Table1_Natural and artificial selection of multiple alleles revealed through genomic analyses.DOCX حسب Jana Biová (11287971)

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

Code حسب Baoqiang Chen (21099509)

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

Core data حسب Baoqiang Chen (21099509)

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

Decoding fairness motivations - repository حسب Sebastian Speer (6489207)

"…Oxford University Press</div><div><br></div><div><u>Participants: </u></div><div><u><br></u></div><div>The reported analyses are based on 31 participants from two separate studies. …"

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

"…</p><h4><b>Case Study 2 - Spatial projection via modelled data</b></h4><h4><b>Description</b></h4><p dir="ltr">Estimate soil pH and electrical conductivity at 45 cm depth across a farm based on values collected from soil samples. This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…"

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

"…</p><p dir="ltr">These were then horizontally and vertically aligned in two alternative ways, producing two time series: one based on local alignment, and one based on global alignment. …"

PresQT - Services to Improve Re-use and FAIRness of Research Data and Software حسب 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. …"

Expression vs genomics for predicting dependencies حسب 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 حسب 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). …"