Test datasets for evaluating automated transcription of primary specimen labels on herbarium specimen sheets by Robert Turnbull (8943344)

“…</p><h2>Evaluation Script</h2><p dir="ltr">We provide a Python script to evaluate the output of an automated pipeline with these datasets. …”

Sonification of Warming Stripes by Christopher Harrison (9448751)

“…The sonification was produced using the STRAUSS sonification Python package.</p><p dir="ltr">Here we release:<br>1. …”

Dataset for the Modeling and Bibliometric Analysis of Business plan for Entrepreneurship by Shofie Galuh Amanda (22121604)

“…For modeling, Python was applied to generate projection analyses of annual scientific production using polynomial regression. …”

Carla Simulator collision scenario DVS Sequences from Bio-inspired event-based looming object detection for automotive collision avoidance by Fabian Schubert (13819882)

“…<p dir="ltr">Data for paper published in <i>Neuromorphic Computing and Engineering</i> (April 2025)<br><br>This dataset comprises 1406 sequences (4 sec each) of simulated dynamic vision sensor data from virtual driving and collision scenarios with cars and pedestrians, created using the CARLA Simulator. This was used to evaluate the capabilities of a neuromorphic looming detector, as presented in <a href="https://doi.org/10.1088/2634-4386/add0da" rel="noreferrer" target="_blank">Fabian Schubert et al 2025 <i>Neuromorph. …”

<b>AutoMated tool for Antimicrobial resistance Surveillance System version 3.1 (AMASS3.1)</b> by Chalida Rangsiwutisak (10501496)

“…;</li><li><i>Enterococcus</i> <i>faecalis</i> and <i>E. faecium</i> have been explicitly included in the pathogens under the survey (while <i>Enterococcus</i> spp. are used in the AMASS version 2.0);</li><li>We have added a few antibiotics in the list of antibiotics for a few pathogens under the survey;</li></ol><p dir="ltr">Technical aspects</p><ol><li>We have added a configuration for “Annex C: Cluster signals” in Configuration.xlsx;</li><li>We have improved the algorithm to support more several date formats;</li><li>We have improved the algorithm to translate data files;</li><li>We have improved Data_verification_logfile report to present local languages of the variable names and values (according to how they were recorded in the data files) in the report;</li><li>We have improved Annex B: Data indicators to support a larger data set;</li><li>We have used only Python rather than R + Python (as used in the AMASSv2.0);</li><li>We have set a default config for infection origin stratification by allowing a specimen collected two calendar days before the hospital admission date and one day after the hospital discharge date into consideration. …”

A potential pitfall in the interpretation of microscope-integrated fluorescence angiography: the center-periphery effect. - raw data by Stolk (20652617)

“…The signal was quantified using tailor-made software in Python. <b>Results</b>: A clear center-periphery effect was present in most settings in both microscopes, with the highest peripheral fluorescence signal loss in the lowest MF: 100% in the Tivato and 83% in the Pentero. …”

Evolutionary Conservation and Hotspot Analysis of the p53 Tumor Suppressor Protein by Efe can Orhan (22308964)

“…All Python scripts and PyMOL scripts used for data processing and figure generation are provided in the associated GitHub repository: <a href="https://github.com/efe4r/p53_Evolution_Project" rel="noopener" target="_new">https://github.com/efe4r/p53_Evolution_Project</a>.…”

Supplemental Material for Perez, 2024 by Marcos Francisco Perez (11788328)

“…These table (sheet 2 imported into R or Python) can be used directly with the decoupleR/decoupler package ('source' refers to TF and 'target' refers to the interaction target) . …”

Online Test-time Adaptation for Interatomic Potentials by Cui Taoyong (18076012)

“…The MD simulations for evaluating the performance of TAIP are conducted using the Atomic Simulation Environment (ASE) Python library. …”

New product development in the industry 4.0 era: a literature review and research agenda by Thiago Augusto Aniceski (22401616)

Replication Package: "The SBOM Gap: Adoption and Compliance in Open Source Software" by anonymous author (14191683)

“…<p dir="ltr">Replication Package Structure:</p><p dir="ltr">The replication package contains all data and scripts necessary to reproduce the analyses and results presented in this study.</p><p><br></p><p dir="ltr">replication_package/</p><p>│</p><p dir="ltr">├── data/</p><p dir="ltr">│ ├── sbom_repo_paths.csv # Repository paths and metadata for analyzed projects</p><p dir="ltr">│ ├── sbom_project_features.csv # Extracted features for SBOM projects</p><p dir="ltr">│ ├── non_sbom_project_features.csv # Extracted features for non-SBOM projects</p><p dir="ltr">│ └── SBOM_files/ # Raw SBOM files collected from selected repositories</p><p>│</p><p dir="ltr">└── code/</p><p dir="ltr"> ├── RQ1_regression/ # Scripts for regression analysis (RQ1)</p><p dir="ltr"> │ ├── regression.R # Main regression analysis script</p><p dir="ltr"> │ └── common.R # Shared functions for data filtering and formatting</p><p> │</p><p dir="ltr"> └── RQ2_compliance/ # Scripts for compliance and coverage checks (RQ2)</p><p dir="ltr"> ├── check_component_name.py</p><p dir="ltr"> ├── check_component_version.py</p><p dir="ltr"> ├── check_supplier.py</p><p dir="ltr"> ├── check_unique_identifiers.py</p><p dir="ltr"> ├── check_sbom_author.py</p><p dir="ltr"> ├── check_timestamp.py</p><p dir="ltr"> ├── check_dependency.py</p><p dir="ltr"> ├── check_hash.py</p><p dir="ltr"> ├── check_lifecycle_phase.py</p><p dir="ltr"> ├── check_license.py</p><p dir="ltr"> ├── check_vex.py</p><p dir="ltr"> ├── check_transitive_dependency.py</p><p dir="ltr"> ├── check_circular_dep.py</p><p dir="ltr"> └── check_all_7_min_req_files.py</p><p dir="ltr"><br></p><p dir="ltr"><br></p><p><br></p><p dir="ltr">Folder Descriptions:</p><p><br></p><p dir="ltr">data/: Contains datasets and raw SBOM files used in the analysis.…”

<b>InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving</b> by Xiyan Jiang (20320758)

“…</b></li></ul><p dir="ltr">The Python codes used to process and analyze the dataset can be found at <a href="https://github.com/zxc-tju/InterHub" rel="noreferrer" target="_blank">https://github.com/zxc-tju/InterHub</a>. …”

Table 1_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.docx by A. L. Back (20719049)

“…This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. …”

Data Sheet 5_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. …”

Data Sheet 8_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. …”

Data Sheet 4_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. …”

Data Sheet 9_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. …”

Data Sheet 15_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. …”

Data Sheet 13_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. …”

Data Sheet 2_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. …”