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2,904 results for “Solute”
A closer look: High-resolution pore-scale simulations of solute transport and mixing through porous media columns
<p>This dataset contains the results of fluid flow (Navier-Stokes) and solute transport (Advection-Diffusion) simulations within columns of granular media generated by virtual gravitational settling of spherical grains. The experiments comprise three media with different degrees of grain-size variability; a range of grain-Peclet numbers is explored. See the homonymous research paper by Sole-Mari et al. (2022, Water Resources Research) for more information.</p> <p>Grains.zip: Positions and radii of the spherical grains for each value of grain-size variability sigma (Matlab's .mat format).</p> <p>ResultsCoarse.zip: Coarse-scale data presented in the aforementioned WRR paper (Matlab's .mat format).</p> <p>Link to the full micro-scale dataset: (soon available)</p> <p>We thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (AECT-2019-3-0014).</p> <p> </p>
A semi-analytical solution for heat transport in rock with parallel fractures and a heat source in both fracture and matrix
<p>In this study, we propose a two-dimensional semi-analytical solution framework based on a Green’s function approach for a flexible heat source definition, including source dimensions, energy delivery strength and duration, and the presence of a heat source in the matrix and/or fracture. The solution fully accounts for heat conduction, advection, dispersion and transient heat exchange between the mobile and immobile phases in a system of parallel fractures. The solution having a strip heat source extending from a fracture into the matrix indicates that one-dimensional heat conduction in the matrix underestimates and overestimates temperature responses at early and later times, respectively.</p> <p>The dataset is for the figures 2-7 in the journal paper. </p>
Solute-solvent clusters
Changed <ul> <li>Reorganized <code>solute</code> and <code>solute.solvent</code> to <code>heterogeneous</code> and <code>homogeneous</code>.</li> <li>Moved selected ABCluster structures into the <code>abc</code> folders instead of having them separate.</li> </ul> Added <ul> <li>Methanol 20mers from Pires & Deturi (DOI: <a href="https://doi.org/10.1021/ct600348x">10.1021/ct600348x</a>) and Yao et al (DOI: <a href="https://doi.org/10.1063/1.4973380">10.1063/1.4973380</a>).</li> <li>Packmol generated structures of 30h2o, 112h2o, 140h2o, 30mecn, 39mecn, 30meoh, 50meoh, and 62meoh.</li> <li>ORCA MP2/def2-TZVP energy+gradient calculation of Yoo et al. boat-b 16mer.</li> <li>Methanol 4-6mer minima from Boyd et al. (DOI: <a href="https://doi.org/10.1021/ct6002912">10.1021/ct6002912</a>) with MP2/def2-TZVP engrads.</li> <li>Acetonitrile 4-6mer minima from Malloum et al. (DOI: <a href="https://doi.org/10.1002/qua.26222">10.1002/qua.26222</a>) with MP2/def2-TZVP engrads.</li> <li>Water 16mer minima from Yoo et al. (DOI: <a href="https://doi.org/10.1021/jz101245s">10.1021/jz101245s</a>) with RI-MP2/def2-TZVP engrad.</li> <li>Water 4-6mer minima from Temelso et al. (DOI: <a href="https://doi.org/10.1021/jp2069489">10.1021/jp2069489</a>) with MP2/def2-TZVP engrads.</li> <li>20 Angstrom box of water from GROMACS solvate.</li> </ul> Removed <ul> <li>Moved 12h2o.su.etal monomers, dimers, and trimers to <a href="https://github.com/keithgroup/mbgdml-h2o-meoh-mecn-engrads">another repository</a>.</li> </ul>
BAS-PRO solutions J81, SA19 and S16
<p><strong>Overview</strong></p> <p>This set of directories contains three solutions from the BAS-PRO proton radiation belt model. These solutions are saved as .cdf files, making available unidirectional, differential proton flux j as a function of time, first invariant mu, second invariant K and third invariant L or phi. The value of the distribution function f is also available (see below format information).</p> <p>The time period modelled is from 1st March 2014, to 1st February 2018.</p> <p>The three solutions correspond to different radial diffusion coefficients ('DLL') like so:<br> <br> - solution 20220203_220342 corresponds to the DLL from Jentsch, 1981 (Equation 18, Selesnick et al., 2007)<br> - solution 20220203_220420 corresponds to the post-1st Jan 2015 DLL from Equation 5, Selesnick & Albert, 2019 (SA19)<br> - solution 20220203_220436 corresponds to the DLL from Equation 12, Selesnick et al., 2016 (S16)</p> <p>There are two .cdf files for each solution: one file is a high resolution static solution, corresponding to 1st February 2018, which is the final output of each model run; the second (larger) file is a lower resolution dynamic (time-dependent) solution, corresponding to the ~four year period above. Both solutions were generated from a high resolution model run, but the dynamic solution was output at a lower resolution to save disk space.</p> <p>These solutions can be considered updates to the work shown in Lozinski et al., 2021 (<a href="https://doi.org/10.1029/2021JA029777">https://doi.org/10.1029/2021JA029777</a>)</p> <p>A set of Python scripts to create plots of the data are available at <a href="https://github.com/AlexisNaN/BAS-PRO_plotting">https://github.com/AlexisNaN/BAS-PRO_plotting</a>. Example plots of each dynamic solution are included in the attached data, produced using this set of Python scripts.</p> <p><strong>Format of the dynamic solution cdf files (i.e. 20220203_220420_solution_dyn.cdf):</strong></p> <p> axis_mu<br> shape: (482,)<br> units: log10(mu/ (1 MeV/G) )</p> <p> axis_K<br> shape: (35,)<br> units: G^0.5 RE</p> <p> axis_L<br> shape: (34,)</p> <p> axis_t<br> shape: (206,)<br> units: seconds since Jan 01 1970 (UTC)</p> <p> axis_t_date<br> shape: (206,)<br> units: datetime CDF object</p> <p> map_KL-aeq<br> shape: (34, 35)<br> units: degrees</p> <p> axis_phi<br> shape: (34,)<br> units: T m2</p> <p> f<br> shape: (206, 482, 35, 34)<br> units: km-6 s3<br> desc: distribution function: relativistic phase space density multiplied by proton rest mass cubed</p> <p> energy<br> shape: (1, 482, 35, 34)<br> units: MeV<br> desc: energy at each data coordinate, assumed constant in time (ignoring secular variation)</p> <p> j<br> shape: (206, 482, 35, 34)<br> units: cm-2 s-1 str-1 MeV-1<br> desc: unidirectional differential proton flux</p> <p> </p> <p><strong>Format of the static solution cdf files (i.e. 20220203_220420_solution.cdf):</strong></p> <p> axis_mu<br> shape: (700,)<br> units: log10(mu/ (1 MeV/G) )</p> <p> axis_K<br> shape: (35,)<br> units: G^0.5 RE</p> <p> axis_L<br> shape: (171,)</p> <p> axis_t<br> shape: (1,)<br> units: seconds since Jan 01 1970 (UTC)</p> <p> axis_t_date<br> shape: (1,)<br> units: datetime CDF object</p> <p> map_KL-aeq<br> shape: (171, 35)<br> units: degrees</p> <p> axis_phi<br> shape: (171,)<br> units: T m2</p> <p> f<br> shape: (1, 700, 35, 171)<br> units: km-6 s3<br> desc: distribution function: relativistic phase space density multiplied by proton rest mass cubed</p> <p> energy<br> shape: (1, 700, 35, 171)<br> units: MeV<br> desc: energy at each data coordinate, assumed constant in time (ignoring secular variation)</p> <p> j<br> shape: (1, 700, 35, 171)<br> units: cm-2 s-1 str-1 MeV-1<br> desc: unidirectional differential proton flux</p> <p> </p> <p> </p>
Analysis of particles size distributions in Mg(OH)2 precipitation from highly concentrated MgCl2 solutions
<p>Magnesium is a raw material of great importance, which attracted increasing interest in the last years. A promising<br> route is to recover magnesium in the form of Magnesium Hydroxide via precipitation from highly concentrated<br> Mg2+ resources, e.g. industrial or natural brines and bitterns. Several production methods and<br> characterization procedures have been presented in the literature reporting a broad variety of Mg(OH)2<br> particle sizes. In the present work, a detailed experimental investigation is aiming to shed light on the<br> characteristics of produced Mg(OH)2 particles and their dependence upon the reacting conditions. To this<br> purpose, two T-shaped mixers were employed to tune and control the degree of homogenization of reactants.<br> Particles were analysed by laser static light scattering with and without an anti-agglomerant treatment based<br> on ultrasounds and addition of a dispersant. Zeta potential measurements were also carried out to further assess<br> Mg(OH)2 suspension stability.</p>
Exact solution and Majorana zero mode generation on a Kitaev chain composed out of noisy qubits
<p>Attached are the data sets in forms of python pickle files from the following submission https://arxiv.org/abs/2108.07235</p> <p>Abstract:</p> <p>Majorana zero modes were predicted to exist as edge states of a physical system called the Kitaev chain. Such zero modes should host particles that are their own antiparticles and could be used as a basis for a qubit that is to large extent immune to noise - the topological qubit. However, all attempts to prove their existence gave inconclusive results. Here, I experimentally show that Majorana zero modes do in fact exist on a Kitaev chain composed out of 3 noisy qubits on a publicly available quantum computer. The signature of Majorana zero modes is a degeneracy with the ground state which is not lifted by noise of the quantum computer. I also confirm that Majorana zero modes have a number of theoretically predicted features: a well-defined parity with switches at specific points and a non-conserved particle number. Furthermore, I show that Majorana zero modes favour long-range Majorana pairing at low chemical potential and short-range pairing at large values of the chemical potential. The results presented here are a most comprehensive set of validations ever conducted towards confirming the existence of Majorana zero modes in nature. I foresee that the findings presented here would allow any user with an internet connection to perform experiments with Majorana zero modes. Furthermore, the noisy intermediate scale quantum computing community can start building topological processors composed out of contemporary noisy qubits.</p>
Supplementary data for Atomistic Mechanism of the Nucleation of Methylammonium Lead Iodide Perovskite from Solution
<p>Supplementary data for "Atomistic Mechanism of the Nucleation of Methylammonium Lead Iodide Perovskite from Solution"</p>
Dataset of the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"
<p>This archive contains the artifacts (datasets) for the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"</p>
Contact tracing solutions for COVID-19: applications, data privacy and security :: Suplementary Material
<p>Supplementary Material for the paper "Contact tracing solutions for COVID-19: applications, data privacy and security"</p>
FORECASTING MOLECULAR DYNAMICS SIMULATIONS OF POLYMER-LIPIDS IN SOLUTION WITH RNNs
<p>Files and scripts pertaining to our work: </p> <ul> <li>GROMACS files for the topology (DSPE+PEG.top) and the initial structure of the aggregate (DSPE+PEG_EA_NPT.gro)</li> <li>GROMACS topology file for the ethyl acetate molecule: EA_SI.top</li> <li>Scripts to submit the <em>GROMACS</em> utilities for calculation of the interaction energies are described in README.txt (Subset_energy.sh , Interaction_energies.sh)</li> <li>Scripts pertaining to <em>PyTorch</em> use and access of methods are described in README.txt (Multiple-run.sh. Job.sh, Pytorch_train-model.py)</li> <li>Scripts pertaining to <em>scikit learn </em>access for the Expectation Maximization clustering are described in the README.txt (Job_EM.sh, EM_Clustering.py)</li> <li>Files with the time series of the potential energy (PE) and interaction energy (IE) of the DSPE-PEG aggregate with the ethyl acetate solvent. Series contain 500,000 snapshots taken every 10 fs along the NVT Molecular Dynamics trajectory at 300 K and 906.3 kg/m<sup>3</sup> density. The molecular solution is in a cubic box of edge length 13.76 nm, containing 16,000 ethyl acetate molecules and one aggregate of 4 DSPE-PEG-amide macromolecules (224,000 atoms): Data_Andrews_etal_DSPE-PEG_2022.zip</li> <li>ArXiv preprint: https://doi.org/10.48550/arXiv.2203.00151 (JAndrews_etal_arXiv-doi.pdf)</li> </ul>
A Comprehensive Solution for Securing Connected and Autonomous Vehicles (presentation video)
<p>Video recording of the online presentation for the publication M. Kamal et al., "A Comprehensive Solution for Securing Connected and Autonomous Vehicles," 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022, pp. 790-795, doi: 10.23919/DATE54114.2022.9774594.</p>
Dataset used in "Exploring Scientific Discourse on Marine Litter in Europe: Review of Sources, Causes and Solutions"
<p>Marine litter is a transboundary environmental issue that affects all the world’s oceans. Marine litter research is a young discipline but one that has exploded during the last five years. However, the increased knowledge of sources and underlying causes to marine litter, as well as knowledge regarding solutions, lack systematic review and synthesis. This study reviews the scientific discourses around plastic marine litter in Europe, and more specifically, in Norway and Denmark, and explores emerging discourse coalitions. Four main thematic storylines on the source-cause-solution causal relationship, as well as two emerging storylines within marine litter research, are found. This study concludes that in order to secure sustainability of solutions and to avoid risk transformation and greenwashing, more interdisciplinary research, including life cycle assessment, is needed.</p> <p>The data set contains three elements:</p> <p>*Full sample* contains all data (both excluded and included articles. Coloums can be sortet and filtered to focus on specific topics. Analysis concept based on PRISMA</p> <p>*Timeline* harvest relevant data from 'full sample' for table 4 in the article. Cross year topic-counts are used for figure 3 in the article.</p> <p>*Storyline-connections* harvest relevant data from 'full sample' for figure 3 in the article</p>
Characterisation of the conformations of amyloid beta42 in solution that may mediate its initial hydrophobic aggregation
<p>General: This is a data repository comprising all the bias-exchange metadynamics simulation files performed to characterise the structural ensemble of Amyloid-ß 42 peptide in solution.<br> The simulation was performed as an explicit model however the solvent was removed while uploading the trajectory to reduce file size.</p> <p>Files:<br> 1. XTCs : Cat19r0tp.xtc, Cat19r1tp.xtc, Cat19r2tp.xtc<br> 2. TPR : 19-mdrun0.tpr, 19-mdrun1.tpr,19-mdrun2.tpr<br> 3. HILLS and COLVAR files <br> 4. Others: Input.gro, PLumed input file, and topol.top</p> <p>for any further information<br> 1. Krushna Sonar : k.sonar@postgrad.curtin.edu.au<br> 2. Ricardo L. Mancera: <a href="mailto:R.Mancera@curtin.edu.au">R.Mancera@curtin.edu.a</a>u</p>
Space of Optimal Solutions of the Correlation Clustering Problem for Complete Signed Graphs
<p><strong>Description. </strong>This is the data used in the experiments of the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs,” <em>Journal of Complex Networks </em>8(6):cnaa025, 2020. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02994011">hal-02994011</a>⟩</li> </ul> <p>This dataset contains:</p> <ul> <li>Plot files used in the article;</li> <li>Input signed networks;</li> <li>All optimal solutions (i.e. optimal solution space) of the corresponding networks;</li> <li>Evaluation files.</li> </ul> <p><strong>Source code. </strong>The code source is accessible on GitHub: <a href="https://github.com/CompNet/Sosocc">https://github.com/CompNet/Sosocc</a></p> <p><strong>Citation. </strong>If you use the data or source code, please cite the above article.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs},</code><br><code> journal = {Journal of Complex Networks},</code><br><code> year = {2020},</code><br><code> volume = {8},</code><br><code> number = {6},</code><br><code> pages = {cnaa025},</code><br><code> doi = {10.1093/comnet/cnaa025},</code><br><code>}</code><br><br></p> <p>--------------------------------------------</p> <p><strong>Details.</strong></p> <p><br><strong># PLOT FILES</strong><br>* `<em>Figure1.zip</em>`: Figures showing that there might be many distinct optimal solutions of a small-sized network.<br>* `<em>Figure2.zip</em>`: Figures showing that distinct optimal solutions of a given network might be partition-wise very similar or different.<br>* `<em>Figure4: All Results.zip</em>`: Figure 4 in the article contains only a few plots regarding the results for space considerations. This zip file contains all plots, and it is organized by the values of `<em>l<sub>0</sub></em>`. In each `<em>l<sub>0</sub></em>` folder, the results are shown in three different perspectives:<br>--- Detected Imbalance Percentage vs Graph Order (i.e. number of vertices)<br>--- Prop mispl vs Graph order<br>--- Graph order vs Prop mispl<br>* `<em>workflow.pdf</em>`: The workflow of the methodology used in the article.<br>* `<em>Syrian network With All Solutions.pdf</em>`: Syrian network (on top) with core part information through node colors, and its optimal solutions in which node colors represent partition information (on bottom).<br> </p> <p><strong>#NETWORKS</strong><br>All networks are in `<em>Input Signed Networks.tar.gz</em>`.<br>Networks are generated through a simple random model (available in <em>https://github.com/CompNet/SignedBenchmark</em>) designed to produce complete (or uncomplete) unweighted networks with built-in modular structure.<br>There are 3 parameters used for the generation:</p> <ol> <li>number of nodes (`<em>n</em>`)</li> <li>initial number of modules (`<em>l<sub>0</sub></em>`)</li> <li>proportion of misplaced links, i.e. proportion of frustrated links, (`<em>q<sub>m</sub></em>`)</li> </ol> <p>Inside `<em>Input Signed Networks.tar.gz</em>`:<br>NETWORKS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>The number of nodes, the initial number of modules and the network density are given. The network density is always 1, since we treat only complete signed networks.<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>Proportion of misplaced links is given.<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>Proportion of negative links (`<em>q<sub>n</sub></em>`) is specified. `<em>q<sub>n</sub></em>` changes depending on `<em>n</em>` and `<em>l<sub>0</sub></em>`. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters.<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>Network numbers are shown.<br>In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format.<br><br><strong># PARTITIONS</strong><br>All partition results are in `<em>Partition Results.tar.gz</em>`. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: <em>https://github.com/arinik9/ExCC</em><br>Inside `<em>Partition Results.tar.gz</em>`:<br><br>PARTITIONS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br>................|__"<em>ExCC-all</em>"<br>....................|__"<em>signed-unweighted</em>"<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>- The fifth hierarchy => the folders are named as follows: "<em>ExCC-all</em>"<br>The name of the partitioning method are shown. Since an exact partitioning method is used to obtain all distinct optimal solutions, it is named as "<em>ExCC-all</em>".<br>- The sixth hierarchy => the folders are named as follows: "<em>signed-unweighted</em>"<br>The type of signed networks are shown: signed and unweighted</p> <p>In the end, the partition results are located, and the file names are named as follows: <em>membership.txt</em>. Note that the first partition result number starts from zero.</p> <p> </p> <p><strong># EVALUATIONS</strong><br>Evaluation results related to our plots are in `<em>Evaluation Results.tar.gz</em>. Note that the hierarchy of this folder is the same as that of 'Partitions'. Inside `<em>Evaluation</em><em> Results.tar.gz</em>`:</p> <p>- `Best-k-for-kmedoids.csv`: It contains three columns. 1) the number of solution classes via kmedoids, 2) the best Silhouette score, 3) the best clustering in terms of Silhouette score, which represents solution classes.</p> <p>- `class-core-part-size-tresh=1.00.csv`. It indicates the proportion of core part size for each solution class.</p> <p>- `exec-time.csv`: It indicates the execution time in seconds.</p> <p>- `imbalance.csv`: It contains the information of imbalance as 1) count and 2) percentage</p> <p>- `nb-solution.csv`: It indicates the total number of solutions<br>--------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
Microclimate simulation output: "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens"
<p>The following microclimate simulation dataset supports the paper "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens" by Mathias Schaefer, published in Urban Ecosystems (2022).</p> <p>"T0Simulation_11082020_output" contains data about the status quo simulation of the area of interest (500 m x 500 m x 60 m), whereas "T1Simulation_11082020_output" shows the results of the Green Infrastructure scenario described in the research article above. Please ensure enough memory space on your device, as both files have a size of approximately 25 GB (unzipped).</p> <p>The output files can be visualized with the ENVI-met Leonardo extension. The ENVI-met LITE-version is freely available and can be downloaded at the <a href="https://envi-met.info/doku.php?id=files:download">ENVI-met homepage</a>. Alternatively, the included .NETCDF files can be imported as a multidimensional raster dataset in ArcGIS Pro.</p> <p>Files in the folder "atmosphere" represent meteorological parameters such as potential air temperature [°C], relative humidity [%], or wind speed [m/s]. Air pollution calculations like particulate matter concentrations [µg/m³] can be found in the folder "pollutants". The folder "buildings" contains building data for 3D visualizations of surface temperatures [°C].</p>
Supplementary Material for Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study
<p>This repository delivers the supplementary material for the paper: <em>Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study.</em></p> <p>In the following, we present the abstract of the study:</p> <p><strong>Context:</strong> Agile Software Development (ASD) is a disruptive process compared to traditional software development. Therefore, traditional Requirements Engineering (RE) forms may not be the best way to do RE for ASD (RE-ASD). <strong>Objective:</strong> Working with ASD using traditional RE ways could limit ASD's potential. Thus, it is necessary to investigate what academia and industry have done in RE to take full advantage of all of the capabilities of ASD beyond traditional RE. <strong>Method: </strong>We conducted a Tertiary Study looking for solutions for RE-ASD using the Systematic Literature Review (SLR) protocol described by Kitchenham and Charters. We then categorized the solutions into families using Targeted Coding and Constant Comparison, tools from Socio-Technical Grounded Theory (STGT). Afterward, we classified the solutions as disruptive using our model based on the Hype Level Curve concept, assessing their hype (popularity) in the software engineering community using Google Trends and Google Colab tools. <strong>Results:</strong> After executing the SLR protocol, we accepted 37 studies and encountered 136 solutions used by academia and industry for RE-ASD. We categorized these solutions into 21 solution families, six of which we classified as disruptive. Design Thinking (DT) and Artificial Intelligence (AI) were the two families of solutions that stood out the most. We also identified the type of solution (e.g., process, method, technique, tool, model, framework) and domain (academia or industry). Furthermore, we cataloged the challenges presented by the solutions. <strong>Conclusion:</strong> We concluded that only a few solutions that have been used for RE-ASD have the power to successfully challenge the mainstream Agile Software Development process by using innovation (26 out of 106). There is a gap between academia and industry regarding these disruptive solutions, and some challenges still need to be addressed in using these solutions.</p> <p>The repository contains the following:</p> <ul> <li>Dataset from the Tertiary Study: <ul> <li>Data of the retrieved studies. It presents the classifications of the documents as 'Accepted,' 'Rejected' (with the indication of the step of the protocol the authors rejected the study), or 'Duplicated.'</li> <li>Data of all solutions retrieved from the accepted studies</li> </ul> </li> <li>Socio-Technical Grounded Theory (STGT) tools <ul> <li>Result of the use of Targeted Coding and Constant Comparison</li> </ul> </li> <li>The Google Colab Notebook <ul> <li>Code in python</li> <li>Results</li> </ul> </li> </ul> <p> </p>
1H Hyperpolarization of Solutions by Overhauser Dynamic Nuclear Polarization with 13C-1H Polarization Transfer
<p>The dataset here contains the raw data used for the publication 10.1021/acs.jpclett.2c01956</p> <p>There are two folders and one readme file. NMR data in JCAMP or Topspin are in these two folders and are organised according to the figure or table mentioned in the publication. For details, please refer to the readme file. </p>
Supplementary material and supplementary data files for: Handling logical character dependency in phylogenetic inference: Extensive performance testing of assumptions and solutions using simulated and empirical data
<p>Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological datasets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in datasets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become "inapplicable" across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological datasets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded dataset on palaeognathid birds. We find that in small, simulated datasets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger datasets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a dataset, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space.</p>
Focal mechanism solutions and relocated earthquake catalog for the Charlevoix Seismic Zone (CSZ)
<p>The relocated catalog for the CSZ (Relocated_earthquakes_CSZ.dat) represent a combination of the catalogs from Yu et al. (2016, BSSA) and Onwuemeka et al. (2018, GRL). The focal mechanism solutions catalog (FMS_CSZ.txt) includes original data combined with the solutions from Mazzotti and Townend (2010, Lithosphere). </p>
QENS spectra of myoglobin in solution to be used with the analysis codes deposited under 10.5281/zenodo.7058345
<p>Quasielastic Neutron Scattering spectra of myoglobin in solution recorded on the IN5 spectrometer at the Institut Laue-Langevin in Grenoble, France. The data sets are to be used with the analysis codes deposited under DOI:10.5281/zenodo.7058345, which are in turn related to the publication A. Hassani, A. M. Stadler, and G.R. Kneller, Quasi-analytical resolution-correction of elastic neutron scattering from proteins, to appear in the Journal of Chemical Physics (DOI:10.1063/5.0103960). </p> <p>The data can be freely used, citing properly the reference concerning the original data, A. M. Stadler, F. Demmel, J. Ollivier, and T. Seydel, Picosecond to Nanosecond Dynamics Provide a Source of Conformational Entropy for Protein Folding. Phys. Chem. Chem. Phys., 18(31):21527–21538, 2016 (DOI: 10.1039/c6cp04146a).</p> <p> </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.