u/Cobblerunionfan

Following up on my backyard bird-call + weather tracker (previous post here). I've been running the numbers all summer and a few things stood out that don't need any bird-nerd background to appreciate:

• Birds go quiet when it's hot. Once you account for time of day, hotter-than-normal temps were tied to noticeably less birdsong — the strongest weather effect I found, by far.
• "Nice, clear weather" didn't mean more birdsong — if anything, slightly less. Higher barometric pressure (the kind that usually means calm, sunny days) was tied to quieter mornings, not louder ones. Not what I expected going in.
• Humidity and rain barely moved the needle. Of everything I tracked, those had the weakest, shakiest effect — temperature and pressure told me a lot more about when birds go quiet.

Bonus: not every species keeps the same hours — American Robins are already loud at 5am, but Fish Crows don't really get going until 2pm.

Live charts (including which species peak when) at winstonsalembirds.com.

https://preview.redd.it/0uai809ohpjh1.png?width=1120&format=png&auto=webp&s=4be406c9862baa292f704271f197baa1d9b45a85

https://preview.redd.it/jl5hj0w4z4hh1.png?width=772&format=png&auto=webp&s=5ab300508635bde0d4b9e0695c448a1863eb29b2

A few weeks ago I shared my backyard project to automatically identify birds by their call and also collect several types of weather data.

Based on some great feedback I've overhauled and expanded the dashboard. If you want the quick breakdown, the site now live-tracks:

• Local Bird Bioacoustics: Automatically logs and ranks bird species by frequency and timestamps.
• Full Weather Telemetry: Real-time tracking for temperature, humidity, barometric pressure, solar radiation, and rain totals.
• Fancy mathematical modeling results regarding correlation between weather variables. Also, a Bayesian model to identify which variables drive bird calling behavior.
• More project history photos and storyline.
• Activity Trends: 15-minute activity graphs to see peak calling times throughout the day.

If you want to check out the live site, weather logs, and current species rankings, you can view it here: winstonsalembirds.com

I’m a local data science professional and I’ve built a sensor network of weather/environmental and acoustic bird detection tools.

I’m looking to connect with a Master’s/PhD candidate, or advanced researcher who could use this rich data set as the foundation for a thesis or dissertation project. I've already done some statistical modeling on it myself.

The telemetry infrastructure includes:

• WittBoy 7-in-1 Weather Station: Real-time localized meteorological metrics streamed via an ultrasonic anemometer, haptic rainfall sensor, solar/UV meters, and precision thermo-hygrometers.
• Acoustic Monitoring (Cornell BirdNET / BirdNET-Pi): 24/7 localized bioacoustic telemetry running on edge hardware, tracking continuous local bird species identification, audio logging, and environmental soundscape patterns.
• Raspberry Pi Architecture: The underlying hardware stack handling the edge computing, continuous data pipelines, database storage, and custom automated environmental scripts.

Everything is clean, structured, and ready to explore. If your research focus touches on data science, time-series forecasting, machine learning, ecology, or environmental modeling and you need a robust, real-world data source, drop a comment below or shoot me a DM and I will send a link to the current site where all the data is being captured and analyzed (every 10 minutes).

I’m a local analytics professional in Winston-Salem and I’ve built a sensor network of weather/environmental and acoustic bird detection tools.

I’m looking to connect with a WFU Master’s student, PhD candidate, or advanced researcher who could use this rich, continuous data set as the foundation for a thesis, dissertation, or capstone project. I've already done some statistical modeling on it myself.

The telemetry infrastructure includes:

• WittBoy 7-in-1 Weather Station: Real-time localized meteorological metrics streamed via an ultrasonic anemometer, haptic rainfall sensor, solar/UV meters, and precision thermo-hygrometers.
• Acoustic Monitoring (Cornell BirdNET / BirdNET-Pi): 24/7 localized bioacoustic telemetry running on edge hardware, tracking continuous local bird species identification, audio logging, and environmental soundscape patterns.
• Raspberry Pi Architecture: The underlying hardware stack handling the edge computing, continuous data pipelines, database storage, and custom automated environmental scripts.

Everything is clean, structured, and ready to explore. If your research focus touches on data science, time-series forecasting, machine learning, ecology, or environmental modeling and you need a robust, real-world data source, drop a comment below or shoot me a DM and I will send a link to the current site where all the data is being captured and analyzed (every 10 minutes)

Using the BirdNET-pi package for the raspberry pi4, here is what I've identified so far today in the backyard: https://winstonsalembirds.com/

Just curious if there are tinkerers like me who like to talk about their Pi projects.

I've completed my first build project but the Sparkfun 2 inch sensors just don't get deep enough in the yard to measure water content for larger plants. All my searches has turned up nothing for the pi, just gikfun models for the arduino.