<b>Dataset for manuscript: </b><b>Phylogenetic and genomic insights into the evolution of terpenoid biosynthesis genes in diverse plant lineages</b> by Puguang Zhao (19023065)

“…</p><p dir="ltr"> The two scripts will calculate median Ka/Ks values and median TPM values</p><p dir="ltr"> 5.2.3. …”

Bacterial persistence modulates the speed, magnitude and onset of antibiotic resistance evolution by Giorgio Boccarella (22810952)

“…</p><p dir="ltr">complete_data.xlsx</p><p dir="ltr">A single Excel file containing 18 sheets with data from all figures:</p><p dir="ltr">Sheet names and descriptions:</p><ul><li>Fig_1: Probability of emergence contour data</li><li>Fig_2_b: MIC evolution simulation data</li><li>Fig_2_c: Speed of resistance evolution data</li><li>Fig_2_d: Time to resistance data</li><li>Fig_2_a_d_time_series_sim7: Simulation time series data (representative simulation, low persistence)</li><li>Fig_2_a_d_MIC_values_sim7: MIC values from simulation (representative simulation, low persistence)</li><li>Fig_2_a_p_time_series_sim5: Simulation time series data (representative simulation, high persistence)</li><li>Fig_2_a_p_MIC_values_sim5: MIC values from simulation (representative simulation, high persistence)</li><li>Fig_3_a-b: Distribution plot simulation data</li><li>Fig_3_a-b_empirical: Distribution plot empirical data</li><li>Fig_4_a: Mutation count simulation data</li><li>Fig_4_b: Mutation count empirical data</li><li>Fig_4_c: Mutation functional data</li><li>Fig_5_a-b: Large-scale simulation results (heatmap data)</li><li>Fig_5_c_mic: MIC heatmap empirical data</li><li>Fig_5_c_extinction: Extinction heatmap empirical data</li><li>Fig_6: Population size analysis simulation data</li><li>S1_figure: Supplementary experimental survival data</li></ul><p dir="ltr">Column naming convention</p><p dir="ltr">All sheets use consistent, tidy column names.…”

Tissue specific expression of ACE2 and ENPEP in 55 human tissues. by Antti Arppo (20416905)

“…Each boxplot displays the distribution of expression values for the gene-tissue intersection: the box represents the interquartile range (IQR), the lower boundary marks the 1st quartile (Q1), the upper boundary marks the 3rd quartile (Q3), and the horizontal line within the box indicates the median. …”

Sonification of Warming Stripes by Christopher Harrison (9448751)

“…<p dir="ltr">We present a sonification of the temperature anomalies (deviations from an average, reference value) on the Earth’s near-surface over the period 1930–2024. …”

Oka et al., Supplementary Data for "Development of a battery emulator using deep learning model to predict the charge–discharge voltage profile of lithium-ion batteries" by Kanato Oka (20132185)

“…</p><p><br></p><p dir="ltr">02_lstm_reuse_pthmodel.py</p><p dir="ltr">This file is a Python script that reads the .pth file output by 01_lstm_model_making.py and predicts the voltage profile from the given current value and the integrated value of current and time (proportional to SOC). …”

Supplementary Material for: The prediction of hematoma growth in acute intracerebral hemorrhage: from 2-dimensional shape to 3-dimensional morphology by figshare admin karger (2628495)

“…Amongst all prediction models, the PCM presented the highest predictive value for active bleeding. Conclusions: The Sphericity≤0.56 and SurfaceArea >55 cm2 could represent the optimal threshold for HG prediction. …”

<b>Engineered Muscle-Derived Extracellular Vesicles Boost Insulin Sensitivity and Glucose Regulation</b> by Hagit Shoyhet (21090650)

“…</p><p dir="ltr"><b>miR_path_target_enrichment.csv</b></p><p dir="ltr"><b>Description:</b> KEGG pathway enrichment analysis results of shared mRNA targets of miRNAs miR-16-5p, miR-122-5p and miR-486-5p ranked by their interaction score defined in our paper. this includes the pathway name, the enrichment p-value, number of genes found in the term and number of miRNAs targeting these genes</p><p dir="ltr"><b>Code/software</b></p><p dir="ltr">Data were analyzed using R-V4.0.4, Python-V3.9.2 and GraphPad software. miRNA analyses were run in R-V4.0.4 Differential expression analysis was conducted using the “DEseq2” package and corrected for multiple hypotheses by FDR. …”

Comparison of MODIS and SGLI Albedo Retrievals Over the Sea of Okhotsk (January-May 2021) by Yingzhen Zhou (19723405)

“…Values are provided for the period January-May 2021 on a grid with a spatial resolution of 1 km.…”

MCCN Case Study 3 - Select optimal survey locality by Donald Hobern (21435904)

“…</p><p dir="ltr">This is a simple implementation that uses four environmental attributes imported for all Australia (or a subset like NSW) at a moderate grid scale:</p><ol><li>Digital soil maps for key soil properties over New South Wales, version 2.0 - SEED - see <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html" target="_blank">https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html</a></li><li>ANUCLIM Annual Mean Rainfall raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer</a></li><li>ANUCLIM Annual Mean Temperature raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer</a></li></ol><h4><b>Dependencies</b></h4><ul><li>This notebook requires Python 3.10 or higher</li><li>Install relevant Python libraries with: <b>pip install mccn-engine rocrate</b></li><li>Installing mccn-engine will install other dependencies</li></ul><h4><b>Overview</b></h4><ol><li>Generate STAC metadata for layers from predefined configuratiion</li><li>Load data cube and exclude nodata values</li><li>Scale all variables to a 0.0-1.0 range</li><li>Select four layers for comparison (soil organic carbon 0-30 cm, soil pH 0-30 cm, mean annual rainfall, mean annual temperature)</li><li>Select 10 random points within NSW</li><li>Generate 10 new layers representing standardised environmental distance between one of the selected points and all other points in NSW</li><li>For every point in NSW, find the lowest environmental distance to any of the selected points</li><li>Select the point in NSW that has the highest value for the lowest environmental distance to any selected point - this is the most different point</li><li>Clean up and save results to RO-Crate</li></ol><p><br></p>…”

Image 1_An explainable analysis of depression status and influencing factors among nursing students.png by Yingying Li (50341)

“…Data cleaning was performed in Excel, and statistical analyses were conducted using SPSS Statistics version 27.0 and Python 3.9.</p>Results<p>The incidence of depression among nursing students is 28.60%. …”

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…</p><p dir="ltr">All data are stored in GeoTIFF (.tif) format and can be accessed and processed using ArcGIS, ENVI, R, and Python. Each GeoTIFF file contains grid-based predictions of habitat suitability, with values ranging from 0 to 1. …”

Indirect Reciprocity and the Evolution of Prejudicial Groups by Gualtiero Colombo (19078925)

“…A reputation system based on social comparison is maintained to help agent decisions to donate in order to prevent exploitation by defective individuals. Two reputation values are maintained per individual representing the views of the agent’s own group (group reputation) and those of others external to the group (universal reputation). …”

Dataset for the Modeling and Bibliometric Analysis of E-business in Entrepreneurship (1997–2024) by Aggie Moses Zeuse (21460466)

“…This curated corpus represents the contributions of 750 authors affiliated with 75 different countries, offering a rich, global perspective on the evolution of the field.…”

Endogenous giant viruses in polar microalgae by Mohammad Moniruzzaman (20325651)

“…Log2foldchange and adjusted p values (padj) are provided along with other statistics.…”

Data Sheet 1_Establishing a real-time biomarker-to-LLM interface: a modular pipeline for HRV signal acquisition, processing, and physiological state interpretation via generative A... by Morris Gellisch (18627744)

“…</p>Methods<p>Using a validated heart rate variability sensor, we decoded Bluetooth-transmitted R-R intervals via a custom Python script and derived core heart rate variability metrics (HR, RMSSD, SDNN, LF/HF ratio, pNN50) in real time. …”

End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting by Jamie Hathaway (10285367)

“…</p><h3>policy/</h3><p dir="ltr">This folder contains pickled trajectories, in the form of a Python list.</p><p dir="ltr">The list's elements are TrajWithRew dataclass objects from the Imitation Python library (https://imitation.readthedocs.io/en/latest/)</p><p dir="ltr">TrajWithRew contains 4 main fields</p><ul><li> obs - the (unnormalised) observations, in the form of a [WINDOW_LENGTH * NUM_CHANNELS] array</li><li> acts - the actions in the form of a [WINDOW_LENGTH - 1 * NUM_ACTS] array</li><li> infos - the info values at each timestep, as a [WINDOW_LENGTH - 1] array of dicts</li><li> terminals - boolean indicating if that trajectory segment is a terminal segment</li><li> rews - the rewards as a [WINDOW_LENGTH - 1] array</li></ul><p dir="ltr">Each TrajWithRew represents not a full episodic trajectory, as is usually the case with Imitiation - rather they represent segments of a full episodic trajectory, of length WINDOW_LENGTH. …”

The global dryland lake storage (GDLS) dataset by Gang Zhao (18368925)

“…"occurrence_max" represents the maximum occurrence value for a lake. …”

Related to Figs 1, 2 and S1. by Tyron Chang (9922533)

“…Sheet B, C, D, and E in S1 Table (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1012673#ppat.1012673.g002" target="_blank">Fig 2</a>): Log₂TPM (transcripts per million) RNA-Seq values and statistics for representatives from ISG subclasses ‐ 2 high, 2 low, and 5 variable ‐ across 578 human lung tissue samples (Sheet B in S1 Table), 226 human liver tissue samples (Sheet C in S1 Table), 241 human spleen tissue samples (Sheet D in S1 Table), and 755 human whole blood tissue samples (Sheet E in S1 Table). …”

Dataset for CNN-based Bayesian Calibration of TELEMAC-2D Hydraulic Model by Jose Zevallos (21379988)

“…</li><li>Files starting with <code>y_part</code> are flattened output arrays representing corresponding water depth values.</li></ul></li></ul><p dir="ltr">The <code>.npy</code> files were loaded and processed using the following approach in Python:</p><p dir="ltr"># Load the input and output numpy arrays</p><p dir="ltr">input_path = "..…”

Code by Baoqiang Chen (21099509)

“…For gene-level RNA features, we used the value from the highest-expressing transcript to represent the gene-level RNA feature. …”