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Dataset results
10 results for “Multiple uncertainties”
Glacier runoff projections and their multiple sources of uncertainty in the Patagonian Andes (40-56°S)
<p>This dataset contains the catchment scale results of the study: "<strong>Unravelling the sources of uncertainty in glacier runoff projections in the Patagonian Andes (40–56° S)</strong>". The results are disaggregated in the following files (for more details, please read the README file):</p> <p><em>- basins_boundaries.zip:</em> Contains the polygons (in .shp format) of the studied catchments. Each catchment is identified by its "basin_id".</p> <p><em>- dataset_historical.csv: </em>Summarises the historical conditions of each glacier at the catchment scale (area, volume and reference climate).</p> <p><em>- dataset_future.csv: </em>Summarises the future glacier climate drivers and their impacts at the catchment scale. </p> <p><em>- dataset_signatures.csv: </em>Summarises the glacio-hydrological signatures of each glacier at the catchment scale.</p> <p><strong>Citation (preprint under review): </strong></p> <p>- Aguayo, R., Maussion, F., Schuster, L., Schaefer, M., Caro, A., Schmitt, P., Mackay, J., Ultee, L., Leon-Muñoz, J., and Aguayo, M.: Assessing the glacier projection uncertainties in the Patagonian Andes (40–56° S) from a catchment perspective, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2325, 2023.</p>
Replication package for: Resolving Failed Banks: Uncertainty, Multiple Bidding & Auction Design
<p>"Resolving Failed Banks: Uncertainty, Multiple Bidding, and Auction Design," Jason Allen, Robert Clark, Brent Hickman and Eric Richert, forthcoming, <em>Review of Economic Studies.</em></p> <p> </p> <p>DATA AND CODE NEEDED TO REPLICATE ANALYSIS</p>
Data from: Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA
Understanding factors that influence observation processes is critical for accurate assessment of underlying ecological processes. When indirect methods of detection, such as environmental DNA, are used to determine species presence, additional levels of uncertainty from observation processes need to be accounted for. We conducted a field trial to evaluate observation processes of a terrestrial invasive species (wild pigs- Sus scrofa) from DNA in water bodies. We used a multi-scale occupancy analysis to estimate different levels of observation processes (detection, p): the probability DNA is available per sample (θ), the probability of capturing DNA per extraction (γ), and the probability of amplification per qPCR run (δ). We selected four sites for each of three water body types and collected 10 samples per water body during two months (September and October 2016) in central Texas. Our methodology can be used to guide sampling adaptively to minimize costs while improving inference of species distributions. Using a removal sampling approach was more efficient than pooling samples, and was unbiased. Availability of DNA varied by month, was considerably higher when water pH was near neutral, and was higher in ephemeral streams relative to wildlife guzzlers and ponds. To achieve a cumulative detection probability greater than 90% (including availability, capture, and amplification), future studies should collect 20 water samples per site, conduct at least 2 extractions per sample, and conduct 5 qPCR replicates per extraction. Accounting for multiple levels of uncertainty of observation processes improved estimation of the ecological processes and provided guidance for future sampling designs.
Experiment results for the paper "Uncertainty-Aware Ship Location Estimation using Multiple Cameras in Coastal Areas" to appear in MDM'2024
<p>After decompression, there are 16 folders which corresponding to the 16 multi-camera settings in the paper.</p> <p> </p> <p>Under each folder, there are two files: trajs.csv and trajsGuess.csv.</p> <p> </p> <p>1. trajs.csv contains the trajectories of ships that are located inside the monitored area of the mult-camera setting.</p> <p> The first four columns are MMSI (ship identity), timestamp, lon, and lat.</p> <p> The following columns are the corresponding pixel of the coordinate (lon, lat) in each camera, where (-1,-1) means (lon, lat) is outside the monitored area by a camera.</p> <p> A pixel is a pair of integers. </p> <p> xPos1 and yPos1 are for the 1st camera, and xPos2 and yPos2 are for the 2nd camera, and so on so forth.</p> <p> </p> <p>2. trajsGuess.csv contains the estimated ship locations by using the proposed approach in the paper.</p> <p> There are 6 columns.</p> <p> The 1st column is timestamp.</p> <p> The 2nd column is used to distinguish between the different pixel polygon intersections.</p> <p> The 3rd column and the 4th column can be either a pixel coordinate or a spatial point coordinate in lon/lat.</p> <p> The 5th column is either the cameraID of a pixel, or the order of a boundary point for a spatial polygon. The cameraID starts from 1.</p> <p> The 6th column is the type of the record, which can be</p> <p> pixel,</p> <p> or intersection1 (a polygon),</p> <p> or center1 (center of intersection1),</p> <p> or intersection2 (a polygon),</p> <p> or center2 (center of intersection2).</p> <p> Note that intersection2 and center2 appear rarely in the 6th column.</p>
Dataset & Code related to article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'
<p>This record contains the 7768 lung masks <strong>manual annotations, implementation code, and pre-trained models</strong> related to the article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'</p> <p>Also we include the visualised, selected top D reliable CT slices for all COVID-19 patients in the test dataset for better understanding. </p> <p>For the detailed usage of the data and code, please refer to https://github.com/smallmax00/BAGCN-Covid19</p> <p> </p>
Decision Making in Multiple Sclerosis Care Under Uncertainty
ClinicalTrials.gov study NCT04035720. IPD Sharing: NO. Countries: 1. Publications: 1.
Phylogeny and secondary sexual trait evolution in Schizocosa wolf spiders (Araneae, Lycosidae) shows evidence for multiple gains and losses of ornamentation and species delimitation uncertainty
Open the record for dataset details and reuse information.
Data from: Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA
Open the record for dataset details and reuse information.
Stochastic hydrodynamic solution data with multiple uncertainties
<p>Raw tabulated data generated by <a href="https://www.seamlesswave.com/">SEAMLESS-WAVE</a> stochastic models.</p>
Communicating Multiple Uncertainties Associated With the Benefits and Risks of New Cancer Drugs
ClinicalTrials.gov study NCT07110597. 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.
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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.