 So, I want to stand up here in my role as representing the National Ecological Observatory Network and tell you about NEON as a data provider, potentially for this community to work with big data. So, the National Ecological Observatory Network is a National Science Foundation funded program and we're really looking at continental scale ecological observations. We're a program that's managed by Battelle and our goal is really to provide free and open data that can be used in a variety of different ecological contexts including looking at responses to climate change and other ecological changes. Also providing a standardized and reliable framework for research and experiments and that includes PIs working with the NEON infrastructure to conduct experiments that they're interested in in conjunction to the normal NEON data streams. And we're also really interested in interoperability of our data with other existing long-term projects. So, we're providing these highly coordinated data from across the entire continent to be able to look at different temporal and spatial questions and to be able to ask ecological questions across these different scales. So, the design of NEON is there are 20 different eco-climatic domains that are scattered across the United States. We have 81 different field sites, slightly over half of them are terrestrial field sites. The others are freshwater aquatic sites in a variety of different from lakes to streams to larger rivers. And we have a geographic distribution that includes the continental US, Alaska, Hawaii and Puerto Rico. And from these different sites we collect about 175 or provide I should say about 175 different data products. So, the data products are collected using a variety of different observational or sampling systems. So, a variety of them are automated instruments so you can think of temperature, your solar radiation, those types of measurements that are coming from automated instruments. We have a whole suite of observational sampling data as well so people going out and collecting it by hand, plant phenology, viruses that are carried by mice and mosquitoes, those mice and mosquitoes, that sort of data. And we also have airborne observational sampling which is your remote sensing data including LiDAR, waveform, LiDAR, hyperspectral remote sensing data and camera imagery. And so all of these sampling systems are applied across all of the different field sites, different suite in the freshwater or in the aquatic sites and terrestrial ones, but across the same field sites. So, you have standardized data collected from across the entire country. And one of the important things is both within a single field site and within co-located freshwater and aquatic and terrestrial sites, we collect related measurements so you can get connectivity of your measures across the local spatial scale. So, if we look at water related measurements, you can see precipitation is collected on flux towers. It's also collected on the ground for throughfall under the forest canopy and we have precipitation measures at aquatic sites as well. Whereas water quality and elevation, we're collecting both surface water and ground water so you can see flow and movement of those different characteristics that you might be interested in through the ecosystem and make connections across the entire ecosystem where these field sites are. The basis of this is our field operation staff so making sure that what is being collected out in the field is reliable, all of the calibration and validation is done for the instruments. This is a huge part. This is also an entry level position for a lot of folks and people are making careers out of it. So there are about 200 seasonal field staff, actually I think 300 seasonal field staff hired every year to do this. So for students that are interested in getting into the field components of ecological field work, this is potentially a way to get lots of students in and get experience. And then the data processing is also incredibly important for the whole process. So if you think of making temperature available, we're collecting it in ohms but we don't think of temperature in ohms. We think of temperatures in Celsius, maybe Fahrenheit. And so the data processing and data quality is a huge part of that. It's something I've heard in conversations throughout this week. When we're talking about big data, open data, where is it coming from and how reliable it is. So it's a huge part of the whole NEON program is making sure the data are reliable that are going out there for people to use it. So where can you get this data? Data.NeonScience.org is the data portal. If you have questions about it, I'm here and happy to answer. There are also a variety of other people out here that are happy to answer about it. There is API access and direct programmatic access as well. But it's not just data in the ones and zeros. It's samples and specimens. The Arizona State University now hosts the majority of the NEON by a repository. And those can be sampled. And it's also infrastructure. So integrating with the infrastructure and having PI led research for that. Education is an incredible important part of this as well. And part of that is what we're doing here, but also working throughout the area. So with that, I'm going to close and we'll turn it over to the next speaker.