Multi-Task Learning in Analyzing the Working capacity of MOFs by Junhui Kou (20327073)

“…</p><ul><li><b>CIF files</b>: CIF files for 252,352 MOFs;</li><li><b>Geometric descriptors</b>: 14 geometric descriptors;</li><li><b>Chemical descriptors</b>: 176 chemical descriptors;</li><li><b>Methane_v, Methane_g</b>: Volumetric and gravimetric working capacities for methane adsorption, including methane adsorption data under six pressures across three application scenarios (landfill gas treatment, methane purification, and methane storage);</li><li><b>MTL4MOFsWC</b>: Python code for training the MTL models to predict the working capacity of methane adsorption in MOFs;</li><li><b>best_model_v_full, best_model_v_sim, best_model_g_full, best_model_g_sim</b>: Pre-trained MTL models.…”

Supplementary file 1_An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction.docx by Songpeng Zhao (21563714)

“…Three machine learning algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (Gradient Boosting)-were integrated into a multi-level stacking ensemble, with Support Vector Regression serving as the meta-learner. …”

Hippocampal and cortical activity reflect early hyperexcitability in an Alzheimer's mouse model by Marina Diachenko (19739092)

“…</b></p><p dir="ltr">*Correspondence: Klaus Linkenkaer-Hansen (klaus.linkenkaer@cncr.vu.nl)</p><p dir="ltr"><br></p><p dir="ltr">In this study, we investigated fE/I, θ-γ PAC, and epileptiform features in hippocampal and cortical local field potentials (LFPs) recorded weekly in freely behaving male APPswe/PS1dE9 (APP/PS1) mice (<i>n</i> = 10) and wildtype controls (<i>n</i> = 10) between 3 and up to and including 11 months of age.</p><p dir="ltr">All data are available upon request. The standalone Python implementation of the fE/I algorithm is available under a CC-BY-NC-SA license at <a href="https://github.com/arthur-ervin/crosci" target="_blank">https://github.com/arthur-ervin/crosci</a>. …”

<b>An Empirical Evaluation of Software Quality Classification Based on User Feedback Aligned</b><b>with ISO/IEC 25010</b> by mesut polatgil (22408147)

“…<p dir="ltr">Evaluating software quality without access to the source code is a challenging task, as traditional metrics and testing approaches often rely on internal code analysis. …”

Processed Dataset for “Optimization of Mixed-Model Multi-Manned Assembly Lines for Fuel–Electric Vehicle Co-Production under Workstation Sharing” by Lingling Hu (22555691)

“…It can be used to replicate the analysis and experiments presented in the paper or to test new optimization algorithms for mixed-model assembly line balancing.</p><p dir="ltr">The data are provided in <code>.xlsx</code> format and can be freely accessed for <b>academic and non-commercial purposes only</b>.…”

Processed Dataset for “Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism” by Lingling Hu (22555691)

“…</p><p dir="ltr">The data are provided in <code>.xlsx</code> format and may be freely accessed and reused for <b>academic and non-commercial purposes only</b>.…”

Text-to-SQL Verification Methods and Benchmark by Tarfah Alrashed (15442229)

“…The code allows for the reproduction of our verification experiments and the evaluation of other verification algorithms on our new benchmark.…”

Classification of cell types in the leaf and the sepal. by Frances K. Clark (5617169)

“…Associated with <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003469#pbio.3003469.s010" target="_blank">S10 Fig</a>. The code and data associated with this figure can be found at Open Science Framework (osf.io), <a href="https://doi.org/10.17605/OSF.IO/RFCWS" target="_blank">https://doi.org/10.17605/OSF.IO/RFCWS</a>.…”

Data Sheet 1_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…Background<p>Water and nitrogen are essential elements prone to deficiency during plant growth. Current water–fertilizer monitoring technologies are unable to meet the demands of large-scale Glycyrrhiza uralensis cultivation. …”

Data Sheet 2_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…Background<p>Water and nitrogen are essential elements prone to deficiency during plant growth. Current water–fertilizer monitoring technologies are unable to meet the demands of large-scale Glycyrrhiza uralensis cultivation. …”

The 2009 FDA PRO guidance, Potential Type I error, Descriptive Statistics and Pragmatic estimation of the number of interviews for item elicitation by Josh Fleckner (22075124)

“…The URN model provides a framework to demonstrate that an algorithm such as “first interview with zero new codes” may establish that all codes have been elicited. …”

TreeMap 2016: A tree-level model of the forests of the conterminous United States circa 2016 by Karin L. Riley (19657882)

“…We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). …”

ImproBR Replication Package by Anonymus (18533633)

“…<br><br>**Import Errors:**<br>Make sure you're in the replication package directory:<br>```bash<br>cd ImproBR-Replication<br>python improbr_pipeline.py --help<br>```<br><br>## Research Results & Evaluation Data<br>### RQ1: Bug Report Improvement Evaluation (139 reports)<br>**Manual Evaluation Results:**<br>- [`RQ1-RQ2/RQ1/manual_evaluation/Author 1 Responses.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Author 1 Responses.csv</u>) - First evaluator assessments<br>- [`RQ1-RQ2/RQ1/manual_evaluation/Author 2 Responses.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Author 2 Responses.csv</u>) - Second evaluator assessments <br>- [`RQ1-RQ2/RQ1/manual_evaluation/Final Results.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Final Results.csv</u>) - Consolidated evaluation results<br><br>**Inter-Rater Agreement (Cohen's Kappa):**<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_s2r_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_s2r_label.png</u>) - Steps to Reproduce κ scores<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_ob_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_ob_label.png</u>) - Observed Behavior κ scores<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_eb_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_eb_label.png</u>) - Expected Behavior κ scores<br><br>**Algorithm Results:**<br>- [`RQ1-RQ2/RQ1/algorithm_results/improbr_outputs/`](<u>RQ1-RQ2/RQ1/algorithm_results/improbr_outputs/</u>) - ImproBR improved reports<br>- [`RQ1-RQ2/RQ1/algorithm_results/chatbr_outputs/`](<u>RQ1-RQ2/RQ1/algorithm_results/chatbr_outputs/</u>) - ChatBR baseline results<br>- [`RQ1-RQ2/RQ1/algorithm_results/bee_analysis/`](<u>RQ1-RQ2/RQ1/algorithm_results/bee_analysis/</u>) - BEE tool structural analysis<br><br>### RQ2: Comparative Analysis vs ChatBR (37 pairs)<br>**Similarity Score Results:**<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_tfidf.csv`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_tfidf.csv</u>) - TF-IDF similarity scores<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_word2vec.csv`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_word2vec.csv</u>) - Word2Vec similarity scores<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/exact_string_comparisons.json`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/exact_string_comparisons.json</u>) - Complete TF-IDF comparison with scores for each comparison unit (full debugging)<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/word2vec_comparisons.json`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/word2vec_comparisons.json</u>) - Complete Word2Vec comparison with scores for each comparison unit (full debugging)<br><br>**Algorithm Outputs:**<br>- [`RQ1-RQ2/RQ2/algorithm_results/ImproBR_outputs/`](<u>RQ1-RQ2/RQ2/algorithm_results/ImproBR_outputs/</u>) - ImproBR enhanced reports<br>- [`RQ1-RQ2/RQ2/algorithm_results/ChatBR_outputs/`](<u>RQ1-RQ2/RQ2/algorithm_results/ChatBR_outputs/</u>) - ChatBR baseline outputs<br>- [`RQ1-RQ2/RQ2/dataset/ground_truth/`](<u>RQ1-RQ2/RQ2/dataset/ground_truth/</u>) - High-quality reference reports<br>## Important Notes<br><br>1. …”

Supplementary information files for "Explainable machine learning models for predicting the ultimate bending capacity of slotted perforated cold-formed steel beams under distortion... by L Simwanda (19921680)

“…Utilizing a dataset from 432 non-linear finite element analysis simulations of CFS Lipped channels, ten ML algorithms, including four basic and six ensemble models, were evaluated. …”

Confusion_Matrix_Data.zip by Mohammad Farhad Bulbul (21003494)

“…<p dir="ltr">This research paper proposes a novel approach for human activity recognition using depth video data, focusing on improving accuracy by effectively capturing motion information and utilizing a robust classification method. Here's a breakdown of the key elements:</p><p dir="ltr"><b>. …”

Supporting files for thesis "Deep-learning-based Morphological Modelling: Case Study in Soft Robot Control, Shape Sensing and Deformation" by Yingqi Li (9151304)

“…The algorithm of deep deterministic policy gradient (DDPG) along with domain randomization and offline retraining facilitates fast initialization and stable path following, even under varying tip load, demonstrating its advantages over Jacobian model-based and supervised-learning-based control methods. …”

Integrating drought warning water level with analytical hedging for reservoir water supply operation by Wenhua Wan (8051543)

“…</p><p dir="ltr">2. R codes for the HR-based DP algorithm, the processes deriving seasonal DWWL, and the statistical performance of HR with DWWL during typical drought years.…”

Gaze Inputs for Targeting: The Eyes Have It, Not With a Cursor by Ajoy S. Fernandes (22293773)

“…If the participant looked out of the grid boundary, on button press, we chose to select the last targeted element, but no other algorithms to enhance performance were employed. …”

TreeMap 2020 CONUS: A tree-level model of the forests of the conterminous United States circa 2020 by Scott N. Zimmer (20807459)

“…We used a Random Forest machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE) and Daymet (Daymet). …”

TreeMap 2022 CONUS: A tree-level model of the forests of the conterminous United States circa 2022 by Rachel M. Houtman (19658365)

“…We used a Random Forest machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE) and Daymet (Daymet). …”