<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.…”

Data_Sheet_1_An analysis of the effects of learner-centered software education and required support strategies.docx by Seongjin Ahn (1561396)

“…</p>Methods<p>A study with 190 students utilized a learner-centered approach, incorporating design thinking and computational problem-solving, and evaluated its effectiveness through pre-and post-tests and structural equation modeling.…”

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.…”

A multi-paradigm and longitudinal EEG dataset including the “sixth-finger” and “affected-hand” motor imagery of stroke patient by Zhuang Wang (22542833)

“…The dataset comprehensively covers the complete longitudinal stages of stroke rehabilitation: pre-training, post-training, and follow-up periods. …”

<b>SAFE: </b><b>s</b><b>ensitive </b><b>a</b><b>nnotation </b><b>f</b><b>inding and </b><b>e</b><b>xtraction from multi-type Chinese maps via hybrid intelligence and knowledge grap... by jiaxin ren (20482655)

“…<p dir="ltr">Sensitive annotations typically contain key geographic elements or sensitive information vital for geographic information security. …”

Table 4_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 3_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 2_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 1_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Institutions in the Complexity-Relatedness Framework by İbrahim Tuğrul Çınar (12058328)

“…<br>Variable construction and filters (5‑year rolling averages, presence matrix, complexity algorithm, relatedness density, normalisation) follow the manuscript.manuscript_numarali</li></ul><p dir="ltr">Variables (grouped overview)</p><ul><li>Identifiers: <code>reg_nuts2</code>, <code>NUTS1</code>, <code>NUTS0</code>, <code>prio_year</code></li><li>Outcomes / initial levels: <code>gdp_c_gr3</code>, <code>gva_c_gr3</code>, <code>totalemp_gr3</code> (3‑year growth rates), <code>lngdp_c</code>, <code>lngva_c</code></li><li>Controls: <code>cap_stock</code>, <code>gfcf</code>, <code>lnhwork</code> (hours worked, log), <code>capdens**</code> (capital intensity variants used in robustness), <code>population density</code> proxy (see manuscript tables)manuscript_numarali</li><li>Composite capability indices & spatial averages:</li><li><ul><li><code>tcrd_mean</code> (CD‑Mean),</li><li><code>fdenseqi_geo</code> (CDQ‑Geo),</li><li><code>tcrd_eqi</code> (CDQ),</li><li><code>_W1</code> / <code>_W0</code> suffixes = within‑NUTS‑1 / within‑country averages.…”

Robust Multi-Model Subset Selection by Anthony-Alexander Christidis (6949055)

“…The <a href="https://doi.org/10.1080/10618600.2025.2596057" target="_blank">supplementary material</a> contains all theoretical proofs, additional algorithmic and computational details, and the code and data to reproduce our numerical results.…”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”

Trust as moral currency: Perspectives of health researchers in sub-Saharan Africa on strategies to promote equitable data sharing by Qunita Brown (19751520)

“…High quality data from Africa will afford diversity to global data sets, reducing bias in algorithms built for artificial intelligence technologies in healthcare. …”

Table 1_From meal to malfunction: exploring molecular pathways, biomarkers and interventions in postprandial cardiometabolic health.docx by Claudia Reytor-González (19917545)

“…Practical strategies to shorten the “damage window” include Mediterranean-style meals with low glycemic index swaps and unsaturated fats, earlier distribution of daily energy and early time-restricted eating, a small pre-meal protein portion, and brief post-meal walking. …”

<b>Leveraging protected areas for dual goals of biodiversity conservation and zoonotic</b> <b>risk reduction</b> by Li Yang (13558573)

“…Supplementary Tables</p><p dir="ltr">The analysis generates several supplementary tables stored in ./3Spatial_result/5sp_tables/<b> (after you run the code)</b>:</p><p dir="ltr">Table S1. Top 30 Countries with the largest key HCN-HZR Areas, their PA Coverage on key HCN-HZR Areas, and economic income level for each country. …”