Table 8_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Table 4_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Table 1_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Field attributes and satellite data for "How vegetation recovery and fuel conditions in past fires influences fuels and future fire management in five western U.S. ecosystems": 2nd... by Benjamin C. Bright (19657240)

“…<br>This data publication is a second edition, which includes some minor data corrections (basal area was calculated incorrectly for some variable-radius plots) and the addition of tree-level data. The data were also slightly reconfigured and are now available in separate files: field and satellite attributes, seedling and sapling densities, and tree-level data.…”

The dendrite suppression CIOP database by Zeyi Wang (19846350)

“…</p><p dir="ltr">10 common SEI containing elements are included and will be updated based on more experimental data later. …”

Supplementary data for the publication in Clinical Epigenetics, <b>Allelic Expression Patterns of Imprinted and Non-imprinted Genes in Cancer Cell Lines from Multiple Histologies.... by Julia Krushkal (18276223)

“…Additionally, 6 summary and cutoff files are provided for the pancancer dataset including all 108 cell lines at the gene, isoform, and exon levels.</p><p dir="ltr">The source code for the pipeline for generating the data is provided in the archive <b>Source_code.zip</b>. …”

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)

“…The m6A modification of long non-coding RNAs (lncRNAs) plays a critical regulatory role in RA pathogenesis. …”

Table 1_An interpretable machine learning model for early prediction of Escherichia coli infection in ICU patients.docx by Shu Yang (381226)

“…E. coli infection was identified based on microbiological results and diagnostic codes. Missing data were imputed using the missForest algorithm. …”

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. …”

The Guardian Reading Dataset by Frans Van der Sluis (18365826)

“…Each participant evaluated 18 articles sampled at three levels of textual complexity (low, medium, high), determined by a readability algorithm (Van der Sluis, 2014). …”

Wasserstein-Kaplan-Meier Survival Regression by Yidong Zhou (580573)

“…We propose an innovative regression model for right-censored survival data across heterogeneous populations, leveraging the Wasserstein space of probability measures. …”

<b>Road intersections Data with branch information extracted from OSM</b> & <b>C</b><b>odes to implement the extraction </b>&<b> I</b><b>nstructions on how to </b><b>reproduce each... by Zihao Tang (19794537)

“…</li></ul><h3>(4) Folder: <b>maxRadiusBuffer</b></h3><p dir="ltr">This folder focuses on the code of a greedy algorithm that produces the largest nonoverlapping buffers for a given collection of points.…”

Figure 1 from Decoding the Genomic and Functional Landscape of Emerging Subtypes in Ovarian Cancer by Giulia Micoli (22547985)

“…The red dashed lines represent discretization thresholds determined using the Jenks natural breaks algorithm. CNV, copy number variant; LINE, long interspersed nuclear elements; SV, structural variant. …”

Table 1_High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion.docx by Zhongqiang Song (21669041)

“…</p>Methods<p>This study established the first fine-grained dataset encompassing aphid Crawling Locomotion(CL), Leg Flicking(LF), and HE behaviors, offering standardized samples for algorithm training. A rapid adaptive motion feature fusion algorithm was developed to accurately extract high-granularity spatiotemporal motion features. …”

iNCog-EEG (ideal vs. Noisy Cognitive EEG for Workload Assessment) Dataset by Fariya Bintay Shafi (21692408)

“…Inside each folder, four <b>.EDF</b> files represent the workload conditions:</p><pre><pre>subxx_nw.EDF → No Workload (resting state) <br>subxx_lw.EDF → Low Workload (easy multitasking) <br>subxx_mw.EDF → Moderate Workload (medium multitasking) <br>subxx_hw.EDF → High Workload (hard multitasking) <br></pre></pre><ul><li><b>Subjects 01–30:</b> Clean EEG recordings</li><li><b>Subjects 31–40:</b> Noisy EEG recordings with real-world artifacts</li></ul><p dir="ltr">This structure ensures straightforward differentiation between clean vs. noisy data and across workload levels.</p><h3>Applications</h3><p dir="ltr">This dataset can be applied to a wide range of research areas, including:</p><ul><li>EEG signal denoising and artifact rejection</li><li>Binary and hierarchical <b>cognitive workload classification</b></li><li>Development of <b>robust Brain–Computer Interfaces (BCIs)</b></li><li>Benchmarking algorithms under <b>ideal and noisy conditions</b></li><li>Multitasking and mental workload assessment in <b>real-world scenarios</b></li></ul><p dir="ltr">By combining controlled multitasking protocols with deliberately introduced environmental noise, <b>iNCog-EEG provides a comprehensive benchmark</b> for advancing EEG-based workload recognition systems in both clean and challenging conditions.…”

<b>Supplementary Materials for:</b> <b>Pan-Cancer Analysis Reveals LncRNA CDKN2B-AS1 as an Immune-Related Biomarker Validated in LIHC and ACC</b> by Dandan Lin (18192946)

“…S8-S10: Comprehensive analysis of the relationship between CDKN2B-AS1 expression and the infiltration levels of diverse immune cells using TIMER2.0 and CIBERSORT algorithms.…”

SPM Shape-PCA model by Yael Balbastre (6779027)

“…<p dir="ltr">This dataset contains the parameters of a random orbit model, where the template represents the mean shape of a population and deviations from this mean shape are encoded by geodesics sampled from a multivariate Gaussian distribution.<br></p><ul><li><code>shape_pca_template_{01234}.nii</code> contain the template with increasingly refined levels of details.…”

Table 1_Rapid forensic ancestry inference in selected Northeast Asian populations: a Y-STR based attention-based ensemble framework for initial investigation guidance.xlsx by Kyo-Chan Koo (22263367)

“…We developed a machine learning architecture centered on an attention-based ensemble mechanism that incorporates three complementary algorithms: a One-vs-Rest Random Forest, XGBoost, and Logistic Regression, each configured to effectively manage imbalanced datasets.…”

Collaborative research: CyberTraining: Implementation: Medium: Training users, developers, and instructors at the chemistry/physics/materials science interface by Francesco Paesani (5128004)

“…The primary objectives are to establish a robust community of materials modeling developers and to enhance computational training at both undergraduate and graduate levels. The project seeks to recruit and train future leaders in materials modeling, foster community engagement, and promote coding literacy across disciplines.…”

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …”