I am young and inexperienced. Whether or not that gives me the credentials to speak to the title of this post, I can’t be sure. What I can be sure of, though, is that I’ve come away from this summer appreciating the opportunity I had to conduct research. I want to tell you why.

If you read my blog on drone flying, you’ll have some idea of the work that I did this summer. In short, I joined an ongoing comparative agricultural research initiative that studies crop growth at different local farms and runs experiments on several plots of land. I had a supporting role, primarily data acquisition, but with a bit of data analysis at the tail-end of the summer. My days were often split in half: mornings, in the lab tagging insects and adding them to a digital library; afternoons, out in the field measuring leaves, collecting bugs, and flying the project drone. I worked alongside a team of students in my grade and one grade higher.

At the beginning of our summer on the team, none of us had any idea really where our research would be going. We were hired on as interns to do the observational grunt-work. But because we were doing the observational grunt work, we had to design and implement systems of recording and organizing all of the different kinds of information we collected in a way that we could modulate, build, understand, and share.

This is the first and perhaps most useful aspect of research for the young and inexperienced: research demands that you slow down at the start, think things through, and design systems that are organic enough to change under stress but robust enough to handle large quantities of interconnected data.

Organic flexibility and scalable robustness are not unique to research. Quite on the contrary, flexible and robust systems are just beneath the skin of any collection in this universe that has any permanence. I mean this very generally, and I think this thought will withstand some level of scrutiny.

Researching when you’re young and inexperienced gives you the opportunity to learn how to begin thinking in ways that are flexible and robust. You’re presented with a problem that at first seems simple, but then, as you begin to become acquainted with the environment, the language, the history around the question, you realize how many layers and angles there are to it. And then you formulate layered and angled ways of getting at the question, not knowing completely how all the pieces of any approach will fit together in the end, but trusting systematic logic, your mentors, and your co-workers. Now in the age of computers, you’re forced to learn about file types, about machine language, about algorithm efficiency, and you begin to see how everything in computers makes sense at a very fundamental logical level. And you learn how to begin structuring your workspace on a computer in a way that mirrors the underlying logic of the computer.

Because you’re young and inexperienced, you do it wrong. You make big mistakes early on. You don’t record the data that you need to when you’re out in the field; you fail to make a spreadsheet in a way that lets you add a new variable; you wrangle with equipment and learn to filter your data for erroneous measurements. Your systems don’t fall into place from experience, they grow up in the heat of your inexperience. At first, your computer workspace, your physical workspace, your strenuous relationship with your co-workers, your displeasure at your work, and your feelings of inability are all a mess. But then, as you learn the logic of systematic research, trust the rhythms of work-without-seeing-the-end, and build healthy personal habits, your systems begin to knit together in strong, organic, and sometimes unexpectedly robust ways.

If you realize you’re inexperienced, you can learn from all this. Your next systems, next workspaces, next spreadsheets, next observational and experimental methods don’t start out as inside-out and upside-down. You learn to take ponderously slow and thoughtful first steps, learn to read all the documentation, learn to really listen to your mentors the first time they say it.

This is good.

The second and no less important reason why research is good for the young and inexperienced is that data is messy, and the sooner you learn to cultivate it, live with it, and be comfortable with its failings, the better you’ll be at thinking through most life situations.

When I say data, I mean the stuff in the system. In any system. In a book system, the characters, words, sentences, paragraphs, chapters, are data. In a computer system, organized 1s and 0s are data. In ecological systems, the location and composition of every organic and inorganic molecule, every plant, every animal, every rock, the downwelling solar radiation, and an infinite number of variables is data.

How are ecological systems able to handle so many variables? Because they’re organically flexible, and they’re robust.

But back on track. Data is everywhere. And, like I said, it’s messy. Messy because it’s the stuff stripped of the structure. Most of the time, we live in and navigate through the world interfacing with the structure of things. Structure is what gives things meaning, and meaning is what we were made for. But when you begin to look past the structure in any system, you’re able to make true inferences about that system that aren’t immediately recognizable from the outward structure. You are also, then, able to see how and why the system functions the way it does, the structure being only half the story.

I think this is what it means to really learn. When we first come in contact with some new idea, some new belief, some new segment of society, we immediately label and paint it based on its outward structure and how that structure integrates with our systematized thinking. It’s not until we sit with the new idea for a while and begin to look past its structure that we can make informed statements, thoughtful arguments, and work the data into our own systems of thought.

This may be part of what happens when a child learns how to play an instrument for the first time. At first, the object is inert and inoperable; the child knows that in the hands of others it makes beautiful music, but when it falls to them it sits like so much deadweight in their hands. But then the child learns the data. How to blow air just right. How to close those holes, depress those keys, strum just this way to make just those notes. Then, how for any instrument those notes in that pattern make this general sound. Slowly, the instrument comes to life in the child’s hand.

Well, when you’re young and inexperienced and you start researching, the whole world feels like it’s opening up to you like that instrument. Things that seemed inert, like they just are, just sit there, that other people understand but that you could never approach, begin to liven a little bit. You begin to see past your own ignorance and become confident enough to embrace that ignorance and try playing the instrument anyway. Slowly, the world comes to life in a deeper, more inviting way.

After you’ve observed all you’re going to observe for your experiment; after you’ve measured all the leaves and counted all the deer and captured thousands of infrared pictures of a cornfield; after all the spreadsheets are full and your report deadline is looming, it’s time to take the instrument in your hands and learn to play.

The data is inert. Doesn’t seem to fit together. How do I make a beautiful G-chord out of 5,000 odd rows of bug data? How do I play that spreadsheet in rhythm to the photographic data, to the environmental data, to the leaf-lengths, to the farmer input reports? How to get it all on the same time-scale, in the same key, to make something beautiful, to make something meaningful?

You begin to wrestle it. Strum it this way, see what happens. Like an amateur carpenter, stick a few nails in a new way and see if it holds a little better. Try a different wrestling stance.

Slowly, slowly, if it’s your first time (like it was mine), the kinks start coming out. You see how edges align, how notes harmonize, and how all the muddled mess that sat inert can make something bordering on a tune. You learn how to search and sort efficiently, script programs modularly and generally for later application and reuse. You spend hours writing this program, scrap it because you started wrong. You manually pick through half a thousand rows before realizing you could’ve done it all in one click. You sleep a little less. The deadline looms a little closer. But inside, in your head, the music is swelling. It’s making sense.

Then, when you’re young and inexperienced and you’ve finished your first research project, the world looks a little different. The structure is a little less opaque, no less beautiful. If you wanted to, you know which routes you could go down to start wrestling with the right data. You know what the data might sing if played exactly right. You also know a hundred–scratch that, a thousand ways not to start going about things.

When you put together your first real report, you put all the effort into it that you can. Spend hours getting everything just right. A pixel to the left here, a pixel up over there. Presentation time. Submission. Feel elated.

Grade back. Feedback. Your report was mediocre.

You’re crushed for a little bit.

Then you begin to realize that you were taking yourself a bit too seriously all along. You look back at the blog post you’ve written and wonder why you waxed so poetic bout such little things. You realize that it was your first report after all, and, since you’re young and inexperienced, you have a lot of time to sort things out.

You start again, this time a thousand light years ahead.

More importantly, the world is a little more open to you. All the grey clouds have more recognizably silver edges, because you can start to think a little more deeply about where the clouds came from in the first place. Books are a bit deeper. Computers a little less complicated.

If you’re young and inexperienced, like me, and you’re reading this, I encourage you to try out a little bit of real, extended research. Pick something you love or think you love.

If you think you’re not young, but you want to start researching, then you’ve betrayed the fact that you really are young–young enough to dream of growing, stretching, and stepping out in the ignorance that research demands.

If you think you’re not inexperienced, then you’re probably somewhat full of yourself and could use re-exposure to research fundamentals and a little poeticism.

đź‘‹ My Concluding Remarks

Give research a shot, whoever you are, in whatever way you think is possible for you right now. Tinkering in the garage or scavenging through the woods qualifies as research if you’re being thoughtful enough. In fact, most things do.

Forgive my brashness in this post. I’ve been reading too many brash detective novels and they’ve gone to my head.

As always, thanks for reading! May the peace of our Lord Jesus Christ be with you always.