8 Research-Ruining Data Mistakes

Researchers often painfully discover critical steps they forgot to think about and find they don’t have enough data to interpret their results.

Mistakes that kill your data

Getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Researchers often painfully discover critical steps they forgot to think about and find they don’t have enough data to interpret their results.

Eliminate nasty surprises

Most data mishaps are avoidable with quality equipment (i.e., research-grade data loggers and sensors, etc.), a good data management system, some careful forethought, and a small amount of preparation. The result? Usable, publishable data.

In this webinar, METER research scientist Dr. Colin Campbell discusses:

  • 8 common data collection mistakes
  • Critical best practices you should never miss
  • How to make field discovery as pain-free as possible

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Our scientists have decades of experience helping researchers and growers measure the soil-plant-atmosphere continuum.


Dr. Colin Campbell has been a research scientist at METER for 19 years following his Ph.D. at Texas A&M University in Soil Physics.  He is currently serving as Vice President of METER Environment. He is also adjunct faculty with the Dept. of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics, a class he took over from his father, Gaylon, nearly 20 years ago.  Dr. Campbell’s early research focused on field-scale measurements of CO2 and water vapor flux but has shifted toward moisture and heat flow instrumentation for the soil-plant-atmosphere continuum.


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Hello everyone, and welcome to 8 Research Ruining Data Mistakes. Today’s presentation will be 30 minutes followed by 10 minutes of Q&A with our presenter Dr. Colin Campbell, whom I’ll introduce in just a moment. But before we start a couple of housekeeping items. First, we want this to be interactive. So we encourage you to submit any and all questions in the Questions pane. And we’ll be keeping track of these for the Q&A session toward the end. Second, if you want us to go back or repeat something you missed, no problem. We’re recording the webinar and we’ll send around the recording via email within the next three to five business days. Alright, let’s get started. Today we’ll hear from Dr. Colin Campbell, who will discuss common data mistakes, critical best practices, and how to make discovery as pain free as possible. Colin has been a research scientist at METER for 19 years following his PhD at Texas A&M University and soil physics. He is currently serving as Vice President of METER environment. He is also adjunct faculty with the Department of Crop and Soil Sciences at Washington State University, where he co teaches environmental biophysics, a class he took over from his father Gaylon, nearly 20 years ago. Collin’s early research focused on field scale measurements of co2 and water vapor flux, but has shifted toward moisture and heat flow instrumentation for the soil plant atmosphere continuum. So without further ado, I’ll hand it over to Colin to get us started.

Thanks, Brad. And welcome, everybody. I want to have this conversation be today something more like kind of hanging around a table dinner, just talking about some of the things that I learned and others here at METER have learned over nearly 100 years of experimentation in the field. So sit back and relax. And let’s just talk about ways to make research go well.

I’m showing here a picture of a study we did several years ago, together with a group out of Brigham Young University in Utah. Our goal here was to study how climate change affected species diversity, specifically invasive species that were coming into this desert area. And out competing some of the native species that were there. A particular interest was how precipitation affected the diversity of species there. And we had several treatments where we kept rodents out of particular areas or in areas where a fire was burned areas or didn’t burn. And you can see these treatments in these plots in a drone flyover of the area that I’ll just play while we’re talking. So we had some really interesting things happening, you can see that in each of these zones, we actually were able to limit some of the precipitation in those areas. So precipitation obviously was going to be a very important piece of this study. And when we went out to the research site to do some testing, at one point, during this experimentation, what we found was our weather station was actually tipped over at about a 22 degree angle from normal, just because it had lost its kind of footing and had tipped over. And so one of the key variables that we needed to know, which was how much precipitation is actually coming down in this area was ruined for us, because we couldn’t see it anymore. You know, it was leaning to 20 degrees. And our solar radiation data that we needed, was also affected by this. We didn’t know about it. And we didn’t know how long our system had been in that steady state. So a big thing when we think about going to the field is considering how we’re going to make measurements and making sure that those measurements are going to be useful later.

So let me go ahead and start with our 10 biggest or eight biggest mistakes. So number one, failing to collect enough metadata. My dad when I was young used to tell me this all the time. He said the shortest pencil is longer than the longest memory. And I always thought he was pretty funny when he told me that and you know, why dad seriously, I can remember things perfectly well. As I’ve grown older, I’ve found the wisdom in that statement. You know, when you get down to writing manuscripts, they require details that maybe you don’t remember, after a couple of years since something happened. Or you put sensors into the ground, and it seemed perfectly clear when you did it. At what depths you actually buried them but later, what were those steps again? Or wires from buried sensors that you don’t know exactly what they’re coming from, are they water content sensors? Are they water potential sensors? Are they temperature or there’s something else? They just come out of the ground. And if you don’t take information on and record information on those sensors, you may never know.

There are a lot of great solutions to this problem. Brainstorm everything you would want to know about the field before you go out into the field. And ask a friend to review your ideas on what you should be noting. And one thing I particularly like to do is keep a site electronic notebook, at least a notebook but notebooks get lost easily. If it was an E-notebook that was saved, like a Google Sheet, you could save those and give access to everybody on the project. And then everybody would have all the data you needed. So be rigorous in your note taking when you go in the field. If you’re using something like a Google Sheet, edit that sheet right in the field, right at the project site, so that you can make sure to get all the data down when it’s freshest in your mind. Note things like sensor serial numbers that may come up that if you have a question about a particular sensor, when it was made, or even the firmware on the sensor, make a note of that so you can get to it later. Record how deep the sensors are buried. That’s something I mentioned just now, that’s really important. It’s funny how I always think I’ll remember how deep I buried the sensors at each site. But now with many sites spread out over several different locations, I don’t remember that as well as I thought I would, and then store the information in a cloud, or at least record data where they can be found, again. Ideally in the cloud is a great place to do that.

Here’s just a look at our digital field notebook from one of the sites I’m working on. We just put it together a list of things that we thought would be important. You can see we’ve got things like date and time, the people that were there, the site, the data loggers that we were using at that particular site, the sensors that we were working with, and any notes. So every time we go to the field, if there’s something we’re going to work on, we leave notes about what we did. So we’ll remember, sometimes we don’t use these, and they’re just sitting there, as written notes that aren’t very useful. But many times we go back and realize, oh, that’s what I did that day, I updated the firmware on that sensor, or I changed out that sensor because there was an issue. Also, we recorded things here like the sensor depths as I imagined, as I mentioned, and also things like data loggers that are at the site. You can see I replaced some data loggers at the site, because eventually, METER built new data loggers, the new ZL6. And I wanted to test them out and use them at this research site because they were so fun to use. But I really needed to record when those data loggers were switched out. So I can go find the data when I’m ready to write the experiment. And we’ve run into that just right recently, as we prepared papers for AGU and ESA.

A great place to store metadata is actually in a program like ZENTRA Cloud. ZENTRA Cloud is our web based system for gathering data. And one of the things that it does with the new ZL6 data logger is record a lot of the data that I often am looking for. So here on this page we’re looking at the calibration we’re using for the water content sensors, the depths that we installed the mat, the firmware that’s on the sensor, and even the serial number. So when questions come up, as they relate to what we did, they’re all right here and so we can remember them. And finally, what about a sight picture? We’ve been playing around with a a game camera from a company called Barn Owl, that that really has been pretty fun to use. This is our research side up at about 3000 meters in central Utah. And you can see the change in just what our system is going through from the fall that’s on the left hand side to just a couple of days ago, which is on the right hand side. And we’re continually getting snow there. And of course there are things that I’d like to know like snow depth, and we put it out at a stake. You can see in the third picture from the left to tell us how deep the snow was. But on the fourth picture, obviously the snow is deep enough now that it’s actually covered that stick. And so we’re going to have to kind of benchmark off our METER logical stand there with our ATMOS 41 all in one weather station on top. But we can see things like snow depth, we can monitor plant height, and we can also just see how our station is doing at any time with these pictures. So that’s another great way to collect information about the system while you’re not actually using sensor data in particular.

Number two, installing sensors in the wrong place to test a hypothesis. The problem is that in things that I constantly ask is, how do I know I’m in the right spot to make the measurement? And there are several solutions, one that we’ve turned to recently, which is using historical satellite data. So we look at wetness changes over time, over multiple years and look at maybe an average location that would be representative of a particular site we’re interested in. Other studies I’ve done we’ve done pretty intensive soil sampling where we’ve gone out and taken lots of samples across an area, analyze those samples and decide where the most representative site or statistically interesting sites are where we’re going to place our sensors. If we are in an agronomic application, maybe look at yield over time if we had access to yield data in fields and try to match where the average yield was, or there’s always an opportunity to talk to experts. There are probably several people that you know who know a lot about about your research area. Ask those people or those people who are most familiar with sensors, provide a detailed diagram of the experiments so others can give comment, and understand exactly what you’re going to do and articulate the project goals clearly so they understand maybe some of the parameters that ought to be taken into account if they’re giving you suggestions. Here’s an example of some research we did last year in an agronomic setting where we were looking at potatoes, and potato yields and trying to tie those into soil water potential. We only had enough budget to stick one sensor site in each one of those fields. And we went to satellite data to look at where the driest spots were in each field and where the average spot was in each field, we ended up deciding to use the average spots. And it really worked out well, we were pleased with how well those measurement sites actually represented the average yield in the field.

Number three, failing to add lab data to get a complete picture. And this has happened to me several times, I’m preparing a paper. And almost out of the gate, when I start the Materials and Methods section I’m right exactly where we were. The next thing I want to talk about is the soil, because that’s something that people are going to want to understand so that they can get the context for your particular research. So if you haven’t put together lab data to tell something about the soil type for example, they’re not going to understand the context or the experience. So you need this to understand both the sensor behavior and for providing context in papers. There are other things that would probably be useful. Moisture release curves come to mind, it gives you information about our entry point, or understanding sensor behaviors there they wet and dry in the field, and also hydraulic conductivity of the soil. All these can be generated from using lab instrumentation to provide more data. And when I think of this, I think of a couple of METER instruments that I use quite a bit, we just barely finished doing some analysis of one of our studies where we went and took soil cores and put them in the system on the right called HYPROP where we did moisture release curves, that was really important to understand the soil variability between the different sites. On the right we have the PARIO instrument that does automated sand, silt, and clay fractionations to get our particle size analysis. And this instrument was pretty critical because we’re trying to understand the dry down curves that we were seeing in the field of the soil. And the differences between each site and understanding that the soil type was really helpful to understand what we were doing in the field.

Number four, not installing enough sensors to capture variability. So the problems an obvious one, the budget limitations limit the number of monitoring sites, we never have enough money to be able to monitor all the sites and all the depths that we want and correlations are questioned because of the lack of statistical significance and this graph here, this is one of the graphs we put together from our field work last summer where we were limited to those single sites. And one of the challenges with connecting the things that we can measure in situ with things like yield is if we don’t have enough sites, our correlations are sometimes they’re not that great and that’s what I’m showing here. Now, of course this is coming from someone who’s a manufacturer of sensors and systems. So of course, I’d be interested in you buying more sensors and systems to put out there. But honestly, I mean, it’s not really that, it’s just doing more robust projects comes with more robust instrumentation budgets, there’s a lot of money that you’re spending on salaries, on maintenance, and on instrumentation. And when you’re putting that all together, remember that it’s really important to get enough instrumentation in the field to be able to provide the correlations that are going to be useful when you write this up. So prepare well for that. But one of the things that we’ve been working on here lately is trying to understand how we can upscale data from point measurements in the field using other data sources, like satellite data, like soil sampling, and testing to show field variability and connecting those across scales like drone remote sensing, and even using above ground sensors, like in DVI sensors that we can put up higher and that can cover larger footprints. And so connecting a lot of these things helps us to avoid getting lost in the lack of sensing all the variability in the field. And so something that you could spend a little time on, as you’re preparing to go to the field to make sure all of that is taken care of.

Number five, failing to review data regularly. If I have had bad experiences in the field, it probably would be a lot related to number five. The issues are pretty obvious that when things go wrong, at an experiment site, if nobody’s reviewing the data, issues go undetected for months, and what may have been a relatively minor problem, if caught in just a day or two can turn into major problems where the goals of the project are lost because data hasn’t been reviewed. There are a lot of ways this happens; data on the server is there but no one looks at them till the end of the season, statistics that might have been run to analyze how things are going are put off till the end so that adjustments aren’t possible if we haven’t addressed certain issues. Logger batteries die and so nothing’s collected. This happened several times in the old days when I was experimenting as a graduate student or the criteria for good data is not well understood. What do I mean by that? Well, a sensor reads 5% water content. Well, you may think that’s just fine. But you don’t understand the context where it’s actually sitting in a wet silt loam, where a 5% reading would mean there was an actual issue with the sensor. As I mentioned, data can compile up on a logger for occasional download where it’s not a connected logger. So you go to the field, you download the data, and then you check it for errors. And key sampling moments can be missed like emergence, flowering, senescence, even rain events, or floods or droughts or other things that actually happened where you may need to do some sampling, you may need to look at some things more in depth, and you simply miss them because the data aren’t being reviewed. And finally, this is one of my colleagues threw this in, mice chewing through wires. This happened to us at a rice field research site that we worked on where mice were really enjoying both our data logger boxes and the wires around and we lost several of them, and that created problems for data analysis.

So what are some of the solutions? Well, using a cloud based visualization software is really important. We put together ZENTRA Clodud for this exact reason that when I was running experiments over the years, I always imagined the ability to just turn on a simple piece of software and be able to see what was going on at my research site. Use those kind of services because they’re exceptionally useful and put together graphs so that the data are automatically brought in and graphed so you know what’s going on. And it doesn’t take much time to be able to evaluate the performance. New things like APIs, which are software connections into clouds software. Use those things to draw data out and put them in your favorite data software if you have one like R, MatLab, or Excel, so those things can be brought in automatically. And you can see the data and just the way you want to, and set up alerts to email you when things are going wrong. I’m showing a small graph here from our research site up at that 3000 meter site, that some of the data loggers are in tree islands where they’re buried in snow, and they’re not getting any recharge. And so I’m constantly looking at battery percentages. And now that one’s dropped below 40%, because it never gets any sun, I am getting an alert from the data logger saying hey, there’s a problem. Your data are outside expectations. So nothing’s building up. I’m aware of everything going on. And in this case, I’m not actually going to go change anything because I don’t want to dig those loggers out and they’ll eventually melt. And even when the communication doesn’t happen from the data logger, these data loggers are designed so that when they don’t get solar radiation, they’ll just turn off their cellular modem and keep taking data until they do. So come spring, sunlight will light their solar panels again, and we’ll continue to get data.

Here’s one of the reasons I just love getting data at my computer. This is data from the project I mentioned earlier that’s still going out in the desert in Rush Valley, Utah, there’s not really any snow out there. And I was looking at the water content trace that’s shown in gray. And I was trying to figure out, you know, wow, that water content’s chipping, way up and way down, and it kind of looks like it’s going along with the precipitation, every time the precipitation goes up, we do see some jumping water content. So maybe it’s reading right. But I was kind of scratching my head because water content traces are supposed to look like that yellow line here that are fairly smooth and not the gray line. But using data just straight up that I could look at instantaneously, I quickly saw that the temperature here shown in green, and this is right in the middle, we have the zero degree line, every time that water content line is dropping way low and bouncing up and down, that’s because we’re getting this freezing event where more and less water is being frozen. And we’re seeing that in the water content signal. So immediately, I knew that while I thought that maybe the sensor had become unburied, and maybe some rodent had dug it up or something that in fact it was nothing but the temperature changing from freezing to thawing, and that water content disappearing and appearing to an electromagnetic sensor that can’t sense ice.

Number six: losing data. Problem is, and this happens all the time even today, the data are stored somewhere on field loggers, on local drives or obscure servers and the data are lost. How many times have I heard the sad story about a local hard drive being the repository for field data? It’s not backed up and it fails. And I know this thing can happen because literally two weeks ago, my beloved computer hard drive failed and all the data on there was gone. But luckily, I had backed it all up completely. And I didn’t lose anything. But these things happen all the time. But that’s not all. Sometimes you can have a catastrophic occurrence in the field. We hear this regularly probably because a lot of people are wanting to measure near rivers, in flood zones, but loggers that were perfectly fine actually get flooded out by climate change activity. This happened fairly recently where one of our customers had got a data logger flooded out. And even though they hadn’t realized that their data were all backed up onto the cloud by the ZL6. And they called saying oh no. What am I gonna do with my data? Can you recover it off this flooded data logger that was just full of all the material you might see in a flood. And I said, well no, the data logger is lost, but luckily the data are all backed up on the cloud. And they were pretty excited about that.

There are other things that happen. These have all happened to us, animals savaging the logging system, for some reason some animals get angry about data loggers or sensors and tear the system up. And ants love to attack electronics, especially in the southern regions or the warmer regions. That happened to us when we were doing research down in Texas. Our entire data logger box got filled with ants, and they love to attack the electronics. Data are also saved to unknown server locations and you forget where they are, or students who are working on projects move on and take the system knowledge with them. Now we’ve heard sad tales about each of these. So the solutions are similar to what we’ve talked about saving data on the cloud and sharing all those data with the stakeholders. So everybody who’s involved in the project has access to those things. Use ZENTRA Cloud system, so everybody can easily find and interact with the data and download it every time. It’s there permanently so that you can grab your data whenever you need it. And one of our people here said, you know, think about it this way. Hardware can be replaced, data cannot. And I liked that statement, it’s a good way to approach research.

Number seven, failing to install sensors properly. One of the biggest problems is that improper installations dooms an experimental finding. And this happens over and over. I mentioned what happened there at that Rush Valley site where we went out, and the weather station was sitting kind of tipped over. But that’s certainly not all the errors that happen. Most people when they go to the field just assume that they understand how to install a sensor properly. But over and over, we see people failing to make good contact with the soil, with their sensor, whether it’s a water content sensor, or a water potential sensor, that they need to get good contact with the soil or they’re poorly leveling their above ground instrumentation. And we’ve even seen situations where people have sent us pictures of their research site, all proud of what’s going on. And we see protective covers still covering something like a solar radiation sensor, it’s really important to know exactly what you’re doing out in the field.

There are also problems because compromises almost always are made when you get out to the realities of the experimental site. I put this picture in here, because this was an installation we did where we were really confident that we could go in and just push in our TEROS 12 water content temperature and electrical conductivity sensors. And when we got to the site, it looks great on the surface, there looks to be a lot of soil and only a little bit of rock. But even though the picture is not really telling the whole story, when we started digging down in the soil, it was mostly rocks, or at least it felt like that. So it just took a long time to find where we could install those sensors into the soil. And at the bottom of the hole there, you’ll notice some rocks that was as deep as we could dig. It felt like we hit a literal layer of rocks. And we had planned on going all the way down to beyond 30 centimeters in this hole. But when we got that deep, that was just the limit of what we could do with the time that we had. And so we just had to make some changes to our experimental design, which we did note in our notebook that we put a sensor down at something like 30 centimeters.

So how do we deal with these problems? Well, one great way is to use an installation tool, often getting to the depths we want and the time that we want to take just involves planning and using this installation tool helps us reach those steps quickly and helps make sure the sensors get put in the soil. So they make good connections. The other opportunity is to really avail yourself of online tools. There are tons of videos, or step by step guides that are out there. METER has many other manufacturers provide them. And they provide a great resource to prepare to go to the field and make sure that you do these things right. And don’t forget that for example, METER has several people who are here to answer your questions. So if you’re worried about how to go about getting your experiment done right, whether you’re older and have done this a lot of times or whether you’re brand new and going to the field for the first time, we’ve got experts here that are anxious to help you make sure things go right, because we love to make sure to help on the front end, where there’s a lot we can do about data that might not be as good as you want versus on the back end where you call us and say I don’t think I got the data I wanted to. These water content data don’t look right or these precipitation data don’t look right. We are really anxious to make sure things go well. So help us do that by by talking to us early.

Okay, the last one, forgetting to verify everything before leaving the installation site. You know, things seldom work the first time that we do them, and this is really important to think about to make sure your research goes well. The other thing to think about is that fieldwork is exhausting. And it’s really hard in the heat of the moment to take time to check. Now a lot of my installs have been hot, sweaty affairs where we’ve been out there, the sun’s beating down, you know, we’re in the middle of Texas, it’s hot, it’s humid. And all I can think about is going and hopping back into the air conditioned truck. But I know many of you have experienced the opposite, where it’s cold, it’s raining, or even snowing, you’re trying to get instrumentation out, because that’s the only time you can do it and it just takes a while.

I know these things happen. And other problems, acceptable ranges of the sensors are poorly understood, and you don’t see that there’s an error in the sensor, or the system isn’t configured correctly. And you simply don’t notice, maybe you forgot to turn the data logger on in software to get it to read, or it’s reading at the wrong end of interval that you wanted to set up. Or you have not configured the ports right when you’re preparing to when you’ve prepared the logger. So here are a few solutions that really have worked for me. Always set up the system in the lab before going into the field. It’s so tempting for you probably, certainly for me just to grab the instrumentation and go to the field. We’re excited to get out there, I can honestly tell you a day in the field is something I just relish. I love going out there, being with the experimental site, it feels like I’m really doing science. And so sometimes you’re so excited to get that done, you don’t prepare well do it. Set up the the system in the lab and make sure everything is working. Also prepare a checklist of plausible readings for each sensor. It’s a great idea to see if a friend will be back in the lab and check values online for you to make sure everything works. For example, in ZENTRA Cloud, when you plug in all your sensors, the data logger automatically just goes on to the cloud, as long as it’s reading an interval and stores your data. So your friend could be back at the at the office. And they can tell you whether things are reading well, which is great. That means you don’t have to do it yourself.

Mentally plan time for verification. If you’re out there exhausted on a field day, it’s hard to plan that in if you are not thinking about hey, after I installed the sensor in the soil and put all the screws in for my mat stand and do all the things that I need to do to set this thing up, I’m going to spend time verifying that everything’s working, just plant it in mentally and then use some devices that we built for you to make sure that everything is working well, we just released something called a ZSC. It’s a Bluetooth reader that you can check individual sensor readings. And you can just put the sensors in. And then on your smartphone, you can see if they’re reading well. It’s super easy to use, and great for just making sure everything’s going all right. Also, you can just read through your data logger using ZENTRA utility mobile, a mobile app that connects directly to the ZL6. And you can just make sure everything’s reading well. The data logger is at the time interval you want and that sensors are installed correctly.

So I’m showing a couple of pictures here on the right is something your friend would see back at the lab, it’s just on our list view and they can just look through and make sure all your sensors are reading like they should. It’s important to leave a list of kind of plausible answers they should be looking for like that five centimeter TEROS 11. Is it supposed to be reading .065 meters cubed per meter cubed or 6.5%? volumetric water content? If you’re in a clay that’s wet, that’s probably not the right reading. And maybe you’ve got some air gaps. So leave some information to make sure they know what they’re looking for. But they can quickly check down through this table and make sure everything’s working. For example on the ATMOS 41, What is the solar radiation reading? If it’s reading zero, we’ve got a problem if it’s during the day, of course if it’s night that’s probably all right. But 625, is that a reasonable reading for what it should be and look at the x and y level down here to make sure you’ve leveled the instrument and it’s prepared to go. Unlocked outside there’s someone using the ZSC, it’s right here this white box. It’s a very simple little device that your sensor just can plug into. And there on your smartphone you can verify that this sensor is reading like you want it to.

Well, that’s all eight of the of the data ruining problems that I wanted to talk about. You know when we started the effort to make the ZENTRA system, the ZL6, the ZSC Bluetooth reader, and especially ZENTRA Cloud. These were culminations of a desire of mine and others here to make research go well, ZENTRA Cloud especially, to be able to monitor data over time and really, really feel the experience of data coming in and looking at trends over time, making sure it goes well, and developing hypotheses as we go. These were pieces that we put together because this opportunity to get great data in the field and the process of discovery was something we wanted everybody to feel.

And so as we think about these eight challenges, a lot of the things that we’ve been doing have been focused on trying to get over these things. So failing to collect enough metadata, put together a notebook, or even better an E-notebook, even better use ZENTRA Cloud and collect the metadata you’re going to need later on in the experiment. Don’t be short sighted installing sensors in the wrong place to test hypothesis. Prepare well, figure out where you need to put the sensors to make sure you’re understanding what you need to. Number three: failing to add lab data to get a complete picture. Don’t forget about that part. When you go install the system, take samples. It’d be great if you could run a moisture release curve. But certainly you need to do the soil type. And the PARIO is a great way to do that. Number four, not installing enough sensors to capture variability. Remember to make a robust instrumentation budget and to use other tools like satellite and other things to get you where you need to go. Number five, failing to review data regularly. If I could get you to do one thing, it would be connecting on something like ZENTRA Cloud so that you could be watching your data daily. For many people that I interact with, I find that they really enjoy this just setting up dashboards in ZENTRA Cloud of the data, they want to see how they’re coming in the way they want them to come in and then setting up alerts. Some people tell me that they look at this multiple times a day to learn things that they never could before, it’s a great opportunity. Losing data. Be so careful of this, this has cost me dearly. One situation where I went to write up an experiment, I lost, or someone lost the book full of leaf area index data we had taken. And that was it, we couldn’t write that portion of the experiment up because leaf area index was gone. Save your data where everybody can find it, it’d be great if it were on the cloud. Things like ZENTRA Cloud allow everybody, all stakeholders to share all the data from the logger without any extra cost. So there’s no reason to lose data anymore. Number seven, failing to install the sensors properly. Use the information that you have, both online, from experts around. Get those sensors installed well. Help things go well right from the outset of the experiment. And finally, before you leave the experiment site, verify everything so you know everything is working. It was amazing. One of my experiments that I did, we got it all set up, we were leaving. And little did I know somebody kicked one of the sensors and it pulled one of the wires out that was in a wire log into the data logger. And we lost that sensor for an entire year. Don’t make that mistake, there’s no reason to make that mistake these days, especially with something like ZENTRA Cloud, I could have looked there and been able to see that that sensor wasn’t working, even before I drove down off the hill from the research site. So I hope this has been useful. This is kind of what I’ve learned through many years of field experimentation, and also why we put together the ZENTRA system to help things go better.

Awesome, thank you, Colin. We hit our half hour mark. So I think we’ll use the next five to 10 minute,s we’re still good on time to answer some questions from the audience. And again, thank you to everybody who’s asked a question already. There’s still time to submit your questions there in that questions pane if you’d like. And we’ll try to get to as many as we can. Before we finish, I do want to make to let you know that if we do not get to your question during the session right now, we do have your questions recorded. And so then either Colin or someone else from our METER environment team will be able to get back to you and respond to you individually via email. So yeah, ask any and all questions and we’ll try to get to as many as we can. One thing I want to get this Colin first, just kind of break the ice, you’ve mentioned a lot of fun, quote unquote fun data disasters or setup disasters, what’s the worst off the top of your head data ruining mistake that you have either done yourself or have seen in your experience?

So in my PhD, we had a special data logger that we were going to measure water depth in our rice field. And I actually made an instrument to measure water depth that I just found on the internet somewhere and put it together and put this CR7 data logger from Campbell Scientific together, worked really well, set it all out and just left it reading. So we were gonna monitor it every time I went to the field, I checked it to see this water depth device was working well. And I was just waiting to get the correct data to come in. And when we looked inside the box, a mouse family had built a house inside the data logger, even though we thought we stopped up all the holes. And the baby mice really enjoyed the cables. So we didn’t get a lot of water depth out of that. And that was a real disappointment. So I needed to be checking that a lot more often. There was a lot of things to check there. This was a conditional sampling system that had probably 50 sensors going on, so I wasn’t really thinking about that particular data logger. And we didn’t get data from it.

Great. Nobody’s perfect. Yeah. Hopefully these steps are points that Colin has shared will be able to help everyone to have better success than that. This first question is dealing with installation. And they’re asking do the sensors have to be installed vertically so that the prongs are horizontal? It sounds like this is specifically asking you about some of our TEROS line, TEROS 10, 11, 12? Yeah, can you talk to that?

So the sensor needs to be installed so that it doesn’t impede water flow down through the source. So you can install it with the body horizontal or the body vertical, but the prongs of the sensor should be to the side of the sensor. So the way I wouldn’t like to see it is if the the body of the sensor was above the prongs so that it basically was an umbrella of water over the sensors. So you can do it horizontally or vertical. In our installations you were seeing there, we always use installation tools if we can, or by hand going into the side of a borehole, which really makes installation fast. You can use a borehole, an auger. And so they usually go into the side of the hole and the body of the sensor is usually vertical.

Okay. Along with that you’ve mentioned the installation tool, could you just go into just a minute or less about about how that actually works?

So the installation tool is actually something you can rent from METER. And it’s an auger, a four inch or a 10 centimeter auger that you can auger down to about two meters in the soil. And then there’s a device that comes along with it, this install tool that you can put the sensor in a carriage lowered down into the hole, and then a handle turns and pushes that sensor into the side of the borehole. And it does it exactly perpendicularly and with very little error in terms of shaking, like my hand would do if I pushed it in by hand. And so it can get really good, consistent installations that way. 

And that kind of goes along with another question here about do those sensors need to be installed perfectly level? You talked about having them perpendicular…

Oh yeah. So I kind of talked about level all the time, that really refers to our meteorological instruments, the all in one weather station or any weather station. If you’re measuring solar radiation, precipitation, wind speed, all of those things need to be carefully leveled. In the ground, we’re not so concerned with that. I mean ideally, the biggest thing is to make sure you don’t shield the prongs as water moves down through but any way you want to get them in into undisturbed soil is I think great.

Okay, and how do you deal with sensor degradation over time?

So for water content sensors and water potential sensors over time is something we test quite a bit and we just don’t see any degradation over time. The new TEROS 10, 11, 12 series from our tests in a lab look like it can last 10 years or more and those sensors now have a long warranty which is great. So we expect them to last a long time. For atmospheric sensors from a meteorological standard, you’re gonna want to recalibrate according to manufacturer’s recommendations. And those will be found in the manuals. You want to make sure that you keep a handle on your especially your pyranometer, your solar radiation sensors, you’re going to want to make sure everything is working well over time, your temperature, relative humidity sensors, check to make sure they’re performing well. Those are things you want to do.

Okay. Say you did mess up, and your experiment went poorly. And you basically did set up the whole thing wrong. In your mind would it just be best to just start all over again? Or are there any kind of other shortcuts to kind of speed up the process of redoing an experiment?

Well, so in our rice study we had a black bird that would go out and leave little presents on top of our pyranometer. And we didn’t notice this, again, another problem of the data that the solar radiation data was coming in, why wasn’t it? You know, it was good, right? Well, I didn’t have a range to check that. And so I wasn’t checking, I was just seeing that the solar radiation data was coming in. And toward the end of the season I realized that we didn’t get any solar radiation. And this was actually a huge issue, because we were measuring radiation use efficiency and the rice. And so we actually did clean the sensor off, it took us about four weeks to figure it out. And what I did was go back and had a photosynthetically active radiation sensor out there too. And I was able to use those data to back correct the solar radiation, the pyranometer data. Now it’s not perfect, we are making an assumption of that 45% of solar radiation is photosynthetically active radiation. And it’s not exactly that in the literature, it’s 42%, or a little bit less, sometimes I think the best numbers we had was 45%. But these are some things, we can use other sensors in the field to maybe try to figure out what we missed. But if you’ve truly missed something, there’s nothing better to do than try to get it set up right and keep going.

Okay, I think we’re getting close to the end of our time here. So I think this is going to be our last question. They’re asking about data, can data be pulled from ZENTRA Cloud repository to your local machine, similar to a GitHub repository? Also, do the loggers and sensors have built in components to connect to the ZENTRA Cloud system? Or is there an additional component needed?

Okay, so a couple of questions that is… so on ZENTRA Cloud, you can easily download your data directly from each data logger. So that’s available. And anybody, so any data logger can be shared with any number of people. So you just buy a subscription for the data logger. And then any data logger can be, or anyone can view that data logger. Right now you can download those data. Soon, we’re actually going to have an opportunity to connect the ZENTRA Cloud into whatever software you like through the API. So you can pull that in, put it in your own repository like GitHub, you can pull that into into R or MatLab or any of these kinds of things. So that’s coming up. Watch for that, it should be released in the next couple of months. And the other question, the sensors themselves do not connect onto the cloud yet, you know, you’ll see that ability I think in the future. That’s something that through IOT everybody’s interested in. Right now, our IOT device is the ZL6 Data Logger. It seems to really do well as an intermediary. And usually you want to measure more than one sensor at a site anyway. So you plug these into the ZL6. And the ZL6 does all the heavy lifting of putting it up on the web.

Great. Thank you, Colin. And thank you again to everybody who’s joined us. That’s going to wrap it up for us today. We we had a lot of questions that we did not get to. Again, we have those recorded and either Colin or somebody else from our METER Environment team will be able to get back to you via email to answer your questions individually. We hope you enjoyed this discussion. Thank you again for your great questions. Please consider answering the short survey that will appear after this webinar is finished to to let us know what type of webinars you’d like to see in the future. Also, for more information of what you’ve seen today, please visit us at metergroup.com. And finally, look for the recording of today’s presentation in your email. And stay tuned for future METER webinars. Thanks again and have a great day.

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