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.
4
datasets available to search
ShareScore release 0.9.0
Dataset results
4 results for “cloud base height”
NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data for SPLASH
<p>This dataset contains daily files from a CL31 ceilometer manufactured by Vaisala that was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The files contain backscatter profiles, cloud base heights, and visibility. The ceilometer measures vertical profiles of backscatter using laser technology. From the backscatter profiles, cloud base height and vertical visibility are determined using with the Vaisala software CL-view. For details on the instrument specifics and the methods, see the manufacturer manual (cl31usersguide.pdf). </p> <p>The file format is the original Vaisala format (.DAT). For a description of the format see the manufacturer manual. To convert the .DAT file format to netcdf format, the open source command line Python program ‘cl2nc’ (https://github.com/peterkuma/cl2nc) can, for example, be used. </p> <p>The file naming conventions for the .DAT files are as follows:</p> <p>NOAA_PSL_CL31_Roaring Judy_yyyymmdd_HH.DAT</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p>HH: Hour when the first sample was written to the file</p> <p>The time stamp of all data is in UTC.</p>
Unified Model Atmospheric Forecast Model Data for Machine Learning Cloud-Base Height
<p>Unified Model data, in pp format, for machine learning of cloud-base height based on profiles of temperature, humidity, pressure and cloud fraction. The model configuration is Global Atmosphere 6, running with a resolution of N320 (which is coarser than what was running operationally at the time). Each simulation is run for 24 hours, re-initialising every 24 hours. A separate data file is provided every 6 hours. Data points are on a latitude-longitude grid in the horizontal and on a stretched grid in the vertical. See https://gmd.copernicus.org/articles/10/1487/2017/ for details of the model configuration.</p> <p>Data from January 2016 is for training.</p> <p>Data from July 2017 is for development/validation</p> <p>Data from October 2017 is for final testing.</p> <p> </p>
Data from: Trait‐based signatures of cloud base height in a tropical cloud forest
<p>Clouds have profound consequences for ecosystem structure and function. Yet, the direct monitoring of clouds and their effects on biota is challenging especially in remote and topographically complex tropical cloud forests. We argue that known relationships between climate and the taxonomic and functional composition of plant communities may provide a fingerprint of cloud base height, thus providing a rapid and cost-effective assessment in remote tropical cloud forests. To detect cloud base height, we compared species turnover and functional trait values among herbaceous and woody plant communities in an ecosystem dominated by cloud formation. We measured soil and air temperature, soil nutrient concentrations, and extracellular enzyme activity. We hypothesized that woody and herbaceous plants would provide signatures of cloud base height, as evidenced by abrupt shifts in both taxonomic composition and plant function. We demonstrated abrupt changes in taxonomic composition and the community-weighted mean of a key functional trait, specific leaf area, across elevation for both woody and herbaceous species, consistent with our predictions. However, abrupt taxonomic and functional changes occurred 100 m higher in elevation for herbaceous plants compared to woody ones. Soil temperature abruptly decreased where herbaceous taxonomic and functional turnover was high. Other environmental variables including soil biogeochemistry did not explain the abrupt change observed for woody plant communities. We provide evidence that a trait-based approach can be used to estimate cloud base height. We outline how rises in cloud base height and differential environmental requirements between growth forms can be distinguished using this approach.</p>
Data from: Trait‐based signatures of cloud base height in a tropical cloud forest
Open the record for dataset details and reuse information.
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.