A Dataset on the Biodiversity Footprints and Sectoral Differences in China by haiyue wang (20803663)

“…It comprises the following components: (1) China’s raw species spatial data, sourced from the IUCN and BirdLife International, with 446 threatened and near-threatened species attributes provided in a CSV file. (2) Biodiversity footprint data of China's 19 economic sectors across 30 provinces (2017), with two versions: Version 1: 352 species (excluding NT species) Version 2: 446 species (including NT species) Stored as “2017 China Provincial Biodiversity Footprint Data” in Shapefile format. (3) Biodiversity footprint data by taxonomic group (Mammal, Amphibian, Reptile, and Bird) across 30 provinces (2017), with two versions as above, stored as “2017 China Taxonomic Biodiversity Footprint Data” in Shapefile format. (4) A procedural demonstration of matrix operations with detailed algorithmic steps for specific species, stored as “An example detailing the computational steps for specific species” in PDF format. (5) Code.…”

Collaborative Research: SCIPE: Interdisciplinary Research Support Community for Artificial Intelligence and Data Sciences by Sanmukh Kuppannagari (21798863)

“…<br><br>To achieve 10x-100x reduction in DNN energy consumption, a holistic approach is pursued, which encompasses: (1) new circuit designs that leverage emerging CMOS+X technologies; (2) a novel near-memory architecture in which processing elements are seamlessly integrated with traditional Dynamic RAM (DRAM); (3) novel 3D-matrix-based per-layer DNN computations and data-layout optimizations for kernel weights; and (4) algorithms and hardware/software co-design tailored for near-real-time DNN-based signal classification in next-generation wireless systems. …”

A Dataset on the Biodiversity Footprints and Sectoral Differences in China by Haiyue Wang (20698763)

“…<p dir="ltr">(1) China’s species data stored in the file “2017 China Species Spatial Data” in CSV format, spatial data sourced from the IUCN and BirdLife International.…”

Is There a Universal Dimensionality Reduction Technique for Feature Extraction? – A Comparative Analysis by Anandkumar Balasubramaniam (22474380)

“…These techniques aim to extract the most important information from high-dimensional data, reducing it to a lower-dimensional representation that can be easily processed by machine learning algorithms. However, with the availability of a multitude of dimensionality reduction techniques and heterogeneous datasets, it can be challenging for researchers to select the most appropriate one for their specific application. …”

<b>Multi-object detection method based on YOLOv9 for labor protection equipment</b><b> </b><b>wear</b><b>ing</b><b> </b><b>condition </b><b>of offshore platform operators</b> by Guo Xiaosai (21358637)

“…Aiming at the missed detection problem of small targets of labor protection equipment, SAHI algorithm is introduced to slice the input image into multiple pieces, and the very small labor protection targets has been detected by the slicing detection method. …”

DataSheet1_A novel dataset and deep learning object detection benchmark for grapevine pest surveillance.pdf by Giorgio Checola (20418239)

“…Assisted by entomologists, we performed the annotation process, trained, and compared the performance of two state-of-the-art object detection algorithms: YOLOv8 and Faster R-CNN. …”

Dataset for Partial Parallelism Plot Analysis in Neurodegeneration Biomarker Assays (2010–2024) by Axel Petzold (7076261)

“…<br></p><p dir="ltr">Each dataset entry is annotated with:</p><ul><li>Sample type (serum, plasma, cerebrospinal fluid)</li><li>Assay platform and dilution steps</li><li>Classification of outcome (partial parallelism achieved or not)</li></ul><p dir="ltr"><b>Use cases:</b><br>This dataset is designed to help researchers, assay developers, and meta-analysts to:</p><ul><li>Reproduce figures and analyses from the published review</li><li>Benchmark or validate new assay performance pipelines</li><li>Train algorithms for automated detection of dilutional non-parallelism</li></ul><p dir="ltr"><b>Files included:</b></p><ul><li><code>.csv</code> files containing dilution–response data</li><li>Metadata spreadsheets with assay and sample annotations</li></ul><p></p>…”

Both Ankle fNIRS MI dataset by Hammad Gilani (8060012)

“…</p><p dir="ltr">The dataset includes:</p><p><br></p><ul><li><b>Raw data:</b> Original <code>.nirs</code> files for each participant.…”

Right Knee fNIRS MI Dataset by Hammad Gilani (8060012)

“…</p><p dir="ltr">The dataset includes:</p><p><br></p><ul><li><b>Raw data:</b> Original <code>.nirs</code> files for each participant.…”

Right Ankle fNIRS MI Dataset by Hammad Gilani (8060012)

“…</p><p dir="ltr">The dataset includes:</p><p><br></p><ul><li><b>Raw data:</b> Original <code>.nirs</code> files for each participant.…”

Left Knee fNIRS MI Dataset by Hammad Gilani (8060012)

“…</p><p dir="ltr">The dataset includes:</p><p><br></p><ul><li><b>Raw data:</b> Original <code>.nirs</code> files for each participant.…”

Left Ankle fNIRS MI Dataset by Hammad Gilani (8060012)

“…</p><p dir="ltr">The dataset includes:</p><p><br></p><ul><li><b>Raw data:</b> Original <code>.nirs</code> files for each participant.…”

Both Knees fNIRS MI dataset by Hammad Gilani (8060012)

“…</p><p dir="ltr">The dataset includes:</p><p><br></p><ul><li><b>Raw data:</b> Original <code>.nirs</code> files for each participant.…”

RabbitSketch by tong zhang (20852432)

“…The similarity analysis of 455GB genomic data can be completed in about 5 minutes using RabbitSketch with Python code. …”

<b>The abundance of ground-level atmospheric ice-nucleating particles and aerosol properties </b><b>in the North Slope of Alaska</b> by Aidan Pantoya (19416546)

“…All Python codes used for processing input data and generating output data are paired with the readme files and archived in folders. …”

Landscape Change Monitoring System (LCMS) Puerto Rico USVI Annual QA Bits by U.S. Forest Service (17476914)

“…</p><p>Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). …”

Landscape Change Monitoring System (LCMS) Hawaii Annual Landcover by U.S. Forest Service (17476914)

“…</p><p>Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). …”

Landscape Change Monitoring System (LCMS) Hawaii Annual Landuse by U.S. Forest Service (17476914)

“…</p><p>Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). …”

Landscape Change Monitoring System (LCMS) Alaska Annual QA Bits by U.S. Forest Service (17476914)

“…</p><p>Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). …”

Landscape Change Monitoring System (LCMS) Puerto Rico USVI Annual Landcover by U.S. Forest Service (17476914)

“…</p><p>Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). …”