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.
3,688
datasets available to search
ShareScore release 0.7.1
Dataset results
3,688 results for “Computer”
Research data from the two surveys on IoT implementation for Article "User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe"
<p>The file includes data collected through two online surveys linked to the article "User and Professional Aspects for Sustainable Computing Based on the nternet of Things in Europe" published by journal Sensors in January 2023:</p> <ul> <li>Survey on factors that inlfuence IoT Adoption by non technical users</li> <li>Survey on recommended profile focused on IoT implementation for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician.</li> </ul>
Phantom imaging data and analysis macros for the article "Monochromatic computed tomography using laboratory-scale setup"
<p>The raw and processed data and analysis macros of the article <em>A.-P.</em> <em>Honkanen et S. J. Huotari, Monochromatic computed tomography using laboratory-scale setup, Scientific Reports (2023), doi:<a href="http://doi.org/10.1038/s41598-023-27409-6">10.1038/s41598-023-27409-6</a></em></p> <p>The data set consists of the raw and reconstructed computed tomography projection data taken of an PMMA phantom embedded with three different chemical species of selenium taken with a monochromatic X-ray imaging setup based on a laboratory-scale Johann-type crystal X-ray spectrometer. In addition to the imaging data, the set contains also the Jupyter Notebooks used to process and analyse the data. The details of the instrument and the analysis are presented in the article.</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International License <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
The computation results of coupled hydrological and hydrodynamic modelling application for the Nemunas River watershed – Curonian Lagoon – South-Eastern Baltic Sea continuum
<p>The datasets provided here were used to analyse the cumulative impacts of climate change in a Nemunas River watershed – Curonian Lagoon – South‑Eastern Baltic Sea continuum by applying a state-of-the-art coupled modelling system, which consists of hydrological and hydrodynamic models.</p> <p>Meteorological data used for running the models were acquired from CORDEX (Coordinated Regional Downscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model (RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models:</p> <table> <tbody> <tr> <th>Abbreviation in datasets</th> <th>Model</th> <th><strong>Institution</strong></th> </tr> </tbody> <tbody> <tr> <td>ICHEC</td> <td>EC-Earth</td> <td>Irish Centre for High-End Computing</td> </tr> <tr> <td>IPSL</td> <td>IPSL-CM 5A-MR</td> <td>The Institut Pierre-Simon Laplace</td> </tr> <tr> <td>MOHC</td> <td>HadGEM2-ES</td> <td>Met Office Hadley Centre</td> </tr> <tr> <td>MPI</td> <td>MPI-ESM-LR</td> <td>Max Planck Institute for Meteorology</td> </tr> </tbody> </table> <p> </p> <p>Climate change scenarios and periods:</p> <ul> <li>Historical/reference (1970-2005);</li> <li>RCP4.5 (2005-2100);</li> <li>RCP8.5 (2005-2100).</li> </ul> <p>The datasets consist of time series for the parameters of:</p> <ul> <li><strong>Ice thickness</strong> - average ice thickness in the Curonian Lagoon;</li> <li><strong>Meteorological data</strong> - bias-corrected temperature and precipitation data for the marine and terrestrial areas;</li> <li><strong>Nemunas River discharge</strong> - simulated average daily values for the discharge and water temperature;</li> <li><strong>Salinity</strong> - selected points in the south-eastern Baltic Sea and one point next to Juodkrantė (in the Curonian Lagoon);</li> <li><strong>Water fluxes</strong> - through four predefined cross-sections in the Curonian Lagoon;</li> <li><strong>Water level</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea;</li> <li><strong>Water residence time</strong> - in the total Curonian Lagoon area, as well as its northern and southern parts;</li> <li><strong>Water temperature</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea.</li> </ul> <p>Some of the datasets (zip files) have additional information (coordinates, data column explanations, units, etc.) in READ_ME.txt files.</p>
Data from: Testing background matching and disruptive colouration in a sexually dichromatic grasshopper: a computer detection experiment.
<p>Cryptic colouration is an adaptative mechanism against predators. Colour patterns can become cryptic through background matching and disruptive colouration, which breaks up the outlines of an animal because the pattern does not coincide with the shape and outline of the animal’s body. Background matching could be advantageous in chromatically homogeneous microhabitats, whereas disruptive colouration can be favoured in visually heterogeneous microhabitats. Grasshoppers of the genus <em>Sphenarium</em> (Orthoptera: Pyrogomorphidae) inhabit very heterogeneous environments and exhibit both strategies. Adults show substantial continuous variation in colouration and longitudinal and transverse bands on the thorax and abdomen. However, males often exhibit considerably more variation in the number of longitudinal and transverse bands than females, which tend to have more uniform colouring (flatter patterns). In this study, we analysed the cryptic properties of the colour patterns of males and females of <em>Sphenarium </em><em>zapotecum</em><em> </em>Sanabria-Urbán, H. Song & Cueva del Castillo and tested the effectiveness of background matching and disruptive colouration using humans as ‘predators’ in a computer detection experiment. We found that the females and males are dichromatic and seem to follow different cryptic strategies in their colouration: males are more disruptive to the background than females, whereas females have a higher level of background matching. In addition, in visually heterogeneous areas, predators spent most time searching for striped male morphs with lower background matching and higher disruptive properties, as well as for female morphs with high background matching, potentially increasing prey survival. As background matching is associated with females and disruptive colouration with males, our results could help explain the evolution of sexual dichromatism in this and other species of grasshoppers of the genus <em>Sphenarium.</em></p> <p> </p>
Computational analyses of dynamic visual courtship display reveal diet-dependent and plastic male signaling in Rabidosa rabida wolf spiders
<p>It has long been a challenge to quantify the variation in dynamic motions to understand how those displays function in animal communication. The traditional approach is dependent on labor-intensive manual identification/annotation by experts. However, the recent progress in computational techniques provides researchers with toolsets for rapid, objective, and reproducible quantification of dynamic visual displays. In the present study, we investigated the effects of diet manipulation on dynamic visual components of male courtship displays of <em>Rabidosa</em> <em>rabida</em> wolf spiders using machine learning algorithms. Our results suggest that (i) the computational approach can provide an insight into the variation in the dynamic visual display between high- and low-diet males which is not clearly shown with the traditional approach and (ii) males may plastically alter their courtship display according to the body size of females they encounter. Through the present study, we add an example of the utilization of recent computational techniques for understanding the evolution of animal behaviors.</p>
Data: More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop
<p>APSIM-Wheat <strong>(</strong><a href="">www.apsim.info</a><strong>)</strong> was used to simulate a data set (for details, see Casadebaig<em> et al.</em>, 2016) with 9100 virtual genotypes (<em>N</em><sub>gen</sub>= 9100) grown under 9000 environments (<em>N</em><sub>env</sub>=9000). In short, virtual genotypes were created by varying the value of 90 independent physiological parameters in a range of ±20% from the reference cultivar <em>Hartog</em>. Environments in the dataset contain historical climate data of 125 years (1889-2013) in four locations (Emerald, Narrabri, Yanco and Merredin) in Australia, in combination with two CO<sub><sup>2</sup></sub> levels (380 and 555 ppm), three nitrogen levels (low: 50%, control: 100% and high fertilization: 100% plus 50 kg‧ha<sup>-1</sup>) and three sowing dates (early, control and late).</p>
Supplementary Data to *Informative and adaptive distances and summary statistics in approximate Bayesian computation*
<p>Supplementary code and data to <strong>Informative and adaptive distances and summary statistics in approximate Bayesian computation</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_slad, of which this archive is a snapshot.</p>
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
Data supplement for the paper "An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures"
<p>This dataset contains the the following data created for the paper "An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures".</p> <p>Data listing:</p> <p>Cell line-specific drug resistance signatures (CDRSR)</p> <p>Patient-specific drug resistance signatures (CTR-DB)</p>
98 selected publications analysed in-depth in the context of a systematic literature review on Computational Thinking in compulsory education
<p>This is the list of the <strong>98 publications from between 2016 and 2021 </strong>analysed in-depth through a review matrix in the context of the <strong>"</strong><a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347">Reviewing Computational Thinking in Compulsory Education: State of Play and</a> <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347"> Practices from the Field</a><strong>"</strong> <strong>research study.</strong> The <a href="https://computhink2study.eu/">study</a> was designed, funded, and followed by the European Commission’s Joint Research Centre (JRC) to investigate <strong>how Computational Thinking (CT) is currently positioned within compulsory school education in Europe’s various Member States, as well as outside the EU</strong>. The study was carried out from April to December 2021 by the Institute for Educational Technology of the Italian National Research Council (CNR-ITD), together with European Schoolnet (EUN) and Vilnius University (VU).</p> <p>Bocconi, S., Chioccariello, A., Kampylis, P., Dagienė, V., Wastiau, P., Engelhardt, K., Earp, J., Horvath, M.A., Jasutė, E., Malagoli, C., Masiulionytė-Dagienė, V., & Stupurienė, G. (2022). <em>Reviewing Computational Thinking in Compulsory Education</em>. Publications Office of the European Union. <a href="https://doi.org/10.2760/126955">https://doi.org/10.2760/126955</a></p> <p> </p>
Structure of the complete review matrix developed in the context of the "Reviewing Computational Thinking in Compulsory Education: State of Play and Practices from the Field" research study
<p>This is the structure of the complete review matrix used to analyse in-depth <strong>98 selected publications from between 2016 and 2021 </strong>in the context of the "<a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347">Reviewing Computational Thinking in Compulsory Education: State of Play and</a> <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347"> Practices from the Field</a>" research study. The <a href="https://computhink2study.eu/">study </a>was designed, funded, and followed by the European Commission’s Joint Research Centre (JRC) to investigate <strong>how Computational Thinking (CT) is currently positioned within compulsory school education in Europe’s various Member States, as well as outside the EU</strong>. The study was carried out from April to December 2021 by the Institute for Educational Technology of the Italian National Research Council (CNR-ITD), together with European Schoolnet (EUN) and Vilnius University (VU).</p>
Medication and condition codes used to develop a computable phenotype for Crohn's Disease incident cases
<p>Lists of medication and condition concepts used for Crohn's disease incident case phenotyping as described in my Master's Thesis "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records From a Large Integrated Health System".</p> <ul> <li>ibd_medication.csv contains medication names, OMOP Concept IDs, RxNorm codes and a flag indicating whether the medication is IBD-specific (i.e., antibiotics and glucocorticoides are marked as unspecific)</li> <li>ibd_conditions.csv contains condition names, OMOP Concept IDs, SNOMED CT codes and a categorical column indicating whether the condition refers to Crohn's Disease (CD), Ulcerative Colitis (UC), or Inflammatory bowel disease unclassified (IBD-U)</li> <li>ibd_symptoms.csv contains symptom names, OMOP Concept IDs, containing symptom group categories, and a flag indicating whether the symptom was added because it is a SNOMED CT descendent code of another code on the list. The list was created based on the IBD symptoms Read Code list provided by Blackwell et. al, 2021, doi:10.1093/ecco-jcc/jjaa146</li> </ul> <p>IBD, Inflammatory Bowel Disease; OMOP, Observational Medical Outcomes Partnership; SNOMED CT, Systematized Nomenclature of Medicine Clinial Terms.</p>
Computed tomography scan of Roman window glass from Ephesos
<p>Computed density scan of a window glass sample performed at Lucerne University of Applied Sciences and Arts (Luci, https://www.hslu.ch/luci). The glass fragment from Ephesos was provided by the Austrian Archaeological Institute (inventory ID EVH12/1017/1322), Vienna, Austria. The measurement covers an area of approx. 12 mm by 12 mm. The measured data is provided as a stacked Tag Image File Format (TIFF) image. A surface mesh that represents the boundary of the glass volume has been derived from the volumetric data and is included in this dataset in GL Transmission Format (gltf).</p>
Data from: Longitudinal effects of early psychosocial deprivation on macaque executive function: Evidence from computational modelling
<p><span>Executive function (EF) describes a group of cognitive processes underlying the organization and control of goal-directed behaviour. Environmental experience appears to play a crucial role in EF development, with early psychosocial deprivation often linked to EF impairment. However, many questions remain concerning the developmental trajectories of EF after exposure to deprivation, especially concerning specific mechanisms. Accordingly, using an 'A-not B' paradigm and a macaque model of early psychosocial deprivation, we investigated how early deprivation influences EF development longitudinally from adolescence into early adulthood. The contribution of working memory and inhibitory control mechanisms were examined specifically via the fitting of a computational model of decision-making to the choice behaviour of each individual. As predicted, peer-reared animals (i.e. those exposed to early psychosocial deprivation) performed worse than mother-reared animals across time, with the fitted model parameters yielding novel insights into the functional decomposition of group-level EF differences underlying task performance. Results indicated differential trajectories of inhibitory control and working memory development in the two groups. Such findings not only extend our knowledge of how early deprivation influences EF longitudinally, but also provide support for the utility of computational modelling to elucidate specific mechanisms linking early psychosocial deprivation to long-term poor outcomes.</span></p>
Supplementary data and code to "Known allosteric proteins have central roles in genetic disease" by G. Abrusan, D. Ascher and M. Inouye, PLOS Computational Biology 18(2):e1009806.
<p>Scripts and data to reproduce the figures and supplementary figures of "G. Abrusan, D. Ascher and M. Inouye (2022) Known allosteric proteins have central roles in genetic disease." PLOS Computational Biology 18(2):e1009806. https://doi.org/10.1371/journal.pcbi.1009806.</p>
QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods
<p>This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. <em>J. Phys. Chem. A</em> <strong>2023</strong>, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes:</p> <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul> <p> </p>
Dataset: A Systematic Study on the Redox Potentials of Phenazine- Derivatives in Aqueous Media: A Combined Computational and Experimental Work
<p>Dataset for the results shown in the publication "A Systematic Study on the Redox Potentials of Phenazine-Derivatives in Aqueous Media: A Combined Computational and Experimental Work"</p>
Output files of the computationally reviewed literature data on enantiomeric metabolism
<p>The computationally reviewed literature data on enantiomeric metabolism. The chiral drug selection based on quantum chemistry's role in the enantiomeric metabolism.</p>
Raw Data to "Computational Investigation of Explicit Solvent Effects and Specific Interactions of Hydroxypyrene Photoacids in Acetone, DMSO, and Water"
<p>This data is a supplement to the publication entitled "Computational Investigation of Explicit Solvent Effects and Specific Interactions of Hydroxypyrene Photoacids in Acetone, DMSO, and Water" in <em>Physical Chemistry Chemical Physics</em> (DOI: 10.1039/D3CP00800B). It contains the structures (as '.xyz' files) and HF/DFT energies from the quantum chemical (QC) calculations using TURBOMOLE (version 7.6).</p> <p>Additional information on the file structure is given in the README file.</p>
Gamma-hemolysin components: computational strategies for LukF-Hlg2 dimer reconstruction on a model membrane
<p>Project files provided as supporting information to the manuscript “Gamma-Hemolysin Components: Computational Strategies for LukF-Hlg2 Dimer Reconstruction on a Model Membrane”. The data set contains the following folders:</p> <ul> <li>LukF_Hlg2_distance: files with the minimum distance between LukF and Hlg2 as a function of time for the simulated replica (Fig. S1); files with the minimum distance between each LukF residue and the Hlg2 monomer and vice versa, averaged over the last 600 ns of simulation in the replica where the spontaneous dimerization is observed (fig. S5).</li> <li>HADDOCK_dimer_crystal_pore_displacement: files with the displacement between Hlg2 residues in the HADDOCK model dimer and the same dimer in the crystal pore, after alignment on the LukF monomer (for both the HADDOCK model in presence and in absence of the LukF N-ter) (Fig. S13)</li> <li>interface_area: files with the interface area between the LukF and the Hlg2 monomers in the replica where the spontaneous dimerization on the membrane is observed, as a function of time (total interface and contribution of LukF and Hlg2 rim domains) (Fig. S4)</li> <li>angles: files with the histograms of the angle between LukF and the axis perpendicular to the membrane, for the simulation of the single LukF monomer and for that capturing the spontaneous dimerization on the membrane (Fig. 4); file with the angle between the LukF and the Hlg2 axis as a function of time in the replica where the spontaneous dimerization is observed (Fig. S6)</li> <li>HADDOCK_scores: files with the HADDOCK scores of the predicted LukF-Hlg2 dimers and their RMSD values computed with respect to the same dimer in the crystal pore. The data are reported for the four top-scored models of each cluster ( for both the HADDOCK models in the presence and in the absence of the LukF N-ter) (Fig. 5).</li> <li>RMSD: files with the RMSD as a function of time for the LukF and the Hlg2 monomers in the replica where the spontaneous dimerization on the membrane is observed (Fig. 2, Fig. S2, Fig. S3)</li> <li>RMSF: files with the RMSF of the LukF and the Hlg2 residues in the replica where the spontaneous dimerization on the membrane is observed and in the simulations of the single monomers (Fig. 2)</li> <li>interaction_persistences: files with H-bond (side chain + backbone and backbone only atoms) salt-bridge, and hydrophobic contact persistence matrices for the single LukF monomer simulated alone (299 x 299) and for the LukF-Hlg2 dimer (299+280 x 299+280) (Fig. 4, Fig. S7 + interactions reported in the manuscript)</li> <li>distance_protein_membrane: files with the minimum distance between each monomer and the membrane, in the last 200ns of the simulation of spontaneous dimerization on the membrane (Figure S9).</li> <li>distance_residues_interface: files with the distance between functionally relevant residues measured along the simulation of the HADDOCK dimer in the absence of LukF N-terminus (Figure S14).</li> </ul>
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.