ER&L 2016: Agents of Change: The Ongoing Challenges of Managing E-books and Streaming Media


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change
“change” by samantha celera

Presenters: Steven R. Harris and Molly Beisler, University of Nevada, Reno

Evolution doesn’t happen in slow increments. Moments of punctuations happen quite suddenly. Ebooks are kind of like that in the evolution of the book.

In 2005, they were putting all formats on one record, manually updating the electronic content. As the quantity of ebooks increased, and the various licensing terms expanded, they were struggling to maintain this. In 2008, they began batch loading large numbers of eresources materials, with one person maintaining QA and merging records.

Then discovery services came in like an asteroid during the dinosaur age. They finally shifted from single record to separate records. They began tracking/coding ebooks to distinguish DDA from purchased, and expanded the ERM to track SU and other terms. This also prompted another staff reorganization.

They developed workflows via Sharepoint for new eresources depending on what the thing was: subscriptions/standing orders, one-time purchases with annual fees, and one-time purchases without annual fees. The streaming video packages fit okay in this workflow.

Streaming media has more complex access and troubleshooting issues. Platform as are variable, plugins may not be compatible. There are also many different access models (DDA, EBA) and many come with short-term licenses. Feel like the organization structure can support them as they figure out how to manage these.

They use a LibAnswers queue to track the various eresources problems come up.

Reiteration of the current library technology climate for eresources, with various challenges. No solutions.

The future comes with new problems due to next-gen ILS and their workflow quirks. With the industry consolidation, will everybody’s products work well with each other or will it become like the Amazon device ecosystem? Changing acquisitions models are challenges for collection development.

Be flexible. Do change. Agents.

ER&L 2016: Lightning Talks


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Eric Frierson, EBSCO
Demoing a mobile interface called “Launch Pad”. Search results you can swipe left/right for the things you want, and then email the ones you want. The email includes persistent links, as well as other search suggestions and possibly a tutorial video as a kick-starter for the project you began a preliminary search on. The plan is that the code will eventually be released on GitHub.

Letitia Mukherjee, Elsevier
ScienceDirect APIs for institutional repositories, which helps with the metadata and embargoes for hosting final versions of articles. Looking for pilot institutions for embedding accepted manuscripts.

Bonnie Tijerina, IdeaDrop
Looking to take what they’ve been doing for the past four years and expand it. Would like support via the NewsChallenge.org site.

?, ?
Developed an “adapter” for digital files that can be retrieved and played/viewed on an open source tool.

Todd Carpenter, NISO
There is a standard for technical reports which is entirely print-based. Need people who are interested in taking this specification and modernize it, but no one has stepped up, yet. There is another standard on something something mono-lingual controlled vocabularies — linked data — but the name isn’t getting the volunteers in place to revise the 2005 revision to modern practices and technologies. If you are interested in technology, revise the old standards!

Todd Carpenter’s beard, NISO
Looking to develop a standard/best practice for text and data mining. Another project regarding the ebook reading experience and how libraries can manage that through API. Another thing about sharing human subjects’ data while maintaining privacy. Open to other ideas about things/problems/issues that need to be resolved.

Lana Zental, California Digital Library
Hiring a new data analyst.

Kate Sudowsky, ?
Plug for Usus.

ER&L 2016: Trying Something New: Examining Usage on the Macro and Micro Levels in the Sciences


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Cheaper by the yard
“Cheaper by the yard” by Bill Smith

Speakers: Krystie (Klahn) Wilfon, Columbia University; Laura Schimming and Elsa Anderson, Icahn School of Medicine at Mount Sinai

Columbia has reduced their print collection in part due to size, but more because their users prefer electronic collections. Wilfon has employed a systematic collection of cost and data over time, a series of analysis templates based on item type and data source, and an organized system of distributing the end product. [She uses similar kinds of metrics I use in my reports, but far more data-driven and detailed. She’s only done this for two years, so I’m not sure how sustainable this is. I know how much time my own reports take each month, and I don’t think I would have the capacity to add more data to them.]

Mount Sinai had a lot of changes in 2013 that changed their collection development practices. They wanted to assess the resources they have, but found that traditional metrics were problematic. Citation counts don’t factor in the resources used but not cited; journal impact factors have their own issues; etc. They wanted to include altmetrics in the assessment, as well. They ended up using Altmetrics Explorer.

Rather than looking at CPU for the journal package as a whole, she broke it up by journal title and also looked at the number of articles published per title as a percentage of the whole. This is only one picture, though. Using Altmetric Explorer, they found that the newsletter in the package, while expensive in the cost per use, had a much higher median Altmetric score than the main peer reviewed journal in the package (score divided by the number of articles published in that year). So, for a traditional journal, citations and impact factor and COUNTER usage are important, but maybe for a newsletter type publication, altmetrics are more important. Also, within a single package of journal titles, there are going to be different types of journals. You need to figure out how to evaluate them without using the same stick.

ER&L 2016: COUNTER Point: Making the Most of Imperfect Data Through Statistical Modeling


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score card
“score card” by AIBakker

Speakers: Jeannie Castro and Lindsay Cronk, University of Houston

Baseball statistics are a good place to start. There is over 100 years of data. Cronk was wishing that she could figure the WAR for eresources. What makes a good/strong resource? What indicators besides usage performance should we evaluate? Can statistical analysis tell us anything?

Castro suggested looking at the data as a time series. Cronk is not a statistician, so she relied on a lot of other folks who can do that stuff.

Statistical modeling is the application of a set of assumptions to data, typically paired data. There are several techniques that can be used. COUNTER reports are imperfect time series data sets. They don’t give us individual data points (day/time). They are clumped together by month, but aside from this, they are good for time series. There is equal spacing and time of consistently measured data points.

Decomposition provides a framework for segmented time series. Old data can be checked by newer data (i.e. 2010-2013 compared to 2014) without having to predict the future. Statistical testing is important in this. Exponential smoothing eliminates noise/outlier, and is very useful for anomalies in your COUNTER data due to access issues or unusual spikes.

Cronk really wanted to look at something other than cost/use, which was part of the motivation to do this. Usage by collection portion size is another method touted by Michael Levine-Clark. She needed 4+ years usage history for reverse predictive analysis. Larger numbers make analysis easier, so she went with large aggregator databases for DB and some large journal packages for JR.

She used Excel for data collection and clean-up, R (studio) for data analysis, and Tableau (public) for data visualization. R studio is a lot more user-friendly than the desktop. There are canned analysis packages that will do the heavy lifting. (There was a recommendation forRyan Womack’s video series for learning how to use R.) Tableau helped with visualization of the data, including some predictive indicators. We cannot see trends ourselves, so these visualization can help us make decisions. Usage can be predicted based on the past, she found.

They found that usage over time is consistent across the vendor platforms (for journal usage), even though some were used more than others.

The next level she looked at was the search to session ratio for databases. What is the average? Is that meaningful? When we look at usage, what is the baseline that would help us determine if this database is more useful than another? Downward trends might be indicators of outside factors.

ER&L 2016: Finding Time: From Industrial Mythology to Chronemic Literacy


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Time
“Time” by cea +

Speaker: Dawna Ballard, Moody College of Communication, University of Texas at Austin

She studies human interaction, particularly the symbols we use in communication. She studies the lived experience of time beyond what is on the clock.

Time has been called the “silent language”. What we see is the tip of the iceberg. It’s not only non-verbal (looking at watch, tapping toes), it’s also full of deeply hidden assumptions that are masked. These hidden assumptions are often seen as truth, and each culture has their own interpretation/approach. We need to develop a chronemic literacy.

Industrial time is visible through the clock. There are a lot of hidden assumptions behind that. For one, it’s not even in tune with the Earth (see also: leap year). Three basic hidden assumptions of industrial time are that people work a lot like machines, that all times are the same, and that we can control the people and events around us. We think this is the way it’s always been, but in fact there are many other ways to orienting to time. This is the chronos aspect of orienting to time.

Pre-industrial and post-industrial time have more in common with each other than with industrial time. Pre-industrial time was based on “the event” (i.e. farming). Assumptions: people work nothing like machines, all times are not the same, and life unfolds through the people and events around us. This is the kairos aspect of time.

The industrial mythology comes with three related myths.

The first one is that better time management skills and tools will make you more productive — the right app will change your life. Time-management originated with factory work, and was wildly successful in that environment. It doesn’t function so well in the office work of today. The reality is that time management is not related to productivity. All it does is help you feel that things are being managed, which is good if that makes you feel happier about your work. It will not solve your time problems. Productivity is a long-term proposition — what is sustainable for you?

The second one is that if you love what you’re doing, it doesn’t feel like work. (“That’s bullshit.”) Be wary of language that tries to mask work as something else. There are still human limits to work, and no matter how much you enjoy it, you can’t do it all the time forever. Focusing on balance can create unending frustration. Lower-wage workers often don’t even know what this means, or assume it’s just for managers.

Thirdly, focusing on work-life balance will solve your problems. Balance is something that machines do, and it doesn’t really apply to human beings. Work and life as separate terms doesn’t appear until the 1960s, and it was about industrial work. Life should be in our work — our lives are a lot of work. We think that if we can find work/life balance, we think our lives will be centered and at peace. Work has never looked or felt like that. We end up holding on to one or two things that are “necessary”, usually work, and getting that done to the detriment of the others.

Consider alignment. Being mindful of our alignment is being aware of all the interrelated parts that are needed to move forward. When all the parts work together, we get an efficiency of movement. We cannot let something stay out of alignment for too long without expecting repercussions. We can get help from experts (therapists) and support networks (family/friends), and it’s important for our long-term sustainability.

What are your hidden assumptions? What are the things you are thinking about or not? What are the things you believe that are shaping your hidden assumptions? What might be impeding the alignment you would like to achieve?