Table 2_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 1_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 7_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 3_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 6_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 10_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 3_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 5_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 9_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 4_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 11_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 6_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 2_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Supervised Classification of Burned Areas Using Spectral Reflectance and Machine Learning by Baptista Boanha (22424668)

“…<p dir="ltr">This dataset and code package presents a modular framework for supervised classification of burned and unburned land surfaces using satellite-derived spectral reflectance. …”

Non-Conjugate Variational Bayes for Pseudo-Likelihood Mixed Effect Models by Cristian Castiglione (21656583)

“…<p>We propose a unified, yet simple to code, non-conjugate variational Bayes algorithm for posterior approximation of generic Bayesian generalized mixed effect models. …”

Framework of the Methodology. by Yibin Zhang (1426579)

“…Tracking is the most crucial data processing step to generate accurate and reliable trajectories of road users from raw point clouds collected from LiDAR sensors. …”

Supporting documents for the <i>APSB</i> manuscript "<i>Metabolic reactions based molecular networking for xenobiotic profiling of complex samples</i>" by Haodong Zhu (20627600)

“…</li><li><b>Node and Edge files of MRMN</b><b> </b>for LC-MS data of biological samples following the administration output by the YaoLab@JNU platform: These files are intended for offline processing of MRMN with Cytoscape</li></ol><p></p>…”

Table 1_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.docx by Yuwen ShangGuan (22633190)

“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”

Table 1_Non-obtrusive monitoring of obstructive sleep apnea syndrome based on ballistocardiography: a preliminary study.docx by Biyong Zhang (20906192)

“…This can reduce both the data to be stored or transmitted and the computational load. …”

Opponent Style Representation Learning Method Based on Spatio-Temporal Features by Jinpeng Zhang (22759856)

“…</p><p dir="ltr"><b>Data Sources</b>: Experimental data combines:</p><ol><li>Logs from the <a href="https://github.com/vwxyzjn/gym-microrts-paper" target="_blank">gym-microrts-paper</a> benchmark suite</li><li>Built-in agent logs from the MicroRTS platform</li></ol><p dir="ltr">Generate Log Data (using selected agents):</p><ol><li>The following three files are logs generated by running selected intelligent agents:<br><br>1model_vs_model.py<br>2model_vs_model.py<br>3odel_vs_model.py</li><li>datatool1 is a data processing tool. …”