Deciphering pseudouridylation profiles and their biogenesis from deep sequencing data using psiFinder by zhirong chen (20863529)

Animal bite risk map for Mymensingh district in 2022 and 2023. by Chandra Shaker Chouhan (17262842)

Data Sheet 1_The development and use of data warehousing in clinical settings: a scoping review.pdf by Shiyang Lyu (20910008)

“…We searched four databases (PubMed, CINAHL, Scopus and IEEE-Xplore), identifying peer-reviewed, English-language studies from 1st January 2014 to 1st January 2024, that focus on data warehousing in healthcare, covering either general or specialised data warehouse applications. Python programming was used to extract the search results and transform the data into a tabular format for analysis.…”

Affinity Molecular Assay for Detecting Candida albicans Using Chitin Affinity and RPA-CRISPR/Cas12a by Deqiang Wang (19817560)

“…Raw reads were obtained from Tsingke Biotechnology (Beijing, China) and analyzed using Python scripts (Python 3.11.8), filtering reads with an average Phred quality (Q score) of at least 25. …”

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

“…Our platform combines individually addressable electrode arrays with HardPotato, a Python application programming interface for potentiostat control, to automate electrochemical experiments and data analysis operations. …”

<b>MSLU-100K: A multi-source land use dataset of Chinese major cities</b> by Yao Yao (7903457)

“…<p dir="ltr">The project includes the code of a deep learning model related to the paper "MSLU-100K: A Multi-Source Land Use Dataset for Major Cities in China". This paper presents a model for classifying irregular land parcels by land use. …”

<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. …”

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. …”