From The Ground Up: Science For The Community

 My last post talked about how ideas get transferred from the laboratory to markets so that they can be used by millions of people. What I’m going to talk about today is the other side of the coin – the way millions of people can use smartphones and today’s tech to help advance scientific research and improve the world.

 

In other words – citizen scientists and how they help the clean tech and big data fields.

One place where the community has been essential in understanding what’s going on in our world is in biodiversity and wildlife monitoring. Collecting data about where the different species are, what’s going on with their habitats has always been something that is hard and expensive to do for scientists. Imagine the effort it takes to distribute sensors and collect enough data about animals like tigers and bears!

 

Scientists and policy makers have always relied to some extent on data collected by enthusiastic amateurs to help round out their data collection efforts in these fields – bird watching societies, indigenous peoples’ observations and school science projects. What’s really interesting is the way tools today, especially the smartphone, are helping to make the citizen science datasets even more useful and valuable.

 

INaturalist is an excellent example of how citizen science, big data and clean tech come together. It’s an app on the phone that can be used to record observations about animals, plants, bugs and other biota – data which is then sent to the cloud and shared with scientists working on biodiversity issues. As more people in different regions use the app, the data collected becomes larger and more useful – and the scientists can use it to help answer different questions – how species are adapting to a changing climate for example.

 

The exciting part from the data science perspective is the way that an app like this marries data collection, data processing and social networking. Figuring out how to store, manage and process the data that gets uploaded is one of the problems that data scientists in all fields wrestle with every day. Adding a social component where the community of users can discuss and help each other out with the data makes the problem even more interesting – now we can look at questions like which species seem to be the most easily identified, how do people with different expertise and skill levels contribute to the data collection…..?

 

This is one of the fun parts of integrating clean tech and data science – it’s not just about ads or chatting with friends – we get to incorporate all the interesting work on communications, social networks and big data on a really cool problem that helps us understand the world around us.

What our community are reading

Moonshots, Models, IoT and Machine Learning in Agriculture

  What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems?     Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively -     scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
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Our online community space is now live!

 Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !
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How much water should an email consume? Data centers and water use

  A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
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