GameOfLife Prediction Dataset by David Towers (12857447)

“…Excluding 0, the lower numbers also get increasingly unlikely, though more likely than higher numbers, we wanted to prevent gaps and therefore limited to 25 contiguous classes</p><p dir="ltr">NumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. …”

Code by Baoqiang Chen (21099509)

“…We utilized the cliff’s delta function from the effsize package to compute Cliff's delta for each feature. …”

Core data by Baoqiang Chen (21099509)

“…We utilized the cliff’s delta function from the effsize package to compute Cliff's delta for each feature. …”

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images by Shuyu Li (18401358)

“…Additionally, the acquired MRI scans were converted from DICOM to the Neuroimaging Informatics Technology Initiative (NIfTI) format and organized in accordance with the Brain Imaging Data Structure (BIDS) format by employing the BIDScoin Python application (version 4.3.0) and stored in <i>rawdata_BIDS</i> directory.…”

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

“…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata from Step 1</li><li><code>perception_module/trained_models/</code> - Pre-trained models</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python -m perception_module.pred \<br> --model-weights .…”

Polygon vector map distortion for increasing the readability of one-to-many flow maps: data and codes by Laetitia Viau (14057463)

“…This directory contains: </p> <p>- README file,</p> <p>- server.py: can be run from the command line to create a local server (`python3 server.py or python server.py`). …”

UMAHand: Hand Activity Dataset (Universidad de Málaga) by Eduardo Casilari (3333906)

“…</p><p dir="ltr">Finally, the SCRIPTS subfolder comprises two scripts written in Python and Matlab. These two programs (named Load_traces), which perform the same function, are designed to automate the downloading and processing of the data. …”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.</p><p dir="ltr">The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 2.ipynb)</p><h4><b>Research Activity Identifier (RAiD)</b></h4><p dir="ltr">RAiD: https://doi.org/10.26292/8679d473</p><h4><b>Case Studies</b></h4><p dir="ltr">This repository contains code and sample data for the following case studies. …”

PresQT - Services to Improve Re-use and FAIRness of Research Data and Software by Sandra Gesing (4501198)

“…PresQT services are easily integratable and target systems can be added via extending JSON files and Python functions. Data is packaged as BagITs for uploads, downloads and transfers. …”

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)

“…We first computed a grid of co-registration point using arosics.COREG_LOCAL function and used the highest reliability point position to globally align to the closest date orthomosaics. …”

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.…”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”