Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Data for predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors

The data was used in the analysis presented in the manuscript: Predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors. The manuscript is published in Animal journal. The data is for piglet survival survival at different time-points from birth to weaning from two research farms.

opencc-by-4.0Nov 2024View details →

Perturbative gravitational wave predictions for the real scalar extended Standard Model, dataset

This deposit contains data from a perturbative study of cosmological phase transitions in the real singlet scalar extension of the Standard Model (xSM). The data relates to the paper "Perturbative gravitational wave predictions for the real scalar extended Standard Model". Everything is contained within the archive file xsm_results.tar.gz, a tarball compressed with Gzip. The data covers phase transition properties for a scan of 100,000 parameter points in the xSM. Further details on the contents of the dataset are explained in the README.md within the tarball.

opencc-by-4.0Jun 2021View details →

pKaDatabase for Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge

A curated a database of small molecules with experimentally measured pKa values.  This pickle file can be loaded into memory using Pandas. In the code block below we will print out the columns of the DataFrame: <pre><code class="language-python">import pandas as pd df = pd.load("pKaDatabase.pkl") print(df.keys()). # print the columns</code></pre> <blockquote> ['deprotonated microstate ID', 'protonated microstate ID', 'deprotonated microstate smiles', 'protonated microstate smiles', 'AM1BCC partial charge (prot. atom)', 'AM1BCC partial charge (deprot. atom)', 'AM1BCC partial charge (prot. atoms 1 bond away)', 'AM1BCC partial charge (deprot. atoms 1 bond away)', 'AM1BCC partial charge (prot. atoms 2 bond away)', 'AM1BCC partial charge (deprot. atoms 2 bond away)', 'Gasteiger partial charge (prot. atom)', 'Gasteiger partial charge (deprot. atom)', 'Gasteiger partial charge (prot. atoms 1 bond away)', 'Gasteiger partial charge (deprot. atoms 1 bond away)', 'Gasteiger partial charge (prot. atoms 2 bond away)', 'Gasteiger partial charge (deprot. atoms 2 bond away)', 'Extented Hückel partial charge (prot. atom)', 'Extented Hückel partial charge (deprot. atom)', 'Extented Hückel partial charge (prot. atoms 1 bond away)', 'Extented Hückel partial charge (deprot. atoms 1 bond away)', 'Extented Hückel partial charge (prot. atoms 2 bond away)', 'Extented Hückel partial charge (deprot. atoms 2 bond away)', '∆G_solv (kJ/mol) (prot-deprot)', 'SASA (Shrake)', 'SASA (Lee)', 'Bond Order', 'Change in Enthalpy (kJ/mol) (prot-deprot)', 'pKa','href', 'num ionizable groups', 'Weight', 'pKa source'] </blockquote>   For more information regarding feature calculations, please read the following paper: <blockquote> Raddi, Robert, and Vincent Voelz. "Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge." (2021). <a href="https://doi.org/10.26434/chemrxiv.14650302.v1">10.26434/chemrxiv.14650302.v1</a> </blockquote>

opencc-by-4.0Jul 2021View details →

Predicted locations and nitrate pollution of groundwater discharge from D3pl karst aquifer in Latvia

Description A georeferenced raster data layer [5_predicted_D3pl_GW_discharge_zone.tif] showing predicted likelihood that groundwater polluted with nitrate (NO3-) is discharging as springs or diffuse seepage from the Upper Devonian Pļaviņas (D3pl) dolomite karst aquifer in Latvia is presented. The cell value indicates the likelihood (0 – low, 1 - high) that groundwater with nitrate contamination is discharging from the D3pl aquifer at this location. Value of 0 means that no groundwater is discharging there. The BalticTM 93 (EPSG:25884) references system is used. The rationale and methodology for elaborating the map of groundwater discharge as springs or diffuse seepage from the D3pl dolomite karst aquifer is described in the main article (Kalvāns et al. under review). In short, a 3D regional geological model (Popovs et al. 2015), land surface elevation model and bedrock surface elevation model (Popovs et al. under review) were combined to identify locations where aquitard at the base of D3pl aquifer is outcropping at bedrock surface and in the nearby depressions (in a distance up to 0.25 km) the land surface was below the surface of this aquitard. The likely contamination with NO3- was estimated from proportion of arable land (European Environment Agency 2018) within 4.75 km window. It is assumed that the NO3- contamination in the D3pl karst aquifer is likely only close to its distribution margins, where groundwater table is deeper than the top of the aquifer. This work was supported by the EU Interreg Est–Lat program project GroundEco No. Est-Lat62, and base funding grant from the Latvian Ministry of Education and Science to the University of Latvia, No. ZD2016/AZ03. References European Environment Agency (2018) Corine Land Cover 2018. https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download (CLC). Accessed 1 Jun 2020 Popovs K, Kalvāns A, Jemeljanova M, et al (under review) Bedrock surface topography map of Latvia. J Maps Popovs K, Saks T, Jātnieks J (2015) A comprehensive approach to the 3D geological modelling of sedimentary basins: example of Latvia, the central part of the Baltic Basin. Est J Earth Sci 64:173–188. https://doi.org/10.3176/earth.2015.25

opencc-by-4.0Oct 2021View details →

Discoba protein sequences for protein structure predictions

Comprehensive database of Discoba protein sequences, gathered for the purpose of improving protein structure predictions of Discoba species (including Trypanosoma and Leishmania) by AlphaFold and RoseTTAFold. Originally gathered for use with: https://github.com/zephyris/discoba_alphafold

opencc-by-4.0Jan 2023View details →

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data

The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.

Blind Prediction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations

Masonry vaults play a much relevant role in the seismic response of heritage masonry buildings, ranging from housing to the greatest cathedrals. Acting as both a ceiling and a structural horizontal diaphragm with significant mass, their mechanical behaviour affects the overall seismic response of buildings, in terms of strength, stiffness, and ductility. Moreover, local damage and collapse of vaults may produce significant losses in terms of cultural assets and casualties. In spite of the importance of this topic, the evaluation of the complex three-dimensional behaviour of vaults is still an important challenge for researchers. The main objectives of the present research project are: 1)    to better understand the seismic behaviour of masonry cross vaults by means of shaking table tests on both full-scale and small-scale models; 2)    to assess the capability of different modelling/analysis approaches to predict the seismic response of these masonry structures. In particular, three sets of shaking table tests are planned: a.    Tests on a 1:1 scale model of a brick unreinforced masonry cross vault:  to investigate the behaviour of brick masonry cross vaults under different seismic inputs, in terms of damage, displacement capacity and peak acceleration. b.    Tests on a 1:1 scale model of a brick reinforced masonry cross vault: to evaluate the effectiveness of reinforcing techniques to repair the vaults tested in a). In addition to the experimental tests, a blind prediction competition is performed to assess the efficacy of different modelling strategies and analysis techniques. The final aims are to improve the safety assessment procedures proposed for historic masonry buildings in Eurocode 8.3 and to provide better seismic assessment techniques and strengthening measures.

MHD Model of Ganymede's Magnetosphere: Predicted magnetic field on Juno's trajectory

This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish predicted magnetic field components on Juno's trajectory that can be compared to MAG measurements and are displayed in Figure 3 of Duling et al. (2022). Each file contains data from one model. The dataset includes all models with parameter variations from Duling et al. (2022). These are summarized in Table 1 of Duling et al. (2022) and displayed in Figure 3 with the gray lines. If not varied, all models are run with the following parameters: Upstream Jovian background magnetic field B0 = (−15,24,−75) nT Upstream plasma velocity v0 = 140 km/s Upstream plasma mass density \(\rho\)0 = 100 amu/cm3 Upstream plasma thermal pressure p0 = 2.8 nPa Ionization frequency \(\nu_{ion}\) = 2.2e-8/s Atmospheric surface mass density \(n_{n,0}\) =  8e6/cm3 Dipole Gauss coefficient \(g_1^0\) = −716.8 nT   The published data files correspond to the following models with each one parameter variation: <table> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Value</th> <th scope="col">Filename Suffix</th> </tr> </thead> <tbody> <tr> <td>default model</td> <td> - </td> <td>default</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured before flyby)</td> <td>B0 = (−16,3,−70) nT</td> <td>B0before</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured after flyby)</td> <td>B0 =  (−14,43,−80) nT</td> <td>B0after</td> </tr> <tr> <td>Upstream plasma velocity (min)</td> <td>v0 = 120 km/s</td> <td>v-</td> </tr> <tr> <td>Upstream plasma velocity (max)</td> <td>v0 = 160 km/s</td> <td>v+</td> </tr> <tr> <td>Upstream plasma mass density (min)</td> <td>\(\rho\)0 = 10 amu/cm3</td> <td>rho-</td> </tr> <tr> <td>Upstream plasma mass density (max)</td> <td>\(\rho\)0 = 160 amu/cm3</td> <td>rho+</td> </tr> <tr> <td>Upstream plasma thermal pressure (min)</td> <td>p0 = 1.0 nPa</td> <td>p-</td> </tr> <tr> <td>Upstream plasma thermal pressure (max)</td> <td>p0 = 5.0 nPa</td> <td>p+</td> </tr> <tr> <td>Ionization frequency (min)</td> <td> \(\nu_{ion}\) = 0.5e-8/s</td> <td>prod-</td> </tr> <tr> <td>Ionization frequency (max)</td> <td> \(\nu_{ion}\) = 10.0e-8/s</td> <td>prod+</td> </tr> <tr> <td>Atmospheric surface mass density (min)</td> <td> \(n_{n,0}\) =  1.6e6/cm3</td> <td>nn-</td> </tr> <tr> <td>Atmospheric surface mass density (max)</td> <td> \(n_{n,0}\) =  40e6/cm3</td> <td>nn+</td> </tr> <tr> <td>Dipole Gauss coefficient (min)</td> <td> \(g_1^0\) = −702.5 nT</td> <td>dipole-</td> </tr> <tr> <td>Dipole Gauss coefficient (max)</td> <td> \(g_1^0\) = −731.1 nT</td> <td>dipole+</td> </tr> </tbody> </table> Magnetic Field components and Juno's position are in GPhiO system. GPhiO is defined by the primary direction z parallel to Jupiter’s rotation axis, the secondary direction y is pointing from Ganymede's towards Jupiter's barycenter and x completes the right-handed system approximately in direction of plasma flow. Columns: Spacecraft time [UTC] Bx modeled magnetic field in GPhiO [nT] By modeled magnetic field in GPhiO [nT] Bz modeled magnetic field in GPhiO [nT] B modeled magnetic field magnitude [nT] x of Juno in GPhiO [km] y of Juno in GPhiO [km] z of Juno in GPhiO [km]

Prediction of Conformational Variability for RRM proteins in inter3m data base

Predictions for protein Conformational Variability for the entries in InteR3M (<a href="https://inter3mdb.loria.fr/">https://inter3mdb.loria.fr/</a>), performed with the software ConforMine (in preparation).

Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm

CHARACTERISTICS Planet: Venus Radius: RV = 6051.8 km (volumetric mean planetary radius) Spacecraft: ESA/Venus Express Spacecraft coordinates system: Venus Solar Orbital (VSO) equivalent to Sun-State coordinate system: <ul> <li>+XVSO points towards the Sun from the planet’s centre,</li> <li>+ZVSO towards Venus’ North pole and perpendicular to the orbital plane defined as the XVSO–YVSO plane passing through the centre of Venus,</li> <li>YVSO completes the orthogonal system.</li> </ul> Time span: 01/04/2006 to 25/11/2014 Total number N of candidate bow shock crossings in the database: N = 4950 Number of quasi-parallel bow shock crossings: N|| = 844 Number of quasi-perpendicular bow shock crossings: N\(\perp\) = 4106 ORIGINAL DATASETS USED The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used. METHOD To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in: Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, Journal of Geophysical Research, 127, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a> They consist of two consecutive steps:  <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane \(\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)\)performed on the datasets of Persson et al. (2023), Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, Zenodo (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted θBn (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that: <ul> <li>45∘<θBn<135∘: quasi-perpendicular shock condition</li> <li>θBn≤45∘ and θBn\(\geq\)135∘: quasi-parallel shock condition</li> </ul> Uncertainty on these angles is estimated to be ± 5º.  For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. VARIABLES DESCRIPTION This database contains the following ASCII variables: <ul> <li>Bow shock times in Venus Express' database (1-s resolution): Tbs</li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius RV (RV = 6051.8 km): XVSO, YVSO, ZVSO and Euclidean distance \(R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}\) (in RV)</li> <li>Solar Zenith angle in degrees: SZA = \(\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º, calculated with atan2(norm(cross(B,ñ),dot(B,ñ)), with B the magnetic field vector and ñ the vector normal to the shock surface): <ul> <li>45 < ThetaBn <  135 deg: quasi-\(\perp\) shock</li> <li>ThetaBn \(\leq\) 45 deg & ThetaBn \(\geq\) 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, Bx, By, Bz (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath \(\longrightarrow\) solar wind (2447 events)</li> <li>flag = 1: solar wind \(\longrightarrow\) magnetosheath (2503 events)</li> </ul> </li> </ul> WARNING <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of: Persson et al. (2023), Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, Zenodo (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a first indicator of the shock location, and with caution: it CANNOT, and WILL NOT substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 RV (with RV = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> Current formatting optimised for MATLAB. ACKNOWLEDGEMENTS C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36.     LICENSE AND RIGHTS This database is shared under a Creative Commons CC-BY-4.0 license. Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF),                                                              Austrian Academy of Sciences, 2022-10-05 Contact email:        cyril.simon.wedlund@gmail.com

opencc-by-sa-4.0Oct 2018View details →

Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon

This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include: <ul> <li> new updated global mangrove coverage map (contact Thomas Worthington), </li> <li> spatiotemporal predictions to account for differences in spectral reflectance at the time of field work, </li> <li> additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file). </li> </ul> To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points. Production steps (ensemble predictions using SuperLearner) are explained in detail at:  <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>. <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>

opencc-by-4.0Jul 2023View details →

Dataset for "Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns"

This archive contains the source code and data sets presented in the publication "Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns". Kabus, D., De Coster, T., de Vries, A. A., Pijnappels, D. A., & Dierckx, H. (2024). Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns. Computers in Biology and Medicine, 107949. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.compbiomed.2024.107949" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compbiomed.2024.107949</a>

opencc-by-4.0Aug 2023View details →

Raw data of healthy young adults in the Weather Prediction Task

Raw data of 22 healthy young adults (11 females; average age: 26.29 years; range: 21.72–30.82) in the Weather Prediction Task with 100 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials. Raw data of 15 healthy young adults (9 females; average age: 26.58 years; range: 20.37–28.84) in the Weather Prediction Task with 200 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials. Bochud-Fragnière E, Banta Lavenex P and Lavenex P (2022) What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task. Front. Psychol. 13:886339. doi: 10.3389/fpsyg.2022.886339

opencc-by-4.0Mar 2023View details →

With or without you: Gut microbiota does not predict aggregation behaviour in females of the European earwig

Recent studies suggest that the gut microbiota could be one of the main driving forces behind the evolution of group living. However, these studies are mainly based on our knowledge of species in which non-social individuals are rare and abnormal, calling into question the adaptive value of the reported association between group-living and gut microbiota. In this study, we addressed this issue by testing this association in females of the European earwig, an insect showing frequent, naturaland wide inter-individual variation in the expression of group living. We video-tracked 320 field-sampled females to quantify their natural variation in aggregation and then tested whether the most and least gregarious females had different gut microbiota. We also compared the general activity, boldness, body size and body condition of these females and examined the association between each of these traits and the gut microbiota. Contrary to our predictions, we found no difference in gut microbiota between the most and least gregarious females, as well as no difference between these females in terms of general activity, boldness, body size and condition. We did show that the gut microbiota of females was overall linked to their body condition, even though it was also unrelated to the other measurements. Overall, these results demonstrate that a host's gut microbiota is not necessarily a major driver of aggregation behaviour in species with inter-individual variation in group living and call for future studies to investigate the determinants and role of gut microbiota in earwigs.

opencc-by-4.0Oct 2023View details →

Output data for "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023)

Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023).Output data is included for 50 nm particles containing sodium myristate (c14na) and myristic acid (myristica), mixed with NaCl (nacl) in different surfactant mass fractions. Data about the critical points is also included for particles containing sodium myristate for particle size range 50-200 nm.Plotters have been provided for the following:<ul><li>Part2_plotter_50_200_nm: Plots the critical supersaturations, diameters, and the relative change in cloud droplet concentrations for dry particles with 50-200 nm diameters containing c14na</li><li>Part2_plotter_50nm: Plots the Köhler curves, surface tension and partitioning factors for 50 nm particles containing c14na</li><li>Part2_plotter_50nm_myristica: Plots the Köhler curves and surface tensions for 50 nm particles containing myristica and also plots c14na for comparison (separate output files for the compounds and c14na data here is different than for the Part2_plotter_50nm plotter)</li></ul>Each plotter needs the user to set the location where the output files are stored. In addition, a function is included:<ul><li>relative_change_in_cloud_droplet_number_conc: This function is called in "Part2_plotter_50_200_nm" and calculates the relative change in cloud droplet number concentration from the critical supersaturations.</li></ul>

opencc-by-4.0Oct 2023View details →

DeepSurge storm surge predictions for HighResMIP tropical cyclones

DeepSurge is a newly presented deep-learning approach to modeling the storm surge generated by a tropical cyclone (TC). This dataset is a collection of DeepSurge outputs for synthetic TCs in the North Atlantic generated by the HighResMIP project (Haarsma et al. 2016) for a simulated historical (1950-2014) and future (2015-2050) climate under the climate scenario SSP585. The data generation process and data analysis is detailed in an upcoming publication. The storm surge data presented here intentionally does not include the effects of sea level rise, rainfall, or other factors, in order to isolate the effects of changing TC climatology on future storm surge risk. <h4>Dataset format</h4> The data comes in the form of maximum surge levels at 2846 near-coastal locations for each synthetic TC. Each TC is defined by the corresponding track in the HighResMIP TempestExtremes dataset (Roberts 2019). The data is presented in NetCDF format, with two dimensions:  <ul> <li>'nodes', the number of near-coastal locations, always 2846.</li> <li>'tracks', the number of tracks in the simulation, which is different in each file.</li> </ul> There are 6 variables in each file: <ul> <li>'lons' and 'lats', the coordinates of the nodes in degrees North and East respectively.</li> <li>'track_valid' is a binary indicator (zero for false, one for true) indicating whether the TC occurs within the region of interest (HighResMIP tracks are global, but we only simulate those in the North Atlantic)</li> <li>'track_done' is another binary indicator for whether the track has been simulated. It should indicate true for all tracks for which 'track_valid' is true.</li> <li>'max_zeta' provides the predicted maximum surge height, in meters, for each storm at all 2846 nodes. This data is only valid in entries for which the corresponding 'track_done' and 'track_valid' indicators are true.</li> <li>'years' is the year in which each simulated TC occurs.</li> </ul>

openCC (other)Dec 2025View details →

Predicted Beaver Dam Building Capacity in the Minneapolis-St. Paul Metro Area

The Beaver Restoration Assessment Tool (BRAT) (MacFarlane et al., 2015) is a predictive model that integrates hydrology, topography, vegetation, and land use data to predict existing and historical beaver dam building capacity within a watershed. The model was run on the HUC8 Mississippi River Twin Cities Watershed (07010206) in March 2025. The output displayed is a shapefile of the Conservation Restoration Model, which includes existing and historical dam building capacity as well as restoration opportunities.

openCC (other)Apr 2020View details →

Lake chloride concentrations and model predictions for 49,432 lakes in the Midwest and Northeast United States.

Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but we have little knowledge of the prevalence and spatial distribution of the problem. The majority of salt pollution in north temperate regions stems from road salt application but other chloride sources include water softeners, synthetic fertilizers, and livestock excretion. Although chloride contamination of lakes is well documented, it is unknown how many lakes are at risk of long-term salinization. We used a quantile regression forest to leverage information from 2,773 lakes to predict the chloride concentration of all 49,432 lakes greater than 4 ha in a 17-state area. The QRF used 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest interstate and road. Model predictions had an r2 of 0.94 for all chloride observations, and 0.87 for predictions of the mean chloride concentration observed at each lake.

openCC (other)Jan 2021View details →

Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression

This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.