What does your Spotify account say about your music taste?
I was curious – so I built a little app to find out. Using Python, Streamlit and Plotly, I visualised my top tracks, favourite genres and the release years of the songs I listen to most.

What you need:
Access to the Spotify Web API
A bit of Pandas for analysis
A few lines of code with Plotly
Streamlit to build and run the app

→ The result? A personal, interactive music year in review

I put together a step-by-step guide (beginner-friendly) including code, screenshots, and how to deploy the app on Streamlit Cloud.

Check out the full article here: bit.ly/3SF3VTw

#python #programming #technology #streamlit #spotify #Datavisualization #datascience #datascientist #opensource #api

Friends-Link: https://medium.com/data-science-collective/visualize-your-spotify-data-with-python-and-streamlit-4d4c46bbd39e?sk=12cb785242472daf6d77d024a90ad57c

Critical Data Study fresh from the press!

In this contribution to the latest special issue of the Swiss Journal of Sociology, I critically examine how Convolutional Neural Networks can be used to explore historical photo collections. In essence, the writing examines potentials and challenges of human-machine collaboration by juxtaposing human and machine ways of seeing. Clustering 48,000 negatives from the collection Ernst Brunner the analysis reveals how sociotechnical imaginaries in infrastructure act as an epistemological Trojan horse and emphasizes the need for thematic data sets to utilize machine-learning approaches for visual data analysis.

Open Access https://www.socio-journal.ch/article/view/6907

Thank you Sebastian W. Hoggenmüller for the great editing, to Kurt Fendt and Tobias Hodel for their critical remarks and to the anonymous reviewers who have given tough but constructive feedback.

From research prototype to production! This project began as a research idea during my internship with Ozette this time last year: what if we could create a chat interface for a single-cell phenotyping visual analytics dashboard, and actually have an agent *reconfigure the visualization for you*? One year later, after lots of testing, our system architecture is deployed in production Paper forthcoming! Read more: https://www.linkedin.com/posts/ozette_asgct2025-lifesciences-pharma-activity-7326304968980811776-aozr/

DesignBoom : BIG’s interactive water installation reveals power of roca connect at milan design week 2025 https://www.designboom.com/design/big-interactive-water-installation-roca-connect-milan-design-week-04-30-2025/ #steelarchitectureanddesign #interactiveinstallation #bjarkeingelsgroup/BIG #MilanDesignWeek2025 #smarthometechnology #datavisualization #bathroomdesign #design #roca

¡Reto #30DayChartChallenge 2025 COMPLETADO! 30 días, 30 visualizaciones con #RStats y #ggplot2.

Ha sido un viaje increíble explorando comparaciones, distribuciones, relaciones (¡animales!), series temporales (sociales, económicas) e incertidumbre (riesgo, exoplanetas, mapas...).

Puedes ver la galería completa (y todo el código) en mi repositorio:
https://github.com/michal0091/dataviz/tree/main/R/30DayChartChallenge2025

¡Gracias por seguir el reto! #dataviz #DataVisualization #DataStorytelling #ChallengeComplete #Rprogramming

#30DayChartChallenge ¡Día 30 y FIN! Último tema: National Geographic . Mi mapa: Riesgo de Desertificación en España (Península, Baleares y Canarias), estilo NatGeo. #UncertaintiesWeek #Mapping

Visualizando la vulnerabilidad territorial (riesgo/incertidumbre) con datos del PAND (MITECO 2008). Colores de amarillo pálido (Bajo) a rojo oscuro (Muy Alto).

Intenté capturar la esencia NatGeo: paleta, fuentes (Lato/Gudea), escala, norte y la famosa ¡banda amarilla! (añadida con grid). Canarias colocadas con {mapSpain}.

¡Un desafío cartográfico para terminar el mes! ¡Encantado de haber completado los 30 días!

#rstats #ggplot2 #sf #ggspatial #mapSpain #grid | Data: MITECO PAND | Theme: Custom NatGeo
Código Final del Reto: https://t.ly/Ol06w

post: Data viz - Exploring recipe data set - where and what

In the third instalment of recipe data set exploration let's look at mainly where the recipes are from and discuss what's in them.

https://www.sarahgebauer.com/post/data-viz-exploring-recipe-data-set-where-and-what/

post: Data viz - Exploring recipe data set - cooking time

This is a second post about exploring the recipe data set. After exploring the servings let's look at the cooking time and the prep time.

https://www.sarahgebauer.com/post/data-viz-exploring-recipe-data-set-cooking-time

The CFP for the 2025 Information Plus conference is now out and available at our website here: https://informationplusconference.com/2025/submissions.html

Please consider submitting a presentation, workshop, or exhibition for the conference at Northeastern and MIT November 14-16! #InfoPlus #DataVisualization #DataJournalism #HCI #Humanities #Data #Art

Have you ever wondered how bilingualism shapes reader preferences for annotated charts? Don't miss "Lost in Translation" at #CHI2025 next week ft. Anjana, Chris, and @lace ! https://www.youtube.com/watch?v=Nr7DVbjCUoI

They'll be presenting at the Visualization and Language Communication track on Weds. April 30th at 10:12am

Headed to #CHI2025? Don't miss honorable mention paper "The Many Tendrils of the Octopus Map" by Eduardo Puerta & Shani Spivak (co-first-authors) and Michael Correll @Birdbassador - a retrospective analysis of these visual manifestations of conspiratorial thinking https://www.youtube.com/watch?v=AyqyTkog_yY

Mon, 28 Apr | 12:10 PM - 12:22 PM

@ACM @chi #HCI #DataVisualization #Maps #Mapstodon

Dive into a GRASS tutorial!

Discover how to create plots directly in GRASS using tools powered by the matplotlib library. No conversion needed! Visualize your raster, vector, and time series data effortlessly. Check it out and give it a try!

#Tutorial #DataVisualization #GRASS #GIS #Python #Matplotlib

https://grass-tutorials.osgeo.org/content/tutorials/good_looking_plots/good_looking_plots_in_grass.html
https://grass-tutorials.osgeo.org/content/tutorials/good_looking_plots/good_looking_plots_in_grass.html

We're proud to announce that @janeadams , Melanie Tory, @michelle_borkin & collaborators from The Jackson Laboratory have won an honorable mention for best paper at @EuroVis for "Gridded Visualization of Statistical Trees for High-Dimensional Multipartite Data in Systems Genetics"! #Genetics #DataVisualization #MachineLearning

#30DayChartChallenge Día 16: Negative Relationship FOUND!

¡Lo conseguimos! Tras ajustar por masa corporal, la relación entre Tasa Metabólica Específica (W/kg) y Longevidad Máxima (años) en ~530 especies animales (AnAge DB, outliers quitados) SÍ es negativa (Pearson ρ ≈ -0.42, p < 2.2e-16). #RelationshipsWeek #Animals

El gráfico log-log muestra la tendencia: mayor intensidad metabólica por kilo se asocia con vidas más cortas. ¡Apoya la idea del "ritmo de vida"! Colores por Clase Taxonómica.

Un recordatorio de la importancia de normalizar variables y limpiar datos para ver la señal correcta. ¡Ciencia en acción!

#rstats #ggplot2 #ggpubr | Data: AnAge | Theme: #theme_week3_animals
Código/Viz: https://t.ly/ouLN0

#30DayChartChallenge Día 15: Complicated Relationships!

Hoy, una matriz de scatter plots con ggpairs para explorar las relaciones entre medidas corporales (Long. Pico, Long. Aleta, Masa Corporal) en los pingüinos de Palmer. ¡Perfecto para el prompt "Complicated"! #RelationshipsWeek #Animals

La matriz lo enseña todo:
* Diagonal: Distribución de cada medida (densidad).
* Abajo: Scatter plots de cada par de medidas (coloreado por Especie).
* Arriba: ¡La correlación $ entre ellas!

Se ven las fuertes relaciones positivas (más grande = aleta más larga) y cómo las especies (Adelie, Chinstrap, Gentoo) forman clusters distintos en este espacio de rasgos. ¡Una forma densa de ver muchas relaciones a la vez!

#rstats #ggplot2 #GGally | Data: #palmerpenguins | Theme: #theme_week3_animals
Código/Viz: https://t.ly/GATJi

#30DayChartChallenge Día 13: Clusters Animales! Explorando la relación Masa Corporal vs Longevidad Máxima. #RelationshipsWeek

Usando un dataset de Kaggle (+170 especies, ¡gracias S. Banerjee!) y tras una divertida limpieza de datos con rangos/unidades mixtas , este scatter plot log-log revela patrones.

Coloreamos por Dieta: Carnívoro(verde) Herbívoro(ocre) Omnívoro(azul).
Se ve la tendencia general (más grande = más longevo), pero los clústeres por dieta sugieren distintas **estrategias de historia de vida**. ¿Cómo gestionan su energía y longevidad según lo que comen?

¡Una visualización para explorar la alometría y la diversidad ecológica!

#rstats #ggplot2 y mi nuevo tema #theme_week3_animals.
Código/Viz: https://t.ly/ehPiu