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209 results for “probabilistic”
ss-PDMFA: size-specific, probabilistic, dynamic material flow analysis
<p><strong>Description of the dataset:</strong></p> <p>This dataset is related to the following publication:</p> <p>Title: Size-specific, dynamic, probabilistic material flow analysis of titanium dioxide releases into the environment</p> <p>Authors: Yuanfang Zheng, Bernd Nowack </p> <p>Submitted to the journal Environmental Science & Technology in November 2020.</p> <p>The files contain input files, final codes and raw results of the ss-PDMFA model.</p> <p> </p>
Interaction data for probabilistic ending tournament for the Axelrod Python project.
<p>This is the data from the tournament described here: http://axelrod-tournament.readthedocs.org/en/latest/prob_end/ordinary_strategies.html</p> <p>A description of the format is available here: http://axelrod.readthedocs.org/en/latest/tutorials/further_topics/reading_and_writing_interactions.html</p>
Inbred Strain Variant Database (ISVdb): A repository for probabilistically informed sequence differences among the Collaborative Cross strains and their founders
<p>Data files for the development of a database for storing (and a GUI for retrieving) the imputed variants for 72 Collaborative Cross strains of mice. Files include the inputs for the imputation, as well as the final results. See File_S1_Readme for more details on the included files.</p>
Probabilistic state-level estimates of US coastal storm property damages from climate change
<p>Probabilistic estimates of property damage (2010 USD) by damage mechanism, sea level measure, region, and time period. Values are derived from <em>Estimates of US coastal damages by local sea level </em>(doi: https://doi.org/10.5281/zenodo.820149) using the code at https://github.com/ClimateImpactLab/acp-impacts</p> <p>Direct (surge + wind) and business-interruption storm damage estimates as well as estimated property below sea level are projected by estimating damages as a function of local sea level (LSL). These damage functions are then applied to probabilistic estimates of local sea level from Kopp et al (2014) using a Monte Carlo simulation. Percentiles of the resulting distributions are presented in the included files. Estimates of property below sea level are calculated for both mean sea level (MSL) as well as mean higher high water (MHHW).</p> <p>In each period, estimates of the cumulative value of inundated property, or property below sea level, are removed from the exposure data set when computing future storm damages; therefore, projections of future storm damages differ between the two measures of sea level.</p>
Probabilistic volumetric speckle suppression in OCT using deep learning: Dataset
<p>This file contains a retinal OCT intensity volume as a demo dataset to generate volumetric speckle-suppressed training data using our non-local-means despeckling (TNode) script and four OCT intensity volumes and their corresponding TNode-processed intensity volumes of different tissue samples to train and test our deep learning framework used in "Probabilistic volumetric speckle suppression in OCT using deep learning" by Chintada et al. 2023. The TNode code for generating the training data and the source code for our deep learning framework are available at https://github.com/bhaskarachintada/DLTNode.git</p>
Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
<p>The rapid expansion of wild pigs (<em>Sus scrofa</em>) throughout the United States (US) has been fueled by unlawful introductions, with invasive populations causing extensive crop losses, damaging native ecosystems, and serving as a reservoir for disease. Multiple states have passed laws prohibiting the possession or transport of wild pigs. However, genetic and phenotypic similarities between domestic pigs and invasive wild pigs – which overwhelmingly represent domestic pig-wild boar hybrids – pose a challenge for the enforcement of such regulations. We sought to exploit wild boar ancestry as a common attribute among the vast majority of invasive wild pigs as a means of genetically differentiating wild pigs from breeds of domestic pigs found within the US. We organized reference high-density single nucleotide polymorphism genotypes (1,039 samples from 33 domestic breeds and 382 samples from 16 wild boar populations) into five genetically cohesive reference groups: mixed-commercial breeds, Durocs, heritage breeds, primitive breeds, and wild boar. Building upon well-established genetic clustering approaches, we structured the test statistic to describe the difference in the likelihood of a given genotype's ancestry vectors (<em>sensu</em> genetic clustering analysis) if derived strictly from the four described domestic pig reference groups versus allowing for admixture from the wild boar group. By fitting statistical distributions to test statistics of reference domestic pigs, we characterized the distribution of the null hypothesis – that a given genotype descends strictly from domestic pig reference groups. We tested the approach with simulated genotypes and empirical data from an additional 29 breeds of domestic pig represented by 435 unique genotypes; all associated test statistics for simulated and empirical domestic pig challenge sets fell within the distribution of reference domestic pigs. We then evaluated 6,566 invasive wild pigs sampled across the contiguous United States, of which 63% exceeded the maximum threshold for domestic pigs and could be statistically classified as possessing wild boar ancestry. This approach provides a scientific foundation to enforce regulations prohibiting the possession of this destructive invasive species. Further, this computationally efficient and generalizable approach could be readily adapted to quantify gene flow among ecological systems of conservation or management concern.</p>
3D reconstruction of plants using probabilistic voxel carving
<p>This is a companion dataset for the paper titled "3D reconstruction of plants using probabilistic voxel carving." by Feng, J., Saadati, M., Jubery, T., Jignasu, A., Balu, A., Li, Y., Attigala, L., Schnable, P.S., Sarkar, S., Ganapathysubramanian, B. and Krishnamurthy, A., published in <em>Computers and Electronics in Agriculture</em>, <em>213</em>, p.108248, 2023.</p> <p> </p>
Probabilistic classification of Fermi LAT gamma-ray sources (effect of covariate shift)
<p>Version 1:</p> <p>These are data products connected to <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2307.09584</a>, where an analysis of the effect of covariate shift on the probabilistic classification of the Fermi LAT gamma-ray sources from the 4FGL-DR3 catalog is performed.</p> <p>The files </p> <p>4FGL-DR3_6class_GMM_nmin100_prob_cat.csv<br>4FGL-DR3_6class_GMM_nmin100_weighted_prob_cat.csv</p> <p>contain probabilistic classification into 6 classes (determined in <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2301.07412</a>) with random forest and neural networks methods. The catalog in "4FGL-DR3_6class_GMM_nmin100_weighted_prob_cat.csv" is constructed including weights for associated sources used in training in order to account for the difference in the distribution of associated (training dataset) and unassociated (target dataset) sources. The catalog in "4FGL-DR3_6class_GMM_nmin100_prob_cat.csv" is constructed with unweighted training samples.</p> <p>The files</p> <p>4FGL-DR3_6class_GMM_nmin100_summary.csv<br>4FGL-DR3_6class_GMM_nmin100_weighted_summary.csv</p> <p>contain the corresponding summaries of the definition of classes and predicted numbers of sources for the RF and NN algorithms for associated sources (averaged over cases when the sources are in the testing samples) and unassociated sources.</p> <p>Detailed description of the construction of the catalogs can be found in <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2307.09584</a>.</p> <p>Version 2: update for the Fermi LAT 4FGL-DR4 catalog.</p> <p>The filenames slightly change.<br>Probabilistic catalogs with unweighted and weighted training respectively:<br>4FGL-DR4_6classes_GMM_prob_cat.csv<br>4FGL-DR4_6classes_GMM_weighted_prob_cat.csv<br><br>The corresponding summary files:<br>4FGL-DR4_6classes_GMM_summary.csv<br>4FGL-DR4_6classes_GMM_weighted_summary.csv</p> <p>Version 3: catalogs corresponding to the published version of the paper. The filenames and the format are the same as in Version 2.</p>
Data for "Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada"
<p><a name="_Toc157072893"></a><strong>Abstract</strong>. This study presents a probabilistic model that partitions the precipitation phase based on hourly measurements from a network of radar-based disdrometers in eastern Canada. The network consists of 27 meteorological stations located in a boreal climate for the years 2020-2023. Precipitation phase observations showed a 2-m air temperature interval between 0-4°C where probabilities of occurrence of solid, liquid, or mixed precipitation significantly overlapped. Single-phase precipitation was also found to occur more frequently than mixed-phase precipitation. Probabilistic phase-guided partitioning (PGP) models of increasing complexity using random forest algorithms were developed. The PGP models classified the precipitation phase and partitioned the precipitation accordingly into solid and liquid amounts. PGP_basic is based on 2-m air temperature and site elevation, while PGP_hydromet integrates relative humidity. PGP_full includes all the above data plus atmospheric reanalysis data. The PGP models were compared to benchmark precipitation phase partitioning methods. These included a single temperature threshold model set at 1.5°C, a linear transition model with dual temperature thresholds of –0.38 and 5°C, and a psychrometric balance model. Among the benchmark models, the single temperature threshold had the best classification performance due to a low count of mixed-phase events. The other benchmark models tended to over-predict mixed-phase precipitation in order to decrease partitioning error. All PGP models showed significant phase classification improvement by reproducing the observed overlapping precipitation phases based on 2-m air temperature. In terms of partitioning error, PGP_full had the lowest RMSE and the least variability in performance. The RMSE of the single temperature threshold model was the highest and showed the greatest performance variability. The improvement of mixed-phase prediction remains a challenge. This study establishes a basis for integrating automated phase observations into a hydrometeorological observation network and developing probabilistic precipitation phase models.</p>
Multi-cell type deconvolution using a probabilistic model for single-molecule DNA methylation haplotypes
<p>Files required to run deconvolution with CelFIE-ISH and Epistate, in U250 regions from Loyfer et al. 2023, in both "pat" and "epiread" formats. </p>
Groundwater level data used in the manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions"
<p>The standardized semimonthly groundwater levels collected from 30 monitoring wells in Dalad County, China. The data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The data are used in our submitted manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions". If you find this dataset useful for your research, please consider to cite our manuscript upon publication. </p>
Probabilistic linear inversion of satellite gravity gradient data applied to the northeast Atlantic
<p>% MATLAB scripts to calculate and plot figures as in manuscript by<br> %<br> % Minakov, A., & Gaina, C. (2021).<br> % Probabilistic linear inversion of satellite gravity gradient data applied<br> % to the northeast Atlantic. Journal of Geophysical Research: Solid Earth,<br> % 126, e2021JB021854. https://doi.org/10.1029/2021JB021854<br> % <br> % Last modified by alexamin@uio.no, 26/11/2021<br> %<br> % version v1.1<br> % </p> <p>% Contents of arhcive<br> % /data contains requiried and generated datasets <br> % /fig folder for output figures <br> % /plot scripts to produce figures <br> % /tools additional matlab tools and routines</p> <p>% Dataset in ..data/GOCE_NEATLANTIC is structure containing the full model<br> % <br> % Cm: [6670×6670 double] posterior model covariance matrix<br> % m: [29×23×10 double] mean denstity perturbation model<br> % Cd: [667×667 double] data covariance matrix<br> % d: [29×23 double] data vector (Trr)<br> % r: [1×10 double] distance<br> % lat: [29×1 double] latitute<br> % lon: [23×1 double] longitude<br> %<br> % Run /plot/fig_results.m to produce all figures <br> %<br> % Some scripts require GMT (Wessel et al. 2019) and SHBUNDLE (Sneeuw et al. 2018) software to be installed</p> <p>% and corresponding folders must be added to the matlab search path.</p> <p>% Also ScientificColorMaps7 by F. Crameri (2021) maybe required and have been included in the archive.</p>
The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates
<p>This repository contains the socioeconomic and emissions projections generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al., "<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates</a>" , forthcoming at the <em>Brookings Papers on Economic Activity</em>. The data take the form of a Monte Carlo simulation with n = 10,000 draws.</p> <p>File structure and column metadata are described here: </p> <p>--- <br> emissions/ <br> --- </p> <p>- rffsp_co2_emissions.csv <br> - rffsp_ch4_emissions.csv <br> - rffsp_n2o_emissions.csv </p> <p>Each file in this folder contains 3 columns: sample, year, and value. For each row: </p> <p>- sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000). <br> - year contains the calendar year of the prediction. <br> - value contains the projected annual global emissions of the gas specified in the filename. IMPORTANT: Units are as follows: "rffsp_co2_emissions.csv" is in gigatons C (not CO2), "rffsp_ch4_emissions.csv " is in megatons CH4, and "rffsp_n2o_emissions.csv " is in megatons N2 (not N2O). </p> <p>--- <br> pop_income/ <br> --- </p> <p>- rffsp_pop_income_run_1.feather <br> - rffsp_pop_income_run_2.feather <br> - rffsp_pop_income_run_3.feather <br> . <br> . <br> . <br> - rffsp_pop_income_run_9999.feather <br> - rffsp_pop_income_run_10000.feather </p> <p>This folder contains 10,000 files in the .feather file format (https://arrow.apache.org/docs/python/feather.html), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row:</p> <p>- Country contains the ISO Alpha-3 code (https://www.iso.org/iso-3166-country-codes.html) of the country whose GDP and population are projected. <br> - Year contains the calendar year of the predictions. <br> - Pop contains the projected population for a given country and year, in units of thousands of people. <br> - GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD. </p> <p>--- </p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. </p> <p>The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), based in part on those from Müller, Stock, and Watson (forthcoming). </p> <p>---</p> <p><strong>References </strong></p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a> </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a> </p> <p>Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., Ševčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. Brookings Papers on Economic Activity. Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/ </a></p>
Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events
<p>Data, code and supplementary Figures for paper "Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events".</p>
Probabilistic forecasts of the daily maximum of the Kp index produced by the SERENADE prototype model and three empirical models for the period 2010-2018
<p>This dataset contains the probabilistic outputs of SERENADE's first prototype model dedicated to the forecasting of the <span class="math-tex">\(\textit{Kp}_{\textrm{max, 24 h}}\)</span> index for forecasting horizons ranging between 2 and 7 days. The period covered is the one of the SDOML dataset, which is 2010-05 --- 2018-12. All data is contained in a single pickle file, that can be opened in Python, using the following code lines:</p> <p><span class="math-tex">\(\texttt{import pickle}\\ \texttt{with open(DATA_PATH+`/serenade_outputs.pkl', `rb') as f:}\\ ~~~~\texttt{dict_outputs = pickle.load(f)}\)</span></p> <p>The pickle file contains a dictionnary, which itself contains Pandas DataFrames. Each DataFrame corresponds to a forecasting horizon. The dictionnary's keys are the forecasting horizons stored as strings, that is:</p> <p><span class="math-tex">\(\texttt{dict_output.keys() = [`2',`3',`4',`5',`6',`7']}\)</span></p> <p>The DataFrames are indexed by datetime. They contain the observed (true) hourly values of the daily maximum of the Kp index, the forecasts provided by SERENADE and three baseline models (Climatology model, Persistence model and 27-day Recurrence model). The forecast values include the mean and the standard deviation of the forecast normal distributions. Missing moments are due to the absence of EUV images needed to provide the forecast at the given moment.</p> <p> </p>
Data from: Learning of probabilistic punishment as a model of anxiety produces changes in action but not punisher encoding in the dmPFC and VTA
<p>Previously, we developed a novel model for anxiety during motivated behavior by training rats to perform a task where actions executed to obtain a reward were probabilistically punished and observed that after learning, neuronal activity in the ventral tegmental area (VTA) and dorsomedial prefrontal cortex (dmPFC) represent the relationship between action and punishment risk (Park & Moghaddam, 2017). Here we used male and female rats to expand on the previous work by focusing on neural changes in the dmPFC and VTA that were associated with the learning of probabilistic punishment, and anxiolytic treatment with diazepam after learning. We find that adaptive neural responses of dmPFC and VTA during the learning of anxiogenic contingencies are independent from the punisher experience and occur primarily during the peri-action and reward period. Our results also identify peri-action ramping of VTA neural calcium activity, and VTA-dmPFC correlated activity, as potential markers for the anxiolytic properties of diazepam.</p>
Probabilistic Shaping: Experimental Optical Back-to-Back Data Set
<p>Data set containing experimental results on B2B operation of single-carrier probabilistic shaping optical signals spanning from net bit-rates of 200G to 250G.</p>
Probabilistic Shaping: Experimental Loop Recirculation Data Set - 250G
<p>Data set containing experimental results on the propagation of probabilistic shaping optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 250G.</p>
Probabilistic Shaping: Experimental Loop Recirculation Data Set - 225G
<p>Data set containing experimental results on the propagation of probabilistic shaping optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 225G.</p>
Probabilistic Shaping: Experimental Loop Recirculation Data Set - 200G
<p>Data set containing experimental results on the propagation of probabilistic shaping optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 200G.</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.