Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
567
datasets available to search
ShareScore release 0.9.0
Dataset results
567 results for “Simulated datasets”
Processed Monthly Datasets from X-SHiELD simulations
<p>Processed monthly datasets during 2020-2021 from X-SHiELD simulations (see Cheng et al. (2022) and Harris et al. (2023) for details on the X-SHiELD model and experiment setups), including mesoscale convective system occurrence frequency, surface precipitation, 500-hPa vertical velocity (ω500), 0-6 km wind shear (shear06), storm relative helicity (SRH), convective available potential energy (CAPE), and convective inhibition (CIN), for both the current climate and a warming scenario with a uniform 4-K increase in sea surface temperature.</p>
Datasets used for the paper "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations"
<p>Atmospheric model and processed data for reproducing the results of "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations" currently submitted to Geophysical Research Letters.</p>
Media Bias Aware Simulation Dataset
<p>We utilized the Hyperpartisan News Detection Dataset, released with the SemEval-2019 Task 4 Hyperpartisan detection task, due to its extensive bias labels. To ensure accurate bias labels, we used the Overlap-checking (1:1) model, retaining only articles where the distant supervision bias labels matched the model's predictions. This validation process resulted in 409,757 articles.</p> <p>These articles span from 1960 to 2018, with a sparse distribution in earlier years. We focused on articles from May 1, 2017, to December 31, 2017, resulting in a subset of 72,940 news articles, ensuring a consistent daily news flow. We processed this subset by removing HTML tags and special characters and generating news summaries using PEGASUS. We used Latent Dirichlet Allocation (LDA) to categorize the articles into 20 news themes, based on perplexity scores.</p> <p>This dataset was then fed into the simulation framework, with a cut-off date of June 24, 2017.</p> <ul> <li><strong>News Recommendation Dataset</strong>: Includes user-item interaction records from May 1 to June 24, providing users' reading histories and interacted news articles. <ul> <li><strong>Training Split</strong>: Data from May 1 to June 17, used to train news recommendation algorithms.</li> <li><strong>Evaluation Split</strong>: Data from June 17 to June 24, used to evaluate the trained recommendation algorithms.</li> </ul> </li> <li> <p><strong>Candidate News Dataset</strong>: News articles published from June 25 to December 31, presented to users during simulations.</p> </li> </ul> <p>For more information, please visit <a href="https://github.com/ruanqin0706/UserRecSimulation" target="_new" rel="noreferrer">https://github.com/ruanqin0706/UserRecSimulation</a>.</p>
simulated datasets for evaluating polygenic detection methods
<p>This dataset contains simulation files corresponding to a combination of each demographic model (1/2/3), environment (linear/quadratic), selection duration (200/400/600/800/1000), and simulation replicate(1-20). This resulted in 600 simulation files with 600 unique combinations of demographic models, environments, selection durations, and simulation replicates. For each individual in the genotype data file, we have the files containing the values of selective pressure(linear and quadratic environment) in the metadata folder. </p> <p>The variant position are 1-based which is default SLiM output. To compare the results with the causal loci user must make the positions 0-based (i.e. POS-1). The details are provided in a github tutorial.</p> <p>Please refer to the documentation for a detailed description of the files and folder structure.</p> <p>The article describing the simulated data and its application is accepted for publication in Nucleic Acids Research (https://doi.org/10.1093/nar/gkae1027).</p>
Dataset from the paper "The boring history of GAIA BH3 from isolated binary evolution": IIC - simulation outcomes, fiducial 1E8
<p>This repository contains (a part) of the population synthesis outputs analysed in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. In particular, it contains the results of the subsets that compose the model fiducial2E8 (see <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>). The complete model includes also the results from the fiducial model stored in the repository <a href="https://doi.org/10.5281/zenodo.11992265" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11992265</a></p> <h2>Content</h2> <p>Each tar file contains the outputs of a given simulation set from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The name of the files corresponds to the name of the set as reported in Table 1 of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The details of the sets can be found in Section 2.2 and Section 2.3 of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The outputs stored in this repository contain only the systems that satisfy the following conditions:</p> <ul> <li>The two objects are in a binary system</li> <li>An object is a black hole and the other one is a star in any evolutionary phase prior to the ignition of the helium burning in the core</li> <li>The two objects are not currently interacting except for wind mass transfer (no ongoing Roche-Lobe overflow, common envelope or merger).</li> </ul> <p>The tar files contain three different files in gunzipped parquet format (<a href="https://parquet.apache.org">https://parquet.apache.org</a>):</p> <h3><strong>Python scripts</strong></h3> <ul> <li><em>analyse_sample.py</em>: python script we used to produce the file BHMS.pq.gz and BHMS_ic.pq.gz analysing the SEVN outputs</li> <li><em>create_aux.py</em>: python script we used to produce the file BHMS_VSNCE.pq.gz by combining the SEVN log outputs with the BHMS_ic.pq.gz file</li> </ul> <h3> </h3> <h3>BHMS.pq.gz</h3> <p><strong> </strong>File containing all the systems satisfying the above condition.</p> <p><strong>Columns:</strong></p> <ul> <li>ID: unique id of the binary (Long integer)</li> <li>name: unique name of the binary (Long integer)</li> <li>BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) </li> <li>BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.</li> <li>ms: mass of the star in Msun</li> <li>ks: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..543H/abstract">Hurley+2000</a> classification (integer)</li> <li>ks_sevn: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio+2023</a> classification (integer)</li> <li>rs: stellar radius in Rsun</li> <li>Ts: stellar effective temperature in Kelvin</li> <li>Ls: stellar luminosity in Lsun</li> <li>mbh: mass of the black hole in Msun</li> <li>P: binary period in days</li> <li>BEvent: Event corresponding to the SEVN output (see SEVN user guide)</li> <li>Eccentricity: Eccentricity of the binary</li> <li>Semimajor: Semimajor axis of the binary in Rsun</li> <li>bh3dist: "Relative" Euclidean distance of the system from the Gaia BH3 properties:</li> </ul> <p> bh3dist = sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )</p> <p> <strong>Notice-1: </strong>in this file each system (identified by ID and name) can appear more than once. Indeed this file contains all the outputs in which a system satisfy the condition BH-star condition discussed above.</p> <p> <strong>Notice-2: </strong><em>the dataframe is sorted based on the bh3dist column,</em> so the first rows contains the objects that are closer to the properties of Gaia BH3 based on the bh3dist statistic.</p> <p> <strong>Notice-3: </strong>The file has been created by analysing the main SEVN outputs using the script <em>analyse_sample.py </em>included in this repository</p> <h3>BHMS_ic.pq.gz</h3> <p>File containing the initial conditions of all the systems included in the file BHMS.pq.gz</p> <p><strong>Columns:</strong></p> <p><em>Info from BHMS.pq.gz</em>:</p> <p>these columns report the same information as in the BHMS.pq.gz file, but each system (identified by ID and name) appears just once (the occurrence from the BHMS.pq.gz with the minimum bh3dist)</p> <ul> <li>ID: unique id of the binary (long integer)</li> <li>name: unique name of the binary (long integer)</li> <li>BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) </li> <li>BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.</li> <li>ms: mass of the star in Msun</li> <li>ks: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..543H/abstract">Hurley+2000</a> classification (integer)</li> <li>ks_sevn: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio+2023</a> classification (integer)</li> <li>rs: stellar radius in Rsun</li> <li>Ts: stellar effective temperature in Kelvin</li> <li>Ls: stellar luminoisity in Lsun</li> <li>mbh: mass of the black hole in Msun</li> <li>P: binary period in days</li> <li>BEvent: Event corresponding to the SEVN output (see SEVN user guide)</li> <li>Eccentricity: Eccentricity of the binary</li> <li>Semimajor: Semimajor axis of the binary in Rsun</li> <li>bh3dist: "Relative" Eucledian distance of the system from the Gaia BH3 properties:</li> </ul> <p> bh3dist = sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )</p> <p><em>Initial properties</em>:</p> <ul> <li>mzams_0: Initial (ZAMS) mass of the black hole in Msun</li> <li>mzams_1: Initial (ZAMS) mass of the star in Msun</li> <li>Semimajor_ini: Initial semimajor axis of the binary in Rsun </li> <li>Eccentricity_ini: Initial eccentricity axis of the binary </li> <li>seed: Random seed used in the SEVN simulation</li> <li>P_ini: Intial period of the binary in days </li> </ul> <p><strong> Notice: </strong>The file has been created by analysing the main SEVN outputs using the script <em>analyse_sample.py </em>included in this repository</p> <h3>BHMS_VSNCE.pq.gz</h3> <p>A file containing additional information about the evolutionary events of the systems stored in BHMS.pq.gz</p> <ul> <li>ID: unique id of the binary (long integer)</li> <li>name: unique name of the binary (long integer)</li> <li>time_SN: formation time of the black hole (BH) in Myr (all the simulations start from time=0)</li> <li>Vcom: Velocity magnitude of the center of mass after the formation of the BH (before the formation Vcom=0 is assumed)</li> <li>time_CE_preSN: time of the last CE before the BH formation (if empty no CE before BH formation)</li> <li>time_CE_postSN: time of the first CE after the BH formation (if empty no CE after BH formation)</li> <li>time_RL_preSN: time of the last Roche-Lobe overflow before the BH formation (if empty no RLO before BH formation)</li> <li>time_RL_postSN: time of the first Roche-Lobe overflow after the BH formation (if empty no RLO before BH formation)</li> </ul> <p><strong>Note: </strong>The file has been created by combining the file BHMS_ic.pq.gz with the main SEVN outputs using the script <em>create_aux.py </em>included in this repository</p> <h3>How to read the files</h3> <p>The parquet files can be easily read in Python by using the <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_parquet.html">Pandas module</a>:</p> <pre><code>import pandas as pd df_bhms=pd.read_parquet("BHMS.pq.gz") df_bhms_ic=pd.read_parquet("BHMS_ic.pq.gz") df_bhms_vsnce=pd.read_parquet("BHMS_VSNCE.pq.gz")</code></pre> <p> </p> <h2>Notes</h2> <p>The code used in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a> is SEVN (<a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio et al., 2023</a>) version 2.10.1 (commit <a href="https://gitlab.com/sevncodes/sevn/-/tree/a4753f1177aab076da2641afc497a06006b30267">a4753f11</a>). </p>
Dataset from the paper "The boring history of GAIA BH3 from isolated binary evolution": IIA - simulation outcomes
<p>This repository contains (a part) of the population synthesis outputs analysed in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. In particular, it contains the results of our fiducial model and of the models in which we change stellar (e.g. kicks) and binary evolutions parameters (e.g. common envelope parameters).</p> <h2>Content</h2> <p>Each tar file contains the outputs of a given simulation set from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The name of the files corresponds to the name of the set as reported in Table 1 of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The details of the sets can be found in Section 2.2 and Section 2.3 of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The outputs stored in this repository contain only the systems that satisfy the following conditions:</p> <ul> <li>The two objects are in a binary system</li> <li>An object is a black hole and the other one is a star in any evolutionary phase prior to the ignition of the helium burning in the core</li> <li>The two objects are not currently interacting except for wind mass transfer (no ongoing Roche-Lobe overflow, common envelope or merger).</li> </ul> <p>The tar files contain three different files in gunzipped parquet format (<a href="https://parquet.apache.org">https://parquet.apache.org</a>):</p> <h3><strong>Python scripts</strong></h3> <ul> <li><em>analyse_sample.py</em>: python script we used to produce the file BHMS.pq.gz and BHMS_ic.pq.gz analysing the SEVN outputs</li> <li><em>create_aux.py</em>: python script we used to produce the file BHMS_VSNCE.pq.gz by combining the SEVN log outputs with the BHMS_ic.pq.gz file</li> </ul> <h3> </h3> <h3>BHMS.pq.gz</h3> <p><strong> </strong>File containing all the systems satisfying the above condition.</p> <p><strong>Columns:</strong></p> <ul> <li>ID: unique id of the binary (Long integer)</li> <li>name: unique name of the binary (Long integer)</li> <li>BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) </li> <li>BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.</li> <li>ms: mass of the star in Msun</li> <li>ks: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..543H/abstract">Hurley+2000</a> classification (integer)</li> <li>ks_sevn: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio+2023</a> classification (integer)</li> <li>rs: stellar radius in Rsun</li> <li>Ts: stellar effective temperature in Kelvin</li> <li>Ls: stellar luminosity in Lsun</li> <li>mbh: mass of the black hole in Msun</li> <li>P: binary period in days</li> <li>BEvent: Event corresponding to the SEVN output (see SEVN user guide)</li> <li>Eccentricity: Eccentricity of the binary</li> <li>Semimajor: Semimajor axis of the binary in Rsun</li> <li>bh3dist: "Relative" Euclidean distance of the system from the Gaia BH3 properties:</li> </ul> <p> bh3dist = sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )</p> <p> <strong>Notice-1: </strong>in this file each system (identified by ID and name) can appear more than once. Indeed this file contains all the outputs in which a system satisfy the condition BH-star condition discussed above.</p> <p> <strong>Notice-2: </strong><em>the dataframe is sorted based on the bh3dist column,</em> so the first rows contains the objects that are closer to the properties of Gaia BH3 based on the bh3dist statistic.</p> <p> <strong>Notice-3: </strong>The file has been created by analysing the main SEVN outputs using the script <em>analyse_sample.py </em>included in this repository</p> <h3>BHMS_ic.pq.gz</h3> <p>File containing the initial conditions of all the systems included in the file BHMS.pq.gz</p> <p><strong>Columns:</strong></p> <p><em>Info from BHMS.pq.gz</em>:</p> <p>these columns report the same information as in the BHMS.pq.gz file, but each system (identified by ID and name) appears just once (the occurrence from the BHMS.pq.gz with the minimum bh3dist)</p> <ul> <li>ID: unique id of the binary (long integer)</li> <li>name: unique name of the binary (long integer)</li> <li>BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) </li> <li>BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.</li> <li>ms: mass of the star in Msun</li> <li>ks: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..543H/abstract">Hurley+2000</a> classification (integer)</li> <li>ks_sevn: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio+2023</a> classification (integer)</li> <li>rs: stellar radius in Rsun</li> <li>Ts: stellar effective temperature in Kelvin</li> <li>Ls: stellar luminoisity in Lsun</li> <li>mbh: mass of the black hole in Msun</li> <li>P: binary period in days</li> <li>BEvent: Event corresponding to the SEVN output (see SEVN user guide)</li> <li>Eccentricity: Eccentricity of the binary</li> <li>Semimajor: Semimajor axis of the binary in Rsun</li> <li>bh3dist: "Relative" Eucledian distance of the system from the Gaia BH3 properties:</li> </ul> <p> bh3dist = sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )</p> <p><em>Initial properties</em>:</p> <ul> <li>mzams_0: Initial (ZAMS) mass of the black hole in Msun</li> <li>mzams_1: Initial (ZAMS) mass of the star in Msun</li> <li>Semimajor_ini: Initial semimajor axis of the binary in Rsun </li> <li>Eccentricity_ini: Initial eccentricity axis of the binary </li> <li>seed: Random seed used in the SEVN simulation</li> <li>P_ini: Intial period of the binary in days </li> </ul> <p><strong> Notice: </strong>The file has been created by analysing the main SEVN outputs using the script <em>analyse_sample.py </em>included in this repository</p> <h3>BHMS_VSNCE.pq.gz</h3> <p>A file containing additional information about the evolutionary events of the systems stored in BHMS.pq.gz</p> <ul> <li>ID: unique id of the binary (long integer)</li> <li>name: unique name of the binary (long integer)</li> <li>time_SN: formation time of the black hole (BH) in Myr (all the simulations start from time=0)</li> <li>Vcom: Velocity magnitude of the center of mass after the formation of the BH (before the formation Vcom=0 is assumed)</li> <li>time_CE_preSN: time of the last CE before the BH formation (if empty no CE before BH formation)</li> <li>time_CE_postSN: time of the first CE after the BH formation (if empty no CE after BH formation)</li> <li>time_RL_preSN: time of the last Roche-Lobe overflow before the BH formation (if empty no RLO before BH formation)</li> <li>time_RL_postSN: time of the first Roche-Lobe overflow after the BH formation (if empty no RLO before BH formation)</li> </ul> <p><strong>Note: </strong>The file has been created by combining the file BHMS_ic.pq.gz with the main SEVN outputs using the script <em>create_aux.py </em>included in this repository</p> <h3>How to read the files</h3> <p>The parquet files can be easily read in Python by using the <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_parquet.html">Pandas module</a>:</p> <pre><code>import pandas as pd df_bhms=pd.read_parquet("BHMS.pq.gz") df_bhms_ic=pd.read_parquet("BHMS_ic.pq.gz") df_bhms_vsnce=pd.read_parquet("BHMS_VSNCE.pq.gz")</code></pre> <p> </p> <h2>Notes</h2> <p>The code used in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a> is SEVN (<a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio et al., 2023</a>) version 2.10.1 (commit <a href="https://gitlab.com/sevncodes/sevn/-/tree/a4753f1177aab076da2641afc497a06006b30267">a4753f11</a>). </p>
Dataset from the paper "The boring history of GAIA BH3 from isolated binary evolution": IIB - simulation outcomes, different metallicities
<p>This repository contains (a part) of the population synthesis outputs analysed in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. In particular, it contains the simulation sets with different metallicities.</p> <h2>Content</h2> <p>Each tar file contains the outputs of a given simulation set from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The name of the files corresponds to the name of the set as reported in Table 1 of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The details of the sets can be found in Section 2.2 and Section 2.3 of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The outputs stored in this repository contain only the systems that satisfy the following conditions:</p> <ul> <li>The two objects are in a binary system</li> <li>An object is a black hole and the other one is a star in any evolutionary phase prior to the ignition of the helium burning in the core</li> <li>The two objects are not currently interacting except for wind mass transfer (no ongoing Roche-Lobe overflow, common envelope or merger).</li> </ul> <p>The tar files contain three different files in gunzipped parquet format (<a href="https://parquet.apache.org">https://parquet.apache.org</a>):</p> <h3><strong>Python scripts</strong></h3> <ul> <li><em>analyse_sample.py</em>: python script we used to produce the file BHMS.pq.gz and BHMS_ic.pq.gz analysing the SEVN outputs</li> <li><em>create_aux.py</em>: python script we used to produce the file BHMS_VSNCE.pq.gz by combining the SEVN log outputs with the BHMS_ic.pq.gz file</li> </ul> <h3> </h3> <h3>BHMS.pq.gz</h3> <p><strong> </strong>File containing all the systems satisfying the above condition.</p> <p><strong>Columns:</strong></p> <ul> <li>ID: unique id of the binary (Long integer)</li> <li>name: unique name of the binary (Long integer)</li> <li>BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) </li> <li>BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.</li> <li>ms: mass of the star in Msun</li> <li>ks: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..543H/abstract">Hurley+2000</a> classification (integer)</li> <li>ks_sevn: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio+2023</a> classification (integer)</li> <li>rs: stellar radius in Rsun</li> <li>Ts: stellar effective temperature in Kelvin</li> <li>Ls: stellar luminosity in Lsun</li> <li>mbh: mass of the black hole in Msun</li> <li>P: binary period in days</li> <li>BEvent: Event corresponding to the SEVN output (see SEVN user guide)</li> <li>Eccentricity: Eccentricity of the binary</li> <li>Semimajor: Semimajor axis of the binary in Rsun</li> <li>bh3dist: "Relative" Euclidean distance of the system from the Gaia BH3 properties:</li> </ul> <p> bh3dist = sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )</p> <p> <strong>Notice-1: </strong>in this file each system (identified by ID and name) can appear more than once. Indeed this file contains all the outputs in which a system satisfy the condition BH-star condition discussed above.</p> <p> <strong>Notice-2: </strong><em>the dataframe is sorted based on the bh3dist column,</em> so the first rows contains the objects that are closer to the properties of Gaia BH3 based on the bh3dist statistic.</p> <p> <strong>Notice-3: </strong>The file has been created by analysing the main SEVN outputs using the script <em>analyse_sample.py </em>included in this repository</p> <h3>BHMS_ic.pq.gz</h3> <p>File containing the initial conditions of all the systems included in the file BHMS.pq.gz</p> <p><strong>Columns:</strong></p> <p><em>Info from BHMS.pq.gz</em>:</p> <p>these columns report the same information as in the BHMS.pq.gz file, but each system (identified by ID and name) appears just once (the occurrence from the BHMS.pq.gz with the minimum bh3dist)</p> <ul> <li>ID: unique id of the binary (long integer)</li> <li>name: unique name of the binary (long integer)</li> <li>BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) </li> <li>BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.</li> <li>ms: mass of the star in Msun</li> <li>ks: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..543H/abstract">Hurley+2000</a> classification (integer)</li> <li>ks_sevn: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio+2023</a> classification (integer)</li> <li>rs: stellar radius in Rsun</li> <li>Ts: stellar effective temperature in Kelvin</li> <li>Ls: stellar luminoisity in Lsun</li> <li>mbh: mass of the black hole in Msun</li> <li>P: binary period in days</li> <li>BEvent: Event corresponding to the SEVN output (see SEVN user guide)</li> <li>Eccentricity: Eccentricity of the binary</li> <li>Semimajor: Semimajor axis of the binary in Rsun</li> <li>bh3dist: "Relative" Eucledian distance of the system from the Gaia BH3 properties:</li> </ul> <p> bh3dist = sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )</p> <p><em>Initial properties</em>:</p> <ul> <li>mzams_0: Initial (ZAMS) mass of the black hole in Msun</li> <li>mzams_1: Initial (ZAMS) mass of the star in Msun</li> <li>Semimajor_ini: Initial semimajor axis of the binary in Rsun </li> <li>Eccentricity_ini: Initial eccentricity axis of the binary </li> <li>seed: Random seed used in the SEVN simulation</li> <li>P_ini: Intial period of the binary in days </li> </ul> <p><strong> Notice: </strong>The file has been created by analysing the main SEVN outputs using the script <em>analyse_sample.py </em>included in this repository</p> <h3>BHMS_VSNCE.pq.gz</h3> <p>A file containing additional information about the evolutionary events of the systems stored in BHMS.pq.gz</p> <ul> <li>ID: unique id of the binary (long integer)</li> <li>name: unique name of the binary (long integer)</li> <li>time_SN: formation time of the black hole (BH) in Myr (all the simulations start from time=0)</li> <li>Vcom: Velocity magnitude of the center of mass after the formation of the BH (before the formation Vcom=0 is assumed)</li> <li>time_CE_preSN: time of the last CE before the BH formation (if empty no CE before BH formation)</li> <li>time_CE_postSN: time of the first CE after the BH formation (if empty no CE after BH formation)</li> <li>time_RL_preSN: time of the last Roche-Lobe overflow before the BH formation (if empty no RLO before BH formation)</li> <li>time_RL_postSN: time of the first Roche-Lobe overflow after the BH formation (if empty no RLO before BH formation)</li> </ul> <p><strong>Note: </strong>The file has been created by combining the file BHMS_ic.pq.gz with the main SEVN outputs using the script <em>create_aux.py </em>included in this repository</p> <h3>How to read the files</h3> <p>The parquet files can be easily read in Python by using the <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_parquet.html">Pandas module</a>:</p> <pre><code>import pandas as pd df_bhms=pd.read_parquet("BHMS.pq.gz") df_bhms_ic=pd.read_parquet("BHMS_ic.pq.gz") df_bhms_vsnce=pd.read_parquet("BHMS_VSNCE.pq.gz")</code></pre> <p> </p> <h2>Notes</h2> <p>The code used in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a> is SEVN (<a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio et al., 2023</a>) version 2.10.1 (commit <a href="https://gitlab.com/sevncodes/sevn/-/tree/a4753f1177aab076da2641afc497a06006b30267">a4753f11</a>). </p>
Simulated datasets analysed in Rota et al. study "A simple method for data partitioning based on relative evolutionary rates"
<p>Simulated datasets analysed in Rota et al. study "A simple method for data partitioning based on relative evolutionary rates". AS refers to datasets simulated on an asymmetrical trees and SS to those simulated on a symmetrical tree. The datasets are in phylip format. Having 'miss' in the name of a file refers to missing 25% of the data.</p>
Simulated RNA-Seq Datasets
<p><strong>The repository contains a simulated RNA-Seq experiment of Saccharomyces cerevisiae. </strong></p> <p> </p> <p><strong>NCBI Repository</strong></p> <p>ftp://ftp.ncbi.nlm.nih.gov/genomes/archive/old_genbank/Fungi/Saccharomyces_cerevisiae_uid128/</p> <p><strong>Sequencing Simulator <em>ART</em></strong></p> <p><a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm</a></p> <p><strong>RNA-Seq Experiment Simulator <em>Polyester</em></strong></p> <p><a href="https://github.com/alyssafrazee/polyester">https://github.com/alyssafrazee/polyester</a></p> <p><a href="https://academic.oup.com/bioinformatics/article/31/17/2778/183245">https://academic.oup.com/bioinformatics/article/31/17/2778/183245</a></p> <p> </p> <p> </p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
RPA simulated dataset containing noisy I-V and noise-free I-V
<p>Dataset contains information for the simulated ideal and noisy I-V curves used in the paper "A bootstrap based approach for improving measurements by retarding potential analyzers", submitted JGR-Space Physics.</p>
Datasets, figures and simulation scripts for "Quantum circuit compilation with quantum computers"
<p>The files contain the datasets and figures with the results of the manuscript "<a title="Quantum circuit compilation with quantum computers" href="https://doi.org/10.48550/arXiv.2408.00077" target="_blank" rel="noopener">Quantum circuit compilation with quantum computers</a>".</p> <p>The repository URL links to the repository with the simulation scripts used to produce the datasets and figures.</p>
Results for datasets with simulated error (derived from Datasets 1-5)
<p>Datasets w/ simulated error derived from Datasets 1-5.</p> <p>Version 2: Datasets were calculated with an Rstandard (R_std) value of 0.0036765 (<span>Skrzypek, G. and Dunn, P. J. H., Rapid Commun. Mass Spectrom., 34, e8890, <a href="https://doi.org/10.1002/rcm.8890">https://doi.org/10.1002/rcm.8890</a>, 2020</span>), which is more accurate than the atom percent value used in Version 1. Updating the R_std value had essentially no effect on the average values calculated using these datasets.</p> <p>There are 1000 datasets per combination of error level and skewness type.</p> <ul> <li>e1: Low error, no skew</li> <li>e2: Medium error, no skew</li> <li>e3: High error, no skew</li> <li>e4: Low error, left skew</li> <li>e5: Medium error, left skew</li> <li>e6: High error, left skew</li> <li>e7: Low error, right skew</li> <li>e8: Medium error, right skew</li> <li>e9: High error, right skew</li> </ul>
Dataset of simulated room impulse responses in three coupled rooms
<p>This dataset accompanies the publication</p> <blockquote> <div> <div> <div> <p>Georg Götz, Teodors Kerimovs, Sebastian J. Schlecht, and Ville Pulkki. Dynamic late reverberation rendering using the common-slope model. In Proceedings of the AES 6th International Conference on Audio for Games, Tokyo, Japan, April 2024.</p> </div> </div> </div> </blockquote> <div> <div> <div> <p> </p> <div> <div> <div> <p>The dataset includes room acoustic simulations conducted with the hybrid simulation suite Treble, using a transition frequency of approximately 750 Hz between wave-based and GA simulation. We simulated the coupled room geometry depicted in the file room_geometry2.pdf. The orange × indicates the source position, and receivers were uniformly distributed on the xy-plane with 0.3 m resolution. Each room has a height of 3 m and exhibits a uniform absorption distribution. Room R2 is the most reverberant with an absorption coefficient similar to concrete (αR2 = 0.01), whereas R1 and R3 are significantly less reverberant with αR1 = 0.2 and αR3 = 0.1, respectively.</p> <p>The dataset also includes the common-slope analysis results for the omnidirectional responses and also for the sector-based analysis as described in the paper. Please also refer to the following paper for more details on the common-slope analysis:</p> <blockquote> <p>Georg Götz, Sebastian J. Schlecht, and Ville Pulkki. Common-slope modeling of late reverberation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, pp. 3945–3957, September 2023. doi: <a href="https://doi.org/10.1109/TASLP.2023.3317572" target="_blank" rel="noopener">10.1109/TASLP.2023.3317572</a></p> </blockquote> </div> </div> </div> </div> </div> </div>
Megan results, emission input datasets for CMAQ, and simulation results
<p>Megan results, emission input datasets for CMAQ, and simulation results:</p> <p>Megan results: megan_results.zip</p> <p>Emission: emission.zip</p> <p>NO2 simulation: NO2_simulation.zip</p> <p>O3 simulation: O3&MDA8_simulation.zip</p> <p>NO2 and O3 simulation station results: simulation_and_observation_results.zip</p> <p>Process results: ozone_chemical_phyisc_contribution.zip</p> <p> </p> <p> </p> <p> </p>
Simulations of the shock turbulence interaction: magnetic fields and particles datasets
<p>Simulation snapshots for hybrid-kinetic model of a perpendicular shock interacting with pre-existing plasma turbulence. This dataset allows to reproduce the analyses presented in Trotta et al. 2024. The filenames are organised in <level of turbulence>-Variable.txt *(e.g., dB_B_00_B_z.txt is the magnetic field z component for the case with turbulence level dB/B = 0.0). </p> <p>For support in reading the simulation data or if further variables/snapshots are needed for scientific purposes, please be in touch.</p>
Dataset of the simulations in the GōMartini 3: From large conformational changes in proteins to environmental bias corrections
<p>This section describes the data files for the nanomechanics of SARS-CoV-2 RBD in complex with a potent nanobody (H11-H4) by GōMartini approach.</p> <p>(1) The <strong>data_4iu3.tar.xz</strong> file has all the files necessary to reproduce the pulling simulations of PDB ID 4IU3 using GōMartini methodology. The files are described below.</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------</p> <p>https://www.rcsb.org/structure/4iu3</p> <p>Cohesin-dockerin -X domain complex from Ruminococcus flavefacience</p> <p> </p> <p>https://github.com/rams-research/OVrCSU</p> <p>http://pomalab.ippt.pan.pl/GoContactMap/</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------</p> <p>4iu3.pdb : All-atoms PDB file</p> <p>4iu3.map : All-atoms OVrCSU contac map file</p> <p>A.pdb,B.pdb : All-atoms Structures of Cohesin and Dockering, later referenced as molA and molB</p> <p>index.ndx : base index file</p> <p>martini_v3*.itp : Martini3 force field definitions</p> <p> </p> <p>GoMartini, topology files:</p> <p> molA.itp</p> <p> molA_exclusions_VirtGoSites.itp</p> <p> molB.itp</p> <p> molB_exclusions_VirtGoSites.itp</p> <p> go-table_VirtGoSites_interface.itp</p> <p> go-table_VirtGoSites.itp</p> <p> topol.top</p> <p> </p> <p>GoMartini, GROMACS MDP equilibration and final GRO files:</p> <p> Pre-equilibration:</p> <p> min.mdp, min.gro</p> <p> npt.mdp, npt,gro</p> <p> nvt.mdp, nvt.gro</p> <p> Equilibration MD run:</p> <p> md-corr.log</p> <p> md-corr.mdp</p> <p> md-corr.edr</p> <p> md-corr.gro</p> <p> md-corr.tpr</p> <p> </p> <p>Topology and force field definitions:</p> <p> GGG_pull/BB-part-def_VirtGoSites.itp</p> <p> GGG_pull/go-table_VirtGoSites.itp</p> <p> GGG_pull/index.ndx</p> <p> GGG_pull/martini_v3.0.4.itp</p> <p> GGG_pull/martini_v3.0_ions.itp</p> <p> GGG_pull/martini_v3.0_phospholipids.itp</p> <p> GGG_pull/martini_v3.0_solvents.itp</p> <p> GGG_pull/molA_exclusions_VirtGoSites.itp</p> <p> GGG_pull/molA.itp</p> <p> GGG_pull/molB_exclusions_VirtGoSites.itp</p> <p> GGG_pull/molB.itp</p> <p> </p> <p>Pulling MD runs from 100 runs ({1-100}), GROMACS files:</p> <p> GGG_pull/md_pull_{1-100}-trajectory-protein.pdb : Trajectory in PDB format</p> <p> GGG_pull/md_pull_{1-100}_pullf.xvg : force profile</p> <p> GGG_pull/md_pull_{1-100}_pullx.xvg : distance profile</p> <p> GGG_pull/md_pull_{1-100}.log : log files</p> <p> GGG_pull/md_pull_{1-100}.edr : EDR files</p> <p> GGG_pull/md_pull_{1-100}.tpr : TPR files</p> <p> </p> <p>2) The <strong>data_6zh9_1.tar.xz</strong>, <strong>data_6zh9_2.tar.xz</strong>,<strong> data_6ZH9_GM2.tar.xz</strong>,<strong> </strong>and<strong> data_6ZH9_GM3.tar.xz </strong>files contain</p> <p>The dataset comprises a total of 50 replicas, each with a total of 751 snapshots, and the mdp and tpr files, which are located in the CG_Pull/Trajectories folder, for the RBD-H11-H4 complex. The gro, mdp and tpr files for NVT and NPT equilibrations are included.</p> <p>The force and displacement xvg files for each replica are included in the GC_Pull folder (pullf*.xvg and pullx*.xvg, respectively). The output files from GōMartini are included.</p> <p>The system consists of a single-domain antibody (i.e. nanobody) and the receptor-binding domain (RBD) portion of the SARS-CoV-2 spike protein. In this regard, the nanobody named H11-H4 and the RBD form a mechanostable protein complex with PDB ID: 6ZH9. The entire system was modeled by the Martini 3 force field. The same protocol as for the XMod-Doc:Coh complex was employed. The GōMartini model requires a total of 715 contacts divided into 404 and 285 for the RBD and the H11-H4 respectively and the protein complex interface was represented by 26 contacts. The value of the dissociation energy of the LJ potentials was set to a value of ε = 15.0 kJ/mol. The dimensions of the water box for the RBD-Nb complex were 16x12x90 nm3 with a 0.15 M NaCl. There were 135,669 CG water beads representing 542,676 water molecules.</p> <p>To conduct the nanomechanical studies, the positions of RBD residues GLY-526, PRO-527, and LYS-528 were kept frozen in the z-axis and the coordinates of residues SER-126, SER-127, and LYS-128 in H11-H4 were kept frozen in x- and y-axis, and were chosen for SMD simulation at constant speed.</p> <p>Two different protocols according to all-atom SMD simulation were considered. i) in <strong>data_6zh9_1.tar.xz: </strong>Restraints were applied to the backbone atoms of the RBD with a spring contact of kb =1000 kJ/(mol·nm2) to avoid large deformation and the residue in the SMD protocol with velocity equal to 5x10-3 nm/ps was coupled to another harmonic potential with a spring constant equal to 600 kJ/(mol·nm2) and ii) <strong>data_6zh9_2.tar.xz: </strong>Pulling velocity was equal to 1x10-4 nm/ps was coupled to another harmonic potential with a spring constant equal to 600 kJ/(mol·nm2). </p> <p>The supplementary files <strong>data_6ZH9_GM2.tar.xz</strong> and <strong>data_6ZH9_GM3.tar.xz</strong> followed a similar protocol as <strong>data_6ZH9_2.tar.xz</strong>, with the following differences: the box size was set to 10 × 10 × 60 nm, and the spring constant was adjusted to 37.6 kJ/(mol·nm²). For the GōMartini 2 model, the default value of ε = 9.414 kJ/mol was used.</p> <p>3) The <strong>IDP_GoMartini3.tar.gz </strong>file has the following folders:</p> <p>a) Atomistic</p> <p>The _atomistic_ directory contains subfolders for each of the IDPs tested. Each subdirectory contains the topology for the protein and an initial starting structure. There is also a folder of the mdp files used. In each simulation, the correct temperature as described in the manuscript was applied.</p> <p>b) Martini</p> <p>The _martini_ directory contains subfolders for each of the IDPs tested. Each subdirectory contains topology for the corresponding protein model tested, and the starting structure used. The TEMP variable in the mdp files was replaced with the experimentally corresponding temperature in each simulation.</p> <p>c) Condensate</p> <p>The _condensate_ directory contains two systems. The artifical IDP (_aIDP_) condensate system of Dzuricky et al. and the short peptides of Abbas et al. (_peptides_)</p> <p>_aIDP_ has two systems comparing the native and optimised topologies for the phase separation of the aIDP.</p> <p>_peptides_ contains a single system with topologies for protonated and deprotonated synthetic peptides, as well as the starting structures, and topology files for the differently rescaled protein-water interactions used.</p> <p>4) The WALPDATA.tar.gz has the following folders:</p> <p>a) ProteinITPs has all GROMACS itp files for the WALP α-helices – 16, 19, 23, and 27 denoted as WALP16, WALP29, WALP23 and WALP27 as well as the GōMartini inputs</p> <p>b) mdps and the script for preparing the mebrane for the WALP peptides PrepareMembranePeptideSim.ipynb</p> <p>5) The <strong>BetaSheet_GoMartini.tar.gz</strong> file has the following folders:</p> <p>molecule_0.itp is the itp file for beta strand 1.<br>molecule_1.itp is the itp file for beta strand 2.</p> <p>martini.itp is the Martini forcefield itp used.</p> <p>rCSU.map is the rCSU map generated.<br>OV.map is the OV map generated.</p> <p><br>GoMartini files:</p> <p> molecule_0_exclusions_VirtGoSites.itp <br> molecule_0_BB-part-def_VirtGoSites.itp<br> molecule_0_go-table_IDPsolubility.itp <br> molecule_0_go-table_VirtGoSites.itp <br> molecule_0_go4view_harm.itp <br> BB-part-def_VirtGoSites.itp <br> go-table_VirtGoSites.itp </p> <p><br>mdp files used:</p> <p> min.mdp <br> rel_310.mdp <br> prod_310.mdp </p> <p> </p> <p>rada_sheet_canonical.pdb is the PDB file of the AA RADA16 structure.<br>rada_cg.pdb is the PDB file of the Martini RADA16 structure.</p> <p> </p> <p>Inside folder Negative_Control is the topology file for<br>the system with no structural or interaction bias.</p> <p><br>Inside folder GoVirt_Bias is the topology file for<br>the system with a GoMartini model applied between the<br>two strands.</p> <p><br>Inside folder Scaled_Water is the topology file for<br>the system with GoVirt water interaction scaling.</p> <p>6) The <strong>md_inputs_go_IDPs_PETER.tar.gz</strong> file has the following folders:</p> <p>Directories "a1", "hst5", and "pnt" contain the input files for proteins with Go potentials. And in the "controls" directory, input files for the same proteins, without Go potentials.</p> <p>To run the simulations, the user can run the following commands:</p> <p>gmx grompp -f md.mdp -p topol.top -c pre_md.gro -o md<br>gmx mdrun -s md.tpr -deffnm md</p> <p>Additionally, atomistic structures are available in .gro format - {protein}_atomistic.gro</p> <p>7) The <strong>repository_Hafez.zip </strong></p> <p>In the folders 1AOH, 1TIT, 1UBQ, 3W0K, AQP1, and Ist2, you will find the necessary files (ITP, MDP, and GRO) to repeat the simulations.</p> <p>In the Martini3FF folder, you can find the MARTINI3 force field files.</p> <p>8) The <strong>Hairpin-M3-15.tar.xz</strong> and <strong>Hairpin-M2.tar.xz</strong> and <strong>Hairpin-M2-15.tar.xz</strong> </p> <p>contains the information mdp, itp and trajectories generated for the study of folding simulation with GoMartini implemented in 2017 and the latest implementation on top of the Martini 3 force field.</p> <p>The Hairpin-M2, Hairpin-M2-15, and Hairpin-M3-15 folders contain all the necessary files to reproduce the pulling simulations of PDB ID 1GB1 (a novel, highly stable fold of the immunoglobulin binding domain of streptococcal protein G) using the GōMartini 2 and GōMartini 3 methodologies, respectively.</p> <p>Each folder contains:</p> <p>A.pdb: All-atom PDB file of the unfolded structure.<br>B.pdb: All-atom PDB file of the folded structure.<br>A-CG.pdb: Martini coarse-grained representation of the unfolded structure.<br>B-CG.pdb: Martini coarse-grained representation of the folded structure.<br>A-GC.gro: GROMACS format file of the unfolded structure.<br>B-GC.gro: GROMACS format file of the folded structure.<br>system.top: Topology file.</p> <p><br>Force Fields and Topologies:<br>GōMartini 2:<br>martini_v2.2.itp: Martini 2 force field for proteins.<br>martiniv2.2_ions.itp: Martini 2 force field for ions.<br>Protein_A.itp: GōMartini 2 topology for the folded structure.<br>Protein_A_unfolded.itp: GōMartini 2 topology for the unfolded structure.</p> <p><br>GōMartini 3:<br>martini_v3.0.0.itp: Martini 3 force field for proteins.<br>martini_v3.0.0_ions_v1.itp: Martini 3 force field for ions.<br>martini_v3.0.0_solvents_v1.itp: Martini 3 force field for solvents.<br>go_martini.itp: GōMartini 3 topology for the folded structure.<br>go_martini_unfold.itp: GōMartini 3 topology for the unfolded structure.<br>go_molecule1.itp: Additional GōMartini 3 topology file.<br>A.map: Contact map for the unfolded structure.<br>B.map: Contact map for the folded structure.</p> <p>GROMACS Files for Equilibration and MD Runs:</p> <p>Equilibration:<br>minimization.mdp, minimization.gro: Files for energy minimization.<br>nvt.mdp, nvt.gro, nvt.xtc, nvt.tpr: Files for NVT equilibration.<br>npt.mdp, npt.gro, npt.xtc, npt.tpr, npt_dry.tpr: Files for NPT equilibration (dry simulations without waters or ions).</p> <p>Folding MD Runs:<br>dynamic.mdp: File for folding molecular dynamics runs.<br>Folding MD Runs (1-100):<br>MD/hairpin-M2_fit.xtc*: Trajectories (without waters or ions, ε = 9.414 kJ/mol).<br>MD/hairpin-M2-15_fit*.xtc*: Trajectories (without waters or ions, ε = 15.0 kJ/mol).</p> <p> </p> <p>MD/hairpin-CG-M3-_fit*.xtc: Trajectories (without waters or ions, ε = 15.0 kJ/mol).</p> <p>Data Analysis Scripts:<br>MD/Distances_native_WT.ipynb: Jupyter notebook for backbone distance analysis.<br>MD/all_native_graphs.ipynb: Jupyter notebook for native contact analysis.<br>MD/total_contacts_column.ipynb: Python script for contact statistics.</p>
Dataset for the numerical simulation in the article "Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"
<p>This dataset includes the essential source code and the corresponding numerical data for the self-assembly of a M6L4 square-based pyramid (SP) complex. </p> <p>The associated study is described in </p> <p><strong>"Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"</strong>, by T. Abe, S. Takahashi, H. Sato, and S. Hiraoka.</p>
Dataset for manuscript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model"
<p>Datasets and Jupyterlab python script for plotting all figures relevant to the mansucript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model" by Herbert et al.</p> <p>https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1538/</p> <p>Data needs to be unzipped and paths (input and output) updated in the jupyterlab python script.</p> <p> </p>
Borehole observation, in-situ stress and breakout simulation datasets for BS34 in the Xinchang site, Beishan region
<p>Two datasets are available here as supplementary materials for the study of borehole breakout development.</p> <p>For the dataset 'Borehole Data.zip', it contains the natural fractures, breakouts, and drilling-induced tensile fractues, and mini-frac test results obtained from borehole BS34 in the Xinchang site, Beishan region. For the dataset 'Simulation Data.zip', it includes the simulated stress evolution associated with breakout development in the vicinity of a pre-existing frature, and simulated breakout geometry using a finite element model.</p>
ScienceDex guides
Understand access before you commit
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.