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opencc-by-4.0Jul 2024View details →

Data & Codes used in: Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)

<p>Here we provide the data, codes, and outline the procedure to reproduce the results presented in the paper "Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)" by E. Ventura-Macias, P. M. Martinez, Rubén Pérez, and J. G. Vilhena.</p> <p>The following sections contain a detailed description of the data and codes. At the end of this README, you will find instructions on how to generate the classical force-field parameters (Morse and Lennard-Jones) from the potential energy surfaces (PES) computed at the DFT level.</p> <p>The procedure is general and applies to any given pair of molecule and surface. The provided codes will allow you to swiftly generate the PES at the DFT level, fit the Lennard-Jones and Morse potentials, and test them in a LAMMPS MD simulation.</p> <p>For a thorough explanation of the procedure and the relevance of these results, please refer to the original publication (ARTICLE_DOI).</p> <p>If you find this helpful, please consider citing the article (ARTICLE_DOI).</p> <h2>Data structure</h2> <p>The data is organized in the following way:</p> <ol> <li> <p>DFT</p> <ul> <li>The equilibrium adsorption geometry of methanethiol (MTH), propanethiol (PTH), and cysteine (CYS) for the Au-mol configuration with PBE+DFT-D3.</li> <li>Potential Energy Surface (PES) computed at the DFT level (Figure 3 of the main manuscript).</li> <li>The scripts used to generate the PES.</li> </ul> </li> <li> <p>MD</p> <ul> <li>Fitting code and general instructions on how to use it.</li> <li>General input scripts used to generate MD data within LAMMPS.</li> </ul> </li> </ol> <h3>DFT Data</h3> <p>The DFT data in <code>DFT.zip</code> is organized in the following way:</p> <ul> <li> <p><code>DFT_PES/</code></p> <p>This folder contains the DFT potential energy surfaces (PES) for the interaction of the sulfur atom of methanethiolate (<code>mth</code>), propanethiolate (<code>pth</code>), and cysteine (<code>cys</code>) with the Au(111) surface and the necessary scripts to reproduce it.</p> <ul> <li> <p><code>results/</code></p> <p>The PES are given in one csv file per molecule named as <em><code>mol</code></em> + <code>_PES_S-Au111.csv</code>, where <em><code>mol</code></em> is the molecule name. Columns are as follows:</p> <table> <tbody> <tr> <td><strong>Label</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><em>i</em></td> <td>calculation number</td> </tr> <tr> <td><em>site</em></td> <td>adsorption site</td> </tr> <tr> <td><em>z</em></td> <td>distance of the S atom to the surface</td> </tr> <tr> <td><em>deltaz</em></td> <td>distance difference from the minimum energy position of the S atom</td> </tr> <tr> <td><em>pbed3</em></td> <td>PBE+D3 binding energy</td> </tr> <tr> <td><em>pbe</em></td> <td>PBE component of the binding energy</td> </tr> <tr> <td><em>d3</em></td> <td>DFT-D3 component of the binding energy</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li> <ul> <li> <p><code>mth/</code> | <code>pth/</code> | <code>cys/</code></p> <p>Each folder contains the CONTCAR (VASP) file for the optimized geometry of the molecule adsorbed on the Au(111) surface with PBE+D3.</p> </li> <li> <p><code>setup_grid.py</code></p> <p>Python script to set up the POSCAR files for the PES calculations.</p> </li> <li> <p><code>read_results.py</code></p> <p>Python script to read the results of the PES calculations.</p> </li> <li> <p><code>sub_array.sh</code></p> <p>Bash script to submit the PES calculations to an SLURM-based cluster.</p> </li> <li> <p><code>INCAR</code> | <code>KPOINTS</code> | <code>surf.CONTCAR</code></p> <p>VASP input files for the PES calculations.</p> </li> </ul> </li> </ul> <h3>MD fitting</h3> <p>The MD data in MD.zip is are organized as:</p> <ul> <li> <p><code>Fitting/</code></p> <ul> <li><code>optimize_Morse.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of Morse potential.</p> <ul> <li><code>optimize_LJ.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of LJ potential.</p> </li> <li> <p><code>Histogram/</code></p> <ul> <li><code>in.test</code></li> </ul> <p>LAMMPS input script to extract an XY file for the position of the S atom in an NVT simulation.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>sheng.eam</code></li> </ul> <p>EAM potential file in case Au dynamics are wished to be included.</p> </li> <li> <p><code>Single_point_scan/</code></p> <ul> <li><code>in.scan</code></li> </ul> <p>LAMMPS input script to perform single-point energy scan of a given molecule.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>launch.sh</code></li> </ul> <p>Launches the single point scan.</p> <ul> <li><code>plot_scan.py</code></li> </ul> </li> <li> <p><code>Molecules</code></p> <p>LAMMPS sample geometries for the 3 molecules.</p> </li> </ul> <h2>Steps to reproduce the results</h2> <p>The PES calculations were performed using the VASP code and two Python scripts for setup and results parsing.</p> <h3>Requirements</h3> <ul> <li>VASP (tested with version 5.4.4) <ul> <li><code>PAW_PBE</code> pseudopotentials set version 5.4</li> </ul> </li> <li>Python3 with packages: <ul> <li>ASE (Atomic Simulation Environment)</li> <li>Numpy</li> <li>Pandas</li> <li>matplotlib (optional)</li> </ul> </li> </ul> <h3>Steps</h3> <p>Each molecule has its own directory with the necessary files to reproduce the results. The following steps are for the methanethiolate molecule (<code>mth</code>).</p> <ol> <li>Set up the grid of points for the PES calculations by running the <code>setup_grid.py</code> script. It will create a subfolder <code>run/</code> inside the molecule's directory with the POSCAR files for each point in the grid.</li> </ol> <blockquote> <p>python setup_grid.py mth</p> </blockquote> <ol> <li> <p>Create the corresponding <code>POTCAR</code> file and place it in the molecule's directory.</p> </li> <li> <p>Change the <code>sub_array.sh</code> script to match the number of calculations in the array numbers and the MOL variable.</p> </li> </ol> <pre><code>#SBATCH --array=1-number of calculations MOL=mth</code></pre> <ol> <li>Submit the calculations to a SLURM-based cluster by running the <code>sub_array.sh</code> script from the <code>DFT_PES</code> directory.</li> </ol> <blockquote> <p>sbatch sub_array.sh</p> </blockquote> <ol> <li>After the calculations are finished, run the <code>read_results.py</code> script to parse the results and generate the PES csv files. It takes the arguments <code>--surf</code> and <code>--mol</code> to specify the surface and molecule PBE+D3 and D3 total energies.</li> </ol> <blockquote> <p>python read_results.py mth --surf -128.5228 -17.7147 --mol -22.6026 -0.0083</p> </blockquote> <h3>Reference values for the PBE+D3 and D3 surface and molecule energies</h3> <table> <tbody> <tr> <td> </td> <td><strong>Surface</strong></td> <td> </td> <td><strong>Molecule</strong></td> <td> </td> </tr> <tr> <td><strong>Molecule</strong></td> <td><strong>PBE+D3<br></strong></td> <td><strong>D3</strong></td> <td><strong>PBE+D3</strong></td> <td><strong>D3</strong></td> </tr> <tr> <td>mth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-22.6026</td> <td>-0.0083</td> </tr> <tr> <td>pth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-55.8615</td> <td>-0.0086</td> </tr> <tr> <td>cys</td> <td>-128.5228</td> <td>-17.7147</td> <td>-74.0338</td> <td>-0.1611</td> </tr> </tbody> </table> <h3>Fitting procedure</h3> <p>Both the .xyz and .csv files should be located at the same folder as the script. Then, simply run the code (<code>mth</code> is used as an example):</p> <blockquote> <p>python optimize_Morse.py mth</p> </blockquote> <p>or</p> <blockquote> <p>python optimize_LJ.py mth</p> </blockquote> <p>The script will print the optimized parameters: [De, re, α] or [ϵ, σ] for Morse or LJ respectively. It will also plot a fitting plot and a birdview of the resulting PES.</p> <h3>Using the potential. Histogram example</h3> <p>The code will run for the optimized <code>mth</code> Morse parameters and extract a <code>occ.lammpstrj</code> containing the (x,y) positions of the S atom throughout the NVT simulation. Note that only Au-S interaction is included. Start as:</p> <blockquote> <p>lmp -in in.test</p> </blockquote> <p>This is easily adaptable to other routines or molecules and is thought to be a generic LAMMPS starting input.</p> <h3>Single-point energy scan</h3> <p>Go to the 2C) folder and launch the scan with:</p> <blockquote> <p>./launch.sh</p> </blockquote> <p>This will create a folder named <code>fine_scan</code> containing 128 folders. Each folder is assigned to an (x,y) position. Then, inside each folder, a single-point energy evaluation is performed at various Z heights around the absolute minima.</p> <p>The <code>in.scan</code> file should be modified accordingly with the appropriate potentials. It is set to perform the scan with the optimized Morse potential by default.</p> <p>The output is gathered in the <code>E_readout.dat</code> folder with the following structure (all energies in eV):</p> <table> <tbody> <tr> <th>Total Energy</th> <th>Intramolecular energy</th> <th>Au-mol vdW interaction energy</th> <th>Au-S interaction energy</th> </tr> </tbody> <tbody> <tr> <td>-133.62</td> <td>0.123112</td> <td>-0.2403</td> <td>-1.31765</td> </tr> <tr> <td>-133.721</td> <td>0.123112</td> <td>-0.28-401</td> <td>-1.37853</td> </tr> </tbody> </table> <p>Therefore, the total adsorption energy will be the sum of the last two columns.</p> <p>Energies are ordered in increasing Z for the same (x,y) point. That is, the first 12 lines correspond to 12 heights of the starting (x,y) coordinate, the next 12 lines to heights at the second (x,y) configuration and so on.</p> <p>The python script <code>plot_scan.py</code> may be used to plot the results. The <code>E_readout.dat</code> file and <code>.csv</code> must be in the same folder.</p> <blockquote> <p>python plot_scan.py mth</p> </blockquote>

opencc-by-4.0Apr 2024View details →

Supplementary data: A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR

<p><strong>Content:</strong></p> <p>This dataset provides supplementary data to "A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR". It contains trained Neural Network Potentials (NNP), energy and force data used for accuracy evaluation of the NNPs. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOHNa.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>.  Also, the structure files used to calculate NMR properties are involved.</p> <ul> <li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li> <li>"SiAlOHNa.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> <li>"error_stats.zip" - traj files storing energies/forces at the DFT (SCAN+D3(BJ)) and NNP level for all test simulations to calcuate energy/force errors</li> <li>"Structures_CHA17.zip" - the structures files of CHA(17). </li> </ul>

Radiation Belt Forecast Model and Framework (RBFMF) 10 Hour Hindcast Validation Data

<div><strong>Archived data for the manuscript “On the Performance of a Real-Time Electron Radiation Belt Specification Model”  Staples et al., submitted to Space Weather, 2024.</strong></div> <div> </div> <div>Data in these files specify the radiation belt through phase space density (PSD) in adiabatic coordinate system. Simulated PSD is from the Radiation Belt Forecast Model and Framework (RBFMF) 10 hour hindcast, and measured PSD is from an intercalibrated multi-mission observatory (Van Allen Probes, GOES 13, 15, GPS, MMS, and THEMIS). For detailed description of the method used in the computation of this data, see sections 2 and 3 of the submitted manuscript.</div> <div> </div> <div>The THEMIS, Van Allen Probe, MMS, and GOES data used in computations is publicly available via http://cdaweb.gsfc.nasa.gov </div> <div>The GPS data is available via https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/</div> <div> </div> <div>Data Preperation: </div> <div>Adam Kellerman, akellerman@atmos.ucla.edu </div> <div>Frances Staples, frances.staples@atmos.ucla.edu</div> <div> </div> <div>Support for this work was provided by NASA grants 80NSSC20K1402 and 80NSSC23K0096, and NSF grant 2149782.</div> <div> </div> <div><strong>'PSD_10hrHC_Jan2016-Oct2018.mat'</strong></div> <div>Matlab data file format.</div> <div>Data time period: January 2016 - October 2018. </div> <div>            Variable Descriptions:</div> <div>time - Serial date.</div> <div>InvMu - 1st adiabatic invariant coordinate, mu.</div> <div>InvK - 2nd adiabatic invariant coordinate, k.</div> <div>lstar - 3rd adiabatic invariant coordinate, l*.</div> <div>psd_sim - 10 hour radiaiton belt hindcast. Simulated PSD has dimensions corresponding to (time,lstar,mu,k).  </div> <div>psd_obs - PSD observed by multi-mission dataset, with dimensions matching the simulated PSD (time, lstar, mu, k).  </div> <div> </div> <div><strong>'PSD_10hrHC_Mar2019-Dec2020.mat'</strong></div> <div>Matlab data file format.</div> <div>Data time period March 2019 - December 2020. </div> <div> <div>Variable Descriptions:</div> </div> <div>time - Serial date.</div> <div>InvMu - 1st adiabatic invariant coordinate, mu.</div> <div>InvK - 2nd adiabatic invariant coordinate, k.</div> <div>lstar - 3rd adiabatic invariant coordinate, l*.</div> <div>psd_sim - 10 hour radiaiton belt hindcast. Simulated PSD has dimensions corresponding to (time,lstar,mu,k).  </div> <div>psd_gps - PSD observed by the GPS constellation, with dimensions matching the simulated PSD (time, lstar, mu, k). </div> <div> </div> <div><strong>'RBSP_beacondata_Jan2016-Oct2018.mat'</strong></div> <div>Matlab data file format.</div> <div>Data time period January 2016 - October 2018. </div> <div>Variable Descriptions:</div> <div>time - Serial date.</div> <div>InvMu - 1st adiabatic invariant coordinate, mu.</div> <div>InvK - 2nd adiabatic invariant coordinate, k.</div> <div>lstar - 3rd adiabatic invariant coordinate, l*. l* dimensions correpond to the dimensions of the 2nd invariant, K (time, K)</div> <div>psd - real time PSD observed from Van Allen Probe b (beacon data), with dimensions corresponding to (time,mu,k). </div> <div>psd_err - observed error of beacon PSD data (i.e. Beacon_PSD - FinalRBSP_PSD).</div> <div>psd_q - observed quotient of beacon PSD data (i.e., Beacon_PSD/FinalRBSP_PSD).</div> <div> </div> <div><strong>'README.txt'</strong></div> <div>Downloadable file descriptions. </div>

opencc-by-4.0Apr 2024View details →

PubChem Data Mining of OXPHOS inhibitors: scripts, data, and models

<p>README doc, source, and data files from PubChem data mining project to identify OXPHOS inhibitory chemotypes.</p>

Data from: Collection methods and distribution modeling for Strepsiptera in the United States

<p>The twisted-wing parasite order (Strepsiptera Kirby, 1813) is difficult to study due to the complexity of strepsipteran life histories, small body sizes, and a lack of accessible distribution data for most species. Here, we present a review of the strepsipteran species known from New York State. We also demonstrate successful collection methods and a survey of species carried out in an old-growth deciduous forest dominated by native New York species (Black Rock Forest, Cornwall, NY) and a private site in the Catskill Mountains (Shandaken, NY). Additionally, we model suitable habitat for Strepsiptera in the United States with species distribution modeling. We base our models on host distributions and climatic variables to inform predictions of where these twisted-wing parasites are likely to be found. With this work, we hope to provide a useful reference for the future collection of Strepsiptera.</p>

Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria

<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>

opencc-by-sa-4.0May 2024View details →

A corrosion model for bioabsorbable metallic stents: Supporting Data

<p>Data including UMATs, Abaqus input files and experimental measurements related to the paper 'A corrosion model for bioabsorbable metallic stents' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actbio.2011.05.032" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.actbio.2011.05.032</span></a></p> <p> </p> <p> </p>

opencc-by-sa-4.0May 2024View details →

A physical corrosion model for bioabsorbable metal stents: Supporting Data

<p>Data including UMATs and Abaqus input files related to the paper 'A physical corrosion model for bioabsorbable metal stents' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actbio.2013.12.059" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.actbio.2013.12.059</span></a></p>

Data from: Determining critical periods for thermal acclimatisation using a Distributed Lag Non-linear Modelling approach

<p>Rapid changes in thermal environments are threatening many species worldwide. Thermal acclimatisation processes may partially buffer species from the impacts of these changes, but currently the knowledge about the temporal dynamics of acclimatisation remains limited. Acclimatisation phenotypes are typically determined in laboratory conditions that lack the variability and stochasticity that characterize the natural environment. Through a Distributed Lag Non-linear Model (DLNM), we use field data to assess how the timing and magnitude of past thermal exposures influence thermal tolerance. We apply the model to two Scottish freshwater Ephemeroptera species living in natural thermal conditions. Model results provide evidence that rapid heat hardening effects are dramatic and reflect high rates of change in temperatures experienced over recent hours to days. In contrast, temperature change magnitude impacted acclimatisation over the course of weeks but had no impact on short term responses. Our results also indicate that individuals may de-acclimatise their heat tolerance in response to cooler environments. Based on the novel insights provided by this powerful modelling approach, we recommend its wider uptake among thermal physiologists to facilitate more nuanced insights in natural contexts, with the additional benefit of providing evidence needed to improve the design of laboratory experiments. </p>

Data for "Antiferromagnetic phase transition in a 3D fermionic Hubbard model"

<p>This dataset is for research article "Antiferromagnetic phase transition in a 3D fermionic Hubbard model".</p>

opencc-zeroJan 2024View details →

OpenAlex Topic Classification v1 Model Artifacts and Training Data

<p>This is all data used to train the topic classification model and also the model artifacts to deploy the model. Please see the github repo for more information:</p> <p>https://github.com/ourresearch/openalex-topic-classification</p>

Data from: Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies

<p><strong>Background:</strong> Optimizing resuscitation to reduce inflammation and organ dysfunction following human trauma-associated hemorrhagic shock is a major clinical hurdle. This is limited by the short duration of pre-clinical studies and the sparsity of early data in the clinical setting.</p> <p><strong>Methods:</strong> We sought to bridge this gap by linking preclinical data in the porcine model with clinical data from patients from the Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study via a three-compartment ordinary differential equation model of inflammation and coagulation.</p> <p><strong>Results:</strong> The model accurately predicts physiologic, inflammatory, and laboratory measures in both the porcine model and patients, as well as the outcome and time of death in the PROMMTT cohort. Model simulation suggests that resuscitation with plasma and red blood cells outperformed resuscitation with crystalloid or plasma alone, and that earlier plasma resuscitation reduced injury severity and increased survival time.</p> <p><strong>Conclusions:</strong> This workflow may serve as a translational bridge from pre-clinical to clinical studies in trauma-associated hemorrhagic shock and other complex disease settings.</p>

Data from: The PDI model system for parameterizing soil hydraulic properties

<p>The PDI ("Peters-Durner-Iden") model system represents a robust framework for parameterizing soil hydraulic properties, i.e. the water retention curve and the hydraulic conductivity curve, across the entire soil moisture spectrum. This model accounts for water retention and hydraulic conductivity in completely and partially-filled pores, including adsorption and film-flow. The model was developed in stages and a comprehensive overview of the model development and the model equations is provided in Peters et al. (2024). In this repository, we provide a Python file named "pdi.py" which can be used to compute the various submodels (PDI-VG, PDI-KOS, PDI-FX, ...) of the PDI model system. One MS Excel file is provided for easy access to one PDI model, the PDI-VG. The PYTHON functions contained in "pdi.py" can be used to calculate the water retention curve, the unsaturated hydraulic conductivity curve, the specific water capacity function, and the soil water diffusivity function. In addition, we provide five python scripts which illustrate how to call the various PDI functions in different contexts. Notably, "pdi.py" incorporates a utility function, 'export_hydrus_materin', which generates an ASCII file named "MATER.IN". This file serves as input for simulations with Hydrus-1D and Hydrus-2D3D, offering seamless integration with these simulation platforms. It's important to emphasize that the provided Python scripts and accompanying documentation are closely aligned with the research article by Peters et al. (2024). To streamline accessibility, the repository refrains from redundantly restating the theory or equations already detailed in the referenced publication.</p>

Unlabeled AnuraSet: A dataset for leveraging unlabeled data in machine learning models for passive acoustic monitoring

<p>The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification.</p> <p>If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows:</p> <p>Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2</p>

Electrophysiological data of the paper 'Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for parkinsonism'

<p>This excel data set contains the electrophysiological data presented in the paper including figure 1I, 1J, figure 3, figure 5, suppl. figure 2A, suppl. figure 3, suppl. figure 6E, 6G, 6L & 6N.</p> <p> </p> <p>More information about how the data was extracted can be found in the materials and methods section of the paper.  </p>

Integrated Machine Learning model in Early Urban Flooding Warning System - Data

<p>AI_DATA.npy - Inundation data (mm) generated from MIKE+ model that has been converted to numpy array</p> <p>INDEX.npy - The index where inundation is > 0 </p> <p>source.tif - Source tif image for creating map from ML models</p>

Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)

<p><em>Aim:</em> Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold-water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data is scarce.</p> <p><em>Location: </em>Seamounts around the Cabo Verde Archipelago (NW Africa).</p> <p><em>Methods:</em> We used species presence-absence data obtained from Remotely Operated Vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100 m resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (Generalized Additive Model (GAM) and Random Forest (RF)) were employed and single-model predictions were combined into a final weighted-average ensemble model. Model performance was validated using different metrics through cross-validation.</p> <p><em>Results</em>: Terrain orientation, at broad-scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic-topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys.</p> <p><em>Main conclusions:</em> Our study shows how data-poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.</p>

Data from: Fast mvSLOUCH: Multivariate Ornstein-Uhlenbeck-based models of trait evolution on large phylogenies

<p>The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein-Uhlenbeck processes. The results of our analysis demonstrate the ability of the modeling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.</p>

opencc-zeroJun 2024View details →

Data from: A model of marmoset monkey vocal turn-taking

<p>Vocal turn-taking has been described in a diversity of species. Yet a model that captures the various processes underlying this social behavior across species has not been developed. To this end, here we recorded a large and diverse dataset of marmoset monkey vocal behavior in social contexts comprising one, two and three callers and developed a model to determine the keystone factors that affect the dynamics of these natural communicative interactions. While a coupled oscillator model failed to account for turn-taking in marmosets, our model alternatively revealed four key factors that encapsulate much of patterns evident in the behavior, ranging from internal processes, such as the state of the individual, to social context driven suppression of calling. In addition, we show that the same key factors apply to the meerkat, a carnivorous species, in a multicaller setting. These findings indicate that vocal turn-taking is affected by a broader suite of mechanisms than previously considered and our model provides a predictive framework with which to further explicate this natural behavior and for direct comparisons with the analogous behavior in other species.</p>