Fast, FAIR, and Scalable: Managing Big Data in HPC with Zarr by Alfonso Ladino (21447002)

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

“…</p><h4><b>Reproducibility</b></h4><p dir="ltr">All datasets can be regenerated using open-source tools (Python, BLAST, SAS). Fully documented scripts are available [3] to ensure transparency, reproducibility, and adaptability across computational environments, following the methodology described in [2].…”

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

“…The mask uses the same classification scheme as the MODIS mask:<br>• 0: Water<br>• 1: Sea-ice<br>• 2: Sea-covered ice</p><p><br></p><h2>Time Periods Covered:</h2><p dir="ltr"><br>The dataset spans eight weekly time intervals, corresponding to the following periods:</p><p>• 2021-01-08 ~ 2021-01-13<br>• 2021-01-31 ~ 2021-02-06<br>• 2021-02-11 ~ 2021-02-17<br>• 2021-03-05 ~ 2021-03-12<br>• 2021-03-15 ~ 2021-03-20<br>• 2021-04-01 ~ 2021-04-07<br>• 2021-04-22 ~ 2021-04-30<br>• 2021-05-07 ~ 2021-05-12</p><p dir="ltr">Each period represents a weekly aggregate of albedo and surface classification data from both sensors, averaged where valid sea-ice pixels were present.</p><h2>Spatial Coverage:</h2><p dir="ltr">• Latitude: 40° to 64° N<br>• Longitude: 135° to 165° E</p><p><br></p><h2>Format:</h2><p dir="ltr"><br>The dataset is provided in NetCDF format (.nc), which can be read and analyzed using common geospatial and scientific data tools such as Python’s xarray or Panoply.…”

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. …”

Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service) by U.S. Forest Service (17476914)

“…Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. …”

Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management by Ning Li (18882138)

“…. ## Project Overview This research investigates whether LLMs can replicate results from human psychological experiments. By presenting LLMs with identical experimental conditions used in the original studies, we compare LLM responses with human responses to assess their understanding of human psychology and potential utility as research tools. …”