Data Ab Initio

I remember being incredibly paranoid when I was writing my PhD thesis that my computer would crash and I would lose all of my files. After 5 long years of work, I did not want anything keeping me from finally graduating, lost dissertation and data included. Luckily, no such calamity befell me, but I did have a friend whose laptop was stolen in the middle of writing his thesis. He was forced to start over from scratch because he did not have a good backup copy. Sadly, this is not a unique occurrence.

It’s bad enough to deal with the stress of writing a thesis and worrying about moving on from school—you do not need the added paranoia about losing (or difficulty finding) important information on top of that. Thankfully, data management offers some practical tips that can keep your worries focused solely on writing the actual thesis.

Back up your files

One strategy that will save you a lot of thesis stress is having a good backup system. I recently wrote about “the Rule of 3”, and thesis time is a great opportunity to follow it. The rule basically says that you should have 3 copies of your files, two onsite and one offsite. If one of your onsite copies fails, you still have two copies to fall back on; this can reduce a lot of paranoia about losing your important files.

To further allay your fears, I recommend using automated backup systems and test restoring from them. Automation removes any work that you have to do beyond set up because, frankly, you have enough things to work on right now. Once you set up your backups, you should run through the procedure for getting your files back from the system. This ensures that you won’t be frantically searching for the restore procedure if you lose your main copy and that your backup system is actually working.

Finally, I will remind you about the hidden perils of cloud storage. In a way, cloud storage is great for thesis writing, especially if you want access from several locations. But you should definitely read your cloud storage service’s terms of service to be sure that they can’t do anything they want with your thesis files. You thesis is too important to store in cloud storage that doesn’t protect your content.

Organize your information

A small thing that will smooth out the writing process is organizing your thesis documents as you create them. First consider how you want arrange your thesis files. It may be logical to organize things by chapter or section, keeping separate folders for figures, data, references, etc. Pick a system that feels logical to you so you’ll know where to find everything when cross-referencing and assembling the final document.

In conjunction with having a good organization structure for your files, think about consistent file naming. Labeling written draft files differently than figures and tables, and drafts differently than final versions makes it easier to find and use information. You can also tell, at a glance, what is done and what you have yet to do.

Another practice I highly recommend is to version your drafts. This means regularly saving a draft to a new file with a new version number. For example, I might save my first chapter drafts as the files “Ch01_v01.docx”, “Ch01_v02.docx”, etc. with each consecutive version being a more complete draft. The final version of this chapter would be named “Ch01_FINAL.docx”.

Not only does versioning allow you to easily revert to an earlier version of your draft or recover from a corrupt file but it also helps you keep track of the most current copy. This last point is very important if you are writing your thesis on multiple computers; you need to know which is the most current copy so that you don’t repeat effort or have to deal with merging edits.

In the end, you want a clear workflow for where things will go and how they will be named. Taking a few minutes before you start your thesis to come up with a system and sticking with these workflows can save you time later when you are looking for that one particular file right before submission.

Manage your references

I cannot say enough about the value of a good citation manager while writing your thesis. You are going to be citing a lot of sources, so you want a system that both organizes your references and helps you format your actual citations. There are many options available to you—most notably Medeley, Zotero, Refworks, Endnote, and Papers—so pick one and run with it. Writing a thesis without a citation manager is just asking for more frustration and stress.

Think ahead

You should address all of the things mentioned in this post before you actually start writing. It will take a little time at the beginning, but once you have set up your backup systems, established your workflows, and chosen a citation manager, everything should fade into the background behind actually writing. That’s the whole point of data management—to build workflows that make it easier for you, in the long term, to do your work.

So take a few minutes at the beginning of the process to set things up. I can’t promise it will entirely relieve your stress, but at least you’ll be worried about your writing instead of losing your thesis.

I’m surprised that I haven’t discussed this on the blog yet, but there is a pretty fundamental question that needs to be addressed in order to discuss data management: what does “data” even mean?

Coming from a scientific background, it’s easy to imagine large tables of numbers as data or files filled with the repetitive A, T, G, and C’s of genetic code, but data regularly defies these stereotypes (particularly in non-science disciplines). Data can be videos, large collections of text, images, tweets, geospatial information, etc. What can be used as data is only limited by the research question and the creativity of the researcher.

Though research data can be a lot of things, it is still useful to define the term so we know what information needs management. So here is my working definition of data: anything that you can perform analysis upon. It’s a wide definition, but there are so many types of research out there that anything narrower won’t apply. Despite the broad definition it is still possible to break the diversity of data into four general types.

What Data Are

Data is often categorized into the four following groups: observational data, experimental data, simulation data, and derived/compiled data. Not only are the data in each group different, but the way that you should manage each type differs. Let’s go through each group now.

Observational data are tied to a time and place and are a record of something that occurred there. This type of data includes everything from bird counts, to polling data, to weather sensor data, to recordings of dance performances. The proper management of this data is critical because this information is not reproducible.

Experimental data are created under a particular set of conditions that are (hopefully) reproducible. This type of data covers everything from gene sequences, to chromatography data, to measurements from the Large Hadron Collider, to psychology studies. Good data management is important here too because, depending on the experiment, it can be very expensive to reproduce data. Experimental data also requires good documentation so that, should the need arise, the data can be accurately reproduced.

Simulation data are created using models and code. This type of data covers everything from climate models, to economic models, to simulations of experiments/experimental data.  In this group, it is more important to preserve the code that created the data than the data themselves, as the data can be recreated from the code.

Finally, derived/compiled data are compilations of other datasets that can be used for new types of analysis. This type of data covers databases, large corpora used for text mining, collections of images, etc. Standard data management applies, but you’re also more likely to run up against data size concerns and licensing/copyright issues with this data type than the others.

What Data Aren’t

I defined what data are but I think it’s also important to talk about what data aren’t. The OMB Circular A-110 contains a nice round-up of what the government does not consider to be research data.

Research data is defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings, but not any of the following: preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues. This “recorded” material excludes physical objects (e.g., laboratory samples). Research data also do not include:

(A) Trade secrets, commercial information, materials necessary to be held confidential by a researcher until they are published, or similar information which is protected under law; and

(B) Personnel and medical information and similar information the disclosure of which would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study.

That doesn’t mean that these things aren’t important to manage and preserve, just that funders don’t consider them to be data for the purposes of sharing and other policies. I will also point out that while lab notebooks are not technically data nor do they need to be shared, they contain important information that gives context to data and should therefore be preserved alongside any data that they describe. The ultimate point is that research is built on multiple information sources, each with its own information management need, but not all of these sources fall under the umbrella of “data”.

Final Thoughts

It’s important to recognize that the term “data” is more broadly applicable than you may think. Something you would not consider to be data can be the critical foundation for research in another field. But that doesn’t mean that the term “data” applies to all research materials.

The broad definition of data goes hand in hand with the realization that not all data should be managed in the same way. Understanding the diversity and nuances of data allows us to make good management decisions to better preserve data and make research more reproducible.