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87 results for “future projections”
The PRO-FUTURE Project
ClinicalTrials.gov study NCT06382233. IPD Sharing: NO. Countries: 1. Publications: 0.
Laser frequency stabilization and stray light issues for LISA and other future multi-spacecraft missions Project
"The Laser Interferometer Space Antenna (LISA) is a joint NASA/ESA project which will use laser interferometry between drag-free proof masses to measure gravitational waves from many galactic and cosmological sources. The same interferometer technology is also the key to future multi-spacecraft missions such as multi-aperture telescope missions. These missions could include several spacecraft all separated by potentially 10s of km, flying in a fixed formation with sub-wavelength variations in their distances. These multi-aperture or distributed aperture telescopes will revolutionize the angular resolution in the infrared, optical, and even X-ray band. This proposal addresses two components which are both critical to these missions. The first component introduces a new technique to stabilize the laser frequency to an optical reference cavity. Laser frequency noise will be the limiting factor for most of the distributed aperture telescope missions; in contrast, LISA can trade frequency noise against ranging precision. This new technique is based on heterodyne interferometry which is also used to measure changes in the distances between the spacecraft. Because of this similarity, this technology can easily be integrated into the payload. It requires the same photo detectors and digital signal processing systems that are used for the interferometry. It utilizes to a large degree existing components, reducing R&D time and cost for all interferometric space missions. We have already started initial proof of principle experiments and have reached already a performance remarkably close to the performance of the standard and long time-favored modulation/demodulation technique. Now we propose to study this technique in more detail, study the limiting noise sources experimentally and theoretically, and push it to the limitations of the reference cavity itself. The expected final fractional frequency noise should be better than 0.01ppt for measurement times of a 1000s. This
Off-plane Grating Arrays for Future Missions Project
N/A
Future NG-IDF Dataset for CONUS Design Extreme Event Projections
<p><strong>Future NG-IDF dataset</strong> consists of projections of design extreme events in future climates across the CONUS. Future climates are represented by an ensemble of 10 downscaled CMIP5 global climate models (GCMs) under the RCP8.5 emission scenario. The dataset covers over 200,000 locations at a 1/16th-degree resolution. This dataset extends Sun et al. (<a href="10.5281/zenodo.5827028">2019</a>) by including future projections following the Next-Generation Intensity-Duration-Frequency (NG-IDF) approach (Yan et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021290">2018</a>). In contrast to traditional precipitation-based IDF methods such as NOAA Atlas 14, which neglects snow processes, NG-IDF accounts for the total water reaching the land surface (<strong>AWR</strong>) from rainfall, snowmelt, and rain-on-snow (ROS) events.</p><p><strong>Downscaled GCM</strong> climate forcing was derived through the Multivariate Adaptive Constructed Analogs (MACA) method for statistical downscaling and were bias-corrected against the Livneh gridded meteorological dataset (Livneh et al., <a href="https://doi.org/10.1175/JCLI-D-12-00508.1">2013</a>), which is the climate forcing used for the historical CONUS NG-IDF (Sun et al., <a href="10.5281/zenodo.5827028">2019</a>). The selection of the 10 GCMs was based on their divergence in projecting future changes in temperature and precipitation for the CONUS, as well as their accuracy in representing historical climate conditions. The chosen GCMs are bcc-csm1-1, BNU-ESM, CCSM4, CNRM-CM5, CSIRO-Mk3-6-0, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MRI-CGCM3, and NorESM1-M. The MACA downscaled GCM projections for future climate forcing are available at: <a href="http://thredds.northwestknowledge.net:8080/thredds/catalog/NWCSC_INTEGRATED_SCENARIOS_ALL_CLIMATE/macav2livneh/catalog.html">Northwest Knowledge Network</a>.</p><p><strong>Driving Mechanism Classification for Extreme AWR Events:</strong></p><p>Extreme AWR events at each location are classified by their driving mechanisms as follows:</p><blockquote><p>precip: Sum of snowfall and rain, with the assumption that both contribute instantaneously to runoff</p><p>rainfall: Precipitation on snow-free ground.</p><p>melt_only: Decreasing SWE, daily rainfall < 10 mm, and the sum of rainfall and snowmelt has < 20% contribution from rainfall.</p><p>ROS: Rain-on-snow - Decreasing SWE with at least 10 mm of daily rainfall falling on a snowpack with at least 10 mm daily SWE, and snowmelt contributes at least 20% of the total of rain and snowmelt.</p><p>AWR: Amount of water reaching the land surface from rain, snowmelt, and ROS events.</p></blockquote><p><strong>Folder Structure:</strong></p><blockquote><p>historical/matlab_matrix/: baseline period</p><p>rcp85/matlab_matrix/: future period</p></blockquote><p>For each GCM, there are 'historical' and 'rcp85' subfolders for baseline and future periods, respectively. Within the subfolder, files list discrete IDF values by event duration (24h, 48h, and 72h) and mechanism (<i>AWR</i>, <i>precip</i>, <i>rainfall</i>, <i>ROS</i>, and <i>melt_only)</i>.</p><p><strong>File Naming Convention: </strong></p><blockquote><p>The naming convention is [duration]_[mechanism].mat, e.g., 24hr_AWR.mat</p></blockquote><p><strong>Data Dimension</strong>: </p><blockquote><p>The data is structured in a three-dimensional array of 381 x 921 x 7. The first dimension corresponds to the latitude index, and the second to the longitude index, mapping to specific geographic coordinates. The third dimension represents NG-IDF values for return periods of 2, 5, 10, 25, 50, 100, and 500 years. 'NaN' indicates no runoff generated by a specific mechanism, such as ROS. </p></blockquote><blockquote><p><strong>Coordinate Reference:</strong></p></blockquote><blockquote><p>Each latitude and longitude index is directly mapped to the corresponding row or line number in the 'CONUS_livneh_lat.csv' and 'CONUS_livneh_lon.csv' files, which contain 381 and 921 lines, respectively.</p></blockquote><p><strong>Unit of NG-IDF Values</strong>:</p><blockquote><p>millimeter (mm)</p></blockquote><p> </p>
Phytoplankton and cyanobacteria abundances in mid‐21st century lakes depend strongly on future land use and climate projections
<p>These are the data used to create the figures for this publication.</p>
Workflow Diagram for GeoSoft "Geoscience Papers of the Future" (GPF) project - Tzeng
<p>This is a diagram for data provenance for a paper in progress, as part of a software sharing project. It is an earlier version of a the final diagram used in the paper.</p>
Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean
<p>Dataset of PROJECTING FUTURE CLIMATE CHANGE IMPACTS ON THE DISTRIBUTION OF PELAGIC SQUID IN THE SOUTHERN OCEAN</p>
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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)
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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.