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). …”

A Dataset on the Biodiversity Footprints and Sectoral Differences in China by haiyue wang (20803663)

“…It comprises the following components: (1) China’s raw species spatial data, sourced from the IUCN and BirdLife International, with 446 threatened and near-threatened species attributes provided in a CSV file. (2) Biodiversity footprint data of China's 19 economic sectors across 30 provinces (2017), with two versions: Version 1: 352 species (excluding NT species) Version 2: 446 species (including NT species) Stored as “2017 China Provincial Biodiversity Footprint Data” in Shapefile format. (3) Biodiversity footprint data by taxonomic group (Mammal, Amphibian, Reptile, and Bird) across 30 provinces (2017), with two versions as above, stored as “2017 China Taxonomic Biodiversity Footprint Data” in Shapefile format. (4) A procedural demonstration of matrix operations with detailed algorithmic steps for specific species, stored as “An example detailing the computational steps for specific species” in PDF format. (5) Code.…”

Table 1_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.docx by Zhenling Dai (22011134)

“…In this study, we aim to explore the role of the cGAS-STING pathway in breast cancer immunotherapy resistance.</p>Methods<p>Multiple machine learning algorithms were applied to construct an immunotherapy subgroup model and in vitro experiments were performed to verify the HOXC13 in regulating BRCA immunity.…”

Image 2_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.tif by Zhenling Dai (22011134)

“…In this study, we aim to explore the role of the cGAS-STING pathway in breast cancer immunotherapy resistance.</p>Methods<p>Multiple machine learning algorithms were applied to construct an immunotherapy subgroup model and in vitro experiments were performed to verify the HOXC13 in regulating BRCA immunity.…”

Image 1_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.tif by Zhenling Dai (22011134)

“…In this study, we aim to explore the role of the cGAS-STING pathway in breast cancer immunotherapy resistance.</p>Methods<p>Multiple machine learning algorithms were applied to construct an immunotherapy subgroup model and in vitro experiments were performed to verify the HOXC13 in regulating BRCA immunity.…”

Additional file 1 of The origin and evolution of cultivated rice and genomic signatures of heterosis for yield traits in super-hybrid rice by Yiyong Zhao (21466062)

“…Each gene's expression data is associated with a specific tissue sample, using a naming convention that includes the gene identifier, variety code, and tissue type, separated by underscores. …”

Methodological overview. by Jinming Xiao (12517096)

“…<p>(A) The source reconstruction of TMS-evoked potential of each subject was performed using dSPM method based on MNE software library. The time series of cortical activity were extracted through Schaefer 200 parcellation atlas. …”

A Dataset on the Biodiversity Footprints and Sectoral Differences in China by Haiyue Wang (20698763)

“…<p dir="ltr">(1) China’s species data stored in the file “2017 China Species Spatial Data” in CSV format, spatial data sourced from the IUCN and BirdLife International.…”

Dataset for Partial Parallelism Plot Analysis in Neurodegeneration Biomarker Assays (2010–2024) by Axel Petzold (7076261)

“…<br></p><p dir="ltr">Each dataset entry is annotated with:</p><ul><li>Sample type (serum, plasma, cerebrospinal fluid)</li><li>Assay platform and dilution steps</li><li>Classification of outcome (partial parallelism achieved or not)</li></ul><p dir="ltr"><b>Use cases:</b><br>This dataset is designed to help researchers, assay developers, and meta-analysts to:</p><ul><li>Reproduce figures and analyses from the published review</li><li>Benchmark or validate new assay performance pipelines</li><li>Train algorithms for automated detection of dilutional non-parallelism</li></ul><p dir="ltr"><b>Files included:</b></p><ul><li><code>.csv</code> files containing dilution–response data</li><li>Metadata spreadsheets with assay and sample annotations</li></ul><p></p>…”