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855 results for “model system”
Figure 8 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 8 Simulation of inflation, Model B.
Figure 7 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 7 Simulation of inflation, Model A.
Figure 9 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 9 Simulation of inflation, Model C.
Figure 1 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 1 DiSSCo timeline.
Figure 11 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 11 DiSSCo national contribution models.
Figure 2 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 2 DiSSCo general membership fee calculation model.
Figure 7 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 7 Output module.
LI 850 sensor data of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)
<p>LI 850 sensor data of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)</p>
USGS National Map 3DEP 1 Arc-second Digital Elevation Models (DEMs): Full Coverage for U.S. Interstate Highway System
<p>This is an archive of the USGS National Map 3DEP 1 Arc-second Digital Elevation Models (DEMs). While it should be reasonably complete (provided via DVD by USGS), coverage has only been verified to provide full coverage for the U.S. Interstate Highway System (see notes below).</p> <p>Original description:</p> <blockquote> <p>This is a tiled collection of the 3D Elevation Program (3DEP) and is 1 arc-second (approximately 30 m) resolution.The elevations in this Digital Elevation Model (DEM) represent the topographic bare-earth surface. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. [...] The seamless 1 arc-second DEM layers are derived from diverse source data that are processed to a common coordinate system and unit of vertical measure. These data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). All elevation values are in meters and, over the continental United States, are referenced to the North American Vertical Datum of 1988 (NAVD88). The seamless 1 arc-second DEM layer provides coverage of the conterminous United States, Hawaii, Puerto Rico, other territorial islands, and much of Alaska and Canada. The seamless 1 arc-second DEM is available as pre-staged products tiled in 1 degree blocks in Erdas .img, ESRI arc-grid, and grid float formats. The seamless 1 arc-second DEM layer is updated continually as new data become available.</p> </blockquote> <p>The archived data here were acquired from two sources:</p> <ul> <li>DVD purchased from USGS on (DATE), as per process described in README.pdf The list of files stemming from the DVD are listed in "manifest_USGS_DVD.txt"</li> <li>manual download of missing grid cells from <a href="https://apps.nationalmap.gov/downloader/#/">https://apps.nationalmap.gov/downloader</a>/ on (DATE), as per procedure described in README.pdf, files are listed in "manifest_missingrasters.txt"</li> </ul> <p> </p>
Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations
<p>The data provided here has been used to create the figures in the paper submitted to Biogeosciences titled <em>Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations.</em></p>
The effects of fair allocation principles on energy system model designs
<p>This is the associated dataset to <a href="https://github.com/OskarVagero/highRES-Europe-WF/tree/MENOFS">https://github.com/OskarVagero/highRES-Europe-WF/tree/MENOFS </a>, which contains the data necessary to replicate the study. </p> <p>In addition to the data required to replicate the study, we also include six pre-generated model results (.db), which represent the "top performers", as well as the cost-optimal model run. </p> <ul> <li>Weather data is based on ERA5, from ECMWF (https://doi.org/10.1002/qj.3803) </li> <li>Demand data is based on the Interannual Electricity Demand Calculator (https://zenodo.org/records/10820928)</li> <li>Existing hydropower capacity is based on the JRC Hydropower database (https://zenodo.org/records/5215920)</li> <li>Historic electricity generation from hydropower is based on the U.S. Energy Information Administration (https://www.eia.gov/international/data/world/electricity/electricity-generation)</li> </ul> <p>More details on how to use the data can be found in the GitHub repository. </p>
Data set for training a ML model to predict duration of MPI application phases (HPC system) - with previous phase info
<p>This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system.</p> <p>There are 10 different data sets corresponding to different HPC applications.</p> <p>These data sets contain information regarding the previous MPI call with same ID and type.</p>
Data set for training a ML model to predict duration of MPI application phases (HPC system) - without previous phases info
<p>This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system.</p> <p>There are 11 different data sets corresponding to different HPC applications.</p> <p>These data sets do not contain information regarding previous MPI calls</p>
The causal loop diagram model of traceability system rental equipment in oil and gas supporting companies
Open the record for dataset details and reuse information.
Data for "The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)" by Verjans et al.
<p>Results and scripts to reproduce figures of The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)</p> <p>Input files, preprocessing, run control and postprocessing scripts for all simulations are also provided.</p> <p>See readme.txt for details.</p> <p>Update 22 November 2022: use v2 of code files for updated version of StISSM</p> <p>by Verjans et al.</p>
Modeling the thermodynamic properties of cyclic alcohols with the SAFT-gamma Mie approach: application to cyclohexanol and menthol systems. Mol.Phys. 2024
<p>Calculated data and supporting information associated with Bernet et al. publication in Mol. Phys. 2024; Thomas Bernet, Shubhani Paliwal, Ahmed Alyazidi, Riccardo Standish, Andrew J. Haslam, Claire S. Adjiman, George Jackson, and Amparo Galindo</p> <p> </p>
Improving Traumatic Brain Injury Rehab Care With Comm Health Services: a Research Project Within the TBI Model System
ClinicalTrials.gov study NCT06188364. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Safety and Performance Study of the CyPass System Applier Model 241
ClinicalTrials.gov study NCT02228577. IPD Sharing: NO. Countries: 1. Publications: 0.
Hepatic and Systemic Hemodynamic Modeling During Liver Surgery
ClinicalTrials.gov study NCT05339984. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects on Mortality and Clinical Course of a Patient's Choice Model for Opioid Maintenance Treatment for Opioid Dependence - Evaluation of a System Enabling a Large Expansion of Treatment Providers a
ClinicalTrials.gov study NCT05678036. IPD Sharing: NO. Countries: 1. Publications: 0.
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.