A Representative example of organ surface data before and after editing. by Takashi Kimura (178058)

Results derived from <i>D</i>-SPR analysis at 25°C: <i>D</i>-SPR values refer to the <i>D</i> calculated with the Python script; <i>error</i> column reports the standard deviation values and represents the variability within the mean of four experimental replicates. by Gabriele Antonio Zingale (12463357)

“…<p>Results derived from <i>D</i>-SPR analysis at 25°C: <i>D</i>-SPR values refer to the <i>D</i> calculated with the Python script; <i>error</i> column reports the standard deviation values and represents the variability within the mean of four experimental replicates.…”

Representative MSD plots, Fig. 6c,d by Hirotake Udono (21008660)

High-resolution images of <i>C. elegans</i> germlines shown in Fig. 5A for representative images from three genotypes (Wild-type, <i>zim-2</i>, <i>syp-4</i>) with manually annotate... by Oane Gros (20636735)

Prism and scutoid cell shape. by Sophie Theis (4126453)

Application to Drosophila gastrulation. by Sophie Theis (4126453)

Results derived from <i>D</i>-SPR analysis of the lysozyme (349μM) at 25°C: <i>D</i>-SPR values refer to the <i>D</i> calculated with the Python script described previously; the <i>error</i> column reports the standard deviation values and represents the variability within the mean of four experimental replicates; values are consistent with the ones reported in the existing literature [13]. by Gabriele Antonio Zingale (12463357)

“…<p>Results derived from <i>D</i>-SPR analysis of the lysozyme (349μM) at 25°C: <i>D</i>-SPR values refer to the <i>D</i> calculated with the Python script described previously; the <i>error</i> column reports the standard deviation values and represents the variability within the mean of four experimental replicates; values are consistent with the ones reported in the existing literature [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0312594#pone.0312594.ref013" target="_blank">13</a>].…”

Correlation (a) and Mean-Absolute-Error (b) between frequencies at a reference cutoff, δ₀, and different frequencies, , at different cutoff-values, δ_scan ∊ [3... by Guillermo Pérez-Hernández (21156182)

Automatic classification of germline nuclei provides quantifications of meiotic progression. by Oane Gros (20636735)

<i>Spherical Textures</i> and machine-learning classifications of <i>C. elegans</i> germline nuclei. by Oane Gros (20636735)

Catalogue of compact radio sources in Messier-82 from e-MERLIN observations by Sibongumusa Shungube (21197363)

“…The table includes the following columns:</p><p dir="ltr">(1) Source Number (sequential identifier)</p><p dir="ltr">(2) Source Name</p><p dir="ltr">(3) Right Ascension Offset (arcseconds from 09h55m00s J2000)</p><p dir="ltr">(4) Declination Offset (arcseconds from 69d40m00s J2000)</p><p dir="ltr">(5) Peak 1.5 GHz Flux Density (mJy/beam)</p><p dir="ltr">(6) Integrated 1.5 GHz Flux Density (mJy)</p><p dir="ltr">(7) Signal-to-Noise Ratio</p><p dir="ltr"><b>Note: </b>For a subset of sources (2, 12, 18, and 36), where the initial Gaussian fit yielded an integrated flux density lower than the peak flux density, the peak flux density was adopted as the representative integrated flux density for these entries.…”

Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , w... by Ziyan Jin (21021976)

Output datasets from ML–assisted bibliometric workflow in African phytochemical metabolomics research by Temitope Omogbene (18615415)

“…<p dir="ltr">This collection contains supplementary datasets generated during the machine learning–assisted bibliometric workflow for metabolomics and phytochemical research. The datasets represent sequential outputs derived from the integration and harmonisation of bibliographic metadata from <b>Scopus</b>, <b>Web of Science (WoS)</b>, and <b>Dimensions</b>, processed via R and Python environments.…”

An example of how segmentation corrects tracking during execution. by Khelina Fedorchuk (19130089)

Segmentation module training details. by Khelina Fedorchuk (19130089)

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>…”

Fig 3 - Using artificial intelligence tools to automate data extraction for living evidence syntheses by Evan Mitchell (20995743)

Data features examined for potential biases. by Harry Hochheiser (3413396)

Analysis topics. by Harry Hochheiser (3413396)

Overview of one by Guillermo Pérez-Hernández (21156182)