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Great Reef Census 2020 Map and API

Hacker News

Great Reef Census 2020 Map and API

Hello HNers! We've just launched an interactive reporting tool (it's a map) that showcases the 13,000 reef and coral images people took as part of last year's Great Reef Census. In addition, it details a number of key metrics for 5,000 reefs on the Great Barrier Reef, including an importance score that was derived from the 2020 survey data (I can provide more info on this). https://greatreefcensus.org/detailed-reporting (try searching for "Five Reefs") More importantly it's powered by our also-just-launched data API (just a firebase database, but carefully structured so as to be most useful ... for building something like our map). It was actually a super fun experience putting together the API documentation and examples ... which led to many iterations on the API structure itself. Keen to hear any thoughts and feedback on the links. Both are being actively developed in-house at our small non-profit here on the Great Barrier Reef. Cheers! https://greatreefcensus.org/api As an aside, this year's Census launched today. The plan is to have new survey data updated live on the API / map (coming soon!).

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70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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