Table 2_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

Table 1_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

“…Version 3.0 has been deprecated due to the discovery of a data inconsistency in the calculation of net longwave radiation in the source code used to generate the dataset. …”

Additional file 1 of The origin and evolution of cultivated rice and genomic signatures of heterosis for yield traits in super-hybrid rice by Yiyong Zhao (21466062)

“…Each gene's expression data is associated with a specific tissue sample, using a naming convention that includes the gene identifier, variety code, and tissue type, separated by underscores. …”

CIAHS-Data.xls by Yingchang Li (22195585)

“…For this purpose, we employed the Natural Breaks classification method to reclassify factor values. This method identifies inherent natural grouping points within the data through the Jenks optimization algorithm, maximizing between-class differences while minimizing within-class differences37. …”

Cell type classification in the 9-dpg leaves. by Frances K. Clark (5617169)

“…Associated with <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003469#pbio.3003469.s010" target="_blank">S10 Fig</a>. The code and data associated with this figure can be found at Open Science Framework (osf.io), <a href="https://doi.org/10.17605/OSF.IO/RFCWS" target="_blank">https://doi.org/10.17605/OSF.IO/RFCWS</a>.…”

Cell type classification in the leaf and sepal. by Frances K. Clark (5617169)

“…See also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003469#pbio.3003469.s009" target="_blank">S9</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003469#pbio.3003469.s011" target="_blank">S11 Figs</a>. The code and data associated with this figure can be found at Open Science Framework (osf.io), <a href="https://doi.org/10.17605/OSF.IO/RFCWS" target="_blank">https://doi.org/10.17605/OSF.IO/RFCWS</a>.…”

Table 1_Demethylase FTO mediates m6A modification of ENST00000619282 to promote apoptosis escape in rheumatoid arthritis and the intervention effect of Xinfeng Capsule.docx by Fanfan Wang (1669474)

“…</p>Results<p>Retrospective data mining demonstrated that XFC significantly improved immune-inflammatory markers in RA patients. …”

Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…</p>Methods<p>This study integrated 2,743 glioma samples from TCGA, CGGA, and GEO databases, encompassing RNA-seq, single-cell transcriptomic (GSE167960), and mutational data. Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). …”

Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…</p>Methods<p>This study integrated 2,743 glioma samples from TCGA, CGGA, and GEO databases, encompassing RNA-seq, single-cell transcriptomic (GSE167960), and mutational data. Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). …”

Table 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…</p>Methods<p>This study integrated 2,743 glioma samples from TCGA, CGGA, and GEO databases, encompassing RNA-seq, single-cell transcriptomic (GSE167960), and mutational data. Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). …”

Image 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…</p>Methods<p>This study integrated 2,743 glioma samples from TCGA, CGGA, and GEO databases, encompassing RNA-seq, single-cell transcriptomic (GSE167960), and mutational data. Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). …”

Image 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…</p>Methods<p>This study integrated 2,743 glioma samples from TCGA, CGGA, and GEO databases, encompassing RNA-seq, single-cell transcriptomic (GSE167960), and mutational data. Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). …”

Table 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…</p>Methods<p>This study integrated 2,743 glioma samples from TCGA, CGGA, and GEO databases, encompassing RNA-seq, single-cell transcriptomic (GSE167960), and mutational data. Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). …”

Image 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…</p>Methods<p>This study integrated 2,743 glioma samples from TCGA, CGGA, and GEO databases, encompassing RNA-seq, single-cell transcriptomic (GSE167960), and mutational data. Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). …”

Multi-Task Learning in Analyzing the Working capacity of MOFs by Junhui Kou (20327073)

“…</p><ul><li><b>CIF files</b>: CIF files for 252,352 MOFs;</li><li><b>Geometric descriptors</b>: 14 geometric descriptors;</li><li><b>Chemical descriptors</b>: 176 chemical descriptors;</li><li><b>Methane_v, Methane_g</b>: Volumetric and gravimetric working capacities for methane adsorption, including methane adsorption data under six pressures across three application scenarios (landfill gas treatment, methane purification, and methane storage);</li><li><b>MTL4MOFsWC</b>: Python code for training the MTL models to predict the working capacity of methane adsorption in MOFs;</li><li><b>best_model_v_full, best_model_v_sim, best_model_g_full, best_model_g_sim</b>: Pre-trained MTL models.…”

Hippocampal and cortical activity reflect early hyperexcitability in an Alzheimer's mouse model by Marina Diachenko (19739092)

“…</b></p><p dir="ltr">*Correspondence: Klaus Linkenkaer-Hansen (klaus.linkenkaer@cncr.vu.nl)</p><p dir="ltr"><br></p><p dir="ltr">In this study, we investigated fE/I, θ-γ PAC, and epileptiform features in hippocampal and cortical local field potentials (LFPs) recorded weekly in freely behaving male APPswe/PS1dE9 (APP/PS1) mice (<i>n</i> = 10) and wildtype controls (<i>n</i> = 10) between 3 and up to and including 11 months of age.</p><p dir="ltr">All data are available upon request. The standalone Python implementation of the fE/I algorithm is available under a CC-BY-NC-SA license at <a href="https://github.com/arthur-ervin/crosci" target="_blank">https://github.com/arthur-ervin/crosci</a>. …”

<b>An Empirical Evaluation of Software Quality Classification Based on User Feedback Aligned</b><b>with ISO/IEC 25010</b> by mesut polatgil (22408147)

“…<p dir="ltr">Evaluating software quality without access to the source code is a challenging task, as traditional metrics and testing approaches often rely on internal code analysis. …”

Processed Dataset for “Optimization of Mixed-Model Multi-Manned Assembly Lines for Fuel–Electric Vehicle Co-Production under Workstation Sharing” by Lingling Hu (22555691)

“…It can be used to replicate the analysis and experiments presented in the paper or to test new optimization algorithms for mixed-model assembly line balancing.</p><p dir="ltr">The data are provided in <code>.xlsx</code> format and can be freely accessed for <b>academic and non-commercial purposes only</b>.…”

Processed Dataset for “Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism” by Lingling Hu (22555691)

“…</p><p dir="ltr">The data are provided in <code>.xlsx</code> format and may be freely accessed and reused for <b>academic and non-commercial purposes only</b>.…”