Data Sheet 8_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 4_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 9_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 15_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 13_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 2_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 3_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 10_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Supplementary file 1_ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models.docx by Piyachat Udomwong (22563212)

“…The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.…”

Data Sheet 14_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Critical Planck Spin Dynamics (CPSD): A Geometric Quantum Spacetime with Zero Free Parameters by Daniel Zunzunegui (22583093)

“…</p><p><br></p><p dir="ltr">FALSIFIABLE PREDICTIONS (2025-2035):</p><p dir="ltr">- Black hole shadow radius increased by factor φ (Sgr A*: 81 μas vs 50 μas GR) - testable by EHT in 2027</p><p dir="ltr">- Higgs width: 5.082 MeV (vs 4.07 MeV SM) - testable at HL-LHC</p><p dir="ltr">- Axion mass: 58.1 μeV - testable by ADMX</p><p dir="ltr">- LIGO ringdown: golden ratio frequency spacing</p><p><br></p><p dir="ltr">Master equation: S_CPSD = φ² × S_Bekenstein-Hawking</p><p><br></p><p dir="ltr">All 26 Standard Model parameters emerge from a single geometric principle with ZERO free parameters. Complete Python verification code included.</p><p><br></p><p dir="ltr">This work solves the hierarchy problem, explains dark energy as geometric anisotropy, and predicts the universe's ultimate fate as "Big Silence" - eternal expansion at c/φ ≈ 0.618c.…”

Table 2_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.xlsx by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 11_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 1_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 12_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 7_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 6_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Phylogenomics of aquatic bacteria by Krzysztof Jurdzinski (12519700)

“…It contains names of the MAGs in format {data source 2-letter code}_{name of the MAG as in  ENA}, the biome of origin and taxonomic classification. …”

Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management by Ning Li (18882138)

“…This repository contains data and code for a research project replicating human psychological experiments using Large Language Models (LLMs). …”

<b>VISION — an open-source software for automated multi-dimensional image analysis of cellular biophysics</b> by Erdinc Sezgin (10714689)

“…However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. …”