Saturday, February 18, 2017

Librarian & fake news - Bayes, metaknowledge & epistemic humility

The recent rise in interest in fake news has given us librarians a reason to once again trumpet loudly the value of what we do in teaching information or media literacy.

Librarians were quick to establish our turf by calling out articles that mention information literacy without mentioning librarians.

Besides the expected library sources, pieces began to appear in mainstream sources such as the Salon, U.S. News & World Report and most recently PBS began to praise the role librarians can play in fighting the rise of fake news. Many librarians were ecstatic, finally our moment in the sun has come!

So how do librarians fight fake news? A running joke among some librarians is that the librarian's standard solution to all the world's ills is to build a LibGuide.

And indeed, librarians and libraries  such as Cornell University LibraryIndiana University East Library, University of Virginia Library have created or adapted existing material to create guides on fake news.

As I write this, there are at least 1,651 Libguide pages that mention "fake news".

While I salute the efforts of librarians to create guides, I fear the actual impact is more like the sentiment below I saw expressed by someone on Twitter*.

* Modified 21/2/2017 with actual Tweet made by Wilkinson

All this talk about helping or teaching our users deal with fake news, made me curious. What roles can librarians play in this? Is it a matter of teaching the CRAP test or worse some black and white view that only .org/.gov or peer reviewed article is reliable (do you automatically trust information on .gov sites under the Trump administration?) Or does teaching information literacy using various "Threshold concepts" be the solution? Is the line between fake news sites and biased news always clear and distinct?

While the answers to these questions are probably not going to be easy, below are three articles I've read that made me think more deeply on the topic.

1. Boyd’s interesting yet scary argument

Danah boyd is a well known Researcher at Microsoft Research and has been influential in helping us understand how young people relate to technology.

Recently she wrote a very provocative piece Did Media Literacy Backfire?  that made me think.

I covered the whole argument over here at Medium  but in a nutshell her argument seems to be that, certain topics are extremely complicated and that it takes a real expert with years of experience and expertise to pick apart the opposing counter arguments particularly for topics where there are many who for various reasons spend years horning their arguments (think anti-evolution arguments). As such it would be better for people to just accept the consensus judgement of experts.

She argues that media literacy may backfire if we train people to believe they should and are capable of evaluating all arguments and statements. We train them to doubt and make up their own minds.

I would add that ACRL's new Framework for Information Literacy for Higher Education states that “Authority Is Constructed and Contextual”, even gives individuals the license to decide they shouldn't automatically trust mainstream sources.

She writes "If the media is reporting on something, and you don’t trust the media, then it is your responsibility to question their authority, to doubt the information you are being given."
Add the natural tendencies of people to privilege evidence that supports their original beliefs and media literacy backfires.

"People believe in information that confirms their priors. In fact, if you present them with data that contradicts their beliefs, they will double down on their beliefs rather than integrate the new knowledge into their understanding."

As such she implies that in many matters it’s better for people not to try to figure out the truth themselves but to just trust the experts.

You can read my full coverage and response to this scary argument here but it's a interesting question to think about, in our rush to teach students to think for themselves and to evaluate information do we teach them humility to say we don't know enough to decide either way? Do we teach the concept of "Epistemic learned helplessness"?

2. Wilkinson's new research Agenda for information literacy - Bayesian inference

You may be wondering what Boyd means above by "People believe in information that confirms their priors...", this is where the idea of Bayesian inference comes in.

Lane Wilkinson is a  "philosophically-inclined instruction librarian" at the University of Tennessee at Chattanooga. Currently acting as Director of Instruction for the library, he is also known for his heavy criticism of ACRL's new Framework for Information Literacy for Higher Education.

He recently wrote an interesting piece that suggests brings in the idea of Bayeisan inference into information literacy.

It's a fascinating idea, and while I have come across the idea of bayesian models of reasoning in other contexts but like many librarians the idea of its intersection with information literacy passed me by.

One reason I suspect for this is that the concept of bayes thinking is not easy to grasp for many (including me). While many of us memorised the formula for bayes' theorem in school, a intuitive understanding of it eludes many. IMHO some of the best explanations can be found here

Lane setups the traditional explanation by using cancer detection reliability as an analogy. But I will skip this and go directly to the implication (in a simplified manner).

As Lane explains the idea here is that people have "priors", their belief in whether a certain fact is true or not. New information that they learn will shift their beliefs with the magnitude of change depending on how reliable they think the source is.

So say before reading anything a person's belief that Obama was born in the US was say 50%, call this P(A) = 50%. This is their "prior" probability. In this case they are undecided.

Say they read a news story that gives evidence he was indeed born in the US call that B.

Say they perceive the source as quite reliable, in other words the articles from that source are usually correct.

This implies two things, firstly

P(B|A) , the probability that B occurs (the article exists) given that it is true (aka Obama was born in the US = A) is high - assume 80%


P(B| Not A) or P (B | 'A) is low, aka if Obama was not born in the US, an article saying so is unlikely to appear - assume 10%

Throw in the bayes formula (see below) and you find  P(A|B) aka the posterior probability that they should believe A given that (or conditional on the fact) there is a news article B rises to 89%. So reading the news article means they should increase their belief that Obama was born in the US to 89%.

If on the other hand if they think the source is not so reliable say a liberal source (if they are conservatives), then say they feel P(B|A) is only 60% and P(B| 'A) is say 50%, plug into bayes theorem and P(A|B) rises to only 55%.

In more extreme cases where they think the source is more likely to be wrong than right, e.g. P(B|A)< 50%, reading the article makes the reader even more doubtful than before!

Hopefully I got that all right! If you understood all that, then you understand what Boyd was saying earlier.

"People believe in information that confirms their priors. In fact, if you present them with data that contradicts their beliefs, they will double down on their beliefs rather than integrate the new knowledge into their understanding."

In other words, people do not trust sources that disagree with what they already believe. Based on bayes theorem this means evidence from such sources will either not change their belief much or even in extreme cases drives their belief in the opposite direction.

Lane clarifies that he doesn't intend librarians to teach students bayes interference (It can get pretty complicated as this involves philosophical issues in epistemology after all),  but that information literacy can be studied under the lens of bayesian interference. He lists quite a few intriguing questions to study for example he asks "How do we adjust when our trusted, reliable sources publish something false? For example, when peer-reviewed journals retract articles." and "What are the information-seeking behaviors of students researching something they have a strong opinion on?"

3. Improving crowd sourcing by weighting metaknowledge

Lane talks about bringing "theories of cognitive science, psychology, information science, economics, philosophy, law, decision theory, and so on into library studies".

It's an interesting thought, I have been reading quite a bit in the past few years on cognitive biases, decision theory and while like Lane, I don't expect librarians to teach this in information literacy classes, it does seem to be an interesting domain that is related and can inform information literacy.  For a taste of this, recently I read an interesting idea about improving wisdom of the crowds by using meta knowledge. 

You can find the argument on Nature "A solution to the single-question crowd wisdom problem", or if you prefer to read more layman friendly articles at Aeon or coverage by MIT News.

Essentially the problem with Wisdom of the crowds is one averages across everyone equally. The idea is if we can identify who are the "experts" in the crowd, we can improve the reliability of our results by counting their opinions more.

How do we identify such experts if we are not experts ourselves? The key insight is that experts not only have more content knowledge, they also have better metaknowledge. Here's how you exploit this.

From the Aeon article,

"When you take a survey, ask people for two numbers: their own best guess of the answer (the ‘response’) and also their assessment of how many people they think will agree with them (the ‘prediction’). The response represents their knowledge, the prediction their metaknowledge. After you have collected everyone’s responses, you can compare their metaknowledge predictions to the group’s averaged knowledge. That provides a concrete measure: people who provided the most accurate predictions – who displayed the most self-awareness and most accurate perception of others – are the ones to trust"

Confused? Here's a concrete example used in the article. They asked a group of MIT and Princeton Undergraduates the following question " Is Philadelphia the capital of Pennsylvania?"

The correct answer is "No", in fact the capital is Harrisburg. Most people who make this mistake, would say "Yes" and predict that most people say 90% would say "Yes" too as they aren't aware that the answer is in fact wrong.

People who know the answer is "No", mostly will also know that "Yes" is a common error and when asked to predict how many % will agree with them will guess a lower figure say only 30% will agree with them (or alternatively 70% will say "yes"). In other words they have better metaknowledge, they not only know the fact, they know others know less.

When you look at the final result, the "No" group would most probably have a more accurate prediction of the overall yes/no split as the "Yes" group thinks "Yes" is the obvious answer that most will go for.

I highly recommend you read the Aeon article and the Nature article, it goes more in depth into how metaknowledge can be leveraged and the various experiments the authors did to verify the effectiveness of this technique, the way variants of this technique can act as a lie detector and/or "truth serum" when  asking questions that respondents have a bias to hide the truth e.g asking if they have committed plagiarism or made up data.

I find this article fascinating as it provides a partial answer to the question of how to reliably verify the question, how do we know who is an expert?


As I have admitted before, information literacy particularly for freshman hasn't been a big interest of mine. Part of it is because often for me it reduces to teaching Boolean operators (something that I'm of the view is getting less and less necessary) , showing undergraduate how to push buttons in databases , helping freshman who are worried a misplaced dot will get marks deducted or pushing mechanical rules like the CRAP test.

Most probably I'm doing it wrong, but still I do enjoy thinking and discussing deep epistemological questions like "How do we know who is an expert?" , "When should we know to express humility and rely on experts?" etc.

I of course understand that due to the constraints of time and the type of audience, a deep discussion isn't always appropriate, though I see hints of deeper engagement with the new ACRL information literacy model's focus on threshold concepts.

Sunday, February 5, 2017

4 different ways of measuring library eresource usage

How does one measure library eresources usage? This is a question I've bumped into numerous times recently in the course of my work whether it be trying to do correlation studies between student success and electronic usage , choosing the right metric for the library dashboard or even more mundanely just evaluating a database for subscription.

My way of looking at it is two fold.

Firstly you can classify metric by the source, that is where you get the data from. Secondly you can classify by the type of usage metric.

For many electronic resource librarians, when you talk about electronic resource usage, the main source of such statistics would be via publishers, which usually but not always is COUNTER compliant.

But that's not the only possible source. A secondary source of electronic resource usage perhaps less commonly used would be via the library's own systems which typically means via Ezproxy (or perhaps openathen logs).

Of the usage statistics that you can derive from these two sources, I divide them into 2 main types of statistics, download based and non-download (session) based.

This creates a 2x2 grid of possible statistics.

My thoughts on the strengths and weaknesses of the 4 types of electronic usage metric and when you should them are as follows

Type (1) - Publisher based download metrics

This is probably the most common type of usage metric used. Typically for most big journal based publishers, you will get standardised COUNTER compliant statistics (up to Release 4 now). While there are many different type of COUNTER reports, the ones generally most well used are JR1 and BR1 and perhaps BR2

JR1 - Number of Successful Full-Text Article Requests by Month and Journal
BR1 - Number of Successful Title Requests by Month and Title
BR2 - Number of Successful Section Requests by Month and Title

There are others like Multimedia Report 1 (basically JR1 for multimedia) and more complicated ones like "Title Report 1 Mobile", but are rarely known to most librarians.

These three metrics are easy to understand by all and basically tell you how many times the journal article/book title/book chapter was downloaded.

Pros : Easy to understand., after all a download is a download! Heavily used to calculate cost per downloads for decision on renewals. JR1 and BR1 are pretty much industry standard and almost always comparable across vendors if they implement COUNTER statistics.

Cons : While journal based platforms are mostly COUNTER compliant , many resources are not COUNTER compliant (e.g many law and finance/business databases).

Many non-traditional type of resources that don't serve up journal articles or books don't adapt well to the concept of downloads. Most obviously are A&I databases, or even databases that have a variety of different types of content.

A bigger issue is that COUNTER statistics a) only provides monthly reports b) only shows total counts.

As a result if you are doing correlation type studies where you correlate say student GPAs with electronic resource use COUNTER statistics can't be used as you can't relate usage to individuals.

Firstly, COUNTER only statistics would mean you wouldn't be able to track usage of a lot of NON COUNTER resources.  More seriously, using JR1, BR1 is not appropriate as you can't do any granular analysis by discipline much less individual. Even tracking time of heaviest use (beyond month) is impossible.

Type (2) - Publisher based non-download metrics

COUNTER include other statistics that don't count "successful requests" (AKA downloads). These include among others

Journal Report 4 -  Total Searches Run By Month and Collection
Database Report 1 - Total Searches, Result Clicks and Record Views by Month and Database
Book Report 5 -  Total Searches by Month and Title

These are what I call "non download based". They count number of searches made or views. Some people are of the view these are of lesser value than downloads, since one can search or view a lot but still not gain any value. Of course a possible counter is even a download might still be useless when read.

Still they share the advantages of other COUNTER statistics as they are standardised. and theoretically comparable across publishers. Of course they share the same issues in that many content providers are not COUNTER in particular inability to drill down further beyond monthly data.

Type (4) - Log based non-download metrics

The point is do you 100% trust what publishers tell you? What if you want to double check? The main way to do so would be to do an analysis your ezproxy logs.

This is a lot less often done in my experience because of the size and complexity of ezproxy logs. As such, the simplest way that most libraries deal with this is to count "sessions". This can be done fairly easily using various methods.

For those who are unaware, when you start can ezproxy session, a sessionID is created logged and stored in your browser cookie. This will continue until you timeout/signout or close the browser.

As such to measure usage of say Scopus, one can just count the number of unique sessions where there is a request for say So One can count unique session counts for each database or journal of interest.

In practice one just counts unique sessions of domains, though this can sometimes get complicated whether you count subdomains together, or where a content provider might have multiple domains. How much extra work you want to do here is up to you.

The main advantage of using this method is that a) It works for pretty much all types of resources (including those that don't have "Downloads" or aren't COUNTER compliant) as long as they are accessed over ezproxy b) It's fairly simple to get technically and fairly comparable and most importantly c) if you setup your ezproxy config properly you can uniquely identify the individual using it (e.g by NT logins/email).

You can then link up the email with the data sitting in your library management system and you have access to a rich source of data on who , what and when they are accessing your eresources.

Main disadvantage is that for many libraries not all traffic needs to be channeled via ezproxy particularly when in campus. In my current and former institution, all traffic is required to go through the proxy even if in campus so this isn't an issue.

I'm not too familiar with openathens type systems but I understand those by default make it trivial to calculate sessions by users by date/time since those are already recorded in the logs but make it hard to go further and study downloads and other details recorded by ezproxy, but I could be word.

Type (3) - Log based download metrics

Sessions obtained from ezproxy are well and good, but what if you want downloads to calculate cost per download?

This can be very time consuming as you need to setup complicated ruleset to be able to identify from the logs which lines are downloads and for which journals or platforms they refer to and this is the main reason why people tend to stick with COUNTER download statistics or other publisher provided download statics

This is where the open source ezpaarse comes in.

I'm already referred to this open source software in the past. It's a amazing software that can crunch your logs and spit out downloads it recognises. It's a community effort, with rulebases been updated constantly.

It even allows you to create COUNTER like statistics for comparisons!

Once you have obtained the logs crunched out by ezpaarse, you can then further enrich the data with more user information similar to in (4).

Since the last time I tried it, I've done more bulk processing of our logs, my main learning point is that as good as Ezpaarse is, at least for our set of databases, it is still incapable of identifying a lot of aggregator platforms. It could be my setup but for example it doesn't seem to identify Proquest at all for us. Even for platforms it does recognise like EBSCO it can't reliably identify journal titles. A lot more testing is needed.

Of course the project is opensource and always looking for help in creating new rule sets.


Obviously, what type of statistics you use depends on the usage case you are looking for and there's no reason you can't combine the two.

If all you want to do is to evaluate or renewal of a specific journal database and it has COUNTER JR1 statistics, that is the obvious thing to do.

But if you need to go down to the level of which schools or types of users who use the journal (perhaps for allocating costs), then you would need to use some sort of ezproxy/openathen  log based metric.

Another question you need to consider is do you need to compare across a variety of resources? A correlation study that tries to compare usage of library resources vs student grades would obviously need a metric that firstly covers as broad a range of resources as possible and secondly do it in a consistent way.

I've found generally counting sessions from ezproxy/openathens probably fits the bill best here. It's still not perfect since many resources are not (some particularly important like high-end financial databases like bloomberg aren't tracked this way), but that's the best I can do.

For showing data into the dashboard, it is harder to say what would be useful. Perhaps all of them?

Which of the types of electronic usage statistics I have outlined do you use? Are any of them useless to you? Most importantly if you have a library dashboard tracking such statistics, which one do you use?

Thursday, January 12, 2017

The open access aggregators challenge — how well do they identify free full text?

Bielefeld Academic Search Engine (BASE) created by Bielefeld University Library in Bielefeld, Germany is probably one of the largest and most advanced aggregator of open access articles (hitting over 100 million records), others on roughly the same level are CORE (around 60 million records) and OAIster (owned by OCLC).

One way of seeing this class of open access aggregators is to see them as similar to web scale discovery search engines like Summon, EDS, Primo and WorldCat Discovery service. but focusing mainly in the open access context.
How well do web scale discovery engines cover open access?
It seems natural to think that index based solutions like Summon, Primo, EDS should cover both paywall contents as well as open access content, particularly since they typically can use OAI-PMH to harvest the institution’s own Institution Repository. In reality, their coverage of open access material can be spotty. The best ones have indexed OAIster or BASE. But even when open access sources are available in the index, many institution’s choose not to turn them on  for various reasons. This includes unstable links, inability to correctly show only open access material as well as flooding of results by inappropriate data (e.g foreign language or irrelevant subjects).

A unique challenge for open access aggregators

One area where BASE and CORE may differ from Summon and Primo is in that open access aggregators need to be able to tell if an article they harvest from a subject or institutional repository has free full text and this isn’t that easy.

This seems odd if you do not understand the history of open access repositories, but suffice to say when OAI-PMH (which is the standard way of harvesting open access repositories and was established as a way of harvesting metadata only and not full text) was established it was envisioned that most if not all items in such open access repositories would be open access (following the example of Arxiv), so no provision was made to have a standard way or of a mandatory field to indicate if the item is free to access.

In today’s world, of course subject and in particular institutional repositories are a mix of free full text and metadata only records. This happens in particular for institutional repositories because they have multiple goals beyond just supporting open access.
What are the multiple purposes of Institutional repositories?
While most librarians are familiar with Institutional repositories mission to support open access they may not be aware that it is not their only purpose (I also argue even advocates who support self archiving in the open access agenda can have different ultimate aims). Other purposes include
a) “to serve as tangible indicators of a university's quality and to demonstrate the scientific, societal, and economic relevance of its research activities, thus Increasing the institution's visibility, status, and public value” (Crow 2002)
b)"Nurture new forms of scholar communication beyond traditional publishing (e.g ETD,  grey literature, data archiving" – (Clifford 2003)
It is purpose A, tracking the institutional’s output that results in Institutional repositories hosting more than just full text items. Many institutional repositories have in fact more metadata only items than full text. It’s a rare Institutional respository that has more than a third full text records. 

Truth be told, most open access aggregators I have seen simply give up on this problem and just aggregate the contents of whole institutional repositoriesgiving users a mistaken idea that everything is free.

This leads to users wondering if something is wrong when they click through and get led to a metadata only record in the repository. This btw was the reason why I and I suspect many librarians tend not to turn on open access repositories available via Summon/Primo because it doesn’t really show only open access items and it’s a rare few that is say 99% free items (typically ETD or electronic thesis dissertations collections but even though has the occasionally embargoed ones), while many have in fact more metadata only records then full text records particularly if they blindly pull in metadata content via their institution’s research publication systems and/or Scopus/Web of Science.

There are of course ways to identify full text in repositories and Google Scholar seems to do it beautifully on an item level (via intelligent spidering to detect pdfs?) but that doesn’t seem common for non-google systems. As it stands, Google Scholar is current my #1 choice whenever I need to check if free articles exist.

One possibility is for institutional repositories to create “collections” that are 100% or near 100% full text and pull in such items by collections. This usually is what happens for ETD.

The other way of course is to set a metadata tag for each item that has full text but I’m not sure if there is 100% universal standard for this. A good start might be OpenAire’s standard.

BASE indeed does suggest you to support this for optimal indexing. I am not sure how wide spread this is outside the EU.

I’m not a repository manager so I’m not sure how this works, but I get the distinct impression that Digital Commons repositories can definitely reliably identify full text records, given that there can be full-text PDF RSS feeds, I’m just not sure how a third party aggregator can exploit that to identify full text and whether it can be generalised to all Digital commons repositories.

In any case, I think one can probably “hack” and create workarounds to reliably detect full text for one repository the trick is to do it without much work for most of them.
In a sense centralised Subject repositories have the advantage over institutional ones here because by the virtue of their mass, there is great incentive for aggregators to tweak compatibility with them compared to any individual institutional repository.

In any case, both BASE and CORE are capable of identifying full text records in their results, the question is how accurate are they?

How well does BASE and CORE do for identifying full text?

The nice thing about BASE is that it allows you to run a “blank search” which gives you everything that meets the criteria (similar to Summon). So one can easily segment the index based on criteria you desire without crude workarounds like searching for common words that all records would have.

Base results restricted to Source: Singapore

The above shows that when restricted to Singapore sources, BASE knows of

66,934 records from National University of Singapore’s IR — dubbed ScholarBank@NUS (using Dspace)

records from Nanyang Technological University’s IR — dubbed DR-NTU (using Dspace)

records from Singapore Management University’s IR — dubbed INK (using digital commons). [Disclosure I’m a staff of this institution]

Based on my colleague's recent Singapore update on open access figures for total records in each of the repositories — this shows a rough coverage of 67%, 89%, 98% respectively in BASE.

Take this figures with a pinch of salt because the total records I am getting are based on different times, e.g the NUS total record is as of 30 Sept 2016, NTU total record is of 18 October 2016. NUS also has fairly substantial non-traditional records eg. patents and music recordings so that might affect the result. Lastly, I did the search in BASE in early Jan 2017 while the total records are from a quarter earlier, so the actual coverage is probably a bit lower.

Overall, the coverage shown isn’t too bad, but the more important point is how well does BASE identify full text? Let us filter to Access : Open Access

Full text identified by BASE

Not very well it seems.

It is only able to identify 75 free records in National University of Singapore’s IR, 654 free records in Nanyang Technology University’s IR, 143 free records in Singapore Management University’s IR

I did not do a check to see if there were false positives in BASE’s identification of full text but in the best case scenario they are 100% correct, we see only a full text identification ratio of 0.6%, 3.8% and 2.7% respectively!

If you consider the case of Singapore Management University (disclosure again I am staff there), BASE is able to index practically every record in our Repository and yet only identifies 2.7% of our free full text. It’s in the same ballpark for the other Singapore repositories.

Let’s do the same for CORE. How many records does it index for the 3 Singapore repositories?

Here are the results :

National University of Singapore’s Scholarbank.

Records (100,657) + Full text (12)

Keyword : repository: (“Scholarbank@NUS”)

Singapore Management University — INK

Records (18,312) + Full text (166)

Keyword : repository: (“Institutional Knowledge at Singapore Management University”)

Interestingly enough I was unable to find any articles indexed in CORE from Nanyang Technological University’s IR, it’s possible I might have missed them somehow.

In any case, I won’t calculate the percentages for the other 2 IRs, there are broadly similar to the case in BASE, except CORE seems to show substantially more records (including metadata only records) indexed than in BASE.

In fact, CORE is showing more records indexed for both universities then the total records listed in the Singapore update on open access figures (e.g 100k vs 99k in NUS and 18k vs 16k for SMU). This possible because the total records from the Singapore update on open access figures generally refer to 3Q 2016 figures so since then the number of records would have grown.

Still I suspect that’s not the full reason, there could be duplicates archived in CORE inflating the result.

More importantly in terms of records identified as free full text the results for CORE are as dismal as BASE.


Both BASE and CORE are extremely sophisticated open access aggregators. For example they offer APIs (BASE, CORE), are indexed by some web scale discovery services, are doing various interesting things with ORCID, here also, creating recommendation systems or working with OADOI to help surface green open access articles hiding in respositories.

A difference is that BASE currently doesn’t search through full text while I believe CORE does.

However identifying which articles they have harvested has free full text is still problematic, BASE claims to be able to reliably identify 40% of their index as full text though the other 60% is still unknown due to lack of metadata. My own quick tests shows that it’s accuracy is quite bad for certain repositories. My hunch is that BASE either works very well with some respositories or not at all with others.

So this is a major challenge for the open access community and in particular institutional repositories to answer. The alternative is to shrug one’s shoulder’s and let Google Scholar be the default open access aggregator.

Friday, December 30, 2016

Library Discovery and the Open Access challenge - Take 2

Earlier this year, over at medium , I blogged about the Library Discovery and the Open Access challenge and asked librarians to consider how library discovery should react to the increasing pool of free material due to the inevitable rise of open access.

At the limit when nearly everything is freely available it is possible to consider whether library will have a place in the discovery business. After all, if all researchers have access to the same bulk of journal articles, does it really make sense for each institutional library to provide a separate discovery solution? Even today, many researchers prefer using Google Scholar and other non-institutional discovery solutions that operate at web scale and some (mostly students) grudgingly use our discovery systems to restrict discovery to things they have immediate access to.

This of course is the library discovery will be dead scenario when (almost) everything is free  and not everyone agrees. Some argue, that libraries can add value by providing superior and customized personalized discovery experiences because we know our users better (e.g what courses they taking/teaching, their demographics etc). Then there are plans to leverage linked data etc but I know regretfully little of that.

But the day when open access is dominant is still not here. We live in the world where there is a mix of toll based access and rising but uneven free access, Scihub notwithstanding. I opined that for now "if we really want to stay in the discovery business we need to be able to efficiently and effectively cover the increasing pool of open access resources".

So how does you ensure the library discovery system includes as much discovery of free open access articles as possible?

The idea of a open content discovery matrix by Pascal Calarco, Christine Stohn and John Dove comes to mind.

For most academic libraries who subscribe to commercial discovery indexes (Worldcat Discovery, EBSCO Discovery Service, Summon and Primo, with the later two having merged indexes), there isn't much libraries can do beyond hoping that discovery vendors include such content in their index.

Well I recently came across services like  1science's oaFindr that claims to have a high quality 20 million database of open access papers that perhaps could help? There's also a oafinder+ product that can identify green and gold OA articles for your institution only.

Even if  you can find open access metadata for content that is available for indexing, delivery issues still might occur in index based discovery services as link resolvers are infamously bad at linking to hybrid journals and practically ignore Green Open access articles. 

A alternative approach to such "pull" approaches is a push approach. The new service  (and an earlier service DOAI) is one of the more interesting things to emerge from this year's open access week and it can used together with discovery services.

The idea is simple. One of the challenges of discovery of open access journals in particular Green open access articles archived at subject repositories and institutional repositories is that in general there is no systematic easy way to find them.

 With the service, you can feed the service a doi and it will attempt to locate a free version of the paper, and this includes both articles made free via the Green or Gold roads.

Here's an example say you land on this article page,  Grandchild care, intergenerational transfers, and grandparents’ labor supply on Springer and you have no access.

Quick as a flash, you grab the DOI 10.1007/s11150-013-9221-x and look it up like this . And you get autoredirected to the preprint full text on our institutional repository.

Looks like magic! How does it work? The oadoi service uses a variety of means to try to detect if a open access version of an article is available (see below), but it looks to me that the main source for detecting articles on institutional repository in particular is via the aggregator BASE, so make sure your institutional repository is indexed in BASE.

My own limited testing with Oadoi was initially pretty disappointing as it failed to find most of the articles hosted on my Institutional repository (hosted on Bepress digital commons). It's possible that the way our institutional repository exposes the doi was not correctly picked up by BASE, but this seems to have been resolved somewhat. More testing required.


Savvy readers of this blog might already be screaming, why bother? Just use Google Scholar or plugins like Google Scholar button or Lazy Scholar buttons (which use Google Scholar in background) and all your problems are solved.

It's true that Google Scholar is pretty much unbeatable for finding free articles but the value in OADOI is that it offers a API.

Already many have been quick to use it to provide all kinds of services. For enable Zotero uses it as a lookup engine, librarians have created widgets etc.

But it's greatest value lies in the fact that it can be embedded into discovery services and link resolvers.

Here's work done on SFX doi service and alma libraries like Lincoln University have not been slow to include it either.

These are fairly basic uses of oaidoi and enable users to help direct users to open access content. Still such implementations are usually a "last resort, try it if it works " kind of deal currently and there is no guarantee clicking on the link will work. If you are Exlibris customer on Primo do consider supporting this the feature request "Add as an option in uresolver" which proposes " displays as an option if the API's value of is_free_to_read is true".

To DOI or not to DOI?

A lot of the problems about discovery and delivery of open access content lies in the fact that there are different variants of the same content.

In the old days it was pretty straight forward the only thing that we tracked and access was the article that appears in the journal.

Today, we make accessible a wide variety of content (data, blog posts, conference papers, working papers) and even worse different versions of the same content at different stages of the research lifecycle (preprint/postprint/final published version).

This leads to a great challenge for discovery.

It doesn't help there is a terminology muddle (despite NISO's best efforts at standardising terminology on Journal article Version names and license and access indicators), with people using terms like preprint/postprint/final published versions while others use author submitted manuscripts, author accepted manuscripts and version fo record.

But I think even beyond that, the question I always wonder is , how do we identity/address each version and these days it means assigning dois. The final version of record will have a DOI of course but what about the rest?

As such, I've always been confused about the practice of assigning dois to non peer reviewed papers. For example, should one assign dois to preprints? post-prints? working papers? Should they be a different doi from the final published version? It doesn't help that when you upload items to ResearchGate it offers to create a doi.

I could be wrong, but up to recently I don't think there was a clear guide. But in recent months there seems to be two developments that seemingly clarify this.

First crossref announced they are allowing members to register preprints. The intention here seems to be that the doi of the final version of record is to be a different doi, though there are ways to crosslink both papers, There's even a way to show a relationship between preprints and the later versions as explained in the crossref webinar.

The oadoi service mentioned earlier seems to be pushing in the other direction , encouraging postprints listed in repositories to be added using the same doi as the final version of record to make the postprint findable (but does it mean the preprint isn't since it will have a different doi?). This allows you to find the postprint using the oadoi service as both postprint and final version of record as the same doi.

I'm not quite sure if this is a good idea, while studies show most postprints are not that different then the final published version, it does seem to be a good idea to be able to track the two versions differently. Still mulling over this.


This will probably be my last post for 2016. This year I was particularly inspired towards the end of the year with many ideas but didn't have the time to craft them so expect a flood in the coming year.

I also would like to thank all my loyal readers for following this blog and reading my long winded posts. Next year this blog will be celebrating it's 8th anniversary and my 10th anniversary in the library industry and I might do something special.

Till then, stay happy and healthy and have a great new year's day!

Saturday, December 10, 2016

Aggregating institutional repositories - A rethink

In recently months, I've become increasingly concerned about the competition faced by individual siloed institutional repository versus bigger more centralised repositories like subject repositories and commercial competitors like ResearchGate.

In a way the answer seems simple, just get someone to aggregate all the institutional repositories on one site and start building services on top of that to compete. Given that all institutional repositories already support OAI-PMH, so this seems to be a trivial thing to do. Yet I'm coming to believe that in most cases, creating such an aggregator is pointless. Or rather if your idea of a aggregator is simply getting a OAI-PMH harvester , point it at the OAI PMH endpoints of the repositories of your members and dumping everything into a search interface like VUFIND or even using something commercial like Summon or EDS without any other attempt to standardise metadata, and call it a day, you might want to back off a bit and rethink. For the aggregator to add value, you will need to do more work.....

A simplistic history of aggregation in libraries

Let me tell you a story...

In the 90s - libraries began to offer online catalogues to allow users to help themselves find out what was available (in their mostly print) collections. These sources of informations were siloed and while they were on the web, they were mostly invisible to web crawlers. The only way you could find out what libraries had in their collections would be to go to each of their catalogues and searched.

So, someone said "Why not we aggregate them all together" and Union catalogues (including virtual Union catalogues based on federated searching) were built e.g Copac. People could now search across various silos in one place and all was well.

Librarians and Scholars used such union catalogues to decide what and who to do ILL from and to make collection decisions. Many were still invisible to Google and web search engines (except for a few innovators like OCLC), but it was still better than nothing.

By the late 90s and early 2000s, libraries began to create "digital libraries" (e.g Greenstone digital library software). It was the wild west and digital libraries at the time build up digital collections consisting of practically anything of interest such as digitized images of music scores, maps, photographs -  anything except for peer reviewed material. Most material on digital libraries was often difficult to find or invisible via web search engines for various reasons (e.g. non-text nature of content, lack of support of web standards etc) and it made sense for some degree of aggregation at various levels such as national or regional levels.

Today larger collections like Europeana exist and all was well.

Then came the rise of the Institutional repositories, and by 2010s, most universities had one.

Unlike it's predecessors, the main distinguishing point of institutional repositories was that for many it was designed around distributing Scholarly peer reviewed (or likely to be peer reviewed) content.

While it's true many institutional repositories do contain a healthy electronic thesis collection and some even inherited the mission of what would be earlier called digital libraries and carried grey literature and other digital objects such as data the main focus was always on textual journal articles.

The other major difference is that by then all Institutional Repositories worth the name supports the OAI-PMH standard which making harvesting and aggregating metadata of content in them easy....

And of course , the same logic seem to suggest itself again, why not aggregate all the contents together? And today, we have global aggregators like CORE (not this other CORE - Common Opens Repository Exchange) , BASE and OAISTER as well as regional aggregators built around associations and organizations both national and regional.

In my region for example there's the AUNILO (ASEAN University Network inter-library online) institutional repository discovery service that aggregates content from 20 over institutional repositories in ASEAN. Most University libraries in Singapore are also part of PRRLA (Pacific Rim Research Library Alliance) formerly PRDLA., which also has a Pacific Rim Library (PRL) project built around OAI-PMH harvesting.

I'm sure similar projects exist all around the world based on aggregating data by basically harvesting via OAI-PMH harvestors. And yet, I'm coming to believe that in most cases, creating such an aggregator is pointless, unless additional work is done.

Or rather if your idea of a aggregator is simply getting a OAI-PMH harvestor , point it at the OAI PMH endpoints of the repositories of your members and dumping everything into a search interface like VUFIND or even using something commercial like Summon or EDS, and call it a day, you might want to back off a bit and rethink.

I argue that unlike UNION catalogues or aggregation of digital libraries (by this I mean not the traditional Institutional repository of text based scholarly articles), aggregation of institutional repositories is likely to be pointless, unless you bring more to the table.

Here's why.

1. Items in your institutional repository are already easily discoverable

Unlike in the case of most library catalogues, items in your institutional repository are already easily findable in Google and Google Scholar. There is little value in creating an aggregator when such an excellent and popular one as Google and Google Scholar exist.

101 Innovations in Scholarly Communication - 89% use Google Scholar to search for literature/data

Given the immense popularity of Google Scholar, what would your simple aggregator based around OAI-PMH offer that Google Scholar does not that would make people come to your site to search?

2. Most simple repository aggregators don't link reliably to full text or even index full text

Union catalogues existed in a time, where it was acceptable for users to find items that had no full text online. You used it to find which libraries had the print holdings and either went down there to view it, or used Interlibrary loan to get it.

In today's world, direct to full text is the expected paradigm and you get undergraduates wondering why libraries bother to subscribe to online subject indexes that show items the library may not have access to.

Now how much worse do you think they feel when they search one of your repository aggregators and realise they can't figure out which item has full text or not until they click on it? This is where a glaring weakness in OAI-PMH rears its head.

I first encountered this problem when setting up my Web Scale Discovery Service - Summon a few years back, and I was surprised to realise that while I could easily harvest entries from my Institutional Repository (Dspace) into Summon via OAI-PMH, I couldn't easily get Summon to recognise if an item from the Dspace repository had full text or not.

I remember been stunned to be told that there was no field in the default Dspace fields that indicated full text or not.

This sounds crazy by today's standards. But a little understanding of the context of the time (1999) when OAI-PMH came about helps. It's a long story, but correct me I'm wrong but it was conceived at a time where preprint server Arxiv was the model and it was envisioned repositories would be 100% full text items, so there was no need for such a standard field. Today, this is of course not what happened, due to varying goals on what an Institutional repository should be and reluctance of researchers to self deposit we have a mix of both full text and metadata only items.

Another quirk about OAI-PMH that might surprise many is that it only allows harvesting of metadata only not full-text. Again in today's world where full-text is king and people are accustomed to web search engines (and library full text databases that have followed their lead) matching in the whole document and have search habits designed for that, they find aggregators based around OAI-PMH that only contain metadata odd to use. This is the same problem many students have with using traditional catalogues.

I understand there can be algorithmic workarounds to try to determine if full text exists and some aggregators try to do so with varying results but many don't and just display everything they grab via OAI-PMH.

To top it all off, Google Scholar actually has none of these problems. They can pretty reliably identify if the full text exists and where and combine that with the library links program you can easily tell if you have access to the item.

They crawl and index the full text, and can find items based on matching in full text and can often provide helpful search snippets before you even click into the result.

A vanity search of myself allows me to see where my name appears in context in the full text not just in abstracts

3. Aggregation doesn't have much point due to lack of consistency in standards

Think back to Union catalogues of traditional catalogues back then called OPACs. The nice thing about them was most of them were created using the same consistent standards.

There was MARC, Call number schemes like LCC/DDC/UDC, subject headings standards like LCSH/MeSH that you could crosswalk etc. So you could browse by subject headings or call numbers etc.

I'm probably painting a too positive view of how consistent standards are, but I think it's fair to say that in comparison institutional repositories are in an even worse state.

Under the heading for "Minimal Repository Implementation" in "Implementation Guidelines for the Open Archives Initiative Protocol for Metadata Harvesting" we see it advises that "It is important to stress that there are many optional concepts in the OAI-PMH. The aim is to allow for high fidelity communication between repositories and harvesters when available and desirable."

Also under the section on dublin core which today is pretty much the default we see "Dublin Core (DC) is the resource discovery lingua franca for metadata. Since all DC fields are optional and repeatable, most repositories should have no trouble creating at least a minimal mapping of their native metadata to unqualified DC. "

Clearly, we see the original framers of OAI-PMH decided to give repositories a lot of flexibility on what was mandatory and what wasn't and only specified a minimum set.

In addition the "lingua franca for metadata", unqualified dublin core perhaps on hindsight was not the best option, not when most of your content is journal articles.

Even Google Scholar recommends against the use of Dublin core in favour of other metadata schemes like  Highwire Press tags, Eprints tags or BEpress tags.

On the section of getting indexed on Google Scholar, they advise repository owners to "use Dublin Core tags (e.g., DC.title) as a last resort - they work poorly for journal papers because Dublin Core doesn't have unambiguous fields for journal title, volume, issue, and page numbers."

Even something as fundamental today as doi (and in the future ORCID) isn't mandated.

I recently found that out when I realised the very useful service that allows you to input a DOI and find a copy in repositories (among other ways it works is that it searches for items indexed in BASE) failed for our institutional repository because doi indentifer wasn't in our unqualified Dublin core feed and that was picked up by BASE. The lack of standards is holding repositories back.

Leaving this aside, I'm not sure why this happened (I have a feeling that up to recently the same people working on institutional repositories were not the same people working on cataloguing) but most institutional repositories content do not use controlled vocabulary for subject headings or for subject classification, though they could easily do so.

As a result, unlike in catalogues, once you have aggregated all the content, you can easily slice the content by discipline (e.g. LC call range) or by subject headings (e.g. LCSH).

With aggregators of repositories you get a mass of inconsistent data. Your subjects are the equivalent of author supplied keywords and there is no standardised way to filter to specific disciplines like Economics or Physics.

 The more I think about it the more this lack of standardisation is hurting repositories.

For example, I love the digital commons network that allows me to compare and benchmark performance across all papers posted via digital commons repositories in the same discipline. This is possible only because digital commons has a hosted service has a standardised set of disciplines.

What should your aggregator of repositories do?

So if you read all this and are undeterred but still want to create a aggregator of institutional repository what should you do?

Here's some of the things I think you should shoot for beyond just aggregating everything and dumping it into one search box.

a) Try to detect reliably if an entry you harvested has full text

b) Try to index full text not just metadata

CORE seems to match full text in my search?

One way to detect reliably if full text exists or not is to decide on a metadata field that all repositories you are harvesting from has a metadata field indicating full text. But that won't scale currently at a global level. Another way is to try to crawl repositories to extract pdf full text.

Ideally the world should be moving away from OAI-PMH and start exposing content using new methods like resource-sync so not just metadata alone is synced. I understand that the PRRLA is working on a next generation repository among it's member that will use Resource-Sync.

c) Create consistent standards among repositories you are going to harvest

If you are going to aggregate repositories from say a small set of member institutions, it is important to not just focus on the tech but also focus on metadata standards. It's going to be tough, but if all institution members can agree on mapping to a standard (hint look at this), perhaps even something as simple as providing a mapping to Disciplines, the value of your aggregator increases a lot.

d) Value added services and infrastructure beyond user driven keyword discovery

Frankly, aggregating content just for discovery isn't something that is going to be a game changer even if one provides the best experience with consistent metadata allowing browsing, indexes full text etc as services like Google Scholar are good enough already.

So what else should you do when you aggregate a big bunch of institutional repositories? This is where it gets vague, but the ambitions of SHARE . while big show that aggregators should go beyond just supporting keyword based discovery.

See for example this description of SHARE

"For these reasons, a large focus of SHARE’s current grant award is on metadata enhancement at scale, through statistical and computational interventions, such as machine learning and natural language processing, and human interventions, such as LIS professionals participating in SHARE’s Curation Associates Program. The SHARE Curation Associates Program increases technical, curation confidence among a cohort of library professionals from a diverse range of backgrounds. Through the year-long program, associates are working to enhance their local metadata and institutional curatorial practices or working directly on the SHARE curation platform to link related assets (such as articles and data) to improve machine-learning algorithms."

SHARE isn't along there are other "repository networks" include OpenAIRE (Europe), LA Referencia (Latin America) and Nii (Japan), that work along similar lines , trying to standardise metadata etc.

Others have talked about layering a social layer over aggregated data similar to ResearchGate/, or provide a infrastructure for new forms of scholarly review and evaluation.

Towards a next generation repository?

In past posts on institutional repositories I've been trying to work out my thinking on institutional repositories and it's a complicated subject particularly with competition from larger more centralised subject and social repositories like ResearchGate.

I'm coming to think that to counter this individual smaller repositories need to link up together but yet this cannot be currently done in an effective way.

This is where "next generation repositories" comes in and they may have probably heard about this most prominently under the umbrella of COAR (Confederation of Open Access Repositories).

What I have described above is in fact my layman's understanding of what the next generation repositories must achieve (For a more official definition see this) and why.

Officially the next generation repositories focus on Repository Interoperability (See The Case for Interoperability and The Current State of Repository Interoperability )- which includes working groups on controlled vocabulary and open metrics and even linked data.

All this is necessary for institutional repositories to take their place as necessary and equal partners in the scholarly communication network.


I had the opportunity to attend the Asian Open Access Submit in November at Kuala Lumpur and learned a lot, particularly the talk by Kathleen Shearer from COAR, the Confederation of Open Access Repositories on repository networks helped clarify my thinking on the subject.

Friday, November 18, 2016

5 reasons why library analytics is on the rise

Since I've joined my new institution more than a year ago, I've focused a lot on this thing called "library analytics".

It's a new emerging field and books like "Library Analytics and Metrics, using data to drive decisions and services" , following by others are starting to emerge.

Still the definition and scope of  anything new is always hazy and as such my thoughts on the matter are going to be pretty unrefined, so please let me think aloud.

But why library analytics? Libraries have always collected data and analysed them (hopefully), so what's new this time around?

In many ways, interest in library analytics can be seen to arise from a confluence of many factors both from within and outside the academic libraries. Here are some reasons why.

Trend 1 :Rising interest in big data, data science and AI in general

I don't like to say what we libraries deal in is really big data (probably the biggest data sets we deal with is in ezproxy logs which can be manageable depending on the size of your institution) , but we are increasingly told that data scientists are sexy and we are seeing more and more data mining, machine learning, deep learning and all that to generate insights and aid decision making. 

Think glamour projects like IBM Watson and Google's AlphaGo. In Singapore, we have the Smart Nation initiative which leads to many opportunities to work with researchers and students who see the library has a rich source of data for collaboration. 

In case you think these are sky in the pie projects - already IBM Watson is threatening to replace Law librarians , and I've read of libraries starting projects to use IBM Watson at reference desks.

Academic libraries are unlikely to draw hard core data scientists as employees, but we are usually blessed to be situated near pockets of talent and research scientists who can collaborate with the library. 

As Universities start offering courses focusing on Analytics and data science, you will get hordes of students looking for clients to practice on and the academic library is a very natural target as a client to practice on.

Trend 2: Library systems are becoming more open and more capable at analytics

Recently, I saw someone tweeting that Jim Tallman who is CEO of Innovative Interfaces declaring that libraries are 8-10 years behind other industries in analytics.

Well if we are, a big culprit is the  integrated library system (ILS) that libraries have been using for decades. I haven't had much experience poking at the back-end of systems like Millennium (owned by Innovative), but I'm always been told that report generation is pretty much a pain beyond fixed standard reports.

As a sidenote, I always enjoy watching conventionally trained IT people come into the library industry and then hear them rant about ILS. :)

In any case, with the rise of Library Open service platforms like Alma, Sierra (though someone told me that all it does is basically adds SQL but that's a big improvement) etc more and more data is capable of being easily uncovered and exposed.

A good example is Ex Libris's Alma analytics system. Unlike in the old days where most library systems were black boxes and you had great difficulty generating all but the most simple reports, systems like Alma and other Library Service Platforms of its class, are built almost ground up to support analytics.

You don't even have to be a hard core IT person to drill into the data, though you can still use SQL commands if you want.

With Alma you can access COUNTER usage statistics uploaded with Ustat (eventually Ustat is to be absorbed into Alma) using Alma analytics. Add Primo Analytics, Google analytics or similar that most Universities use and a big part of the digital footprints of users is captured.

Alma analytics - COUNTER usage of Journals from one Platform 

Want to generate users and the number of loans by school made in Alma? A couple of clicks and you have it.

Unfortunately there still seems to be no easy way to track usage of electronic resources by users as COUNTER statistics are not granular enough. The only way is by mining ezproxy logs which can get complicated particularly if you are interested in downloads not just sessions.

This is still early days of course, but things will only get better with open APIs etc. 

Trend 3 : Assessment and increasing demand to show value are hot trends

A common trend on Top trends list for academic libraries in recent years (whether lists by ACRL or Horizon reports) is assessment and/or showing value and library analytics has potential to allow academic libraries to do so.

Both assessment (understanding to improve or make decisions) or advocacy (showing value) require data and analytics

For me, the most stereotypical way for a academic library to show value would be to run correlations showing high usage of library services would be highly correlated with good grades GPA. 

But that's not the only way. 

ACRL has led the way with reports like Value of academic libraries report , projects like Assessment in Action (AIA): Academic Libraries and Student Success to help librarians on the road towards showing value.

But as noted in the Assessment in Action: Academic Libraries and Student Success report, a lot of the value in such projects comes from the experience of collaborating with units outside the library.

Academic libraries that do such studies in isolation are likely to experience less success.

Trend 4 : Rising interest in learning analytics

A library focus on analytics also ties in nicely as universities themselves are starting to focus on learning analytics (with UK supported by JISC probably in the lead).

A lot of current learning analytics field focus on the LMS  (Learning management systems) data, as vendors such as  Blackboard, Desire2Learn, Moodle provide learning analytics modules that can be used.

But as libraries are themselves a rich store of data on learning (the move towards Reading list management software like Leganto, Talis Aspire and Rebus:List help too), many libraries like Nottingham Trent University find themselves involved in learning analytics approaches. 

So for example  Nottingham Trent University , provides all students with a engagement dashboard allowing them to benchmark themselves against others . Sources used to make up the engagement score include access of learning management systems, use of library and university buildings. 

Trend 5 : Increasing academic focus on managing research data provides synergy 

From the academic library side , we increasingly focus on the challenges of collecting, curating , managing and storing research data. There are rising fields like GIS, Digital Humanties that put the spotlight on data. We no longer focus not just on open access for articles, but on open data if not open science. 

While library analytics is a separate job from librarians who are involved in research data management , there is synergy to be had between the two job functions as both deal with data. Both jobs requires skills in  handling of large data sets, protection of sensitive data,  data visualization etc.

For example the person doing library analytics can act as a client for the research data management librarian to practice on when producing reports and research papers. In return, the later can gain experience handling relatively large datasets by doing analytics projects.

But what does library analytics entail?  Here are some common types of activities that might fall into that umbrella.

Assisting with operational aspects of decision making. 

Traditionally a large part of this involves collection development and evaluation.

In many institutions like mine it involves using alma analytics,Ezproxy logs, Google analytics, Gate counts and other systems that track user behavior etc.

This in many ways isn't anything new, though these days there are typically more of such systems to use and products are starting to compete on the quality of analytics available.

This type of activity can be opportunistic, ad hoc and in some libraries siloed within individual library areas.

Implementation and operational aspects of library dashboard projects 

A increasing hot trend, many libraries are starting to pull all their data together from diverse systems into one central dashboard using systems like Qlikview, Tableau, or free javascript libraries like D3.js

Typically such dashboard can be setup for public view or more commonly for internal users (usually within-library, ideally institution wide) but the main characteristic is that they go beyond showing data from one library system or function (so for example a Alma dashboard or a Google Analytics dashboard doesn't quite qualify as a library dashboard the way I defined it here).

Remember I mentioned above that library systems are becoming more "open" with APIs? This helps to keep dashboards up-to date without much manual work.

I'm aware of many academic libraries in Singapore and internationally creating library dashboards using commercial or opensource systems like Tableau, Qlikview etc but they tend to be private.

Here are my google sheet list of public ones.

Setting up the dashboard is relatively straightforward technically speaking, more important is sustaining it. What data should we present? How should we visualize the data? Is the data presented useful to decision makers? How can we tell? At what levels of decision makers are we targeting it at? Should the data be made public?

This type of activity breaks down barriers between library functions though it can still be siloed in the sense that it is just the work of a University Library separate from the rest of the University. 

Implementation or involvement in correlation studies, impact studies for value of libraries.

The idea of showing library impact by doing correlation studies of student success (typically GPA) and library usage seems to be popular these days with pioneers like libraries at the University of Huddersfield (with other UK libraries by JISC)University of WollongongUniversity of Minnesota etc leading the way.

Such studies could be one off studies, in which case arguably the value is much less as compared to a approach like University of Wollongong's Library Cube where a data warehouse is setup to provide dynamic uptodate data that people can use to explore the data.

Predictive analytics/learning analytics

Studies that show impact of library services on student success are well and good, but the next step beyond it I believe is getting involved in predictive analytics or learning analytics which will help people whether it be students, lecturers or librarians use the data to improve their own performance.

I've already mentioned Nottingham Trent University's engagement scores, where students can log into the learning management system to look at how well they do compared to their peers.

The dashboard also is able to tell them things like "Historically 80% of people who scored XYZ in engagement scores get Y results".

This type of analytics I believe is going to be the most impactful of all.

Hierarchy of analytics use in libraries

I propose that the activities I list above are listed in increasing levels of capability and perhaps impact.

It goes from

Level 1 - Any analysis done is library function specific. Typically ad-hoc analytics but there might be dashboard systems created for only one specific area (e.g collection dashboard for Alma or web dashboard for Google analytics)

Level 2 - A centralised library wide dashboard is created covering most functional areas in the library

Level 3 - Library "shows value" runs correlation studies etc

Level 4 - Library ventures into predictive analytics or learning analytics

Many academic libraries are at Level 1 or 2 and a few leaders are at level 3 or even level 4.

Analytics requires deep collaboration 

This way of looking at things I think misses a important element. I believe as you move up the levels, increasingly silos get broken & collaboration increases.

For instance while you can easily do analytics for specific library functions in a silos way (level 1), by building a library dashboard that covers library wide areas would break down the silos between library functions (level 2).

In fact, there are two ways to reach level 2.

Firstly, libraries can go their own way and implement a solution specific to just their library. Even better is if there is a University wide platform that the University is pushing for and the library is just one among various departments implementing dashboards.

The reason why the latter is better is if there is a University wide push for dashboards, the next stage is much easier to achieve because data is already on the University dashboard and University wide there is already familiarity with thinking about and handling of data.

Similarly at level 3, where you show value and run correlation studies and assessment studies you could do it in two ways. You could request for one off access to student data (particularly you need cooperation for many student outcome variables like GPA, though there can be public accessible data like class of degree and Honours' lists) or if there is already a University wide push towards a common dashboard platform, you could connect the data together creating a data warehouse. The later is more desirable of course.

By the time you reach level 4, it would be almost impossible for the library to go it alone.

Obviously I've presented a rosy picture of library analytics. But as always new emerging areas in libraries tend to be at the mercy of the hype cycle. Though conditions seem to be ripe for a focus on library analytics, it's unclear the best way to organize the library to push for it.

Should the library highlight one person who's sole responsibility is analytics? But beware of the Co-ordinator syndrome! Should it be a team? a standing committee? a taskforce? a intergroup? It's unclear.

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