Supplementary file 3_The AMerican PREGNANcy Mother–Child CohorT: description and prevalence of baseline outcomes and medication dispensing.docx by Lisiane F. Leal (21986330)

“…We updated and implemented a hierarchical algorithm using ICD-9-CM and ICD-10-CM codes to identify pregnancies in individuals aged 15–45 years (2003–2021). …”

Supplementary file 2_The AMerican PREGNANcy Mother–Child CohorT: description and prevalence of baseline outcomes and medication dispensing.xlsx by Lisiane F. Leal (21986330)

“…We updated and implemented a hierarchical algorithm using ICD-9-CM and ICD-10-CM codes to identify pregnancies in individuals aged 15–45 years (2003–2021). …”

Supplementary file 1_The AMerican PREGNANcy Mother–Child CohorT: description and prevalence of baseline outcomes and medication dispensing.xlsx by Lisiane F. Leal (21986330)

“…We updated and implemented a hierarchical algorithm using ICD-9-CM and ICD-10-CM codes to identify pregnancies in individuals aged 15–45 years (2003–2021). …”

Unsupervised Concept Discovery for Deep Weather Forecast Models with High-Resolution Radar Data by Soyeon Kim (533382)

“…We provide comprehensive analyses of the clustered patterns grounded in meteorological theory, highlighting the potential of knowledge discovery of meteorological mechanisms from nonlinear, data-driven models.</p><p dir="ltr">This repository contains experimental code in Python scripts, Jupyter Notebook files, and a dataset for the extracted psuedo clusters.…”

Data Sheet 1_MACRPO: Multi-agent cooperative recurrent policy optimization.pdf by Eshagh Kargar (20458304)

Supporting Data for "Intelligent prediction of steel corrosion in cementitious materials via machine learning" by Haodong Ji (11129136)

“…</p><ul><li><b>Chapter </b><b>3</b> contains laboratory corrosion data of steel under carbonation conditions. The dataset includes 16 variables and 180 groups, along with code implementing relevant regression algorithms.…”

Australian Institution Co-authorship Collaboration Patterns DataArena exhibition March 4th by Simon Porter (1364613)

“…</p><p dir="ltr"><b>Methodology:</b></p><p dir="ltr">Graph layout: Batchlayout [1]</p><p dir="ltr">Clustering: Leiden Algorithm [2]</p><p dir="ltr">3d Layout: Blender [3]</p><p dir="ltr">Data: Dimensions [4]</p><p dir="ltr">Music for Animation: Scientific Cathedral (Instrumental Mix) <a href="https://soundcloud.com/roaming-mosaics" target="_blank">Roaming Mosaics</a></p><p>[5]</p><p dir="ltr">Music for DataArena: Michele Pasin Dreamy pianos - Study No 1 in C Minor (2023-12) [6]</p>…”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”

Research on Olympic medal prediction based on GA-BP and logistic regression model Extended data by Sanglin Zhao (20599835)

“…</p><p dir="ltr">Including data and code (Matlab)</p>…”

Heavy-load transfer steel platform. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. …”

Traditional scaffolding reinforcement system. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. …”

Iteration curve of the optimization process. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. …”

Survey Data on NSSI, Stress Response, Emotional Regulation Strategies and gender in Chinese Undergraduates by Yilin Ma (21154754)

“…</li></ul><p dir="ltr"><b>Data Collection and Measures</b></p><p dir="ltr"><b>1. …”