I recently published my third first-author paper. It is always a great feeling when a paper gets accepted. As an academic researcher, papers are the primary product of my job. Yes, I have a “real job”. I get paid real money to perform a real service and I output a real product. And yes, this is a point I am defensive about. But to get back on track, this particular paper came out of my graduate work, so it is especially gratifying that it is published.
Publishing a paper is a lot of work. I suppose it is easier for some people or for some disciplines, but I have found it usually to take a significant amount of time and effort. Here is an outline of research from the proposal to the finished paper.
First you have to get an idea for your research project. Then you have to write proposals for either or both funding and data. This process itself can be quite intimidating, but it is often exciting. Writing a proposal is a good time to read papers/research from others and get ideas about what to investigate on your own. I often learn a lot at this stage. After you submit the proposal, it will usually take a few months before the results from the review committee are released. At that point, all you can do is wait - or, more realistically, busy yourself with all the other things you have to do.
Once time/funding/data have been granted you can start your actual research program. Going observing is one of my favorite experiences in astronomy, and maybe I can write up a how-to on observing runs in the future. But let’s say you go observing and collect a bunch of data. Then you get to go home and start analyzing, right? Wrong!
First you have to prepare your data. In astronomy we call this “reducing the raw data”. In other fields you might call it “cleaning the data”. You have to get rid of artifacts from the instrument(s) and perform calibrations so that you can interpret your data properly. The amount of time this takes is highly variable. It depends on several factors including what kind of data you have (imaging, spectroscopy, IFU), and also on how familiar you are with the instrument. It can help to have a pre-made pipeline for data reduction - or it can frustrate the bananas out of you trying to figure out how to run it.
After the data are cleaned you are ready for analysis! This can include an intermediate step, which is referred to by data scientists as “data wrangling”. You want to compare your measurements to some previous study, or mix data from two different sources. Often times these data are in various formats or calibrated to different standards. In order to make everything uniform, you have to perform transformations and/or recalibrate the data.
The analysis will obviously vary depending on the research program, but it might include spectral or image modeling, statistical analysis of data, error and uncertainty estimations, lots and lots of plots, etc. And after many long hours of work you get some results! In some cases this might reduce down to a single point on a single plot, or the results might span many pages of plots.
After all that work, you are ready to begin writing your paper. Some ambitious sorts may have already begun writing as they perform the analysis, but we’ll get to all of that next time on . . . Arbitrary Notations!
Kyle D Hiner
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