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1,600 results for “input”
Kellogg Biological Station site, station Treatment 4, certified oganic, no chemical inputs, annual tillage, rotary-hoed to control weeds, study of aboveground net primary productivity of Veronica in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains aboveground net primary productivity of Veronica measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Kellogg Biological Station site, station Treatment 4, certified oganic, no chemical inputs, annual tillage, rotary-hoed to control weeds, study of aboveground net primary productivity of Vicia villosa in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains aboveground net primary productivity of Vicia villosa measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Kellogg Biological Station site, station Treatment 4, certified oganic, no chemical inputs, annual tillage, rotary-hoed to control weeds, study of aboveground net primary productivity of Zea mays in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains aboveground net primary productivity of Zea mays measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Kellogg Biological Station site, station Treatment 4, certified oganic, no chemical inputs, annual tillage, rotary-hoed to control weeds, study of aboveground net primary productivity in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains aboveground net primary productivity measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Kellogg Biological Station site, station Treatment 4, certified oganic, no chemical inputs, annual tillage, rotary-hoed to control weeds, study of plant species richness in units of numberPerMeterSquared on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains plant species richness measurements in numberPerMeterSquared units and were aggregated to a yearly timescale.
Merged Global Pluvial Floods DataBase (Input Data)
<p>This repository contains the input data used in the Jupyter notebook downloadable from Github <a href="https://github.com/FatimaPillosu/Merged_Global_Pluvial_Floods_DataBase">here</a>.</p> <p>Such input data consists in two main datasets:</p> <ul> <li>A raw (global and regional) flood reports from four different databases.</li> <li>A global daily rainfall analysis from NOAA (CPC_GLOBAL_PRCP_v1.0).</li> </ul> <p>The Jupyter notebook runs a Python code that post-processes the raw flood reports, using information extracted from other datasets, to select some reports of interest (mainly regarding pluvial and flash floods). At a later stage, such reports are merged into a single database for global pluvial/flash flood reports. The Jupyter notebook also runs a Metview-Python code to visualize partial and final results as map plots.</p> <p>The four original databases are:</p> <ol> <li>FloodList, FL (Global domain): <a href="http://floodlist.com/">http://floodlist.com/</a></li> <li>Emergency Events Database, EMDAT (Global domain): <a href="https://www.emdat.be/">https://www.emdat.be/</a></li> <li>European Severe Weather Database, ESWD (Europe): <a href="https://www.essl.org/cms/european-severe-weather-database/">https://www.essl.org/cms/european-severe-weather-database/</a></li> <li>Storm Events Database, SED (USA): <a href="https://www.ncdc.noaa.gov/stormevents/">https://www.ncdc.noaa.gov/stormevents/</a></li> </ol> <p><em>NOTE: </em>For more details about these databases (documentation, licenses, etc.), look at the README.md file.</p> <p><em>NOTE: </em>The data in this repository is intended for an exclusive NON-COMMERCIAL academic or personal use, and it is released under the Creative Commons Attribution-ShareAlike 4.0 International Public License. For more information, look at the LICENSE.md file.</p>
Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
Inputs for Galaxy tutorial on molecular docking on SARS-CoV-2 MPro
<p>Inputs for Galaxy tutorial on molecular docking on SARS-CoV-2 main protease.</p>
GEO4962 input dataset for running CESM 2.1.0 case for the course
<p>Contains the input dataset for running CESM 2.1.0 as part of the GEO4962 course at the University of Oslo.</p>
Input files to run the Iquitos dengue simulator
<p>Input files to run the Iquitos dengue simulator</p>
LAMMPS input scripts for GO in confinements
<p>LAMMPS input scripts and sheet models.<br> (1) Two scripts of the cylindrical and spherical confining;<br> (2) Different size of GO sheets model.</p>
Input files and setup for simulations of ancestral and modern glycosidases
<p>Input files, heme parameters, and representative structures from simulations of ancestral glycosidases and the modern glycosidase from the thermophilic <em>Halothermothrix orenii</em>. For details please see the README file provided.</p>
Model input and output, performance measures and modifications in the source code for PALM simulations on Mäkelänkatu in Helsinki, Finland
<p>This dataset is for air quality simulations conducted on Mäkelänkatu in Helsinki, Finland, using the PALM model system 6.0.</p> <p>By default, simulations use modelled data as boundary conditions. This includes modelled meteorological data from MEPS (MetCoOp Ensemble Prediction System) and air pollutant background concentrations from the ADCHEM model. Alternatively, measured meteorology from the Kivenlahti mast in Espoo, Finland, and aerosol size distribution from the SMEAR III station in Kumpula, Helsinki, is applied.</p> <p>Simulations:</p> <ul> <li>9 June morning: <ul> <li>0609_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>0609_morning_smear: use modelled boundary conditions for meteorology and measured for aerosol size distribution</li> <li>0609_morning_wd_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at Kivenlahti. For aerosol size distribution, use the measured boundary conditions.</li> <li>0609_morning_wdk_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at SMEAR III. For aerosol size distribution, use the measured boundary conditions.</li> <li>precursor_0609_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_morning_allmet: precursor with measured boundary conditions for meteorology</li> <li>precursor_0609_morning_wd: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at Kivenlahti.</li> <li>precursor_0609_morning_wdk: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at SMEAR III.</li> </ul> </li> <li>9 June evening: <ul> <li>0609_evening: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_evening_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>precursor_0609_evening: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_evening_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> <li>12 December morning: <ul> <li>1207_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>1207_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution </li> <li>precursor_1207_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_1207_morning_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> </ul> <p> </p> <p>Datasets are given separately for the root (no suffix), parent (suffix _N02) and child (_N03) domain. The content is following:</p> <ul> <li>input_monitoring_output_usercode <ul> <li>Input data <ul> <li><run_identifier>_chemistry: emission data for gases</li> <li><run_identifier>_dynamic: initialisation and forcing data for meteorological variables and air pollutants</li> <li><run_identifier>_p3d: parameter file for model steering</li> <li><run_identifier>_salsa: emission data for aerosol particles</li> <li><run_identifier>_static: topography information</li> </ul> </li> <li>Simulation performance information <ul> <li><run_identifier>_cpu: information on the CPU time consumed</li> <li><run_identifier>_header: information about the selected model parameters</li> <li><run_identifier>_rc: time step control output</li> </ul> </li> <li>Output data <ul> <li><run_identifier>_av_masked_N03_M01.nc: temporally averaged wind speed data close to the ground</li> <li><run_identifier>_av_masked_N03_M04.nc: temporally averaged aerosol particle concentration data close to the ground</li> <li><run_identifier>_av_masked_N03_M06.nc: temporally averaged aerosol particle concentration data in a vertical column next to the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_av_masked_N03_M07.nc: temporally averaged aerosol particle concentration data in a vertical column on the other side of the street from the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_pr.nc: temporally vertical profile data on meteorological variables</li> <li><run_identifier>_ts.nc: flow statistics data</li> </ul> </li> <li>Modifications made to the source code (PALM revision, https://palm.muk.uni-hannover.de/trac/browser?rev=4416, last access: 10 Sept 2019) <ul> <li>chem_gasphase_mod.f90: chemical mechanism salsa+simple</li> <li>chem_emissions_mod.f90 (modifications indicated with "MONA")</li> <li>user_module.f90 (modification listed under "Current revisions")</li> <li>Makefile (modification listed under "Current revisions")</li> </ul> </li> </ul> </li> </ul> <p>See the PALM model webpage (https://palm.muk.uni-hannover.de) for details.</p> <p> </p>
Input files for iDEP
<p>These are files used in the book chapter entitled: iDEP web application for RNA-Seq data analysis. </p> <p> </p>
Neisseria gonorrhoeae clustering to reveal major European WGS-based genogroups in association with antimicrobial resistance (cgMLST and MScgMLST schemas, allelic profile matrices and GrapeTree input file)
<p>This dataset refers to the gene-by-gene analysis of 3791 <em>Neisseria gonorrhoeae</em> genomes from 21 European countries and includes the used cgMLST and MScgMLST loci schemas prepared for the chewBBACA core suite, as well as the associated allelic profile matrices for all genomes. Additionally a <em>.json</em> file is made available for direct input in the GrapTree vizualization software for data/metadata exploration. </p> <p>All novel raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB36482). Additional raw sequence read data used were retrieved from the following ENA BioProjects: PRJEB14933; PRJEB2124; PRJEB23008; PRJEB26560; PRJEB9227; PRJNA275092; PRJNA348107; PRJNA473385; PRJNA315363. </p>
GFE Driver - Input graphs
<p>The archive contains the input graphs used in the <a href="https://github.com/cwida/gfe_driver">GFE Driver</a>. It consists of the graphs dota-league, graph500-22, graph500-24 and graph500-26, taken from the <a href="https://www.graphalytics.org/datasets">Graphalytics collection</a>, and the syntentic graphs uniform-22, uniform-24 and uniform-26, generated with an <a href="https://github.com/whatsthecraic/uniform_graph_generator">ad-hoc tool</a>. </p>
A narrow ear canal reduces sound velocity to 1 create additional acoustic inputs in a micro-scale insect ear
<p><span><span><span><span><span><span><span><span><span><span><span>Located in the forelegs, katydid ears are unique among arthropods in having outer, middle and inner component, analogous to the mammalian ear. Unlike mammals, sound is received externally, and internally via a narrow ear canal (EC) derived from the respiratory tracheal system. Inside the EC sound travels slower than in free air, causing temporal and pressure differences between external and internal inputs. The delay is suspected to arise as sound propagation changes from adiabatic to isothermal, imposed by EC geometry. If true, a reduction in sound velocity should persist independently of the gas composition in the EC. Integrating experimental (laser Doppler vibrometry, micro-CT) and numerical methods, we demonstrate that the narrow radius of the EC is the major cause of the signal time delay. Results imply that the EC is asymmetrically bifurcated, creating four notable auditory paths for each ear. Implication of methods and findings in avian hearing are discussed.</span></span></span></span></span></span></span></span></span></span></span></p>
Scenario Input files for "The Domestic and International Implications of Future Climate for U.S. Agriculture in GCAM"
<p>The GCAM scenario input files needed for the experiments described in the paper "The Domestic and International Implications of Future Climate for U.S. Agriculture in GCAM".</p>
Input and validation data for the Askervein Hill benchmark
<p>This dataset has been generated within the IEA-Wind Task 31 Wakebench project to validate microscale flow models over complex terrain. The data has been digitized from the original technical reports of the experiment from Taylor and Teunissen (1983/1985).</p>
Input dataset for a single location in Norway ALP1 (61.0243N,8.12343E) for running CTSM/FATES EMERALD
<pre>inputdata_version2.0.0 for a single location in Norway ALP1 (61.0243N,8.12343E) for running CTSM/FATES EMERALD (created by Hui Tang, using tools from https://github.com/NordicESMhub/ctsm/tree/release-emerald-platform2.0.1/tools/emerald_sites_tools) - Same as version version1.0.0, except two additional input files required by fire module are included. - Should work with model platform:release-emerald-platform2.*.* </pre> <p> </p> <p><em>Additional reference:</em></p> <p>Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., et al. (2019). The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. Journal of Advances in Modeling Earth Systems, 11, 4245–4287. <a href="https://doi.org/10.1029/2018MS001583">https://doi.org/10.1029/2018MS001583</a></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.