I'm Benedict Greller. I'm currently working at 52 degrees north and I specialize in the field of geoinformatics and spatial statistics. I did my Ph.D. about 10 years ago in the

supervision at the Ph.D. smart at the University of Munster. I joined 52N five years ago and I'm now there responsible for the research activities that we've been doing. I would agree that it's still the case. Most of these techniques and data sources are still hidden

so they narrow down people using it. But we also see in our daily businesses that private companies and also the public sector start to move on and try to explore these data sets. What they often bring as questions is how complex it's going to be to handle this data

and also they don't really know what's going to be the benefit for them. So there's a deep learning curve and you have to do an investment first before you really know how much you can get from the data. And of course the trouble is where you can get hands dirty and do certain things

and stuff. I guess that's one of the main reasons why it's not really taking up as it kind of could be and maybe should. And why it's kind of up to open geoinformatics and prepare data and provide answers so it can be easier. I think open search and open data are

kind of key components so that the tools we build, they're kind of easy, extendable, adoptable from the first purpose. And also the reuse of data, publishing data so that not everybody has to do all the pre-purposing steps before they can build the model and look at the data so that you can start kind of a bit further up the road and have easy access.

So open data, open science as general and also involving citizens and kind of building on citizen science ideas is kind of very helpful to make this whole data set available. If I want to know if some kind of function I use in this kind of close to solution really

does what I expect it to do, I have hardly any chance to kind of look into it and sort of tackle it. So if I use open source code, I can always kind of open up the source code, well, give them some sort of knowledge about programming languages and so forth. But in general, I can kind of look into the source code and really explore whether the function really does

what I expect it to do, whether it's any trim parameter or kind of change or it could be the problem, especially if the numbers don't look like I expect them to be. So always double checking the results is kind of, well, always a good idea, no matter whether it's open source or source solution. And this is one part of being able to look into the

source code and so on thing, but also being able to extend it myself. So I can use other solutions somebody else has provided, I can build on top of it. And I don't have to start from the beginning just to make small changes to the different things, to adopt it, different use cases, different data sets. I think one problem might be just accessibility.

So kind of really have to get to this data. I think we would need kind of web tools, perfectly co-designed by Susan and users in the end, we kind of have their day-to-day questions. And I guess it's kind of the last dirty mile we have to take from the kind of

big data products that we have these days, kind of down the road, we need to be user in the end and then kind of easy accessible web applications, smart phone applications. So it should be as easy to grow the data set as you grow any web shop for instance. And there are different web

portals and different ideas that we've not yet. And then we have to kind of maybe also make some advertisement really kind of let people know about these tools and how they can actually help in their daily life. So I can imagine that from end users, they just don't know about products that are available and what kind of benefit they could get from it. They would use

it in their daily life.