Table 1_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.docx 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 5_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 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. …”

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. …”

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. …”

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

“…Its efficiency and scalability make it well-suited for early-stage antibody discovery, repertoire profiling, and therapeutic design, particularly in the absence of structural data. The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.…”

<b>Alpha-Synuclein Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…</p><p dir="ltr">Whether your work involves biomarker development, precision neurology, or machine learning, this dataset provides structured, labelled inputs that are ideal for:</p><ul><li>Training supervised models to detect or predict cleavage sites</li><li>Feature extraction from protein sequences</li><li>Clustering or classification of fragment types by mutation or disease context</li><li>Integrating with omics data for multimodal prediction tasks</li></ul><p dir="ltr">Dataset Features:</p><ul><li>Annotated α-synuclein proteolytic fragments</li><li>Includes wild-type and clinically relevant variants</li><li>Tab-delimited ASCII format for compatibility with Python, R, and ML frameworks</li><li>Linked SAS and Python scripts for pipeline reproducibility and updates</li><li>Ready-to-use for computational modelling, AI training, and bioinformatics workflows</li></ul><p dir="ltr">The dataset was generated using a reproducible codes involving Python, BLAST, and SAS. …”

Concurrent spin squeezing and field tracking with machine learning by Junlei Duan (18393642)

“…<p dir="ltr">The dataset contains:</p><ol><li>Steady_squeezing.zip <b>a)</b> data for steady squeezing data and characteraztion <b>b)</b> data for pulse RF magnetormeter</li><li>Tracking1.zip <b>a)</b> data of OU process for Deep learning <b>b)</b> data of OU-jump process for Deep learning</li><li>Tracking2.zip <b>a)</b> data of white noise process in backaction experiment <b>b) </b>data of white noise process in rearrange experiment</li><li>Code <b>a)</b> Randomly signal generating code <b>b)</b> Deep learning codec.data pre-processing code</li></ol><p dir="ltr">The network is implemented using the torch 1.13.1 framework and CUDA 11.6 on Python 3.8.8. …”