Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

Pyrho Validation - Check re-gridded periodic data by Jimmy Shen (12664675)

Illustration of PyNoetic’s 2D and 3D simulation module with visual feedback. by Gursimran Singh (575288)

BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

Multidomain, Automated Photopatterning of DNA-functionalized Hydrogels (MAPDH). by Moshe Rubanov (7289156)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

Fig 3 - by Bill Zhao (11530264)

Principal component analysis (PCA) of sub-layer mean intensities. by Maëlle Vilbert (17101954)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

S1 Graphical abstract - by José M. Rivera-Arbeláez (12418512)

Comparison against the commercial scanner. by Swathi Lakshmi Balasubramanian (12078093)

Sudoku Dataset by David Towers (12857447)

“…To round out the number of samples in the sets, we randomly selected class labels and generated a single sudoku grid of that label so that no label had more than 7,778 samples among the three sets.</p> <p>The labels for this dataset are the possible sudoku cell values (1, 2, 3, 4, 5, 6, 7, 8, and 9). …”

Mechanomics Code - JVT by Carlo Vittorio Cannistraci (5854046)

“…The functions were tested respectively in: MATLAB 2018a or youger, Python 3.9.4, R 4.0.3.…”

GameOfLife Prediction Dataset by David Towers (12857447)

“…Effectively for every cell we only need to look at the surrounding eight cells (3x3 square, minus the centre) which means all information for each cell can be found from a 3x3 Convolution, which is a very common kernel size to use. …”

Datasheet1_A Workflow for Rapid Unbiased Quantification of Fibrillar Feature Alignment in Biological Images.zip by Stefania Marcotti (5896853)

“…Using pre-existing datasets of cell and ECM images, we demonstrate our approach and compare and contrast this workflow with two other well-known ImageJ algorithms to quantify image feature alignment. …”

PySilsub—a toolbox for silent substitution by Joel Martin (11864048)

“…<p>A normal human retina contains several classes of photosensitive cell—rods for low-light vision, three types of cones for daylight vision, and the intrinsically photosensitive retinal ganglion cells (ipRGCs) expressing melanopsin for controlling non-image-forming functions (e.g., pupil size, circadian rhythms). …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><h2>Project Structure</h2><pre><pre>Perception_based_neighbourhoods/<br>├── raw_data/<br>│ ├── ET_cells_glasgow/ # Glasgow grid cells for analysis<br>│ └── glasgow_open_built/ # Built area boundaries<br>├── svi_module/ # Street View Image processing<br>│ ├── svi_data/<br>│ │ ├── svi_info.csv # Image metadata (output)<br>│ │ └── images/ # Downloaded images (output)<br>│ ├── get_svi_data.py # Download street view images<br>│ └── trueskill_score.py # Generate TrueSkill scores<br>├── perception_module/ # Perception prediction<br>│ ├── output_data/<br>│ │ └── glasgow_perception.nc # Perception scores (demo data)<br>│ ├── trained_models/ # Pre-trained models<br>│ ├── pred.py # Predict perceptions from images<br>│ └── readme.md # Training instructions<br>└── cluster_module/ # Neighbourhood clustering<br> ├── output_data/<br> │ └── clusters.shp # Final neighbourhood boundaries<br> └── cluster_perceptions.py # Clustering algorithm<br></pre></pre><h2>Prerequisites</h2><ul><li>Python 3.8 or higher</li><li>GDAL/OGR libraries (for geospatial processing)</li></ul><h2>Installation</h2><ol><li>Clone this repository:</li></ol><p dir="ltr">Download the zip file</p><pre><pre>cd perception_based_neighbourhoods<br></pre></pre><ol><li>Install required dependencies:</li></ol><pre><pre>pip install -r requirements.txt<br></pre></pre><p dir="ltr">Required libraries include:</p><ul><li>geopandas</li><li>pandas</li><li>numpy</li><li>xarray</li><li>scikit-learn</li><li>matplotlib</li><li>torch (PyTorch)</li><li>efficientnet-pytorch</li></ul><h2>Usage Guide</h2><h3>Step 1: Download Street View Images</h3><p dir="ltr">Download street view images based on the Glasgow grid sampling locations.…”

Barro Colorado Island 50-ha plot aerial photogrammetry orthomosaics and digital surface models for 2018-2023: Globally and locally aligned time series. by Vicente Vasquez (13550731)

“…Agisoft LLC. https://www.agisoft.com/pdf/metashape_python_api_2_0_4.pdf.</p><p dir="ltr">Vicente Vasquez. (2023). …”

Expression vs genomics for predicting dependencies by Broad DepMap (5514062)

“…</p><p dir="ltr"><br></p><p dir="ltr">PerturbationInfo.csv: Additional drug annotations for the PRISM and GDSC17 datasets</p><p dir="ltr"><br></p><p dir="ltr">ApproximateCFE.hdf5: A set of Cancer Functional Event cell features based on CCLE data, adapted from Iorio et al. 2016 (10.1016/j.cell.2016.06.017)</p><p dir="ltr"><br></p><p dir="ltr">DepMapSampleInfo.csv: sample info from DepMap_public_19Q4 data, reproduced here as a convenience.…”