MEG Dataset and Analysis Scripts for “The Effects of Task Similarity During Representation Learning in Brains and Neural Networks” by Nicholas Menghi (22426174)

“…</p><h3><b>Contents</b></h3><ul><li><b>MEG data</b> (results of the correlation between empirical and model matrices at different dimensionalities and domains)</li><li><b>Behavioral data</b> (behavioural accuracy performance: "Spatual Source Data")</li><li><b>Analysis script</b></li><li><b>Python package </b>developed to help with retrieving and computing simple operations</li></ul><h3><b>Data format</b></h3><p dir="ltr">Data are organized according to a structured folder layout (see <code>README.md</code> in the repository) and include:</p><ul><li><code>npy</code> MEG files (numpy)</li><li><code>.csv</code> behavioral files</li><li>Python scripts using MNE-Python for statistical analysis and visualization</li></ul><h3><b>Usage</b></h3><p dir="ltr">The provided scripts reproduce the statistical tests and figures presented in the manuscript. …”

dataset by Jingyi Chang (20404166)

“…<p dir="ltr">The R and Python code used to perform the analysis and generate the results and visualizations presented in the forest canopy height, and the related data and results produced in the research analyses.…”

Moulin distributions during 2016-2021 on the southwest Greenland Ice Sheet by Kang Yang (7323734)

“…</p><p><br></p><ul><li>00_Satellite-derived moulins: Moulins directly mapped from Sentinel-2 imagery, representing actual moulin positions;</li><li>01_Snapped moulins: Moulins snapped to DEM-modeled supraglacial drainage networks, primarily used for analyses;</li><li>02_Moulin recurrences: Recurring moulins determined from the snapped moulins;</li><li>03_Internally drained catchments: Internally drained catchment (IDC) associated with each moulin;</li><li>04_Surface meltwater runoff: surface meltwater runoff calculated from MAR for the study area, elevation bins, and IDCs; </li><li>05_DEM-derived: Topographic features modeled from ArcticDEM, including elevation bins, depressions and drainage networks;</li><li>06_GWR: Variables for conducting geographically weighted regression (GWR) analysis;</li></ul><p><br></p><ul><li>Code_01_Mapping moulins on the southwestern GrIS.ipynb: A Jupyter Notebook to analyze moulin distributions, reproducing most of the analyses and figures presented in the manuscript using the provided datasets;</li><li>Code_02_pre1_calculate Strain Rate from XY ice velocity.py: A preprocessing Python script to calculate strain rate for the GWR analysis;</li><li>Code_02_pre2_calculate Driving Stress from ice thickness and surface slope.py: A preprocessing Python script to calculate driving stress for the GWR analysis;</li><li>Code_02_GWR analysis.ipynb: A Jupyter Notebook to conduct the GWR analysis using the provided datasets.…”

CNG-ARCO-RADAR.pdf by Alfonso Ladino (21447002)

“…This approach uses a suite of Python libraries, including Xarray (Xarray-Datatree), Xradar, and Zarr, to implement a hierarchical tree-like data model. …”

M-SGWR model by M. Naser Lessani (17817475)

“…The repo contains all the necessary information, including the python code "M-SGWR", datasets and the instruction of how to reproduce the results presented in the article. …”

<b>Anthropogenic nutrient inputs cause excessive algal growth for nearly half the world’s population</b> by Richard McDowell (7311011)

“…</p><p dir="ltr">Models: R code to explore different models for implementation via Python in ArcGIS</p><p dir="ltr">!geotiffs: GeoTIFF raster files at level 6 of HydroBasins for current, zero human effect and the difference between current and zer human effect.…”

Supporting data for "Optimisation of Trust in Collaborative Human-Machine Intelligence in Construction" by Hao Chen (9696848)

“…The first folder contains Scopus-derived data alongside analytical results that substantiate the figures presented in Chapter 1. The second folder mirrors the structure of the first, encompassing Scopus data and Python source code used to generate the visualizations featured in Chapter 2. …”

Elements: Streaming Molecular Dynamics Simulation Trajectories for Direct Analysis – Applications to Sub-Picosecond Dynamics in Microsecond Simulations by Matthias Heyden (17087794)

“…This eliminates the need for intermediate storage and allows immediate access to high-frequency fluctuations and vibrational signatures that would otherwise be inaccessible. We have implemented this streaming interface in the MD engines NAMD, LAMMPS, and GROMACS</p><p dir="ltr">On the client side, we developed the IMDClient Python package which receives the streamed data, stores into a custom buffer, and provides it to external tools as NumPy arrays, facilitating integration with scientific computing workflows. …”

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

“…</p><p dir="ltr">The Python programming source code used to run the calculation of ET0 and AI is provided and available online on Figshare at:</p><p dir="ltr">https://figshare.com/articles/software/Global_Aridity_Index_and_Potential_Evapotranspiration_Climate_Database_v3_-_Algorithm_Code_Python_/20005589</p><p dir="ltr">Peer-Review Reference and Proper Citation:</p><p dir="ltr">Zomer, R.J.; Xu, J.; Trabuco, A. 2022. …”

NanoDB: Research Activity Data Management System by Lorenci Gjurgjaj (19702207)

“…In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …”

Attention and Cognitive Workload by Rui Varandas (11900993)

“…</p><p dir="ltr">The data for subject 2 do not include the 2nd part of the acquisition (python task) because the equipment stopped acquiring; subject 3 has the 1st (N-Back task and mental subtraction) and the 2nd part (python tutorial) together in the <code>First part</code> folder (file <code>D1_S3_PB_description.json</code> indicates the start and end of each task); subject 4 only has the mental subtraction task in the 1st part acquisition and in subject 8, the subtraction task data is included in the 2nd part acquisition, along with python task.…”

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…</p><p dir="ltr">GPU：NVIDIA GeForce RTX 3090 GPU</p><p dir="ltr">Bert-base-cased pre-trained model: https://huggingface.co/google-bert/bert-base-cased</p><p dir="ltr">python=3.7，pytorch=1.9.0，cudatoolkit=11.3.1，cudnn=8.9.7.29.…”

Global blue carbon losses from salt marshes exceed restoration gains by Yuhan Zheng (21610220)

“…<h4>This repository contains the main code used to generate the figures and results presented in the manuscript.…”

<b>Beyond absolute space: Modeling disease dispersion and reactive actions from a multi-spatialization perspective</b> by Shiran Zhong (14518376)

“…</p><h3>Running the Code</h3><p dir="ltr">· To run the Python code (preferably in Jupyter Notebook), ensure that all dependencies are installed by running: <i>pip install pandas pgmpy</i>. …”

Supplementary Materials for the article: ”Damped sectorial oscillations of an acoustically levitated droplet". by Taisiia Nagorskaia (22557095)

“…</li><li><b>Supplementary Video S3 (</b><i>S3_Video_Damped_oscillations.avi</i><b>):</b> High-speed footage of the damped oscillations used for the quantitative analysis presented in the paper.</li><li><b>Supplementary Code S4 (</b><i>S4_Code_Data_processing.ipynb</i><b>):</b> Python analysis code used for automated processing and for analyzing manually extracted data from the damped oscillations (S3).…”

Olson et al Muscle spindles provide flexible sensory feedback for movement sequences by William Olson (21703679)

“…</li></ul><h4><b>Code</b></h4><p dir="ltr">Accompanying Python scripts and Jupyter notebooks are included for:</p><ul><li>Data preprocessing and formatting.…”

The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network" by Chongshan Wan (19247614)

“…</li></ul><h2>Codes</h2><p dir="ltr">This repository contains the following Python codes：</p><ul><li>`data_processing.py`: Contains the implementation of data processing and feature extraction. …”

<b>Data Availability</b> by Bonyad Ahmadi (20750327)

“…</p><p dir="ltr">python scripts documenting the implementation of the Mixture Density Network (MDN) algorithm, including hyperparameter tuning and uncertainty quantification.…”

<b>Data Availability</b> by Bonyad Ahmadi (20750327)

“…</p><p dir="ltr">python scripts documenting the implementation of the Mixture Density Network (MDN) algorithm, including hyperparameter tuning and uncertainty quantification.…”

Artifact for the IJCAI 2024 paper "Solving Long-run Average Reward Robust MDPs via Stochastic Games" by Krishnendu Chatterjee (15367413)

“…<br></pre></pre><h2>Structure and How to run</h2><p dir="ltr">There are four Python files in the repository.</p><pre><pre>(i) `StrategyIteration.py` is the backend code, containing the implementation of the RPPI algorithm described in the paper.…”