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pre implementation » time implementation (Expand Search), _ implementation (Expand Search), new implementation (Expand Search)
python model » python tool (Expand Search), action model (Expand Search), motion model (Expand Search)
method pre » method age (Expand Search), method red (Expand Search), method see (Expand Search)
pre implementation » time implementation (Expand Search), _ implementation (Expand Search), new implementation (Expand Search)
python model » python tool (Expand Search), action model (Expand Search), motion model (Expand Search)
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2D Orthogonal Planes Split: <b>Python</b> and <b>MATLAB</b> code | <b>Source Images</b> for Figures
Published 2025“…The output files generated by the code include results from both Python and MATLAB implementations; these output images are provided as validation, demonstrating that both implementations produce matching results.…”
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EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit
Published 2025“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”
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The format of the electrode csv file
Published 2025“…To enable efficient calculation of extracellular signals from large neural network simulations, we have developed <i>BlueRecording</i>, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. …”
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The format of the simulation reports
Published 2025“…To enable efficient calculation of extracellular signals from large neural network simulations, we have developed <i>BlueRecording</i>, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. …”
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Comparison of BlueRecording with existing tools
Published 2025“…To enable efficient calculation of extracellular signals from large neural network simulations, we have developed <i>BlueRecording</i>, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. …”
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The format of the weights file
Published 2025“…To enable efficient calculation of extracellular signals from large neural network simulations, we have developed <i>BlueRecording</i>, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. …”
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Phases of implementation.
Published 2025“…<div><p>Objectives</p><p>This participatory, mixed methods study will explore how iHEAL, a woman-led, nurse-delivered health promotion intervention for women who have experienced intimate partner violence (IPV), can be implemented in real-world, community-based health care settings located in 3 Canadian provinces. …”
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ZILLNB_Model
Published 2025“…<p dir="ltr">Acquire latent variables using deep-learning based model implemented in python</p>…”
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Cost functions implemented in Neuroptimus.
Published 2024“…To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. …”
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Data and code for: Automatic fish scale analysis
Published 2025“…</p><h3>Includeed in this repository:</h3><ul><li><b>Raw data files:</b></li><li><code>comparison_all_scales.csv</code> – comparison_all_scales.csv - manually verified vs. automated measurements of 1095 coregonid scales</li></ul><ul><li><ul><li><code>Validation_data.csv</code> – manually measured scale data under binocular</li><li><code>Parameter_correction_numeric.csv</code> – calibration data (scale radius vs. fish length/weight)</li></ul></li><li><b>Statistical results:</b></li><li><ul><li><code>comparison_stats_core_variables.csv</code> – verification statistics (bias, relative error, limits of agreement)</li><li><code>Validation_statistics.csv</code> – summary statistics and model fits (manual vs. automated)</li></ul></li><li><b>Executable script (not GUI):</b></li><li><ul><li><code>Algorithm.py</code> – core processing module for scale feature extraction<br>→ <i>Note: The complete Coregon Analyzer application (incl. …”
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Data Sheet 1_Assessment of acute stroke care, stroke metrics and patient outcomes: analysis from the pre-implementation phase of the IMPETUS stroke study.pdf
Published 2025“…</p>Methods<p>IMPETUS stroke is a multicentric, prospective, multiphase, mixed-methods, quasi-experimental implementation study, comprising three phases. …”