MCCN Case Study 1 - Evaluate impact from environmental events/pressures by Donald Hobern (21435904)

“…filters=eyJTVEFURSI6WyJBQ1QiXX0=&location=-35.437128,149.203518,11.00</a></li><li><b>caladenia_act.csv</b> - Distribution records for orchids in the genus <i>Caladenia</i> between 1990 and present from the ALA in CSV format: <a href="https://doi.org/10.26197/ala.1e501311-7077-403b-a743-59e096068fa0" target="_blank">https://doi.org/10.26197/ala.1e501311-7077-403b-a743-59e096068fa0</a></li></ul><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>Group orchid species records by species</li><li>Prepare STAC metadata records for each data source (separate records for the distribution data for each orchid species)</li><li>Load data cube</li><li>Mask orchid distribution records to boundaries of ACT</li><li>Calculate the proportion of distribution records for each species occurring inside and outside protected areas</li><li>Calculate the proportion of distribution records for each species occurring in areas with each class of vegetation cover</li><li>Report the apparent affinity between each species and protected areas and between each species and different classes of vegetation cover</li></ol><h4><b>Notes</b></h4><ul><li>No attempt is made here to compensate for underlying bias in the areas where observers have spent time recording orchids. …”

<b>Diminished signal-to-noise ratio disrupts somatosensory population encoding and drives tactile hyposensitivity in the </b><b><i>Fmr1</i></b>^{<strong>-/y</strong>}<b> au... by Ourania Semelidou (19178362)

Western Oregon Wet Dry (WOWTDR) annual predictions of late summer streamflow status for western Oregon, 2019-2021 by Jonathan D. Burnett (19659601)

“…Also included is all R code and Python code needed to run and process this model.…”

SPIDER - Synthetic Person Information Dataset for Entity Resolution by Praveen Chinnappa (20835779)

Dipankar_iCAS24.pptx by Anurag Dipankar (19701118)

A Quick Guide to Prompt Engineering: Using Generative AI Wisely in Learning and Work by Uce Indahyanti (21324434)

Probabilistic-QSR-GeoQA by Mohammad Kazemi (19442467)

“…<p dir="ltr">The code and data are related to the paper Mohammad Kazemi Beydokhti, Matt Duckham, Amy L. …”

Linking Thermal Conductivity to Equations of State Using the Residual Entropy Scaling Theory by Zhuo Li (165589)

“…<b>2021</b>, <i>60</i>, 13052) in modeling the thermal conductivity of refrigerants, we present here an RES model that extends Yang et al.’s approach to a wider range of pure fluids and their mixtures. …”

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)

“…Introduction<p>Large language models are capable of summarizing research, supporting clinical reasoning, and engaging in coherent conversations. …”

Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds by Gloria Castañeda-Valencia (20758502)

“…The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. …”

Leue Modulation Coefficients (LMC): A Smooth Continuum Embedding of Bounded Arithmetic Data by Jeanette Leue (22470409)

“…This Zenodo package includes: the full research paper (PDF), a complete Python implementation generating the LMC field and conductivity model, a numerical plot comparing discrete LMC values with the smoothed continuum field, a cover letter and supporting documentation. …”

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

“…Notably, “historical” refers to distribution probabilities modeled using 1970–2000 data, serving as the baseline. …”

Cost analysis of casting techniques. by Víctor Hugo Gonzalo Quispe Mamani (21213092)

“…In conclusion, the study demonstrates that EAC produces dental models with significantly higher compressive strength and a more refined microstructure compared to TC, enhancing restoration durability. …”

Overview of generalized weighted averages. by Nobuhito Manome (8882084)

“…GWA-UCB1 outperformed G-UCB1, UCB1-Tuned, and Thompson sampling in most problem settings and can be useful in many situations. The code is available at <a href="https://github.com/manome/python-mab" target="_blank">https://github.com/manome/python-mab</a>.…”

<b>Alpha B crystallin (CRYAB) Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…<p dir="ltr">This open-access dataset presents Version 1 of the <b>alpha B </b><b>Crystallin (CRYAB) Degradome Foundation Atlas</b>, a curated <b>proteolytic peptide resource</b> designed to advance translational research in protein aggregation myopathies and cardiomyopathies.…”

<b>China’s naturally regenerated forests currently have greater aboveground carbon accumulation rates than newly planted forests</b> by Kai Cheng (18367101)

“…As well as, the Google earth engine code for detecting their ages and extents, python code for modelling the carbon accumulation rate of China’s PYF and NYF, python code for evaluating the influence of various factors on the patterns and differences in AGC accumulation rates between NYF and PYF in China.…”

MEG Dataset and Analysis Scripts for “The Effects of Task Similarity During Representation Learning in Brains and Neural Networks” by Nicholas Menghi (22426174)

“…</p><h3><b>Contents</b></h3><ul><li><b>MEG data</b> (results of the correlation between empirical and model matrices at different dimensionalities and domains)</li><li><b>Behavioral data</b> (behavioural accuracy performance: "Spatual Source Data")</li><li><b>Analysis script</b></li><li><b>Python package </b>developed to help with retrieving and computing simple operations</li></ul><h3><b>Data format</b></h3><p dir="ltr">Data are organized according to a structured folder layout (see <code>README.md</code> in the repository) and include:</p><ul><li><code>npy</code> MEG files (numpy)</li><li><code>.csv</code> behavioral files</li><li>Python scripts using MNE-Python for statistical analysis and visualization</li></ul><h3><b>Usage</b></h3><p dir="ltr">The provided scripts reproduce the statistical tests and figures presented in the manuscript. …”

Supporting data for "Optimisation of Trust in Collaborative Human-Machine Intelligence in Construction" by Hao Chen (9696848)

“…Lastly, the fifth folder comprises the Unity3D model, experimental data (EEG, eye-tracking, and behavioral recordings), and analytical files (Python source code and SPSS files) supporting real-time trust estimation.…”

Moulin distributions during 2016-2021 on the southwest Greenland Ice Sheet by Kang Yang (7323734)

“…</p><p><br></p><ul><li>00_Satellite-derived moulins: Moulins directly mapped from Sentinel-2 imagery, representing actual moulin positions;</li><li>01_Snapped moulins: Moulins snapped to DEM-modeled supraglacial drainage networks, primarily used for analyses;</li><li>02_Moulin recurrences: Recurring moulins determined from the snapped moulins;</li><li>03_Internally drained catchments: Internally drained catchment (IDC) associated with each moulin;</li><li>04_Surface meltwater runoff: surface meltwater runoff calculated from MAR for the study area, elevation bins, and IDCs; </li><li>05_DEM-derived: Topographic features modeled from ArcticDEM, including elevation bins, depressions and drainage networks;</li><li>06_GWR: Variables for conducting geographically weighted regression (GWR) analysis;</li></ul><p><br></p><ul><li>Code_01_Mapping moulins on the southwestern GrIS.ipynb: A Jupyter Notebook to analyze moulin distributions, reproducing most of the analyses and figures presented in the manuscript using the provided datasets;</li><li>Code_02_pre1_calculate Strain Rate from XY ice velocity.py: A preprocessing Python script to calculate strain rate for the GWR analysis;</li><li>Code_02_pre2_calculate Driving Stress from ice thickness and surface slope.py: A preprocessing Python script to calculate driving stress for the GWR analysis;</li><li>Code_02_GWR analysis.ipynb: A Jupyter Notebook to conduct the GWR analysis using the provided datasets.…”

Data for "Are pseudo first-order kinetic constants properly calculated for catalytic membranes?" by Timothy Warner (20222838)

“…</li><li><b>catalytic_membrane_meta-analysis-0.1.3.zip</b>: zip folder containing figures from the publication and python codes and instructions for generating PFO results and individual figures for every research article listed in database.xlsx</li></ol><h2>Data Contents</h2><h3>database.xlsx</h3><h4>Porous Catalytic Membranes (Tab 1):</h4><ul><li>Index - number associated with analysis and figures generated by python codes in catalytic_membrane_meta-analysis-0.1.3.zip</li><li>Author full names</li><li>Title</li><li>Year</li><li>Source title</li><li>DOI</li><li>Abstract</li><li>Keywords</li></ul><h4>Data Table (Tab 2):</h4><ul><li>Index - number associated with analysis and figures generated by python codes in catalytic_membrane_meta-analysis-0.1.3.zip</li><li>Title</li><li>Comments on C/C0 Graph - description on what data was extracted, the format of the data and any modification required for analysis (if any).…”