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  1. 101

    Comparison data 1 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
  2. 102

    Comparison data 2 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
  3. 103

    Comparison data 5 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
  4. 104

    Comparison data 6 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
  5. 105

    PTPC-UHT bounce by David Parry (22169299)

    Published 2025
    “…<br>It contains the full Python implementation of the PTPC bounce model (<code>PTPC_UHT_bounce.py</code>) and representative outputs used to generate the figures in the paper. …”
  6. 106

    Code and data for reproducing the results in the original paper of DML-Geo by Pengfei CHEN (8059976)

    Published 2025
    “…<p dir="ltr">This asset provides all the code and data for reproducing the results (figures and statistics) in the original paper of DML-Geo</p><h2>Main Files:</h2><p dir="ltr"><b>main.ipynb</b>: the main notebook to generate all the figures and data presented in the paper</p><p dir="ltr"><b>data_generator.py</b>: used for generating synthetic datasets to validate the performance of different models</p><p dir="ltr"><b>dml_models.py</b>: Contains implementations of different Double Machine Learning variants used in this study.…”
  7. 107

    Data sets and coding scripts for research on sensory processing in ADHD and ASD by Vesko Varbanov (9687029)

    Published 2025
    “…The repository includes raw and matched datasets, analysis outputs, and the full Python code used for the matching pipeline.</p><h4>Ethics and Approval</h4><p dir="ltr">All procedures were approved by the University of Sheffield Department of Psychology Ethics Committee (Ref: 046476). …”
  8. 108

    DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF by Shivani V. Pawar (20355171)

    Published 2024
    “…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …”
  9. 109

    DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF by Shivani V. Pawar (20355171)

    Published 2024
    “…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …”
  10. 110

    Graphical abstract of HCAP. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  11. 111

    Recall analysis. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  12. 112

    Convergence rate analysis. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  13. 113

    Computational efficiency. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  14. 114

    Analysis of IoMT data sources. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  15. 115

    Prediction accuracy on varying attack types. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  16. 116

    <b> </b> Precision analysis. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  17. 117

    Prediction accuracy analysis over time steps. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  18. 118

    Impact of cyberattack types on IoMT devices. by Mohanad Faeq Ali (21354273)

    Published 2025
    “…The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
  19. 119

    MATH_code : False Data Injection Attack Detection in Smart Grids based on Reservoir Computing by Carl-Hendrik Peters (21530624)

    Published 2025
    “…</li><li><b>3_literature_analysis_and_mapping.ipynb</b><br>Contains the Python code used for executing the systematic mapping study (SMS), including automated processing of literature data and thematic clustering.…”
  20. 120