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648 results for “uncertainties”
Data for Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties
<p>Data for the manuscript "Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties". The data set contains all the generated mechanisms, DFT data used for the construction of the RMG library, a backup of the RMG-database, all results and scripts for the evaluation of the results. </p>
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty
<p>We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function.<br> The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the robust knapsack problem. Both problem sets have been described and used for benchmarking in the paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty", published in Mathematical Programming Computation by Christina Büsing, Timo Gersing and Arie Koster.<br> Furthermore, we provide instances for robust weighted matching on bipartite graphs and robust weighted independent set. The latter are based on graphs for the clique problem of the second DIMACS implementation challenge (1993). Both problem sets have been described and used for benchmarking in the paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty", presented at IPCO 2023 by the same authors.</p> <p> </p> <p>Paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/s12532-022-00232-2"> https://doi.org/10.1007/s12532-022-00232-2</a><br> Paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/978-3-031-32726-1_5">https://doi.org/10.1007/978-3-031-32726-1_5</a><br> For algorithms solving these problems see: <a href="https://doi.org/10.5281/zenodo.7463371">https://doi.org/10.5281/zenodo.7463371</a></p>
Sampling uncertainty results using gumboot package by Clark et al (2021)
<p>Clark et al 2021 based sampling uncertainty assessment.</p> <p>Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., et al. (2021). The Abuse of Popular Performance Metrics in Hydrologic Modeling. Water Resources Research, 57(9). https://doi.org/10.1029/2020WR029001</p>
Model output from Snow Ensemble Uncertainty Project (SEUP) as used in Seasonal Snow Predictability Derived from Early-Season Snow in North America
<p>The files provided here are the output from the median peak snow water equivalent (peak_SWE.mat), 1 December snow water equivalent (Dec1_SWE.mat), and 1 January snow water equivalent (Jan1_SWE.mat) model simulations for the Noah-MP run with MERRA-2 forcing, as used in Lundquist et al. (2023) and described in Kim et al. (2021). We also include the model grid's latitude, longitude and elevation data (SEUPlatlon.mat), and example code for plotting the data (Plotmodeldata.m) as in the Lundquist et al. (2023) paper. </p> <p>Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., et al. (2021). Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. <em>The Cryosphere, 15</em>(2), 771-791.</p> <p>Lundquist, J. D., R. S. Kim, M. Durand, and L. R. Prugh, 2023, Seasonal Peak Snow Predictability Derived from Early-Season Snow in North America, Geophysical Research Letters, (submitted 2023)</p>
Reliable imputation of spatial transcriptome with uncertainty estimation and spatial regularization
<p>Imputation of missing features in spatial transcriptomics is urgently demanded due to technology limitations, while most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. <br> To fill the research gaps, we introduce a computational model, TransImp, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference. Uniquely, we derived a set of attributes that can accurately predict imputation uncertainty, hence enabling us to select reliably imputed genes. Also, we introduced a spatial auto-correlation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms have demonstrated that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.</p>
Metadata with submitted Emission Control Science and Technology Journal manuscript Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems
<p>Metadata with submitted Emission Control Science and Technology Journal <em>Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems</em></p> <p>Link to article: https://link.springer.com/article/10.1007/s40825-025-00260-z</p>
[Supplementary Information] Model uncertainty versus variability in the life cycle assessment of commercial fisheries
<p>Supporting information from the manuscript: <em>Model uncertainty versus variability in the life cycle assessment of commercial fisheries</em>. The study analyses the life cycle assessment of fish species landed by Danish trawlers, comparing sources of uncertainty (such as modelling approaches) with sources of variability (vessel length and years).</p> <p>Supporting information 1: In depth description of the different models used in the study, the fuel disaggregation processes and the sensitivity analysis performed</p> <p>Supporting information 2: Contains the excel table with the datasets and related calculations to replicate the results described in the paper and the R code used to analyze the results.</p>
Microhabitat conditions drive uncertainty of risk and shape neophobic responses in Trinidadian guppies, Poecilia reticulata
<p>In response to uncertain risks, prey may rely on neophobic phenotypes to reduce the costs associated with the lack of information regarding local conditions. Neophobia has been shown to be driven by information reliability, ambient risk, and predator diversity, all of which shape uncertainty of risk. We similarly expect environmental conditions to shape uncertainty by interfering with information availability. In order to test how environmental variables might shape neophobic responses in Trinidadian guppies (<em>Poecilia</em> <em>reticulata</em>), we conducted an in situ field experiment of two high-predation risk guppy populations designed to determine how the "average" and "variance" of several environmental factors might influence the neophobic response to novel predator models and/or novel foraging patches. Our results suggest neophobia is shaped by water velocity, microhabitat complexity, pool width and depth, as well as substrate diversity and heterogeneity. Moreover, we found differential effects of the "average" and "variance" environmental variables on food- and predator-related neophobia. Our study highlights that assessment of neophobic drivers should consider predation risk, various microhabitat conditions, and neophobia being tested. Neophobic phenotypes are expected to increase the probability of prey survival and reproductive success (i.e. fitness), and are therefore likely linked to population health and species survival. Understanding the drivers and consequences of uncertainty of risk is an increasingly pressing issue, as ecological uncertainty increases with the combined effects of climate change, anthropogenic disturbances, and invasive species. </p>
Replication data for: The Effect of IS-Innovation Strategy Alignment on Corporate Performance: Investigating the Role of Environmental Uncertainty by Heterogeneity
<p>A base de dados técnico-científica de 856 empresas brasileiras examinou o alinhamento entre sistemas de informação estratégicos (ISS), na abordagem da estratégia como prática, e inovação de exploration e exploitation, e seu impacto no desempenho corporativo (CP) sobre incerteza ambiental. Os resultados mostram que todos os tipos de alinhamento entre ISS e inovação influenciam positivamente o CP. O alinhamento com inovação ambidestra teve um impacto 62% maior no CP do que o alinhamento com inovações incrementais. Além disso, inovações disruptivas tiveram efeitos positivos em ambientes hostis, enquanto inovações exploratórias e ambidestras tiveram impactos fortes em ambientes altamente dinâmicos.</p>
Data from: Evaluating UCE data adequacy and integrating uncertainty in a comprehensive phylogeny of ants
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Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarization
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Global patterns of taxonomic uncertainty and its impacts on biodiversity research
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Data from: Transformation of measurement uncertainties into low-dimensional feature vector space
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A Bayesian extension of phylogenetic generalized least squares (PGLS): incorporating uncertainty in the comparative study of trait relationships and evolutionary rates
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Microhabitat conditions drive uncertainty of risk and shape neophobic responses in Trinidadian guppies, Poecilia reticulata
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Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
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Data for: Revealing hidden sources of uncertainty in biodiversity trend assessments
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Data from: Comparing alternative harvest strategies to address robustness to recruitment variability and uncertainty: Implications for Alaska Sablefish tested with management strategy evaluation
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Know what you don't know: Embracing state uncertainty in disease-structured multistate models
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Data from: Assessing uncertainties and approximations in solar heating of the climate system
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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)
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.