Behavioural machine activity for benign and malicious Win7 64-bit executables حسب Peter Burnap (19078697)

"…</li><li>The virtual machine used 2GB RAM, 25 GB storage, and a single CPU core running 64-bit Windows 7.</li></ul><p><br></p><p><strong>Dataset 2:</strong></p><ul><li>filename = "data_2.csv"</li><li>2345 benign samples </li><li>2286 malicious samples</li><li>Up to 20 seconds execution per file</li><li>The data was collected in a VirtualBox[1] virtual machine using Cuckoo Sandbox[2] with a custom package written in the python library, Psutil[4] to collect the machine activity data. …"

Performance. حسب Sophie Theis (4126453)

Auxiliary and validation data for SAGEA-fluid حسب Weihang Zhang (17626761)

"…Building upon the core framework and data post-processing capabilities of SAGEA, SAGEA-fluid supports the integration of multi-source surface fluid datasets for geophysical effect estimation. …"

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

"…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. 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. …"

Data and code for: Automatic fish scale analysis حسب Christian Vogelmann (21646472)

"…</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. …"

Dataset for: Phylotranscriptomics reveals the phylogeny of Asparagales and the evolution of allium flavor biosynthesis, Nature Communications，DOI：10.1038/s41467-024-53943-6 حسب Xiao-Xiao Wang (2447920)

"…</p><p dir="ltr">For paired-end reads:</p><p dir="ltr"><i>python2 filter_fq.py taxonID_1.fq.gz taxonID_2.fq.gz Magnoliophyta both num_cores output_dir clean</i></p><p dir="ltr">For single-end reads:</p><p dir="ltr"><i>python2 filter_fq.py taxonID_1.fq.gz Magnoliophyta both num_cores output_dir clean</i></p><p><br></p><p dir="ltr"><b>Step 2: Transcriptome assembly using Trinity (https://github.com/trinityrnaseq/trinityrnaseq)</b></p><p dir="ltr"><i>python2 trinity_wrapper.py taxonID_1.overep_filtered.fq.gz taxonID_2.overep_filtered.fq.gz taxonID num_cores max_memory_GB stranded output_dir</i></p><p><br></p><p dir="ltr"><b>Step 3: Get the longest transcript in each gene from the Trinity assembly and translate transcripts to CDS and PEP sequences</b></p><p dir="ltr">Execute the script <i>get_longest_isoform_seq_per_trinity_gene.pl</i> to get the longest transcripts.…"

Data from: Dairy cows inoculated with highly pathogenic avian influenza virus H5N1 حسب AMY BAKER (17797757)

"…To analyze the Illumina short read data for 82 samples collected during the experimental challenge, we used the “Flumina” pipeline (https://github.com/flu-crew/Flumina) for processing and analyzing influenza data. The pipeline uses Python v3.10, R v4.4 (R Development Core Team 2024), and SnakeMake to organize programs and script execution. …"

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

"…Technical Architecture Overview Computational Environment Specifications Our experimental infrastructure leverages a heterogeneous multi-node computational topology encompassing three distinct hardware abstraction layers: Node Configuration Alpha (Intel-NVIDIA Heterogeneous Architecture) Processor: Intel Core i7-12700K (Alder Lake microarchitecture) - 12-core hybrid architecture (8 P-cores + 4 E-cores) - Base frequency: 3.6 GHz, Max turbo: 5.0 GHz - Cache hierarchy: 32KB L1I + 48KB L1D per P-core, 12MB L3 shared - Instruction set extensions: AVX2, AVX-512, SSE4.2 - Thermal design power: 125W (PL1), 190W (PL2) Memory Subsystem: 32GB DDR4-3200 JEDEC-compliant DIMM - Dual-channel configuration, ECC-disabled - Memory controller integrated within CPU die - Peak theoretical bandwidth: 51.2 GB/s GPU Accelerator: NVIDIA GeForce RTX 3070 (GA104 silicon) - CUDA compute capability: 8.6 - RT cores: 46 (2nd gen), Tensor cores: 184 (3rd gen) - Memory: 8GB GDDR6 @ 448 GB/s bandwidth - PCIe 4.0 x16 interface with GPU Direct RDMA support Node Configuration Beta (AMD Zen3+ Architecture) Processor: AMD Ryzen 7 5800X (Zen 3 microarchitecture) - 8-core monolithic design, simultaneous multithreading enabled - Base frequency: 3.8 GHz, Max boost: 4.7 GHz - Cache hierarchy: 32KB L1I + 32KB L1D per core, 32MB L3 shared - Infinity Fabric interconnect @ 1800 MHz - Thermal design power: 105W Memory Subsystem: 16GB DDR4-3600 overclocked configuration - Dual-channel with optimized subtimings (CL16-19-19-39) - Memory controller frequency: 1800 MHz (1:1 FCLK ratio) GPU Accelerator: NVIDIA GeForce GTX 1660 (TU116 silicon) - CUDA compute capability: 7.5 - Memory: 6GB GDDR5 @ 192 GB/s bandwidth - Turing shader architecture without RT/Tensor cores Node Configuration Gamma (Intel Raptor Lake High-Performance) Processor: Intel Core i9-13900K (Raptor Lake microarchitecture) - 24-core hybrid topology (8 P-cores + 16 E-cores) - P-core frequency: 3.0 GHz base, 5.8 GHz max turbo - E-core frequency: 2.2 GHz base, 4.3 GHz max turbo - Cache hierarchy: 36MB L3 shared, Intel Smart Cache technology - Thermal velocity boost with thermal monitoring Memory Subsystem: 64GB DDR5-5600 high-bandwidth configuration - Quad-channel topology with advanced error correction - Peak theoretical bandwidth: 89.6 GB/s GPU Accelerator: NVIDIA GeForce RTX 4080 (AD103 silicon) - Ada Lovelace architecture, CUDA compute capability: 8.9 - RT cores: 76 (3rd gen), Tensor cores: 304 (4th gen) - Memory: 16GB GDDR6X @ 716.8 GB/s bandwidth - PCIe 4.0 x16 with NVLink-ready topology Instrumentation and Telemetry Framework Power Consumption Monitoring Infrastructure Our energy profiling subsystem employs a multi-layered approach to capture granular power consumption metrics across the entire computational stack: Hardware Performance Counters (HPC): Intel RAPL (Running Average Power Limit) interface for CPU package power measurement with sub-millisecond resolution GPU Telemetry: NVIDIA Management Library (NVML) API for real-time GPU power draw monitoring via PCIe sideband signaling System-level PMU: Performance Monitoring Unit instrumentation leveraging MSR (Model Specific Register) access for architectural event sampling Network Interface Telemetry: SNMP-based monitoring of NIC power consumption during cloud upload/download phases Temporal Synchronization Protocol All measurement vectors utilize high-resolution performance counters (HPET) with nanosecond precision timestamps, synchronized via Network Time Protocol (NTP) to ensure temporal coherence across distributed measurement points. …"

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 حسب Andrew Rogers (17623239)

"…</p><h2>Software and Spatial Resolution</h2><p dir="ltr">The VRE siting model is implemented using Python and relies heavily on ArcGIS for comprehensive spatial data handling and analysis.…"

Phylogenomic analyses shed lights into the adaptation to aquatic environments in Alismatales حسب Ling-Yun Chen (2591623)

"…This process can be assisted by a script 'extract_raxml_infor_ln.py'</p><p dir="ltr">Execute python ln_counts.py. The command will show the results</p><p><br></p><p dir="ltr">5.5. …"