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15 results for “Code of conduct”
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 4: Pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons
<p>In each time step, axonal conduction delays between presynaptic neurons and postsynaptic neurons are examined whether they are equal to the elements of array I_S, to apply the respective spikes.<br> The pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons are shown in Figure 4.</p>
Data and code for "Tuning the lattice thermal conductivity in van-der-Waals structures through rotational (dis)ordering"
<p>This record contains neuroevolution potential (NEP) models for C, BN, and MoS<sub>2</sub> that have been constructed to model the potential energy surfaces of these materials in the presence of interlayer rotations. It also contains databases with the results from density functional theory calculations that were used for constructing the NEP models.</p> <p><strong>Databases</strong><br> The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <p><strong>Models</strong><br> The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <p><strong>Primitive structures</strong><br> Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p>
Parent dataset and code from: Atomistic Mechanisms of the regulation of small conductance Ca 2+ -activated K + channel (SK2) by PIP2
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Code and data for "Machine-learning-boosted ab-initio study of the thermal conductivity of Janus PtSTe van der Waals heterostructures"
<h1>Code and data for <em>Machine-learning-boosted ab-initio study of the thermal conductivity of Janus PtSTe van der Waals heterostructures</em></h1> <p> </p> <h2>Contents:</h2> <ul> <li><strong>neuralil.tar.xz</strong>: version used in the manuscript of the force-field code described in the articles <a href="https://doi.org/10.1021/acs.jcim.1c01380">A Differentiable Neural-Network Force Field for Ionic Liquids</a> and <a href="https://doi.org/10.1063/5.0146905">Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning</a>. General-purpose releases can be found <a href="https://github.com/Madsen-s-research-group/neuralil-public-releases">here</a>.</li> <li><strong>DFT_data.tar.xz</strong>: first-principles data created for training and validating the force field, stored as <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE databases</a> in JSON format.</li> <li><strong>model_params_plain_ensemble_DEEP_413E12A9.pkl</strong>: saved parameters of the fully trained force field.</li> <li><strong>0001-Use-equipartition-occupancies.patch</strong>: patch for <a href="https://phonopy.github.io/phono3py">Phono3py</a> to use classical (equipartition) occupations instead of Bose-Einstein values.</li> </ul>
The source code for a new capillary and adsorption‒force model predicting hydraulic conductivity of soil during freeze‒thaw processes
<p>The source code is related to "A New Capillary and Adsorption‒Force Model Predicting Hydraulic Conductivity of Soil during Freeze‒thaw Processes" (Shufeng Qiao, Rui Ma, Yunquan Wang, Ziyong Sun, Helen Kristine French, Yanxin Wang)</p>
Code in support of: Physical and chemical mechanisms that influence the electrical conductivity of lignin-derived biochar
<p>Lignin-derived biochar is a promising, sustainable alternative to petroleum-based carbon powders (e.g., carbon black) for electrode and energy storage applications. Prior studies of these biochars demonstrate that high electrical conductivity and good capacitive behavior are achievable. These studies also show high variability in electrical conductivity between biochars (~10^-2-10^2 S/cm). The underlying mechanisms that lead to desirable electrical properties in these lignin-derived biochars are poorly understood. In this work, we examine the causes of the variation in conductivity of lignin-derived biochar to optimize the electrical conductivity of lignin-derived biochars. To this end, we produced biochar from three different lignins, a whole biomass source (wheat stem), and cellulose at two pyrolysis temperatures (900 C, 1100 C). These biochars have a similar range of conductivities (0.002 to 18.51 S/cm) to what has been reported in the literature. Results from examining the relationship between chemical and physical biochar properties and electrical conductivity indicate that decreases in oxygen content and changes in particle size are associated with increases in electrical conductivity. Lignin isolated with an acidification process yielded biochar with higher electrical conductivity than lignin isolated with sulfate processes. These findings indicate how lignin composition and processing may be further selected and optimized to target specific energy-related applications.</p>
Data and code for "Efficient calculation of the lattice thermal conductivity by atomistic simulations with ab-initio accuracy"
<p>This data set contains data and code related to the publication "Efficient calculation of the lattice thermal conductivity by atomistic simulations with ab-initio accuracy".</p>
Code in support of: Physical and chemical mechanisms that influence the electrical conductivity of lignin-derived biochar
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Software code for simulations and analyses concerning the calculation of canopy stomatal conductance at eddy covariance sites
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Figure 2 from: Evans N, Marusic A, Foeger N, Lofstrom E, van Hoof M, Vrijhoef-Welten S, Inguaggiato G, Dierickx K, Bouter L, Widdershoven G (2021) Virtue-based ethics and integrity of research: train-the-trainer programme for upholding the principles and practices of the European Code of Conduct for Research Integrity (VIRT2UE). Research Ideas and Outcomes 7: e68258. https://doi.org/10.3897/rio.7.e68258
Figure 2 Overview of the proposed Open platform for Blended learning. [Screenshot inset of Science Communicator, Derek Muller, of Veritasium Science Blog from the TED Talent Search You-Tube channel].
Figure 1 from: Evans N, Marusic A, Foeger N, Lofstrom E, van Hoof M, Vrijhoef-Welten S, Inguaggiato G, Dierickx K, Bouter L, Widdershoven G (2021) Virtue-based ethics and integrity of research: train-the-trainer programme for upholding the principles and practices of the European Code of Conduct for Research Integrity (VIRT2UE). Research Ideas and Outcomes 7: e68258. https://doi.org/10.3897/rio.7.e68258
Figure 1 Academic partners, advisory board and their associated networks. As shown, the networks that VIRT2UE PI's are involved in, together cover all European countries.
Figure 5 from: Evans N, Marusic A, Foeger N, Lofstrom E, van Hoof M, Vrijhoef-Welten S, Inguaggiato G, Dierickx K, Bouter L, Widdershoven G (2021) Virtue-based ethics and integrity of research: train-the-trainer programme for upholding the principles and practices of the European Code of Conduct for Research Integrity (VIRT2UE). Research Ideas and Outcomes 7: e68258. https://doi.org/10.3897/rio.7.e68258
Figure 5 Management structure.
Figure 4 from: Evans N, Marusic A, Foeger N, Lofstrom E, van Hoof M, Vrijhoef-Welten S, Inguaggiato G, Dierickx K, Bouter L, Widdershoven G (2021) Virtue-based ethics and integrity of research: train-the-trainer programme for upholding the principles and practices of the European Code of Conduct for Research Integrity (VIRT2UE). Research Ideas and Outcomes 7: e68258. https://doi.org/10.3897/rio.7.e68258
Figure 4 VIRT2UE Gantt Chart.
Figure 3 from: Evans N, Marusic A, Foeger N, Lofstrom E, van Hoof M, Vrijhoef-Welten S, Inguaggiato G, Dierickx K, Bouter L, Widdershoven G (2021) Virtue-based ethics and integrity of research: train-the-trainer programme for upholding the principles and practices of the European Code of Conduct for Research Integrity (VIRT2UE). Research Ideas and Outcomes 7: e68258. https://doi.org/10.3897/rio.7.e68258
Figure 3 PERT Description of the work packages.
Conducting Perioperative Code Status and Goals of Care Discussions: A Bi-Institutional Study to Develop a Novel, Evidence-Based Curriculum for Anesthesiology Trainees
ClinicalTrials.gov study NCT04045886. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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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.