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648 results for “uncertainties”
Design Space Evaluation for Confidentiality under Architectural Uncertainty - Dataset
<p>This dataset contains additional files used, adapted and/or created for the bachelor's thesis of Oliver Liu. It includes the code for the architecture candidate filter, as well as models and configuration files used for the evaluation.</p>
Data for "Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1"
<p>The domain file and surface data file that used to run ELMv1, and processed ISIMP2a runoff data that used in <a href="https://gmd.copernicus.org/preprints/gmd-2021-401/">https://gmd.copernicus.org/preprints/gmd-2021-401/</a></p> <p>ELM_runoff_parameter_post.nc contains the ELM runoff generation relevant parameter posteriors at a global half degree spatial resolution.</p>
Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package
<p>Here we present field observations of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the PLM1, PLM6, and PLM7 wells in the East River Colorado (USA) sampled in May, 2021. This observation dataset, along with the presented python modeling scripts to interpret the data, can aide in quantifying groundwater residence times and recharge conditions. The README files describes the directories and scripts.</p>
Surface hourly measurement data of O3, NO2 and PM2.5 for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>Surface hourly measurement data of O3, NO2 and PM2.5 during summer of 2017.</p> <p>In the .csv files, the first column contains the ID for each measurement site. "lon", "lat" are longitude and latitude, respectively.</p> <p>Date format is "YYYYMMDD_hour".</p>
Narratives on the present and the future in the time of Covid-19 pandemic: Uncertainty, subjective feeling and the role of positive anticipatory states.
<p><strong>Narratives on the present and the future in the time of Covid-19 pandemic: Uncertainty, subjective feeling and the role of positive anticipatory states.</strong></p>
Optimal policy for uncertainty estimation concurrent with decision making
<p>Dataset for "Optimal policy for uncertainty estimation concurrent with decision making".</p> <p>The dataset should be merged into the code folder, thus the program can work.</p>
Extended Data for Single-cell Transcriptional Uncertainty Landscape of Cell Differentiation
<p>The dataset includes supporting information for the study titled "Single-cell Transcriptional Uncertainty Landscape of Cell Differentiation".</p>
Data for: Uncertainty about old information results in differential predator memory in tadpoles
<p>As information ages, it may become less accurate, resulting in increased uncertainty for decision-makers. For example, chemical alarm cues are a source of public information about a nearby predator attack, and these cues can become spatially inaccurate through time. These cues can also degrade quickly under natural conditions, and cue receivers are sensitive to such degradation. Although numerous studies have documented predator-recognition learning from fresh alarm cues, no studies have explored learning from aged alarm cues and whether the uncertainty associated with this older information contributes to shortening the retention of learned responses (i.e., the 'memory window'). Here, we found that wood frog tadpoles, <em>Lithobates</em> <em>sylvaticus</em>, learned to recognize a novel odour as a predator when paired with alarm cues aged under natural conditions for up to one hour. However, only tadpoles conditioned with fresh alarm cues were found to retain this learned response when tested 9 days after conditioning. These results support the hypothesis that the memory window is shortened by the uncertainty associated with older information, preventing the long-term costs of a learned association that was based on potentially outdated information. </p>
Reducing Uncertainty in Collective Perception using Self-organized Hierarchy
<p>This dataset accompanies an article submission and a <a href="https://github.com/BlueDiamond07/Collective_perception">code repository</a>.</p> <p><strong>Abstract:</strong><br> In collective perception, agents sample spatial data and use the samples to agree on some estimate. In this research, we identify the sources of statistical uncertainty that occur in collective perception and note that improving the accuracy of fully decentralized approaches, beyond a certain threshold, might be intractable. We propose self-organized hierarchy as an approach to improve accuracy in collective perception, by reducing or eliminating some of the sources of uncertainty. Using self-organized hierarchy, aspects of centralization and decentralization can be combined: robots can understand their relative positions system-wide and fuse their information at one point, without requiring, e.g., a fully connected or static communication network. In this way, multi-sensor fusion techniques that have been designed for fully centralized systems can be applied to a self-organized system for the first time, without losing the key practical benefits of decentralization. We implement simple proof-of-concept fusion in a self-organized hierarchy approach and test it against three fully decentralized benchmark approaches. We test the perceptual accuracy of the approaches for time-invariant and time-varying absolute conditions, and test the scalability and fault tolerance of their accuracies. We show that the self-organized hierarchy approach is substantially more accurate, more consistent, and faster than the other approaches, but also that it is comparably scalable and fault-tolerant.</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 Set: Uncertainty
<p>Dataset of the measurements presented in the futureEnergy D8 submission</p>
YudengLin/memristorBDNN: Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning
<p>This code repository is partly to support risk-sensitive reinforcement learning experiment in the manuscript "Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning" submitted to Nature Machine Intelligence.</p>
European migration scenarios with probabilistic uncertainty assessment – Data Description
<p>This open data deposit contains the data and code accompanying used in the report: Bijak (2023): European migration scenarios with probabilistic uncertainty assessment, QuantMig Project Deliverable D9.4. The cover note should be read in conjunction with the report, available via www.quantmig.eu, and with the individual readme files in the data folders that can be found within this Zenodo repository (DOI: 10.5281/zenodo.7954150).</p>
Brewer #150 UV measurements and ancillary data needed for its uncertainty evaluation
<p>All files uploaded are needed to evaluate the uncertainty of the UV measurements recorded by the Brewer MKIII double monochromator (No. 150). The uncertainty analysis was carried out using the Monte Carlo technique, which was implemented in a separate R file (doi: 10.5281/zenodo.8046562). Files contain all the necessary information to characterize Brewer #150 (cosine correction, raw counts, lamp irradiance, responsivity instabilities, noise and wavelength shifts).</p>
Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector. Energy Policy (2023). Supplementary Information on Assumptions and Results
<p>This dataset supplements the article with the title "Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector", published in Energy Policy. </p> <p>The dataset contains the following:</p> <ul> <li>The Latin Hypercube Sample of the multipliers that are applied to the key input parameters of ETSAP-TIAM in order to generate 1000 different states of the world regarding economic and demographic growth, energy resources potentials, energy technology costs, climate sensitivity and radiative forcing, LULUCF CO<sub>2</sub> sink potential, CO<sub>2</sub> sequestration potential, and decoupling between energy consumption and economic development. The multipliers are sampled from the underlying probability distributions described in the article. </li> <li>The results (at the global scale) from four scenario families for each one of the 1000 wofld states. These scenario families are: <ul> <li>BASE_SSP2: Describes the development of the global energy system consistent with recent trends and policies.</li> <li>2C_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 2 °C as the maximum post-industrial temperature change from 2020 to 2100.</li> <li>2C_SSP2_DA30: Delayed climate action. The climate change mitigation policies of 2C_SSP2 start in 2030. </li> <li>1p5c_OS_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 1.5 °C as the maximum post-industrial temperature change from 2020 onwards to 2100</li> </ul> </li> </ul> <p>Key results included in the dataset are: Temperature change, Radiative Forcing, GHG concentrations, CO2 emissions, Marginal abatement cost, Electricity Supply Primary Energy Consumption, and Annual Total Global Energy System Cost.</p> <p>The dataset also includes sectoral results regarding energy consumption and use, such as shares of different electric uses, shares of hydrogen consumption in end-use sectors, hydrogen supply, Demand electrification by sector, Renewable energy consumption by sector, Alternative fuels consumption in transport, and Total final energy consumption by sector. </p>
Genomic data resolve long-standing uncertainty by distinguishing white marlin (Kajikia albida) and striped marlin (K. audax) as separate species
<p>Large pelagic fishes are often broadly and continuously distributed and capable of long-distance movements. These factors can promote gene flow that makes it difficult to disentangle intra- vs. inter-specific levels of genetic differentiation. Here, we assess the relationship of two istiophorid billfishes, white marlin (<em>Kajikia</em> <em>albida</em>) and striped marlin (<em>K</em>. <em>audax</em>), presently considered sister species inhabiting separate ocean basins. Previous studies report levels of genetic differentiation between white marlin and striped marlin that are <a>smaller</a> than those observed among populations of other istiophorid species. To determine whether white marlin and striped marlin comprise separate species or populations of a single globally distributed species, we surveyed 2<a>520</a> single nucleotide polymorphisms (SNPs) in 62 white marlin and 242 striped marlin sampled across the Atlantic, Pacific, and Indian oceans. Multivariate analyses resolved white marlin and striped marlin as distinct groups, and a species tree composed of separate lineages was strongly supported over a single lineage tree. Genetic differentiation between white marlin and striped marlin (<em>F</em><sub>ST</sub> = 0.5384) was also substantially larger than between populations of striped marlin (<em>F</em><sub>ST</sub> = 0.0192–0.0840), and we identified SNPs that allow unambiguous species identification. Our findings indicate that white marlin and striped marlin comprise separate species, which we estimate diverged at approximately 2.38 Mya.</p>
Combined P-V-T Data for hcp-Fe with Reevaluated Pressures and Pressure Uncertainties
<p>This dataset consists of unit-cell volumes, temperatures, and reevaluated pressures, and their related uncertainties for <em>hcp-</em>Fe. The volumes and temperatures are taken from previous studies (indicated under the column "Original Publication"), and the pressures were reevaluated using the internally consistent pressure scales of Fei <em>et al</em>., 2007. The pressure uncertainties presented here propagate uncertainties in the volume and temperature measurements, as well as the uncertainty and covariance in the equation of state parameters used to determine the pressure. Also listed are the beamline the data were originally collected at, and the compression technique (DAC= Diamond Anvil Cell; MAP= Multi-Anvil Press).</p>
Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels.</p> <p> </p> <p>The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. </p> <p> </p> <p>The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat.</p> <p><br> The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (<a href="https://github.com/ai2es/miles-guess">https://github.com/ai2es/miles-guess</a>).</p>
Data set: IEC 60270 Calibration Uncertainty in Gas-Insulated Substations
<p>Data set for the publication named: IEC 60270 Calibration Uncertainty in Gas-Insulated Substations.</p>
Data from: Introgression underlies phylogenetic uncertainty but not parallel plumage evolution in a recent songbird radiation
<p class="MsoNormal"><span>Instances of parallel phenotypic evolution offer great opportunities to understand the evolutionary processes underlying phenotypic changes. However, confirming parallel phenotypic evolution and studying its causes requires a robust phylogenetic framework. One such example is the "black-and-white wagtails", a group of five species in the songbird genus </span><em><span>Motacilla</span></em><span>: one species, the White Wagtail (</span><em><span>M. alba</span></em><span>), shows wide intra-specific plumage variation, while the four others form two pairs of very similar-looking species (African Pied Wagtail </span><em><span>M. aguimp </span></em><span>+ Mekong Wagtail </span><em><span>M. samveasnae</span></em><span><em><span> </span></em>and Japanese Wagtail </span><em><span>M. grandis</span></em><span><em><span> </span></em>+ White-browed Wagtail </span><em><span>M. maderaspatensis</span></em><span>, respectively). However, the two species in each of these pairs were not recovered as sisters in previous phylogenetic inferences. Their relationships varied depending on the markers used, suggesting that gene tree heterogeneity might have hampered accurate phylogenetic inference. Here, we use whole genome resequencing data to explore the phylogenetic relationships within this group, with a special emphasis on characterizing the extent of gene tree heterogeneity and its underlying causes. We first used multispecies coalescent methods to generate a "complete evidence" phylogenetic hypothesis based on genome-wide variants, while accounting for incomplete lineage sorting and introgression. We then investigated the variation in phylogenetic signal across the genome, to quantify the extent of discordance across genomic regions, and test its underlying causes. We found that wagtail genomes are mosaics of regions supporting variable genealogies, because of ILS and inter-specific introgression. The most common topology across the genome, supporting </span><em><span>M. alba</span></em><span> and </span><em><span>M. aguimp</span></em><span> as sister species, appears to be influenced by ancient introgression. Additionally, we inferred another ancient introgression event, between </span><em><span>M. alba</span></em><span> and </span><em><span>M. grandis</span></em><span>. By combining results from multiple analyses, we propose a phylogenetic network for the black-and-white wagtails that confirms that similar phenotypes evolved in non-sister lineages, supporting parallel plumage evolution. Furthermore, the inferred reticulations do not connect species with similar plumage coloration, suggesting that introgression does not underlie parallel plumage evolution in this group. Our results demonstrate the importance of investigation of genome-wide patterns of gene tree heterogeneity to help understanding the mechanisms underlying phenotypic evolution.</span></p>
ScienceDex guides
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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.