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Hey, #GoogleEarthEngine enthusiasts! New #eemont tutorial! 🚀 033 - Exploring ALL #EarthEngine Apps from #Python! 🐍 (using ee-appshot! Samapriya Roy, PhD.) 🖥️ Tutorial: eemont.readthedocs.io/en/latest/tuto… GitHub: github.com/davemlz/eemont #RemoteSensing #GeoPython #EOchat #GISchat #DataViz

“Hello World” our kick-off meeting is in full swing!

dimRed v0.2.5 released! cran.r-project.org/web/packages/d…

Watch a t-SNE of the MNIST data converge youtube.com/watch?v=TZzicX… Animations created in R with the Rtsne package #RStats #tSNE

Since today you can explore data cubes interactively! Congrats to Maximilian Söchting for launching lexcube.org - an interactive #EarthSystemDataCube 🌎 viewer developed @RSC4Earth in close cooperation with #ImageAndSignalProcessingGroup UNIVERSITÄT LEIPZIG. Why do we need this? 🧵

coRanking - Calculates the co-ranking matrix to assess the quality of a dimensionality reduction. #rstats github.com/gdkrmr/coranki…

rstats.wtf will be a mandatory read for all my future students! #RStats

I am excited to announce the next major release for #Lexcube, our interactive #EarthSystemDataCube 🌎 viewer. Now featuring 🎬animations, 🅰️3D axis labels, and a new #CopernicusAtmosphere data set ready to explore at lexcube.org 🧵(1/4) Copernicus ECMWF ECMWF ESA Earth Observation

Questions? Feel free to DM Hannes Feilhauer, me or Guido Kraemer

Major update from #Lexcube! 🌎🔍You can now explore anomalies, ✂️make your own paper cube, and 🎁check out the new sun-induced fluorescence and specific humidity data sets at➡️lexcube.org! Copernicus ECMWF ECMWF Copernicus EU ⤵️🧵

What’s new? ✂️ Paper Cubes: make your own paper cube from any selection and data set! Simply click the printer button, print out the template and cut & glue your own paper cube! High-quality paper recommended 📸

🔍 Anomalies: for each parameter, you can now visualize their anomalies! Simply select the respective option in the parameter drop down.

📊 However, Guido Kraemer and I think that the figure itself needs to be improved. First, the radial bar plot used in PB visuals leads to a quadratic scaling of effect sizes, potentially distorting our perception of environmental risks. #DataVisualization

🎨 Second, the color gradations indicating risk levels in PB visuals are complex and non-linear. This makes interpreting the severity of boundary transgressions challenging and limiting #ScienceCommunication

🔍 Third, the combination of quadratic scaling and specific color coding in PB visuals might unintentionally exaggerate high-risk areas. Yet, given the quantitative nature of the last paper, they need to accurately reflect the underlying data...

🔄 We suggests revising the PB concept's visual language. Maybe using a simpler graphical language helps? Discrete color scales can lead to clearer communication. Simple is better.

Wolfgang Lucht 🇪🇺 @[email protected] Katherine Richardson Jonathan F. Donges Sarah Cornell 💙 Thanks for your thoughts! Guido Kraemer and I answer: 1⃣ Risks are high enough, no need to overstretch. 2⃣ You also have a bar plot, just changed the coordinate system, ➡️ visually more complicated but remains a comparison. 3⃣IPCC schemes are not that nonlinear, thus readable... 🧵