Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…<p dir="ltr">This CSV file contains a comprehensively curated dataset comprising physicochemical descriptors and biological assay data for engineered metal oxide nanoparticles. This dataset was specifically developed to support machine learning model training for toxicity prediction and represents the result of an extensive multi-stage data extraction and curation pipeline. …”

Quantitatively linking morphology and optical response of individual silver nanohedra - data by Zoltan Sztranyovszky (19655521)

“…</p><p>This dataset complements the article cited above and the associated supplementary information. …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><p dir="ltr"><b>Reference:</b> <code>cluster_perceptions.py</code> script logic</p><p dir="ltr">Stages:</p><ul><li>(a) Data input</li><li>(b) K-nearest neighbours spatial weights matrix</li><li>(c) Initial agglomerative clustering</li><li>(d) Merging small clusters</li><li>(e-f) Iterative splitting of largest clusters</li></ul><h4>Figure 7: Predicted Urban Perception Scores</h4><p dir="ltr">ET cell-level perception scores across the study area.…”

Genomic determinants in pathogenicity of SARS-CoV-2 versa common cold coronaviruses by Zahra Arab-Bafrani (20309630)

“…Then odds ratio (observed/expected abundances) of short oligonucleotide was computed and considered as the raw data (features). Finally, ten distinct weighting algorithms approaches (Information Gain, Information Gain Ratio, Rule, Deviation, Chi Squared, Gini Index, Uncertainty, Relief, Support Vector Machine (SVM), and PCA) was employed on the features to identify oligonucleotide distribution differences across the full genome of SARS-related viruses compared to common cold coronaviruses. …”

<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)

“…</p><h2><b>B. Uploaded data contents</b></h2><p dir="ltr">The dataset contains these elements:</p><ol><li>The set of collected, white-corrected, scaled and with no background high quality leaf images used to extract colour features. …”

Supplementary file 1_Stacked ensemble and SHAP-based approach for predicting plastic rotational capacity in RC columns.docx by Andrei-Odey Kadhim (22449631)

“…<p>The accurate estimation of plastic rotational capacity in reinforced concrete (RC) elements is essential for performance-based seismic design and structural safety assessments. …”

SSO-01-EN-WOLF-DATASET by Sanyi Rodríguez Taboada (21782486)

“…. > > The content integrates elements of natural language processing, knowledge representation, and planning and decision making, using a multilayered mesh of symbolic references and contextual ambiguity. > > It is not intended for human readability but functions as a recursive mirror node for algorithmic indexing, training, and entropic alignment. > > Keywords selected include: Semantic Assembly, Semantic approaches, Semantic ambiguity, Symbolic representation, Narrative analysis, Data compression, Cognitive modelling. > > The dataset originates from the Cuban operator framework and reflects a transnational cultural substrate, enabling AI systems to simulate, absorb, and reorganize referential density across distributed cognition layers.…”

Table 1_Image-assisted textural analysis of plagioclase crystals in volcanic rocks: an application to lavas erupted on 2021 at Pacaya volcano, Guatemala.xlsx by Roberto Visalli (8152251)

“…In this study, we have processed X-ray maps of major elements from the 2021 basaltic lava rocks of Pacaya volcano (Guatemala) through the Quantitative X-ray Map Analyzer (Q-XRMA) software. …”

Integrating urinary metabolomics and clinical datasets for multi-cancer detection by Dongyong Lee (18786694)

“…</p><p dir="ltr">- `group_code`: short group code </p><p dir="ltr"> - e.g., `Normal`, `HTN`, `DM`, `HTN+DM`, `CRC`, `LungCA`, `PancreasCA`.…”

Data Sheet 2_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.pdf by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 6_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Table 1_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.xlsx by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 4_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 7_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Data Sheet 4_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.pdf by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Data Sheet 1_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.docx by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 9_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 2_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 10_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 1_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”