<a title="Raw_Neg_ML" href="https://github.com/skschum/MFAssignR/tree/master/MFAssignR/data" target="_blank" rel="noopener">Raw_Neg_ML</a>), containing a raw mass list, measured in a negative ESI mode.</p>
Input files for simulation of potassium channels using the AMOEBA polarizable force field
<p>This dataset contains input Tinker xyz and key files for the simulation of KcsA potassium channels in DOPC bilayer, a simple script for converting CHARMM pdb file to Tinker xyz file, and modified Tinker source code to support one-dimensional position restraints.<br> "params.tar.gz" contains a description of the force field modifications.<br> <br> To use "mod2", add the following lines to the key file.</p> <pre><code>#compatible with amoebabio18.prm polarize 5 1.4500 0.3900 3 polarize 11 1.4500 0.3900 9 polarize 3 1.7500 0.3900 1 5 7 50 225 227 polarize 9 1.7500 0.3900 1 7 11 50 225 227</code></pre> <p> </p>
Temporal patterns of leaf litter inputs into a stream over a four-year period (2011-2014), Arbúcies, Catalonia, Spain.
Data based on estimations of leaf litter inputs from riparian trees into a stream reach over a 4 years period (2011-2014). Data was collected in Arbucies, Barcelona is a forested stream with no human pressure (i.e., pristine). Data contains values from 4 riparian tree species: AL (alder), AS (ash), BL (Black Locust) and BP (Black Poplar). Units are in mg. Estimations were extracted from sampling leaf litter input into the stream during the study period (30 samplings per year) and fitting Gaussian-type models (P<0.001, r2>0.60). Data also includes daily-basis discharge flow estimations based on discrete measure of flow using salt dilution technique and water level sensor data.
Aboveground vegetation cover and biomass in plots with experimentally altered precipitation and nutrient inputs at the Jornada Basin LTER site, 2006-ongoing
This dataset contains cover and biomass data collected starting in 2006 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Plant cover measurements are made annually in each plot, from which biomass or net primary production are derived. This is an ongoing study and the dataset will be updated yearly.
MCSE Model input data at the Kellogg Biological Station, Hickory Corners, MI (1988 to 2020)
Dataset AbstractConsolidated dataset for the ARDEN crop modeling effort. This pulls together several useful data tables into one dataset. Further information can be found at https://agmip.github.io/ARDN/original data source http://lter.kbs.msu.edu/datasets/195
Input Runoff Data for RAPID Model Pre-Processor (RRR) from ECMWF ERA-Interim/Land
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the ECMWF ERA-Interim/Land [<em>Balsamo et al.,</em> 2015] outputs, available from ECMWF Data Server. The ERA-Interim/Land outputs are available in daily temporal resolution. The database contains the following files;</p> <p> ECMWF_Interim_Land_<strong><em>yyyy</em></strong>.tar.gz (Note: <strong><em>yyyy</em></strong> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.</p> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a></p> <p> </p> <p>References:</p> <p>Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a></p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
RAPID Model Input Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins
<p>This database contains Inputs and intermediate files of the RAPID model pre-processor (RRR), and also outputs from the RRR (<em>i.e.</em>, Inputs for RAPID); which were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. If you use this RAPID Model Input Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins in your work, please cite: <em>Sikder et al.</em>, [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>.</p> <p>The database contains;</p> <ul> <li>Global River basin and Network Shapefiles: HydroSHEDS.tar.gz</li> <li>Extracted Basin Shapefile: MIGBM_basin.tar.gz</li> <li>Extracted River Network Shapefiles: MIGBM_<strong><em>res</em></strong>_ntwk.tar.gz (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Catchment Files: rapid_catchment_as_<strong><em>riv</em></strong>_res.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Connectivity Files: rapid_connect_<strong><em>res</em></strong>_MIGBM.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Coordinate Files: coords_<strong><em>res</em></strong>_MIGBM.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Base Parameter Files: <strong><em>p</em></strong>fac_<strong><em>res</em></strong>_MIGBM_1km_hour.csv (Note: <strong><em>p</em></strong> = k or x; <strong><em>res</em></strong> = fine or coarse)</li> <li>Sort Files: sort_<strong><em>res</em></strong>_MIGBM_topo.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Sorted Basin Files: riv_bas_id_<strong><em>res</em></strong>_MIGBM_topo.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Coupling Files: rapid_coupling.tar.gz</li> <li>Parameter Files: rapid_param.tar.gz</li> <li>Volume Files: m3_riv_<strong><em>res</em></strong>_MIGBM_20000101_20091231_<strong><em>prj</em></strong>_<strong><em>LSMsr</em></strong>_<strong><em>tr</em></strong>_utc.nc (Note: <strong><em>res</em></strong> = fine or coarse; <strong><em>prj</em></strong> = GLDAS or GLDAS.2.0 or GLDAS.2.1 or ECMWF; <strong><em>LSM</em></strong> = CLM, MOS, NOAH, VIC, ERAint; <strong><em>sr</em></strong> = 10 or 025; <strong><em>tr</em></strong> = 3H or D)</li> </ul> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a></p> <p>ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a></p> <p> </p> <p>References:</p> <p>Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a></p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
Input files for Dispa-SET for the JRC report "Power System Flexibility in a variable climate"
<p><strong>Input files for Dispa-SET for the JRC report "Power System Flexibility in a variable climate"</strong></p> <p>Here you can find the input files needed to reproduce the results of the <a href="https://doi.org/10.2760/75312">report</a>:</p> <pre><code>De Felice, M., Busch, S., Kanellopoulos, K., Kavvadias, K. and Hidalgo Gonzalez, I., Power system flexibility in a variable climate, EUR 30184 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-18183-5 (online), doi:10.2760/75312 (online), JRC120338. </code></pre> <p>The results in the report are generated with the Dispa-SET power system model, available and explained at <a href="https://www.dispaset.eu/">www.dispaset.eu</a>.</p> <p>A description of the data sources with the references can be found into the report.</p> <p><strong>How to use this dataset</strong></p> <p>This dataset can be used as input data for the Dispa-SET model. We refer to the <a href="https://doi.org/10.2760/75312">report</a> and the <a href="https://www.dispaset.eu">official model documentation</a> for information about the data and the model.</p> <p><strong>Description of the dataset</strong></p> <p>The file <code>EnVarClim.yml</code> is a template of the YAML configuration file used by Dispa-SET. To run a specific climate year the <code>XXXX</code> present in some input files must be replaced with the year.</p> <p><strong>Availability factors</strong></p> <p>In the folder <code>AvailabilityFactors</code> there are the availability factors (from 0 to 1) for the power plants and the renewable generation. There is a subfolder for each simulated zone and inside a file for each climate year: from <code>emh_and_cc_availability_1990.csv</code> to <code>emh_and_cc_availability_2015.csv</code>.</p> <p><strong>Cross-border transmission</strong></p> <p>In the folder <code>DayAheadNTC</code> there is the file <code>merged_constant_NTC.csv</code> containing the capacity (in MW).</p> <p><strong>NOTE</strong>: due to an error in the pre-processing code there are some additional lines for the Western Balkans countries ending with a <code>1</code> (e.g. <code>GR -> MK1</code>). Those lines are ignored by the model because are not associated to any simulated zone.</p> <p><strong>Cross-border historical flows</strong></p> <p>In the file <code>CC_L_flows.csv</code> under the folder <code>Flows</code> are contained the hourly flows between the simulated zones and their neighbours (RU, TR, UA).</p> <p><strong>Fuel prices</strong></p> <p>In the folder <code>FuelPrices</code> are contained a set of files containing the hourly prices for the fuels (biomass, coal, lignite, gas, oil) and CO2 emissions. It is worth noting that in spite of their hourly resolution the time-series are constant through the year.</p> <p><strong>Hourly load</strong></p> <p>In the folder <code>Load_RealTime</code> there are hourly load time-series for each zone considering a different climate year. For the Western Balkans countries we use the same time-series for each climate year.</p> <p><strong>Outage factors</strong></p> <p>The files <code>CC_L_outages.csv</code> in the folder <code>OutageFactors</code> contain the outage factor (from 1, full outage, to 0) for the various generation units. Whenever a simulation zone is missing the model assumes the absence of outages.</p> <p><strong>Power plants data</strong></p> <p>In the folder <code>PowerPlants</code> there is a file named <code>CC_L_plants.mip.csv</code> for each simulated zone. The CSV files contain the data <a href="http://www.dispaset.eu/en/latest/data.html#power-plant-data">needed by Dispa-SET</a>.</p> <p><strong>Water storage levels</strong></p> <p>The folder <code>ReservoirLevel</code> contains the storage level (values from 0 to 1 relative to the size of the storage) for all the simulated zones. The levels have been computed for each climate year using a different inflow using the <a href="http://www.dispaset.eu/en/latest/mid_term.html">mid-term scheduler</a> recently implemented in Dispa-SET. For the Western Balkans countries we use the same time-series for each climate year.</p> <p><strong>Hydro-power inflows</strong></p> <p>In the folder <code>ScaledInflows</code> are contained the inflows used for the hydro-power generation. The values in the CSV files describes how much energy is available for hydro-power generation compared to the installed capacity.</p> <p><strong>Linked resources</strong></p> <ul> <li>Model output files:<strong> </strong>https://zenodo.org/record/3778133</li> <li>Source code for the figures: https://github.com/energy-modelling-toolkit/figures-JRC-report-power-system-and-climate-variability</li> </ul>
Changing Brine Inputs into Hydrothermal Fluids: Southern Cleft Segment, Juan de Fuca Ridge
<p>In 2016 temperature recorders were recovered, temperatures were measured, and fluid samples were collected from Vent 1, a high temperature (338°C) hydrothermal discharge site on the southern Cleft Segment of the Juan de Fuca Ridge. Coupled with previous sampling efforts, this collection represents a 32-year record of discharge from a single chimney structure, the longest record to date. Remarkably, the fluid has remained brine-dominated for more than three decades. This brine formed during phase separation and segregation prior to initial observations in 1984. Although the chloride concentration of the discharging fluid has decreased with time, the fluid temperature has remained nearly constant for at least 3.3 years and probably for 15 or even 22 years. Compositions of the discharging fluids are consistent with inputs from a deep-sourced brine, which was last equilibrated at >400° C at a depth consistent with the base of the sheeted dikes and the brittle-ductile transition. This brine mixed (diffusion or dispersion) with a likely non-phase-separated, hydrothermal fluid prior to discharge. A survey of hydrothermal endmember fluids with chlorinities in excess of 700 mmol/kg shows, with the exception of Fe, a single trend between major ion concentrations and chlorinity even though data are from a range of crustal compositions, spreading rates, and water and magma depths. Calculated deep-sourced brines from hydrothermal fluid data are similar to data based on fluid inclusions and estimates of brine assimilation in magmas. A better understanding of brines is required given their potential duration of discharge and capacity for mobilizing metals.</p> <p>The data in the attached 10 tables represent the supplemental data in a paper published in <em>Geochemistry, Geophysics, Geosystems</em>. The data include temperature data from long-term records, chemical data from hydrothermal effluent from Vent 1 on the Cleft Segment, sediment data, and sulfide chimney data.</p>
RAPID input and output files corresponding to "River Network Routing on the NHDPlus Dataset"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RAPID input and output files that were used in the study reported in:</p> <ul> <li>David, Cédric H., David R. Maidment, Guo-Yue Niu, Zong-Liang Yang, Florence Habets and Victor Eijkhout (2011), River Network Routing on the NHDPlus Dataset, Journal of Hydrometeorology, 12(5), 913-934. DOI: 10.1175/2011JHM1345.1. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Time format</strong></p> <p>The times reported in this description all follow the ISO 8601 format. For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1<sup>st</sup> 2000 (2000-01-01), Central Standard Time (-06:00). Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step. For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps. The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1<sup>st</sup> 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1<sup>st</sup> 2000, Universal Time.</p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 1, obtained from http://www.horizon-systems.com/nhdplus. </li> <li>The National Water Information System (NWIS), obtained from http://waterdata.usgs.gov/nwis. </li> <li>Outputs from a simulation using the community Noah land surface model with multiparameterization options (Noah-MP, Niu et al. 2011, http://www.jsg.utexas.edu/noah-mp). The simulation was run by Guo-Yue Niu, and produced 3-hourly time steps from 2004-01-01T00:00+00:00 to 2008-01-01T00:00+00:00. Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> <p> </p> <p><strong>Software</strong></p> <p>The following software were used to produce files in this dataset:</p> <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.0.0. Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).</li> <li>ESRI ArcGIS (http://www.arcgis.com). </li> <li>Microsoft Excel (https://products.office.com/en-us/excel). </li> <li>CUAHSI HydroGET (http://his.cuahsi.org/hydroget.html). </li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul> <p> </p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to two study domains:</p> <ul> <li>The combination of the San Antonio and Guadalupe River Basins, TX. RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 5,175). The temporal range corresponding to this domain is from 2004-01-01T00:00-06:00 to 2007-12-31 T00:00-06:00.</li> <li>The Upper Mississippi River Basin. RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 182,240). The temporal range corresponding to this domain spans 100 fictitious days.</li> </ul> <p> </p> <p><strong>Description of files for the San Antonio and Guadalupe River Basins</strong></p> <p>All files below were prepared by Cédric H. David, using the data sources and software mentioned above. </p> <ul> <li><em>rapid_connect_San_Guad.csv.</em> This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of COMID. The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE. This file was prepared using ArcGIS and Excel.</li> <li><em>m3_riv_San_Guad_2004_2007_cst.nc. </em>This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007/12/31T18:00-06:00. The values were computed by superimposing a 900-m gridded map of NHDPlus catchments to the outputs of Noah-MP. This file was prepared using ArcGIS and a Fortran program.</li> <li><em>kfac_San_Guad_1km_hour.csv. </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using a wave celerity of 1 km/h. This file was prepared using a Fortran program.</li> <li><em>kfac_San_Guad_celerity.csv. </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using the wave celerity numbers of Table 2 in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_1.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (17) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_2.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (18) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_3.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (19) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_4.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (21) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_1.csv. </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Equation (17) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_2.csv. </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Equation (18) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_3.csv. </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Equation (19) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_4.csv. </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Equation (21) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>basin_id_San_Guad_hydroseq.csv. </em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the San Antonio and Guadalupe River Basins. The river reaches are sorted from upstream to downstream. The values were computed using the following NHDPlus fields: COMID and HYDROSEQ. This file was prepared using Excel.</li> <li><em>Qout_San_Guad_1460days_p1_dtR=900s.nc.</em> This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p2_dtR=900s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p3_dtR=900s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p4_dtR=900s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p1_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p2_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p3_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p4_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>gage_id_San_Guad_2004_2007_full.csv. </em>This CSV file contains the list of COMIDs of rivers containing USGS gauges and with full daily data record. The river reaches are sorted in increasing value of COMID. The time range used for determining a full record is daily from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00. The values were computed using the following NHDPlus field: COMID. This file was prepared using ArcGIS, HydroGET, and Excel.</li> <li><em>Qobs_San_Guad_2004_2007_full.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same COMIDs and are sorted similarly to <em>gage_id_San_Guad_2004_2007_full.csv</em>. The time range for the daily values is from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00. The values were computed using the following NHDPlus field: COMID, and the observations from NWIS. This file was prepared using ArcGIS, HydroGET, and Excel.</li> </ul> <p> </p> <p><strong>Description of files for the Upper Mississippi River Basin</strong></p> <p>All files below were prepared by Cédric H. David, using the data sources and software mentioned above. </p> <ul> <li><em>rapid_connect_Reg07.csv. </em>This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of COMID. The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE. This file was prepared using ArcGIS and Excel. </li> <li><em>m3_riv_Reg07_100days_dummy.nc. </em>This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The time range for this file is for 100 fictitious days. The values were computed using a unique value of 1 cubic meter for all river reaches and all time steps. This file was prepared using a Fortran program.</li> <li><em>kfac_Reg07_2.5ms.csv. </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (22) in David et al. (2011). This file was prepared using a Fortran program. </li> <li><em>xfac_Reg07_0.3.csv. </em>This CSV file contains a first guess of Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The values were computed based on Equation (22) in David et al. (2011). This file was prepared using a Fortran program. </li> <li><em>basin_id_Reg07_hydroseq.csv. </em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the Upper Mississippi River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the following NHDPlus fields: COMID and HYDROSEQ. This file was prepared using Excel.</li> <li><em>Qout_Reg07_100days_pfac_dtR900s.nc.</em> This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_Reg07_hydroseq.csv</em>. The time range for this file spans 100 fictitous days. The values were computed using the Muskingum method with parameters of Equation (22) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>The confluence of the Missouri River and the Upper Mississippi River upstream of Saint Louis, MO was overlooked. The contribution from the Missouri River is therefore not accounted for in the network connectivity corresponding to the Upper Mississippi River Basin. This has no effect on the conclusions of David et al. (2011) since the Upper Mississippi River Basin was studied with synthetic data and solely to evaluate parallel performance of RAPID.</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was partially supported by the U.S. National Aeronautics and Space Administration under the Interdisciplinary Science Project NNX07AL79G; by the U.S. National Science Foundation under project EAR-0413265: CUAHSI Hydrologic Information Systems; by Ecole des Mines de Paris, France; and by the American Geophysical Union under a Horton (Hydrology) Research Grant.</p>
Input files for SICOPOLIS v25
<p>Archives containing the input files (and corresponding READMEs including references) for <a href="https://sicopolis.github.io/sicopolis/" target="_blank" rel="noopener">SICOPOLIS</a>: <br>'ant.tgz' - Antarctica, <br>'grl.tgz' - Greenland, <br>'nhem.tgz' - Entire northern hemisphere, <br>'scand.tgz' - Fennoscandia/Eurasia, <br>'tibet.tgz' - Tibet, <br>'asf.tgz' - Austfonna, <br>'mocho.tgz' - Mocho-Choshuenco ice cap, <br>'eismint.tgz' - EISMINT (Phase 2 SGE and modifications), <br>'heino.tgz' - ISMIP HEINO, <br>'nmars.tgz' - North polar cap of Mars, <br>'smars.tgz' - South polar cap of Mars.</p> <p>Manual download of these archives is not required! This will happen automatically when <a href="https://sicopolis.github.io/sicopolis/" target="_blank" rel="noopener">SICOPOLIS</a> is installed.</p>
Input data and results of the RECC-ODYM model for Greater Oslo study (v1.0)
<p>This repository contains the input data and results of the modified RECC-ODYM model for Greater Oslo study (v1.0) used in "Reducing material use and their greenhouse gas emissions in Greater Oslo" by Lola Rousseau, Jan Sandstad Næss, Fabio Carrer, Sara Amini, Helge Brattebø, and Edgar Hertwich.</p> <p>The publication and its supplementary information are available at: <a href="https://doi.org/10.1111/jiec.13611">https://doi.org/10.1111/jiec.13611</a></p> <p>The following data are included in this repository:</p> <ul> <li>A description (<strong>How_to_use_RECCODYM_Greater_Oslo.pdf</strong>) how to run the code with the database to generate the results </li> <li>The database (<strong>CURRENT_VN1_0.zip</strong>) with the parameters, the master classification file RECC_Classifications_Master_V2.0.xlsx, the model config file RECC_Config.xlsx and the list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The results organized (<strong>results_organized.zip</strong>) by folder depending on the model run (results_organized) </li> </ul> <p>The code used with this database and generating these results is archived as v1.0 (<a href="https://github.com/LolaRousseau/RECC-ODYM/releases" target="_blank" rel="noopener">https://github.com/LolaRousseau/RECC-ODYM/releases</a>). The latest version is available on GitHub: <a href="https://github.com/LolaRousseau/RECC-ODYM" target="_blank" rel="noopener">https://github.com/LolaRousseau/RECC-ODYM</a></p> <div> <p>Please note that this is a modified version of RECC-ODYM with changes made for this study specifically. More general information about RECC-ODYM can be found on: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc">https://www.industrialecology.uni-freiburg.de/odym-recc</a> and the original framework is also described here: <a href="https://doi.org/10.1111/jiec.13023">https://doi.org/10.1111/jiec.13023</a></p> </div>
Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean's biological carbon pump
<p>This repository contains the post-processed model outputs underlying the main figures in the paper "Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean’s biological carbon pump" by Oziel et al. in Nature Climate Change (https://doi.org/10.1038/s41558-024-02233-6). The repository also contains the jupyter notebooks (python) scripts used to produce the figures, the custom model code as well as the mesh informations to reproduce the model run.</p>
Dictionary of ENBIOS processors, Euro-Calliope inputs and ecoinvent .spold filenames
<p>Provides full details of ENBIOS structural processor structure, input sources from Euro-Calliope files, processor names and energy carrier details, and the names of the corresponding life cycle inventory (LCI) data files in ecoinvent .spold format. Note that the LCI .spold files referenced are those for the allocation at point of substitution (APOS) approach within version 3.8 of the ecoinvent database.</p> <p>New version (18 February 2022) includes new listings for biomass and coal as industrial fuels, as now included in ENBIOS base file.</p>
Food and Agriculture Biomass Input–Output (FABIO) database
<p>This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry. </p> <p>The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers <strong>191 countries</strong> + RoW, <strong>118 processes</strong> and <strong>125 commodities</strong> (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on <a href="https://github.com/fineprint-global/fabio">GitHub</a>. For more information please refer to <a href="https://fabio.fineprint.global">https://fabio.fineprint.global</a>.</p> <p>The database consists of the following main components, in compressed .rds format:</p> <ul> <li>Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.</li> <li>Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.</li> <li>X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.</li> <li>L: the Leontief inverse, computed as (I – A)<sup>-1</sup>, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).</li> <li>E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).</li> <li>mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).</li> <li>mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).</li> </ul> <p>A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS</p> <p>Except of <em>X.rds</em>, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes <em>value </em>and <em>mass </em>only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see <a href="http://doi.org/10.1021/acs.est.9b03554">Bruckner et al. 2019</a>). Product, process and country classifications can be found in the file <em>fabio_classifications.xlsx</em>.</p> <p>Footprint results are not contained in the database but can be calculated, e.g. by using this script: <a href="https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R">https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R</a></p> <p> </p> <p><strong><em>How to cite: </em></strong></p> <p>To cite FABIO work please refer to this paper:</p> <p>Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. <em>Environmental Science & Technology</em> 53(19), 11302–11312. DOI: <a href="https://doi.org/10.1021/acs.est.9b03554">10.1021/acs.est.9b03554</a></p> <p> </p> <p><em><strong>License:</strong></em></p> <p>This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at <a href="mailto:martin.bruckner@wu.ac.at">martin.bruckner@wu.ac.at</a>.</p> <p> </p> <p><strong><em>Known issues:</em></strong></p> <p>The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.</p>
ParaTAXIS X-ray Scattering Input & Output
<p>This dataset describes the science case of the SIMEX platform tool chain for EUCALL WP4 Milestone M4.3.</p> <p>The file <em>opt_thick_1.6x1.6x3_micron_10fs_around_laser_max.h5</em> contains the ParaTAXIS density input data in openPMD format for the optically thick case (milestone 4.2.2.10). The data was obtained in a 2D PICLS simulation (milestone 4.2.2.7) which modeled the temporal evolution of a silicon grating irradiated by a <span class="math-tex">\(\tau_\mathrm{FWHM} = 83\,\mathrm{fs},\ \lambda = 800\,\mathrm{nm}\)</span> laser pulse of normalized amplitude <span class="math-tex">\(a_0 = 0.25\)</span>. Here 959 slices which correspond to subsequent PIC time steps of length <span class="math-tex">\(\Delta t_\mathrm{PIC} = 1.042 \cdot 10^{-17}\,\mathrm{s}\)</span> were stacked in propagation direction of the XFEL probe pulse thus taking time evolution of the target during X-ray pulse propagation into account. ParaTAXIS reads the density into a simulation volume of 1024 x 512 x 512 cells. The cell sizes of the PIC and the ParaTAXIS simulations are equally <span class="math-tex">\(3.125\,\mathrm{nm}\)</span> in every spatial direction. Density data is given in units of critical densities with respect to the <span class="math-tex">\(800\,\mathrm{nm}\)</span> laser. One critical density corresponds to <span class="math-tex">\(n_\mathrm{c} = 1.7422 \cdot 10^{27}\,\mathrm{m}^{-3}\)</span>. The time window chosen is situated from <span class="math-tex">\(5\,\mathrm{fs}\)</span> before until <span class="math-tex">\(5\,\mathrm{fs}\)</span> after the optical laser main pulse maximum hits the foil indicating a delay of <span class="math-tex">\(\Delta t = 0\)</span>. The optical laser incidence is in z-direction (ParaTAXIS coordinates) under 0°.</p> <p>The total illuminated area for both the optically thick and thin cases was <span class="math-tex">\(1.6 \times 1.6\, \mathrm{\mu m}\)</span>. We assume a target thickness of <span class="math-tex">\(3\,\mathrm{\mu m}\)</span>. The detector distance was <span class="math-tex">\(d = 1.4\,\mathrm{m}\)</span> and the detector pixel size was <span class="math-tex">\(a_\mathrm{D} = 13.5\,\mathrm{\mu m}\)</span>. For Thomson scattering most photons are scattered in forward direction. We therefore assumed a maximum polar scattering angle of <span class="math-tex">\(0.01\,\mathrm{rad}\)</span> in order to increase statistics on the detector.</p> <p>Via 16 simulations we obtained the detector outputs in the optically thick case which can be found in opt_thick_run_<run-number>_detector_<number-of-simulated-photons>_photons.h5 in openPMD format.</p> <p>The file <em>opt_thin_integrated_1.6x1.6x3_micron_10fs_around_laser_max</em> contains the total electron density data for the optically thin case (milestone 4.2.2.9) integrated over 959 slices in the propagation direction of the probe laser beam. The density is only non-zero in the 6th cell of the simulation volume thus enforcing single-scattering in the ParaTAXIS simulation as can be assumed for an optically thin medium. This data was read by a ParaTAXIS into a simulation volume of 12 x 512 x 512 cells.</p> <p>We launched 10 parallel simulations, each arriving at detector images for <span class="math-tex">\(10^{12}\)</span> simulated photons. The detector output of these simulations can be found in <em>opt_thin_detector_1e12_photons_run<run-number>.h5</em> also in openPMD format.</p> <p> </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.