Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

I periodically post this visualization reminder as summer approaches in the Northern Hemisphere - think heatwaves 🫠 The changing distribution of land temperature anomalies since 1950... Animation is by NASA's Scientific Visualization Studio using GISTEMPv4 data. More information at

98,156 Aufrufe • vor 3 Jahren •via X (Twitter)

11 Kommentare

Profilbild von YakultSmoothie
YakultSmoothievor 3 Jahren

@NASAViz Not only is the global average temperature rising each year, but also the temperature variability is also increasing. It indicates that extreme temperature events are happening more frequently.

Profilbild von Weather, Maps and some AI
Weather, Maps and some AIvor 2 Jahren

Do you have the right weather app for this season?

Profilbild von 2025-TheLiesRevealedWillDestroyAllButTheBlueDogs
2025-TheLiesRevealedWillDestroyAllButTheBlueDogsvor 3 Jahren

@NASAViz Now show the temperatures that weren’t taken at airports or othef heat generating tarmacs or buildings.

Profilbild von Jaap
Jaapvor 3 Jahren

@EC_Kosters @NASAViz Great visualisation!

Profilbild von PhD_Tarantoga
PhD_Tarantogavor 3 Jahren

@NASAViz the global climate has become warmer and more comfortable

Profilbild von Scott Wright
Scott Wrightvor 3 Jahren

@NASAViz What are the axises?

Profilbild von Zack Labe
Zack Labevor 3 Jahren

@NASAViz The PDFs are derived from a kernal density estimator ( with the x-axis as the temperature anomaly relative to a 1951-1980 baseline (

Profilbild von Anita Khalini
Anita Khalinivor 3 Jahren

@NASAViz It would be more helpful to see this further back than 1951. Where does the data come from and what is it measuring?

Profilbild von Svante Törnquist
Svante Törnquistvor 3 Jahren

@NASAViz Nice visual! I find it really interesting that the distribution has widened so much. It means we can still get extreme cold but more seldom. But in the hot end, it´s much higher, witch means it's asymetrical. I'm not sure I can think of an obvious explanation to this, can you?

Profilbild von Zack Labe
Zack Labevor 3 Jahren

@NASAViz In addition to resulting from differences in the spatial distribution in the rate of warming patterns, here is some more information on the statistical appearance:

Profilbild von Darrell Welch
Darrell Welchvor 3 Jahren

@NASAViz Does anyone else see a pattern here or am I the only one. Al Gore is a profiteer

Ähnliche Videos

Most people think Rerun is a visualization tool. In reality, it's a database masquerading as a visualizer. I wanted to showcase this functionality by building a full data pipeline consisting of: ingestion → baseline method → eval → finetuning for SLAM on egocentric data. I'll eventually extend this to the rest of my ego/exo datasets, but I wanted to start with a smaller bunch of datasets first. Rerun allows you to expose your saved .rrd files to a catalog where you store datasets. You can query, filter, and join them like any database using DataFusion under the hood. These are the same .rrd files that are automatically generated whenever you visualize anything in Rerun and decide to save it to disk. I brought in 109 VSLAM-LAB sequences across 14 datasets into the Rerun catalog as an example. These include 7Scenes, Euroc, eth3d, and others. Now I can query them with segment_table, filter_segments, and filter_contents instead of parsing CSVs and YAML files. With a strong set of ground-truth datasets for SLAM, baseline additions become nearly automatic with agents like Opus/Codex. This unification of data and visualization is imo the largest missing part for Physical AI. Visualization becomes a natural byproduct of having your data properly structured and queryable. The catalog API is what makes it a database, not just a viewer. I initially focused on VSLAM-LAB data, but I'll migrate all the egoexo data to this format in the coming days to really show just how useful this is.

Pablo Vela

34,937 Aufrufe • vor 3 Monaten

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,012 Aufrufe • vor 1 Jahr