Eating Our Own Carrot Sticks

One phrase that’s bound to come up at every data management conference is “carrot versus stick” vis-a-vis incentivizing researchers to manage their data better. Carrots are rewards for good practices and sticks are requirements and their consequences relating to data management. There is inevitably discussion over which method is more effective for implementing data management.

Another phrase that I often hear in similar settings is “eating our own dog food” or “drinking our own champagne”. This is another way of saying “practice what you preach”, in that data experts should apply their advice to their own files.

These phrases are used so often that I’ve decided that they need to be combined as “eating our own carrot sticks”. It’s at least more appetizing than some of the other “eating our own…” options and a bit of snarkiness provides relief from predictability.

But to say something serious in this blog post, all of these phrases emphasize the importance of *doing* data management. It’s not enough to have the knowledge or to be given the incentive. It is only in the act of actually managing the data that we get value.

So I challenge you, whether you are a data management novice or an expert, to find one new data management practice to implement this month. Because a carrot stick a day keeps the data disaster away*.

 

* Okay, now I’m taking it too far, I know. I can’t help myself.

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The Long List of Data Management Books

There was a long discussion on twitter yesterday (okay, I went on a rant) about the vast number of data management books that have been published for librarians in the past few years. While not exclusively data management books for librarians, here is the long list of data management books that I’m aware of:

We do not need any more “here’s how to build data services at a large research institution in a western country” books, thank you. I would happily buy books about data services at smaller institutions, in non-western countries, for data service support beyond PhDs and faculty, for building on data information literacy principles, and how to manage data when you’re a researcher (I’m happy to have competition for my own book!).

Please feel free to point people to this post when someone suggests writing/publishing another “building data services for librarians” book.

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It’s My 5-Year Blogiversary!

It is the 5-year anniversary of me starting this blog! I can’t believe that it’s already been 5 years. How did that happen?! I put up my very first post on 2013-02-20:

The Blog I Wish I Had

A lot has changed between then and now – I finished my MLIS, started my current job, published a book, and pursued some pretty interesting data-related research projects – but this blog has continued to be a wonderful project for me.

To celebrate, I’m giving away a softback copy of my book, Data Management for Researchers. I’ll even sign it for you!

Details: Leave a comment on this post by 2018-02-28 describing your worst data disaster; the worse the data disaster the more likely I’ll feel you need my book. A winner will be chosen on 2018-03-01. United States only.

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Wrapping Up a Project, Part 2

Twice in the month of January, I had to find files from an old project. With resignation, I delved into old folders only to find that, wow, there’s a “FinalDocuments” subfolder with everything I need all laid out for me in a well documented way. Both of these times I was so, so thankful that my past self had the forethought to organize things for the future.

Looking back through the blog archives, I realize that I wrote a “Wrapping Up a Project” post 4 years ago! (Related: how the heck is this blog 5 years old?!) I still stand beside the advice I gave in that post that researchers should: back up their notes, convert file formats, use README.txt files, and keep everything together. These are generally useful strategies for all of your files that make it likely for you to still have everything in 5-10 years. However, most often when you go back to your old files you are looking for something specific.

This is why, in this post, I’m recommending that you add a step during project wrap up to select key information to copy to a “FINAL” folder (or some obvious variant of that name). Example documents include: a copy of the final publication, the raw dataset and the analyzed dataset, finalized scripts, key protocols, and JPEG files for figures. Basically you should identify the information that you will mostly likely need to refer to later and place all the final versions together in one folder. And then write a README.txt file to describe the contents of that folder.

Without this added step, you will still have your files and can open them, but you’ll likely waste a lot of time looking for exactly what you need. And even with this step, there will be times when you’ll have to dig through all of the project files to find something specific. But 90% of the time, you will save time by placing these key documents in an obvious place.

Trust me, your future self with thank you for taking 20 minutes, while you still understand the files and their organization, to set aside the important stuff for later.

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Gaining Competency in Data Visualization

I couldn’t be more excited that my latest journal article, “Gaining Competency: Learning to Teach Data Visualization,” was just published in the Journal of eScience Librarianship.

The idea behind the paper is: how do we as librarians teach data skills in an area, specifically data visualization, in which we often have little expertise? Data librarians teach many data competencies, but the “data visualization” competency has always been an awkward one. We see a lot of desire for help in this area but don’t always have the expertise to meet this need. Data visualization has not historically be associated with the library and it isn’t covered in our usual data-management-based curricula. This paper seeks to close this gap.

I admit that it’s a quirky little paper. So many papers in librarianship are in the “we did this awesome thing” mold, and there’s a health dose of that in this paper in that I discuss the data visualization workshop I offer at my institution. However, I decided to go a step further by describing the lead-up process in which I prepared to teach the workshop. I thought it might benefit other librarians to have a framework for developing our own skills to the point where we can help others with data visualization.

So if you’ve ever thought about supporting data visualization but don’t feel like you have the requisite skills, I encourage you to check out my new paper. It’s part of a larger data visualization special issue and I’m certainly looking forward to digging into the whole issue!

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Exit Stage Left

Have you ever fallen down a research rabbit hole? It does not happen to me very often (thankfully) but when it does, I fall deep. All of this is to say that I’m back from my 5-month blogging hiatus. I promise that there will be future posts about this particular rabbit hole, but in the meantime we’re going to talk about exit strategies!

I was involved in a Twitter conversation the other day about the importance of exit strategies.

This all came out of a conversation about the end of Storify and what people should do with all of their content on that platform. @Libskrat discussed the importance of an exit strategy for your online content and I want to spend this post talking about how important that is for research data as well.

The simple fact of the matter is that you should never put your data into something where you can’t get it out again. This applies to many situations: storage platforms, file formats, e-lab notebooks, specialized analysis software, etc. Things happen and if you don’t have Plan B for your data then you sometimes have no data at all.

A couple big areas where researchers get into trouble around exit strategies are using proprietary file types and adopting specialized software/platforms (note: these overlap somewhat). The first area is something that I’ve had experience with. My PhD lab used a specific analysis software, which I lost access to when I left the lab. Unfortunately all of my data was locked up in a proprietary file format that I can no longer open. Good thing I don’t need access to all of that chemistry data as a Data Librarian because it would be a huge problem to get it back. If only I had saved a copy of my data to Excel or as a .csv, I wouldn’t be dealing with this mess.

E-lab notebooks (ELN) are a good example of the dangers of adopting software without an exit strategy. You should assume that you won’t be usuing the same ELN in 10 years. So you need to ask about getting your data out before you put one piece of data in an ELN. Even if your exit strategy is literally printing out every page of your ELN, it’s better than nothing (heck, it’s about equivalent to your paper lab notebook).

So next time you’re adopting a tool for your research, ask the question “do I have an exit strategy out of using this tool?” If the answer is no, run far and fast. That tool is not worth the short-term benefits it might offer.

Also, are you a librarian who reads this blog and wants to know more about delivering data services? I’m teaching a online continuing education class through UW-Madison’s iSchool on Research Data Management from Jan 22 – Mar 18. There is a 10% discount if you register by January 7!

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