Distributions of optimized coupling parameters and median FC strengths for males and females. by Kevin J. Wischnewski (21354521)

Reliability of the optimized frequency parameters and sFC matrix edges in the high-dimensional cases as well as of the GoF. by Kevin J. Wischnewski (21354521)

Reliability of the optimized model parameters of delay , coupling and noise intensity . by Kevin J. Wischnewski (21354521)

3D analysis of the perforated pebbles and the perforations. by Talia Yashuv (12883867)

Table 1_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Table 2_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Table 3_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.docx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

The immune microenvironment association of CENPA in cancers. by Hengrui Liu (2059750)

Supplementary file 1_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.d... by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Data Sheet 3_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…Derived parameters like cerebral oxygenation index or Fast Fourier Transform based coherence estimates between blood pressure and cerebral blood flow have their limitations of use for stationary data and low time resolution. Wavelet transfer function analysis can be employed to estimate coherence, gain and phase relationship between two signals without these restrictions.…”

Data Sheet 1_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…Derived parameters like cerebral oxygenation index or Fast Fourier Transform based coherence estimates between blood pressure and cerebral blood flow have their limitations of use for stationary data and low time resolution. Wavelet transfer function analysis can be employed to estimate coherence, gain and phase relationship between two signals without these restrictions.…”

Data Sheet 2_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…Derived parameters like cerebral oxygenation index or Fast Fourier Transform based coherence estimates between blood pressure and cerebral blood flow have their limitations of use for stationary data and low time resolution. Wavelet transfer function analysis can be employed to estimate coherence, gain and phase relationship between two signals without these restrictions.…”

Data Sheet 4_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…Derived parameters like cerebral oxygenation index or Fast Fourier Transform based coherence estimates between blood pressure and cerebral blood flow have their limitations of use for stationary data and low time resolution. Wavelet transfer function analysis can be employed to estimate coherence, gain and phase relationship between two signals without these restrictions.…”

<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 dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

Data analyzed for the article of <b>Evaluating photoplethysmography-based pulsewave parameters and composite scores for assessment of cardiac function: A comparison with echocardio... by Kulin (20907929)

“…Concurrently, echocardiographic parameters were derived by averaging the data from 1-3 heartbeats, allowing for a direct comparison of cardiac function assessments between the two techniques, by the following. …”

Graphical representation of the diffuse interface approach in one dimension. by Martina Bukač (20161460)

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

The modified SarcGraph pipeline. by Saeed Mohammadzadeh (13492285)

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”

SpatialKNifeY analysis landscape. by Shunsuke A. Sakai (13789939)