Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Supplement to "Discriminating non-stationary flood hazard effects via probabilistic estimation of sparse residuals from rescued and rated stage–discharge data"

This supplement contains the data and script associated with “Sparse hydrometric data rescue for exploratory analyses of conveyance-driven flood hazard trends and controls,” which has been submitted to a journal for consideration. This supplement is deposited on Zenodo.   Code, Data, and Attribute Descriptions   Uploaded are five directories (indicated in bold font, with their contents detailed below) containing input data and various outputs of the analyses performed for our case study on the Pulangi River at Lumayong (Philippines). Please consider this description equivalent to an omnibus README file for the deposited files. Note that we collected and rescued the hydrometric data herein from the archives of the Water Projects Division (WPD) of the Philippine Department of Public Works and Highways (DPWH).   ●      Pln_R_code contains (1) Pln_RC.R, the R script file, and (2) 240601_Pln_RC.RData, which stores objects generated from the script based on the last execution (on 1 June 2024). The script consists of admittedly too many lines of code (e.g., for data wrangling, formal analyses, and producing figures used in the manuscript) that should have been split into multiple .R files. Apologies. Please be guided by the outline and the comments and kindly reach out lest issues with the code arise.   ●      Pln_GH_PDFs contains the scanned gaugekeeper’s reports of gauge heights (“stages”).    ○      The name of each file varies. For example, Pln-GH-1100.pdf contains daily stage readings for all months in the year 2011. But, if the last two digits are not zeroes, as in Pln-GH-1204.pdf, they refer to the month of that year; in this case, for example, the PDF file contains stage data for the month of April in the year 2012. ○      Each scanned sheet contains sub-daily (with readings at “AM,” “NOON,” and “PM”) and mean daily stages for a given month. ○      Also indicated are the gaugekeeper’s remarks on the daily weather (e.g., “fair,” “cloudy”). Under inclement weather, the gaugekeeper would note the duration of rainfall and its intensity and might, at times, record extra stage readings.   ●      Pln_DM_PDFs contains the following PDF files and a sub-directory: ○      Pln-DM-<XXXXXX>.pdf contains the scanned log for each direct stage—discharge measurement, otherwise known as “gaugings.” Each file is named according to the date of gauging (YYMMDD). For example, the log for the gauging performed on 24 February 2011 can be found in the file Pln-DM-110224.pdf. Data from these gaugings are summarized in Pln-DM-filtered.csv in the Pln_In_CSVs directory. ■      The readability of each file varies based on the original quality of the original paper-format data. ■      The first page in each file contains, on the left side, a summary of the gauging data and metadata (e.g., date of measurement, number of gauging verticals or “sections” used, method of crossing or measuring the cross-section), and on the right side, the velocity—area readings at each gauging vertical. ■      The second page in each file contains the plotted cross-section of the channel at the time of measurement. ○      Pln-RC.pdf contains various other paper-format data and some annotations relevant to the historical rating at the station. ■      P. 1: The hydrographic engineer’s comment (dated 19 January 2012) on the evaluation of the discharge data, specifically detailing the periods of validity of the rating curves developed for different sub-periods of monitoring. ■      PP. 2—3: A summary table of all the gaugings performed from 1983 to 2010 whose logs were no longer retrievable (and hence not included as a DM-PDF file in this directory). ■      PP. 4—5: Plots of the official stage—discharge rating curves developed by DPWH hydrographers. ■      PP. 6—8: Rating tables used to convert daily mean stages to deterministic discharge estimates. Two of these rating tables were digitized and can be found in the Pln_RatingTables sub-directory in Pln_In_CSVs. ■      PP. 9—22: Daily stage (m) and its corresponding daily discharge (L/s) for the 2004—2010 sub-period. The stages were digitized and included in Pln-H_arch.csv in the Pln_In_CSVs directory. ○      Pln_DM_unused contains the PDF files of logs (including data and metadata) corresponding to the gaugings that were excluded from our analysis following our filtering step for gauging location consistency.   ●      Pln_In_CSVs contains the following CSV files and two sub-directories; these files were used as inputs to the R script for formal analyses: ○      Pln-H_arch.csv contains the mean daily stage values [“H_bar_arch”] (m) for every day in the 2004—2020 sub-period [“Date”] (YYYY-MM-DD). The stages in this file are already corrected for gross errors. ○      Pln-DM-filtered.csv contains the following information on the gaugings performed on the Pulangi at Lumayong (1983—2020): (i) date [“Date”] (YYYY-MM-DD); (ii) stage [“H”] (m); (iii) discharge in L/s [“Q_lps”] and m3/s [“Q_cms”]; (iv) wetted area [“A_sqm”] (m2); (v) mean flow velocity [“Vel_mps”] (m/s); (vi) channel width [“W_m”] (m); (vii) mean flow depth [“D_ave_m”] (m); (viii) location of the measurement cross-section with respect to the staff gauge, with negative values meaning downstream of the gauge and positive values meaning upstream of the gauge [“XS_loc_wrt_gage”]; (ix) maximum flow depth [“Max_Depth_m”] (m); and (x) minimum streambed elevation [“MINSBE”] (m). Note that this file includes only gaugings that passed our filtering step for measurement location consistency. ○      Pln-DM_oview.csv contains information on the temporal coverage (bounded by “Start_date” and “End_date”) of the available hydrometric data from the station archives. ○      Pln-H_sample_GrossE_corr.csv contains a sample sub-period (2017-06-15 through 2017-11-30) and the corresponding values of stages (m), uncorrected [“H_uncorrected”] and corrected for gross errors [“H_corrected”]. This CSV file was used as an input to the R script to produce one of the figures in the manuscript.  ○      Pln-XS-csv.csv contains data on the gauging transects: gauging ID [“GaugingID”]; date [“Date”] (YYYY-MM-DD); lateral distance from a fixed initial point [“Lat_distance”] (m); width of the gauging vertical [“Width_vert..m.”] (m); depth relative to the water surface [“Depth..m.”] (m); stage at the gauging vertical [“H_m_vert..m.”] (m); and elevation with respect to a fixed arbitrary datum at the station [“Elev..m.”] (m). ○      Pln_Rating_Tables contains two rating tables, Pln-DM - RatingTable_A.csv and Pln-DM - RatingTable_B.csv, prepared and used by DPWH hydrographers for converting stage values to deterministic discharge estimates for the Pulangi River at Lumayong for the 1980s—early 2000s sub-period. Each rating table contains columns for stage [“H”], discharge in L/s [“Q_lps”], and discharge in m3/s [“Q_cms”]. ○      Pln_Q_rated contains two CSV files: Pulangi.csv has the columns “YEAR”, “DAY”, and every month of the year [“JAN” through “DEC”] for the 1983—2003 sub-period, with the values under each month column indicating the deterministic discharge estimates in L/s; Pulangi_trunc.csv contains similarly formatted data, but for the 2009—2010 sub-period.   ●      Pln_Out_CSVs contains two CSV files, the primary outputs of the hydrometric data rescue effort. ○      Pln_Q_recon.csv contains the reconstructed discharge time series (1983—2020) and its associated uncertainties at the 95% credibility interval. It has the following columns: date [“Date”] (YYYY-MM-DD); daily stage [“H”] (m); lower bound of the discharge estimate [“Q_lwr”] (m3/s); median discharge estimate [“Q_med”] (m3/s); and the upper bound of the discharge estimate [“Q_upr”] (m3/s). Pln_H_recon.csv contains the reconstructed and quality-controlled stage time series (1983—2020). It has the following columns: date [“Date”] (YYYY-MM-DD); mean daily stage that has been corrected for gross errors, but not yet filtered through other quality checks [“H_bar_arch”] (m); quality check for low outliers [“Low_Outlier”] (TRUE/FALSE); quality check for flatliners [“Flatliner”] (TRUE/FALSE); difference between the stage values on day i and day i-1 [“Daily_Step”] (m); quality check for large steps [“Large_Step”] (TRUE/FALSE); and the corrected and quality-controlled mean daily stage values [“H”] (m).

opencc-by-4.0Aug 2021View details →

Dataset from: Binary population synthesis with probabilistic remnant mass and kick prescriptions

This contains the data behind the paper "<a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.1380M">Binary population synthesis with probabilistic remnant mass and kick prescriptions</a>" See<a href="https://github.com/TeamCOMPAS/COMPAS"> https://github.com/TeamCOMPAS/COMPAS</a> for code information and instructions on parsing the data.

opencc-by-4.0Aug 2021View details →

ProbFire: a probabilistic fire early warning system for Indonesia

This repository holds ProbFire model input datasets and pre-trained model weights and feature scaling parameters. Model code and description is available at https://github.com/ToFEWSI/ProbFire.

opencc-by-4.0Oct 2022View details →

Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach

20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.

opencc-zeroFeb 2023View details →

TreeFlow: Probabilistic programming and automatic differentiation for phylogenetics

Probabilistic programming frameworks are powerful tools for statistical modelling and inference. They are not immediately generalisable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic programming and automatic differentiation with phylogenetic trees. It implements inference algorithms for phylogenetic tree times and model parameters, given a tree topology. We demonstrate how TreeFlow can be used to quickly implement and assess new models. We also show that it provides reasonable performance for gradient-based inference algorithms compared to specialized computational libraries for phylogenetics.

openncgl-uk-2.0Feb 2023View details →

IMPROVER: the new probabilistic post processing system at the UK Met Office: BAMS paper Data

© Crown Copyright, Met Office This is the data associated with the figures in the IMPROVER BAMS paper 2023: <a href="https://doi.org/10.1175/BAMS-D-21-0273.1">https://doi.org/10.1175/BAMS-D-21-0273.1</a>. Gridded data is in CF-NetCDF with reasonably self explanatory metadata, other data such as for Figure 9's wind speed calibration is in CSV.

opencc-by-4.0Feb 2023View details →

Derived data supporting the analysis of surface albedo changes from Mars 2020 observations: Probabilistic distribution of the Amplitude Spectral Densities of Supercam microphone recordings and Monte-Carlo dust devil simulations.

These files contain derived data used in the analysis submitted for publication in Journal of Geophysical Research: Planets, entitled "Dust Lifting Through Surface Albedo Changes at Jezero Crater, Mars" by Vicente-Retortillo et al. The article was initially submitted on November 14, 2022, and the revised version on March 1, 2023. Files include the derived data and information needed to generate Figures 4 (Microphone_Data.mat and Plot_ASD_from_Microphone_Data) and 6 (remaining files) of the article.

opencc-by-4.0Mar 2023View details →

Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Video Supplement

These videos provide examples of application of RAMEFI (RAndom-forest based MEsoscale wind Feature Identification) for winter storm cases between 2000 and 2019 and an animation of cyclone-relative occurrence (cf. Fig. 7 of 10.5194/wcd-2023-10).

opencc-by-4.0Mar 2023View details →

Probabilistic projections of mean sea level change in Finland by 2100

Paper describing the methods used to calculate these projections: Pellikka, H., Johansson, M. M., Nordman, M., and Ruosteenoja, K.: Probabilistic projections and past trends of sea level rise in Finland, Nat. Hazards Earth Syst. Sci., <a href="https://doi.org/10.5194/nhess-2022-230">https://doi.org/10.5194/nhess-2022-230</a>, 2023. This dataset includes probability distributions of projected mean sea level in Finland in 2030, 2040, ... 2100, as well as time series of projected mean sea level 2005-2100. Data is provided for 13 tide gauge locations and 3 emission scenarios: low (RCP2.6 / SSP1-2.6), medium (RCP4.5 / SSP2-4.5), and high (RCP8.5 / SSP5-8.5). There are two data packages, distributions.zip and timeseries.zip. The data files included in these packages are tab- or space-delimited text files with the file extension .dat. All filenames start with a three-character code xxx that determines the tide gauge (1-letter symbol) and the emission scenario (2 digits). For example: v26 means Vaasa, low emission scenario (RCP2.6 / SSP1-2.6) e45 means Helsinki, medium emission scenario (RCP4.5 / SSP2-4.5) t85 means Turku, high emission scenario (RCP8.5 / SSP5-8.5) The letter symbols and locations of the tide gauges are, from north to south along the coast: a - Kemi (65.67 N, 24.52 E) o - Oulu (65.04 N, 25.42 E) b - Raahe (64.67 N, 24.41 E) p - Pietarsaari (63.71 N, 22.69 E) v - Vaasa (63.08 N, 21.57 E) s - Kaskinen (62.34 N, 21.21 E) m - Mäntyluoto (61.59 N, 21.46 E) r - Rauma (61.13 N, 21.44 E) t - Turku (60.43 N, 22.1 E) d - Degerby (60.03 N, 20.38 E) h - Hanko (59.82 N, 22.98 E) e - Helsinki (60.15 N, 24.96 E) f - Hamina (60.56 N, 27.18 E) 1) distributions.zip > xxx_fitdistr_yyyy.dat These files include the probability distribution (probability density function) of projected mean sea level in year yyyy (2030, 2040, ... 2100). There are two columns: sea level and probability. Sea level values are millimetres in the Finnish N2000 height system. 2) timeseries.zip > xxx_timeseries.dat These files include the time series of projected mean sea level in 2005-2100. The files have 8 columns: year and 7 sea level values representing different percentiles of the probability distribution. The percentiles are 1%, 5%, 17%, 50% (median), 83%, 95%, 99%. Sea level values are centimetres in the Finnish N2000 height system. Please note that all projections for years other than 2100 are indicative and based on a simple 2nd order fit made to the current rate of mean sea level change and the projected mean sea level in 2100. In other words, the projections for intermediate years are based on the 2100 projections assuming constant acceleration in mean sea level change rates. Example figures distributions.png and timeseries.png are included to illustrate the data. The Matlab script slrfinland_figures.m used to produce these figures is also included.

opencc-by-4.0Apr 2023View details →

Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals

Heavens, Nicholas G. (2023), “Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals”, Zenodo, V1, doi: 10.5281/zenodo.7828010.   Title: Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals   Author: Nicholas G. Heavens, Space Science Institute, Boulder, CO, USA and London, UK (nheavens@spacescience.org)   Date: 15 April 2023   Overview: This dataset contains analyses of convective instabilities in individual MRO-MCS temperature profiles for a manuscript currently under review at the Planetary Science Journal.   Averaged data products based on these analyses already have been archived as Heavens (2022):   The data products are estimates of the probability of convective instability derived from diagnosis of convective instability in retrieved temperature profiles from Mars Climate Sounder on board Mars Reconnaissance Orbiter (MRO-MCS)   If you are using this dataset and are feeling confused or wish there were some additional information from the article in this dataset, please contact me. A complete account of the contents and a restatement of this description is included as MCS_CONVINST_INDFILE_SURVEY_DESC.PDF   Acknowledgments: The archiving of this dataset is supported by NASA’s Mars Data Analysis Program (80NSSC19K1215).   Contents:   <ol> <li>MCS_CONVINST_INDFILE_SURVEY_DESC.pdf</li> </ol> A copy of this description in PDF format.   <ol> <li>MCSconvinst_indivncfiles.tar.gz</li> </ol> This compressed tar archive contains 63196 Network Common Data Format (netCDF) files (~ 20 GB uncompressed) named in the form:   xxxxxx_convinst.nc, where xxxxxx is the re-centered orbit number of MRO defined by Heavens et al. (2018). The contents of this file type is:   Variables:   <ol> <li>entrynum: Number to index retrievals in the file</li> <li>Pgrid: The pressure (Pa) level grid</li> <li>lapserateunstableprob_rets: The probability (%) of convective instability in an individual profile at a given pressure level</li> <li>altrets: The altitude grid (km) of each individual profile in relative to the Mars Orbiter Laser Altimeter (MOLA) areoid</li> <li>ampmrets: A flag to indicate whether MRO is in its ascending PM orbit (~ 15:00 LST at the Equator) or in its descending AM orbit (~ 3:00 at the Equator). Precise local time can be estimated by using the MRO-MCS dataset directly or using sclkrets.</li> <li>latrets: The latitude of the profile in degrees north.</li> <li>longrets: The longitude of the profile in degrees east</li> <li>loopflagrets: A flag to indicate the presence of nearby loops; flag = 1 indicates the presence of a nearby loop.</li> <li>lsrets: Areocentric longitude (degrees) of each individual profile</li> <li>myrets: Mars Year of each individual profile</li> <li>sclkrets: SCLK (seconds since 1 January 1980 00:00:00 UTC) for each individual profile</li> </ol>   References   Heavens, N. (2022), Epitomic Data for a Multiannual Record of Convective Instability in Mars's Middle Atmosphere from the Mars Climate Sounder, Mendeley Data, V1, doi: 10.17632/fmgvbpwdk7.1   Heavens, N.G., A. Kleinböhl, M.S. Chaffin, J.S. Halekas, D.M. Kass, P.O. Hayne, D.J. McCleese, S. Piqueux, J.H. Shirley, and J.T. Schofield, 2018, Hydrogen escape from Mars enhanced by deep convection in dust storms, Nature Astron., 2, 126–132, doi: 10.1038/s41550-017-0353-4.

opencc-by-4.0May 2023View details →

3D structural and probabilistic modeling of geothermal reservoir horizons in the Northern Eifel and its foreland

This repository contains the supplementary data to the submitted publication titled "3D structural and probabilistic modeling of geothermal reservoir horizons in the Northern Eifel and its foreland" which was submitted to the Journal "Geothermal Energy" (https://geothermal-energy-journal.springeropen.com/).

opencc-byJun 2023View details →

Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis

Dataset used in Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis [<a href="https://arxiv.org/abs/2211.11784">arXiv: 2211.11784</a>]. This dataset will be helpful to validate different continuum prediction model and study absorption systems. The descriptions of individual files can be found here: <ul> <li><a href="/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-dr16.tar.gz?versionId=427c1aca-6f12-4e84-b91e-f9a3d678329e">sdss-dr16.tar.gz</a> : continuum prediction for ~100,000 quasar spectra from SDSS DR16, see Section 3.1 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>;</li> <li><a href="https://zenodo.org/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-mock-with-dla-with-perturb.tar.gz?versionId=55bc466d-4cbf-43ba-9b83-209ffba4ec30">sdss-mock-with-dla-with-perturb.tar.gz </a> : ~150,000 mock quasar spectra to validate QFA performance with perturbation on  quasar continuum from PCA template, see Section 3.2 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>;</li> <li><a href="https://zenodo.org/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-mock-with-dla-without-perturb.tar.gz?versionId=efa5d05a-2a40-4a43-801e-c95b29a4efb2">sdss-mock-with-dla-without-perturb.tar.gz</a>: ~150,000 mock quasar spectra to validate QFA performance with quasar continuum directly from PCA template, see Section 3.2 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>.  </li> </ul>

publicSep 2022View details →

Data from: Learning of probabilistic punishment as a model of anxiety produces changes in action but not punisher encoding in the dmPFC and VTA

Open the record for dataset details and reuse information.

publicJan 2024View details →

Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)

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publicFeb 2023View details →

TreeFlow: Probabilistic programming and automatic differentiation for phylogenetics

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2020View details →

Data and code for paper "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning"

Simulation results from the NOAA/SWPC Geospace model used in the paper Camporeale et al. (2020) "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning" published in J. Geophys. Res. (2020) MATLAB code is provided to train process the data and train the machine learning model and plot results. Manuscript available on <a href="https://arxiv.org/abs/1912.01038">https://arxiv.org/abs/1912.01038</a>

opencc-by-4.0Jun 2021View details →

Probabilistic Reconstruction of Type Ia Supernova SN 2002bo

SN2002bo_emulator_paper_data <ul> <li>The TARDIS configuration YAML file used as a template for the training spectra.</li> <li>HDF5 file of posterior samples of model parameters and their associated weights from the nested sampler.</li> </ul> SN2002bo_emulator_paper_spectra <ul> <li>Input grid of model parameters and corresponding spectra produced by TARDIS used to generate the training grid of the emulator.</li> </ul>

openapache2.0Jun 2016View details →

WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction - A Model-Driven Approach for Session-Based Application Systems.

Supplementary material for the paper: "WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction". Included in the supplementary material are the evaluation results. The WESSBAS software relevant to the paper is available via https://github.com/Wessbas/ The WESSBAS UI is available as a password-protected (password: wessbasui) ZIP file: https://dl.dropboxusercontent.com/u/81621779/wessbas.ui.zip (--- WESSBAS GUI (license confirmation pending, i.e., not on GitHub, yet))

opencc-by-4.0Jul 2024View details →

Expanding the space of self-reproducing ribozymes using probabilistic generative models

This repository contains the code and data produced in "Expanding the space of self-reproducing ribozymes using probabilistic generative models".