2016: My Year in Reading

All of the data that follows was collected by me throughout the year using a combination of Google Sheets and Google Calendar. All seasonal and monthly calculations are based on the date each title was completed. Average days to read titles are based on the number of days actually spent reading each title, and not necessarily the full span from begun date to completed date.

A complete list of all the books I read in 2016 is at the bottom of this post.


I read 70 books in 2016. This year I overwhelmingly read fiction:

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2015: My Year in Reading

All of the data that follows was collected by me using a combination of Google Sheets and Google Calendar. Once again, I elected not to track pages read—too much discrepancy between formats to generate meaningful comparisons.

A complete list of all the books I read in 2015 is at the bottom of this post.


I read 66 books in 2015. Fiction titles outnumbered nonfiction by 2-to-1:

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The Relevance of Libraries

On April 10, 2015, KCUR’s “Up to Date” program interviewed Prof. John Palfrey about the future of libraries in the Digital Age, the day after he gave a talk on the subject at the Kansas City Public Library. During the interview, KCUR tweeted a question meant to provoke discussion about the future of libraries:

Prof. Palfrey offers an optimistic and robust vision for the future of libraries, but even he frames the discussion in a way that implicitly fuels the fire for those who question their relevance.

I’ve spent a lot of time looking at the data and I have to say—I can’t understand how the relevance of libraries has come into question in the first place. It bothers me that we’ve allowed this question to define the discussion about their future. I can’t think of any other public or civic institution or service that can boast the kind of numbers that libraries do. I tweet-stormed some of the most powerful:

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Infographic – 2014: My Year in Reading

My friend Bil liked my 2014: My Year in Reading post so much, he made an infographic of it:

Infographic - 2014: My Year in Reading
This image is entirely the property of Bil Gaines.

He asked me to name an animal and I chose the three-toed tree sloth.

Bil is an amazing writer / artist / father / husband / shark lover / bland car enthusiast / SEO guru. Please read his blog. Also, if you want any fancy-schmancy infographics, drop him a line.

2014: My Year in Reading

I have a friend who posts a list of all the books they read each year on their Facebook page. This has inspired me to write my own Year in Reading posts.

All of the reading data that follows comes from my Goodreads account. A complete list of all the books I read last year is at the bottom of this post.

EDITOR’S NOTE: I realized after I posted this on February 12, 2015, that I had miscalculated some of my figures based on the data. On February 13, I recalculated all my figures to correct for my previous mistake. This post has been updated to reflect these new calculations. I added a day to my time-to-read figures.


I read 40 books in 2014. It was a nonfiction-heavy year for me.

  • 24 nonfiction
  • 16 fiction

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Thoughts On Automated Recommendation Services for Libraries – A Correction

Re-reading my post from last month, Thoughts On Automated Recommendation Services for Libraries, I realize that I’m somewhat wrong about the role of locality in automated recommendation systems. Amazon and Netflix can (and I think they do) integrate user location data in their recommendation algorithm. They can skew results based on strong trends that appear among proximate user groups.

I’m also somewhat wrong about how recommendations work in BiblioCommons—ratings and reviews for individual titles are aggregated from multiple libraries, as well as user-created reading lists. Reviews from local library staff are prioritized over others, but I don’t know if local library user-created reading lists are prioritized. Regardless, these are individually curated pieces of content, these sections aren’t automated in the same way that Netflix is. This content is related to an individual item in the catalog and doesn’t generate lists of recommendations as one typically expects from Readers’ Advisory services.

The “Similar Titles” type of content in the sidebar in BiblioCommons is what I think of when I look for reading recommendations, and this content is also the most directly analogous to Netflix / Amazon. However, this content doesn’t get generated by BiblioCommons at all—these titles come from third party services like NoveList.

So locality can be a meaningful factor in automated recommendation systems—such systems are smart enough to recognize that local trends are important, even if they aren’t yet intelligent enough to know what local trends mean.

But libraries still don’t have enough data to make such algorithms work. We still rely on curation and the personal touch to create real value for our patrons.

Thoughts On Automated Recommendation Services for Libraries

Librarians talk off and on about the need for us to offer Netflix / Amazon-style automated recommendations for our patrons. It seems almost self-evident that this is something patrons have come to expect. But there’s a self-evident question about this that we must ask:

Have patrons actually told us that they want this type of service from a library?

Or do we just assume that they want this?

A library doesn’t fulfill the same role in people’s lives that Netflix does, or that Amazon does. Our patrons don’t necessarily expect the same service models from us. We may be holding ourselves accountable to a false comparison here. This is a prime example of the need for us to base decisions on verifiable user data.
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