Data_Sheet_1_MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence.docx by Mehdi Foroozandeh Shahraki (9555317)

“…MCIC is freely available as a python package and standalone toolkit for Windows and Linux-based operating systems with several functions to facilitate the screening and thermal and pH dependence prediction of cellulases.…”

Presentation_1_NeuroEditor: a tool to edit and visualize neuronal morphologies.pdf by Ivan Velasco (9463019)

“…Moreover, NeuroEditor can be easily extended by users, who can program their own algorithms in Python and run them within the tool. Last, this paper includes an example showing how users can easily define a customized workflow by applying a sequence of editing operations. …”

Decoding fairness motivations - repository by Sebastian Speer (6489207)

“…</div><div> In the non-social control condition (24 trials), participants played against a computer algorithm, allegedly programmed to mimic human behaviour. …”

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)

“…Agisoft LLC. https://www.agisoft.com/pdf/metashape_python_api_2_0_4.pdf.</p><p dir="ltr">Vicente Vasquez. (2023). …”

datasheet1_ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed.xlsx by Paola Turina (10431428)

“…The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts.…”

presentation1_ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed.pdf by Paola Turina (10431428)

“…The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts.…”

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. …”

Skeletal_ Muscle_MRI_Registration by Lucia Fontana (9435020)

“…</p> <p>wxPython library was employed to develop the GUI, which is composed by two main windows – initial window and registration window – and 5 secondary frames for support functionalities. 3D images are presented with three views – axial, coronal and sagittal – with three sliders to adjust maximum value, minimum value, and gamma correction.…”

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)

“…</p><p dir="ltr">The dataset is freely accessible on IEEE DataPort, a data repository created by IEEE and can be found at the following URL: <a href="https://ieeexplore.ieee.org/document/10218394/algorithms?tabFilter=dataset" target="_blank">https://ieeexplore.ieee.org/document/10218394/algorithms?…”

Expression vs genomics for predicting dependencies by Broad DepMap (5514062)

“…If you are interested in trying machine learning, the files Features.hdf5 and Target.hdf5 contain the data munged in a convenient form for standard supervised machine learning algorithms.</p><p dir="ltr"><br></p><p dir="ltr">Some large files are in the binary format hdf5 for efficiency in space and read-in. …”

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). …”