Vicky Lucas

Vicky Lucas

Training manager for the IEA - interested in all things about learning and the environment.

Location UK


  • Hello and welcome to the course - there will be four of us actively contributing over the next three weeks - myself, Sally, Jon and Liam.

  • Welcome to the course! We have an article on a data visualisation which is part of a renewable energy project for Seychelles in Week 3.

  • We'll be looking at smart meter data in Week 2

  • Large datasets are a challenge for storage and analysis. There are several projects (such as this example from Europe which offer cloud computing to help remove this burden from the user - one step in the right direction.

  • Excellent point - a range of data should be included in order to make conclusions

  • And this site compares the openness of government data by country (2017 data)

  • Good point - knowledge of data reliability and quality is often really important - one way to help this is by providing rich metadata - which we talk about later in this week 2

  • Yes - sounds like the kind of thing the location app does

  • ECMWF (European Centre for Medium-range Weather Forecasts) has this free online module on data assimilation:

  • In week 3 we have at least three steps of interest for planning - one visualising urban benefits, one on land use (Thames estuary) derived from satellite data and another on spotting palm trees by satellites. Welcome to the course!

  • Good ideas - comms is an interesting addition.

  • Absolutely - good visualistaions reduce cognitive overload!

  • Thanks - it is a really good point on trying to summarise highlights out of large amounts of text is not something we have really dealt with here. I have a colleage who is currently facing this having issued a survey and is now trying to collate and make succinct sense of all the free text comments - and sometimes people do use word clouds for this - but any...

  • That's a really interesting point - including more of our senses than just visual. I had wondered about what the climate stripes ( would sound like if each stripe was turned into tones - the rising pitch as it gets warmer over time.

  • Thanks for sharing the Johns Hopkins dashboard - it is an effective dashboard-style visualisation. I like their use of tabs to hide/display and therefore not overwhelm as well as the explicit inclusion of all the data sources.

  • Thanks - good suggestion - we're currently working on a project for diagnosing hot spots within urban areas using satellite data - with a view to assisting city planners on where things like green spaces might help

  • uncluttered, beautiful, informative

  • Thanks - that's a really good point - access to, investigating and valuing all go hand in hand.

  • That's facinating - important work. Thanks for the link.

  • The spheres are made by

  • Great points - so much to consider - I have colleagues whose research is on making consistent datasets from different satellite missions over time - even as far back Nimbus from the 60s and 70s - article from NASA about these pioneering missons here

  • Thanks for the comment - it is sometimes difficult to unpick data sources (we'll come onto metadata later this week).
    @JoEvans - I have some experience of MetOceanView staff (but not products) and they're a friendly bunch - it would be worth contacting them directly.

  • Welcome to week 2 - we'll talk open data, data science and metadata.

    One thing to highlight on open data is that some control can still be asserted on licensed resources e.g., the Creative Commons is an open source way to show the conditions under which your work can be used by others - which may be with no control (known as...

  • We touch very briefly this week on the process of data science - of which quality control and cleaning data is a standard thing to do - to remove e.g., corrupted data and how to account for missing data - depending on the datasets these processes and assumptions could impact any analysis and results.
    In 2010 physicists from Berkeley reanalysed global...

  • I had to look up World Clim - and that reminded me of something that we will touch on in week 2 - which is discovery of data. A group called re3 (registry of research data repositories) list reputable data sources worldwide - and this is the link for World Clim

  • The principle of intelligent/useful filtering of data is a good point - we'll touch on that next week when talking about the challenges of data discovery.

  • Excellent idea!

  • Indeed - using evidence based policies, informed by environmental data is really important. And collecting the right data isn't always easy - one example is that often you have to collect data for a long time before it becomes really useful and reliable trends can be diagnosed.

  • Do check out our 'urban benefits to you' visualisation in week 3 - it analyses the kind of factors that you list e.g. green spaces

  • The interconnectedness of environmental data to so much of our lives!

  • Interesting thought - the benefits of data assimilation to gain the best view of a current spread of a disease (with imperfect data of course - which is also the case for weather) and then model forward... nice idea.

  • There are lots of examples in the course on the applications of big data to environmental science - enjoy the course!

  • You'll find that the course is about some of the underlying needs of big data - such as computing power, access to data and data science skills - along with examples on applications relevant to the full range of environmental data - from weather forecasting to ecology.

  • Agreed - a good way to think of big data - when it gets so large that you struggle to deal with it in the usual ways - it is difficult to store, processing takes a long time (hours or days instead of minutes) etc.

  • If you work in climate change you might be interested in this networking group on applying satellite data to the issue of climate (along with some broader topics) - sign up is free and it is a friendly group

  • Welcome to the course! I'm finding more time to dip into the course that I might normally - and it is great to see all the discussions.

  • Good point - as environmental scientists we're all about the data and analysis, but - as we will touch on when talking about data science in week 2 and for visualisations in week 3 - providing clear information to others for decision-making is vital.

  • Welcome - this will be a nice overview for you of current topics in environmental science and how they are addressed through big data.

  • Welcome to the course! Most of our developers use Python and as an open source language there are several online courses for that - including on FutureLearn. R is also open source and widely used - often by those in environmental science who have a stats or biology or ecology interest.

  • Big Data, in my view, is often used and often not well defined. In week 1 we talk to Victoria at CEDA who deals with big data daily at one of the massive data centres, with dedicated computing power, for environmental science.

  • Welcome to the course - in weeks 1 and 2 we'll describe some of the issues, including computing needs and data science skills - then in week 3 we'll show some visualisations as lots of environmental data needs mapping and showing lots of different variables at once - along the way we'll talk citizen science too!

  • A great comment, depending on what you are trying to convey, simplicity can be more important and more powerful that being impressive or novel.

  • That is an amazing image, thanks!

  • In week 3 we show a visualisation tool we have developed for showing environmental factors in particular neighbourhoods - called BOUNTY

  • Big data is very useful for trends and patterns - of course one of the challenges is making sure there is a true signal to be found in the relationships - which is why sound statistical analysis is vital.