 So, anyway, everybody, so my name is Addis Pemont and I am a Geophysics Data Manager at the UK Polar Data Center. And I'm presenting on how the UK Polar Data Center is supporting data collection in remote environments. So I'm presenting on behalf of my colleagues, so I don't know if they say what I'm presenting, but I will try to do my best. Yeah, so the UK Polar Data Center is part of the NERC Environmental Data Service and it's based here at the British Antarctic Survey. So the UK Polar Data Center coordinates the management of Polar data from UK research and it supports researchers in compliance with national and international data legislation and policies. So reflecting on the multidisciplinary of Polar science, we are covering all scientific disciplines. So we are managing marine, biological, space weather, geophysics, ozone and meteorological data as well as physical samples such as rocks or fossils. So when we talk about data collection in remote environments, we need to overcome several challenges. So we want to collect as much data as we can, so we want to cover a lot of different disciplines. We need to work with quite limited bandwidth and connectivity and also limited resources. We need to deal as well with large range of sensors and we want to continue the long term monitoring activities that we have here as a British Antarctic Survey. So to do that, the role of the Polar Data Center is to support data collection by providing operational data management expertise and create innovative systems. So in this presentation, I will present three examples that showcase how the UK Polar Data Center is supporting data collection in remote environments. So the first example is the collection of biological data in small Antarctic stations. So as a second example, we'll be about the automation of data collection in one of our stations, which is called HALI, and the third example is about the automation of sensor data onboard the world research ship called CRW. So on small station, our activities that we are doing is collecting biological data, which are mainly collecting manually data regarding the wildlife. So it can be like to get a picture as a zoological feed assistant who is collecting, monitoring and collecting samples. So here on albatras. So the history can be the data was kept in feed books only but now we are trying to make sure that the data is also stored on hard drives and then brought back to the UK at the end of the season. But it's quite valuable. So we want to automate a bit more this process. So we are trying to work with a bus scientific teams but also the engineering team at the bus to develop new systems to to collect data. And so one of the examples is a way for it. So at the top you can see a tree grid. So it's on a broad island. And so each thing we start so we can know which thing we're installing from the bridge and we can count it. So at this point it's the only way that we bring up to go through from the seaside to the colony. And so this is a very good way to propose to know how much food they have eaten because we can wait them before they go fishing and when they come back. So also to improve standardization of the data. We are also developing databases. And so there are database on the station. And we are also created some overcome affects application forms. So that's so the assistant is in feelings reform and then the data is directly in the right format. And then it comes back. And it's a following the recommendation from the Commission for the conservation of Antarctic marine life and resources, and then we can report back to them directly to their environmental monitoring program. So here you are the brother, for example, of the animal on the cheese truck. And so a sickness station. And as you can see, we have sometimes some gaps or in 2010 and also in 2020 2014. So it was when the data system didn't arrive before the penguin. And now we to avoid this data gap, and we are trying to find new ways and also to add automation of some of the collection. So, now we are looking at having six cameras but also to look at satellite images, and also to add the counting of the, of the thing where we can look at also other platforms such as the drones. And now we move on to the data collection. So how is it where the red arrow is. So it's on the bright side and it's not really stable so we don't want to have the scientist stays there during the winter. But we still want to keep the very high level data that we have collected so far and to keep the monitoring for the long term. So, to do that, we have totally automated the station. So we have set up a micro-traveling for supply use, but on the system, and there is a data link to a set of agenomist scientific instruments and instrumentation. So this project preserves core science data streams from AI, so it includes meteorology and ozone monitoring, chocospheric chemistry and science, space weather and super atmospheric observation, as well as glass erosion of subordinate high chef. So to do that, we have quite a complex infrastructure. So we are working with six virtual machine. We have a data server on site and a processing server to automatically process some of the data. So visualization and monitoring server. So on the top, you can see a black and white dashboard. So this is to follow our way in the data link history, so what is the speed of the data coming in, but also to look at how well the shooting system is doing as well. And so there is a rough dashboard. So this is an in-house interface that is also showing some of the ideas fly, that is kind of saying whether or not some of the system would like the status of the system on site. So now we can run through the next kind of platform that we are working on. And this is a wide research ship, so that is at Enver or SDA for short. So it's one of the most advanced for our research vessel in the world. So it's a big red ship with lots of sensors on it. So we have lots of sensors, for example, on the front mass, but also on the main mass, and also under the ship. So currently we are getting more than 500 data sensors per second from more than 60 Android sensors. So as data we are collecting in its position, attitude, depth, maximum field, gravity, width, engine, and lab monitoring data. So the sensor data are large data rigour for our data set we are developing with NUC. And then the data inserted into a database and then we can visualize the data. So you are here a dashboard, so it's a graph and a dashboard that give a number of you of everything going on in the ship. So we are over the dive for but also some more specific dashboard for that we use to look at a specific instrument, or also for professional purposes, such as when we are deploying an instrument, we can look at for example which operations, and depth, for example, which are quite useful for real time information. So when the leak, the data range is not sufficient to transfer all the data automatically. So but new technology brings new opportunities in supporting data collection in remote environments. And so we are looking into using the styling collection that could hide improve the data transfer either from the station, but also from the ship. So in conclusion, so you get for another passenger is a focus point for Arctic and Arctic environment for data management in the UK. We are supporting science in remote environments by providing operational data management expertise, and technology and automation gives an opportunity to improve data collection in remote environments. And we have seen that new technology, including satellites and for the new systems, such as drone are increasingly used to running to also think when you're in for production. So that's why I'm encouraging you to look at my poster and if you're using currency, please fill in the survey as a code for the database here. Thank you very much. It's very interesting to hear about. I should say on the slide, he needs to switch now to the spotlight talks one thing. I was going to ask you mentioned at the end about the styling. So is that kind of effort to sort of standardize the way that you do your comments from all of those different senses and things or is it just not so standardize everything. And because we are just filing at the moment, the styling. So now we have one on one of our main stations so that is vertebrae and one on the ship. And so it has to, like at the moment it's more useful personal purposes, because we can't rely on the link because it can drop out whenever and you can't really plan for a rest but we can't rely on the visa connection but having the people like the people on the ship using the styling connection will move all that. Yeah, all of this from the visa that we can use only for scientific purposes so then it's really to test our way of the styling is doing and at the moment it's still a test but that could bring much more data to be collected and then transport kind of in real time. Sounds good. And I was also thinking, like in lots of, well, sort of field work that people at CH do, they can use like tablets and those sorts of things, I guess in those, in the different, the different approaches you have to use in Antarctic conditions to really have your gloves on or you need to have equipment or they can froze with the code so you need to have specific computers for this. So yes, sometimes it's not exactly the same thing. So sometimes you think twice before going outside.