Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Original song waveform transformed into a magnitude-only auditory spectrogram, then transformed back into a waveform using an iterative phase-estimation algorithm. by Ludovic Bellier (188942)

Analysis of the estimated concentration of the presence of ilmenite throughout the pixels of the scene. by Gabriel Prates Hallal (19555976)

Division of work phases. by Anees ul Husnain (18017648)

Data_Sheet_2_COVID-19 UK Lockdown Forecasts and R0.ZIP by Greg Dropkin (3472793)

Data_Sheet_1_COVID-19 UK Lockdown Forecasts and R0.PDF by Greg Dropkin (3472793)

Data_Sheet_2_Comprehensive analysis of the diagnostic and therapeutic value, immune infiltration, and drug treatment mechanisms of GTSE1 in lung adenocarcinoma.docx by Guanqiang Yan (18472116)

Data_Sheet_3_Comprehensive analysis of the diagnostic and therapeutic value, immune infiltration, and drug treatment mechanisms of GTSE1 in lung adenocarcinoma.docx by Guanqiang Yan (18472116)

Data_Sheet_1_Comprehensive analysis of the diagnostic and therapeutic value, immune infiltration, and drug treatment mechanisms of GTSE1 in lung adenocarcinoma.docx by Guanqiang Yan (18472116)

Schematic illustration of the data analysis. by Brigitta Tóth (6411491)

“…<p><b>A) EEG preprocessing</b>. Following primary filtering and ICA based artefact removal data was segmented. …”

Song reconstruction and methodological considerations. by Ludovic Bellier (188942)

Demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies by Antonis Michanikou (8996667)

“…The implementation of this GPS data filling algorithm allowed replacing the missing 5-minute intervals with estimated values. …”

Composite Burn Index (CBI) data and field photos collected for the FIRESEV project, western United States by Pamela G. Sikkink (19657060)

“…FIRESEV (FIRE SEVerity mapping tools) is a comprehensive set of tools and protocols to deliver, create, and evaluate fire severity maps for all phases of fire management. This CBI data describes fire effects for the western U.S. for five vegetation strata after burning in 2008 to 2010 (Key and Benson 1999). …”

Practical implementation of an End-to-end methodology for SPC of 3-D part geometry: A case study by Yulin An (833223)

“…<p>Del Castillo and Zhao have recently proposed a new methodology for the Statistical Process Control (SPC) of discrete parts whose 3-dimensional (3D) geometrical data are acquired with non-contact sensors. The approach is based on monitoring the spectrum of the Laplace–Beltrami (LB) operator of each scanned part estimated using finite element methods (FEM). …”

PROTOCOL - Effects of Ashwagandha (Withania somnifera) on Physical Performance: Systematic Review and Bayesian Meta-Analysis by Diego A. Bonilla (9086201)

“…Selected publications that met all the requirements will go on to the next phase of data analysis and synthesis, where a table of their results and findings comparison will be developed and complemented by the authors considering the items mentioned before (see data items).…”

DataSheet_2_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... by Adam D. Irwin (11011752)

“…Phase 2 of the trial protocol is registered with the ANZCTR (ACTRN12620001122943).…”

DataSheet_1_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... by Adam D. Irwin (11011752)

“…Phase 2 of the trial protocol is registered with the ANZCTR (ACTRN12620001122943).…”

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

“…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …”