Data

We have a very clear set of needs to understand Earth’s climate and nature, including measuring biodiversity, mapping disasters, and monitoring crops, to mention just a few. Yet despite having an enormous amount of diverse, Earth data (both open and commercial), most insights about the Earth are locked in the data, requiring very advanced technical skills, resources, time, and tools to use it. We believe we finally have the missing piece to unlock this situation. To transition Earth Observation beyond being the scribes of doom, and instead support the change we want to see in the world, removing complexity and adding clarity and speed. That last piece we needed is AI. AI for Earth. ...

The Open Data Catalog at the World Bank is a fantastic resource. I’ve used it many, many times. Especially while developing the GAIN Index. I’m sure I’ve spent hours on that domain. It’s astonishing the amount of information they’ve gathered in a consistent way. Our GAIN Index indeed uses, and references, roughly 80% of its components directly back to the Catalog. Proudly, but oddly, a number of people have told me that they use our page instead of the Bank’s to get a glimpse of the data for a particular country. As humbling as that is, it’s odd that our page gets visits to see the components, not the aggregation we do for our purposes. It’s like buying a book for the Bibliography. ...

Last week I ran the NCR Trail Marathon in Baltimore. This is only 4 weeks after my first marathon. I didn’t actually train for it, as it was not on my plans four days before the race. I crossed the start line breaking plenty of recommendations, like getting some time off, new shoes, new clothes, no music for the first time, first trail race, no setting goals or paces, … ...

Last week I ran the 2012 Marine Corps Marathon. It was my first marathon, I did it as vegan, and it was a great experience. Finish time was 3h54m17s, much better than I expected. To get to that point I trained hard, I focused on my diet and my stride, I read papers on strategies, and I geeked with the data. I want to talk about all that. ...

That was the title of my first Data Science DC Meetup. Mike Dewar, from bitly, presented his work on tracking attention. The background hypothesis is that attention leads to revenue, and he wants to measure attention at any given time by means of click rates. With bitly he has access to all clicks on shortened links. The problem is then to transform that stream of single events into an attention function estimate in real time. ...