<b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b> by Marly G F Costa (19812192)

“…<p dir="ltr"><b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b></p><p dir="ltr">The code was developed in the Google Collaboratory environment, using Python version 3.7.13, with TensorFlow 2.8.2. …”

Identifying Reactive Trends in Glycerol Electro-Oxidation Using an Automated Screening Approach: 28 Ways to Electrodeposit an Au Electrocatalyst by Raghuram Gaddam (15285161)

2024 HUD Point in Time Count Data by State and CoC with Serious Mental Illness and Chronic Substance Use Counts by Benjamin Gorman (21648794)

“…</p><p dir="ltr">HUD PIT Count reports for states, Washington, DC, and the 384 CoCs were systematically downloaded from the HUD Exchange website using a Python script developed using Cursor software. …”

R codes and curated dataset for “EnoLEX: A Diachronic Lexical Database for the Enggano Language” by Gede Primahadi Wijaya Rajeg (14592023)

Oka et al., Supplementary Data for "Development of a battery emulator using deep learning model to predict the charge–discharge voltage profile of lithium-ion batteries" by Kanato Oka (20132185)

“…To use this Python script, you need to modify the "CFG" and "Convenient" sections within the script.…”

Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations by Sascha Mausenberger (22225772)

“…We present a novel, flexible framework for electronic structure interfaces designed for nonadiabatic dynamics simulations, implemented in Python 3 using concepts of object-oriented programming. …”

The Twin Prime Conjecture: Collapse-Band Parity Cancellation and Operator Proof by Jennifer Bulyaki (22283308)

Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service) by U.S. Forest Service (17476914)

“…CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. …”

Yokoyama et al., Supplementary Data for "Prediction of Li-ion Conductivity in Ca and Si co-doped LiZr₂(PO₄)₃ Using a Denoising Autoencoder for Expe... by Yumika Yokoyama (20174705)

“…<p dir="ltr"><b>uniDAE.py </b>is the python script used in this study. The script includes a denoising autoencoder for XRD profiles with six attention heads and a deep learning model for regression analysis of the activation energy (Ea) of ion conduction.…”

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation by Renato Soares (20348202)

“…In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds’ targets of newly discovered molecules. A Python programming protocol obtained 3431 cyanobacteria bioactive compounds, 373 unique protein targets, and 3027 molecular descriptors. …”

List of abbreviations. by Jingru Dong (14076094)

“…In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. …”

Heat map of the correlation of features. by Jingru Dong (14076094)

“…In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. …”

Experimental environment configuration table. by Jingru Dong (14076094)

“…In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. …”

Pseudocode for machine learning models. by Jingru Dong (14076094)

“…In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. …”

Base learner parameters. by Jingru Dong (14076094)

“…In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. …”

Data inclusion and exclusion process. by Jingru Dong (14076094)

“…In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. …”

Modeling flowchart. by Jingru Dong (14076094)

“…In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. …”

S1 Data - by Jingru Dong (14076094)

“…In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. …”

JASPEX model by Olugbenga OLUWAGBEMI (21403187)

“…The accompany data were processed and reformatted into its current form using Python programming within Jupyter Notebook enivironment and Shell programming.…”

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…</p> <p>NIMO enables AI-driven automation in self-driving labs by bridging diverse experimental systems, including those using non-Python platforms, greatly enhancing SDL accessibility and flexibility.…”