Vicky Lucas

Vicky Lucas

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

Location UK

Activity

  • 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 https://www.copernicus.eu/en/access-data/dias) 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) https://opendatabarometer.org/?_year=2017&indicator=ODB

  • 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 https://foursquare.com/ does

  • ECMWF (European Centre for Medium-range Weather Forecasts) has this free online module on data assimilation: https://www.ecmwf.int/assets/elearning/da/da1/story_html5.html

  • 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 (https://showyourstripes.info/) 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 pufferfishdisplays.com

  • 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
    https://www.nasa.gov/content/goddard/nimbus-nasa-remembers-first-earth-observations

  • 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 creativecommons.org/choose/ 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 https://www.re3data.org/repository/r3d100011791

  • 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 https://www.nceo.ac.uk/space4climate/

  • 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.