OUseful.Info, the blog…

Not surprisingly, I’m way behind on the two eSTEeM projects I put proposals in for – my creative juices don’t seem to have been flowing in those areas for a bit:-( – but as a marking avoidance strategy I thought I’d jot down some thoughts that have been coming to mind about how the custom search project at least might develop (eSTEeM Project: Custom Course Search Engines).

The original idea was to provide a custom search engine that indexes pages and domains that are referenced within a course in order to provide a custom search engine for that course. The OU course T151 is structured as a series of topic explorations using the structure:

- topic overview
- framing questions
- suggested resources
- my reflections on the topic, guided by the questions, drawing on the suggested resources and a critique of them

One original idea for the course was that rather than give an explicit list of suggested resources, we provide a set of links pulled in live from a predefined search query. The list would look as if it was suggested by the course team but it would actually be created dynamically. As instructors, we wouldn’t be specifying particular readings, instead we would be trusting the search algorithm to return relevant resources. (You might argue this is a neglectful approach… a more realistic model might be to have specifically recommended items as well as a dynamically created list of “Possibly related resources”.)

At this point it’s maybe worth stepping back a moment to consider what goes into producing a set of search results. Essentially, there are three key elements:

- the index, the set of content that the search engine has “searched” and from which it can return a set of results;
- the search query; this is run against the index to identify a set of candidate search results;
- a presentation algorithm that determines how to order the search results as presented to the user.

If the search engine and the presentation algorithm are fixed, then for a given set of search terms, and a given index, we can specify a search term and get a known set of results back. So in this case, we could use a fixed custom search engine, with know search terms, and return a known list of suggested readings. The search engine would provide some sort of “ground truth” – same answer for the same query, always.

If we trust the sources and the presentation algorithm, and we trust that we have written an effective search query, then if the index is not fixed, or if a personalised ranking algorithm (that we trust) is used as part of the search engine, we would potentially be returning search results that the instructor has not seen before. For example, the resources may be more recent than the last time the instructor searched for resources to recommend, or they better fit the personalisation criteria for the user under the ranking algorithm used as part of the presentation algorithm.

In this case, the instructor is not saying: “I want you to read this particular resource”. They are saying something more along the lines of: “these are potentially the sorts of resource I might suggest you look at in order to study this topic”. (Lots of caveats in there… If you believe in content led instruction, with students referring to to specifically referenced resources, I imagine that you would totally rile against this approach!)

At times, we might want to explicitly recommend one or two particular resources, but also open up some other recommendations to “the algorithm”. It struck me that it might be possible to do this within the context of a Google Custom Search approach using “special results” (e.g. Google CSEs: Creating Special Results/Promotions).

For example, Google CSEs support:

- promotions: “A promotion is simply an association between a pre-defined set of query terms and a link to a webpage. When a user types a search that exactly matches one of your query terms, the promotion appears at the top of the page.” So by using a specific search term, we can force the return of a specific result as the top result. In the context of a topic exploration, we could thus prepopulate the search form of an embedded search engine with a known search phrase, and use a promotion to force a “recommend reading” link to the top of the results listing.

Promotion links are stored in a separate config file and have the form:

<Promotions>
  <Promotion id="1"
    queries="wanderer, the wanderer" 
    title="Groo the Wanderer" 
    url="http://www.groo.com/"
    description="Comedy. American series illustrated by Sergio Aragonés."
    image_url="http://www.newsfromme.com/images5/groo11.jpg" />
</Promotions>

- subscribed links: subscribed links allow you to return results in a specific format (such as text, or text and a link, or other structured results) based on a perfect match with a specific search term. In a sense, subscribed links represent a generalised version of promotions. Subscribed links are also available to users outside the context of a CSE. If a user subscribes to a particular subscribed link file, then if there is an exact match against of one the search phrases in the subscribed link file and a search phrase used by a subscribing user on Google web search (i.e. on google.com or google.co.uk), the subscribed link will be returned in the results listing.

In the simplest case, subscribed links can be defined at the individual link level:

If your search term is an exact match for the term in the subscribed link definition, it will appear in the main search results page:

It’s also possible to define subscribed link definition files, either as simple tab separated docs or RSS/Atom feeds, or using a more formal XML document structure. One advantage of creating subscribed links files for use within in custom search engine is that users (i.e. students) can subscribe to them as a way of augmenting or enhancing their own Google search results. This has the joint effect of increasing the surface area of the course, so that course related recommendations can be pushed to the student for relevant queries made through the Google search engine, as well as providing a legacy offering: students can potentially take away a subscription when then finish the course to continue to receive “academically credible” results on relevant search topics. (By issuing subscription links on a per course presentation basis (or even on a personalised, unique feed per student basis), feeds to course alumni might be customised, or example by removing links to subscription content (or suggesting how such content might be obtained through a subscription to the university library), or occasionally adding in advertising related links (so if a student searches using a “course” keyword, make recommendations around that via a subscribed links feed; in the limit, this could even take on the form of a personalised, subscription based advertising channel).

Another way in which “recommended” links can be boosted in a custom search result listing is through boosting search results via their ranking factors (Changing the Ranking of Your Search Results).

In the case of both subscribed links and boosted search results, it’s possible to create a configuration file dynamically. Where students are bookmarking search results relating to a course, it would therefore be possible to feed these into a course related custom search engine definition file, or a subscribed link file. If subscribed link files are maintained at a personal level, it would also be possible to integrate a student’s bookmarked links in to their subscribed links feed, at least for use on Google websearch (probably not in the custom search engine context?). This would support rediscovery of content bookmarked by the student through subscribed link recommendations.

Just by the by, a PR mailing in my inbox today threw up another example of how search and bookmarking might be brought more closely together: SearchTeam (screenshots [pdf]).

The model here is based around defining search contexts that one or more users can contribute to, and then saving out results from a search into a topic based bookmark area. The video suggests that particular results can also be blocked (and maybe boosted? The greyed plus on the left hand side?) – presumably this is a persistent feature, so if you, or another member of your “search team” runs the search, the blocked result doesn’t appear? (Is a list of blocked results and their corresponding search terms available anywhere I wonder?) In common with the clipping blog model used by sites such as posterous, it’s possible to post links and short blog posts into a topic area. Commenting is also supported.

To say that search was Google’s initial big idea, it’s surprising that it seems to play no significant role in Google’s offerings for education through Google Apps. Thinking back, search related topics were what got me into blogging and quick hacks; maybe it’s time to return to that area…

“A booktalk in the broadest terms is what is spoken with the intent to convince someone to read a book.” Wikipedia

Whilst putting together yesterday’s post about personal art collections online (for a wider take on this, see Mia Ridge’s The rise of the non-museum (and death by aggregation), which offers all manner of food for thought around personal collection building…), I started thinking again about how we might use recorded discussions or book talks focussing on particular books as a component in the “content scaffolding” around works that might be used as resources in an informal learning context.

(For an earlier foray in to the book talk world, see my post on BBC “In Our Time” Reading List using Linked Data.)

So the (really simple and obvious) idea is this (and I fully appreciate other sites out there may already exist that do this: if so, please let me know in the comments): how about we build a lookup service that allows you to search by author, book title, ISBN (or cross ISBN), and it returns details for the book as well as links to audio or video recordings of book talks around the book.

I’ve started trying to cobble together a few resources around this, setting up (a not yet complete set of) scrapers (in various states of disrepair) on Scraperwiki to collate books and book talk audio links from:

• TechNation (scraper)
• Authors@Google (scraper) [only author names? No book info? Maybe if the date of upload was captured we could guess at the book tour from the scheduling of it?]
• BBC Radio 4′s A Good Read (scraper)
• BBC Radio 4′s Book Club (scraper)
• BBC Radio 4′s Start The Week (scraper)
• BBC Radio 4′s In Our Time (scraper) [okay, this isn't strictly discussing books; but it is abut ideas and there are links to books in the programme notes]
• Philosophy Bites (scraper) [again, not about books per se, but a good context for conversational sensemaking around ideas contained in easily linked to books on the topic written by the author?]
• [probably untold other podcasts...]
• Guardian Books podcast
• BBC Combined book related podcasts

It might also be appropriate to try to pull in “quality” book reviews* to annotate book listings, given that part of my idea at least is to find ways of enriching reading book references with discussion around them that can help folk make sense of the big ideas contained within the book, as well as maybe encouraging them to buy the book (the all required sustainability model: in this case, Amazon referral fees! Note that several of the sites use Amazon referrals as part of their own sustainability model. So it would only be fair to use their affiliate codes at least part of the time if their playable audio content was embedded on the site (even if that content is openly licensed… Share and share alike, right?! That is, trickle back a portion of any income you do make off the work of others, even if it is openly licensed for commercial use;-)

Another strand to all of this, of course, is sensemaking annotations around books pulled from “OERs” (what is is about education that makes the sector want its content to be somehow regarded as “special” and deserving of all sorts of qualification?!;-)

*Maybe the Guardian Platform API or one of the New York Times APIs could play a role here?

So, as ever, I’ve made a start, and as ever, that’ll probably be the end of it…. Sigh… Nice thought while it lasted though…

PS if I were to do next steps, it would probably to take the scraped data and try to normalise it in some ad hoc way in a triple store, maybe on the Talis platform? Note that in the current incarnation, some of the scraped BBC data contains multiple book references in a single record, and thise should be spearated out; also note that a lot of book references are informal (author/title), though I did manage to grab ISBNs (I think?!) from the IT COnversations/Tech Nation pages.

PPS In passing, I note that some of the older archived episodes of A Good Read have been split into chapters covering the different books reviewed in the programme? Was this some sort of experimental enrichment, or just the start of a more general roll out of chapterisation…?

You know those files that are too large for you to work with, or even open? Maybe they’re not….

Picking up on Postcards from a Text Processing Excursion where I started dabbling with Unix command line text processing tools (it sounds scarier than it is… err… maybe?!;-), I thought it would make sense to have a quick play with them in the context of some “real” data files.

The files I’ve picked are intended to be intimidating (maybe?) at first glance because of their size: in this post I’ll look at a set of OpenURL activity data from Edina (24.6MB download, unpacking to 76MB), and for a future post, I thought it might to interesting to see whether this approach would work with a dump of local council spending data from OpenlyLocal (73.2MB download, unzipping to 1,011.9MB).

To start with, let’s have a quick play with the OpenURL data: you can download it from here: OpenURL activity data (April 2011)

What I intend to do in this post is track my own preliminary exploration of the file using what I learned in the “Postcards” post. I may also need to pick up a few new tricks along the way… One thing I think I want to look for as I start this exploration is an idea of how many referrals are coming in from particular institutions and particular sources…

Let’s start at the beginning though by seeing how many lines/rows there are in the file, which I downloaded as L2_2011-04.csv:

wc -l L2_2011-04.csv

I get the result 289,691; older versions of Excel used to only support 65,536 rows per sheet, though I believe more recent versions (Excel 2007, and Excel 2010) can support over a million; Google Apps currently limits sheet sizes to up to 200,000 cells (max 256 columns), so even if the file was only one column wide, it would still be too big to upload into a single Google spreadsheet. Google Fusion Tables can accept CSV files up to 100MB, so that would work (if we could actually get the file to upload… Spanish accent characters seemed to break things when I tried… the workaround I found was to split the original file, then separately upload and resave the parts using Google Refine, before uploading the files to Google Fusion tables (upload one to a new table, then import and append the other files into the same table).

..which is to say: the almost 300,00 rows in the downloaded CSV file are probably too many for many people to know what to do with, unless they know how to drive a database… which is why I thought it might be interesting to see how far we can get with just the unix command line text processing tools.

To see what’s in the file, let’s see what’s in there (we might also look to the documentation):

head L2_2011-04.csv

Column 40 looks interesting to me: sid (service ID); in the data, there’s a reference in there to mendeley, as well as some other providers I recognise (EBSCO, Elsevier and so on), so I think this refers to the source of the referral to the EDINA openurl resolver (@ostephens and @lucask suggested they thought so too. Also, @lucask suggested “OpenUrl from Endnote has ISIWOS as default SID too!”, so we may find that some sources either mask their true origin to hide low referral numbers (maybe very few people ever go from endnote to the EDINA openurl resolver?), or to inflate other numbers (Endnote inflating apparent referrals from ISIWOS.)

Rather than attack the rather large original file, let’s start by creating a smaller sample file with a couple of hundred rows that we can use as a test file for our text processing attempts:

head -n 200 L2_2011-04.csv > samp_L2_2011-04.csv

Let’s pull out column 40, sort, and then look for unique entries in the sample file we created:

cut -f 40 samp_L2_2011-04.csv | sort | uniq -c

I get a response that starts:

12
1 CAS:MEDLINE
1 EBSCO:Academic Search Premier
7 EBSCO:Business Source Premier
1 EBSCO:CINAHL
...

so in the sample file there were 12 blank entries, 1 from CAS:MEDLINE, 7 from BSCO:Business Source Premier and so on, so this appears to work okay. Let’s try it on the big file (it may take a few seconds…) and save the result into a file (samp_uniqueSID.csv:

cut -f 40 L2_2011-04.csv | sort | uniq -c > uniqueSID.csv

This results of the count will be in arbitrary order, so it’s possible to add a sort into the pipeline in order to sort the entries according to the number of entries. The column we want to sort on is column 1 (so we set the sort -k key to 1; and because sort sorts into increasing order by default, we can reverse the order (-r) to get the most referenced entries at the top (the following is NOT RECOMMENDED… read on to see why…):

cut -f 40 L2_2011-04.csv | sort | uniq -c | sort -k 1 -r > uniqueSID.csv

We can now view the uniqueSD.csv file using the more command (more uniqueSD.csv), r look at the top 5 rows using the head command:

head -n 5 uniqueSID.csv

Here’s what I get as the result (treat this with suspicion…):

9181 OVID:medline
9006 Elsevier:Scopus
7929 EBSCO:CINAHL
74740 www.isinet.com:WoK:UA
6720 EBSCO:jlh

If we look through the file, we actually see:

1817 OVID:embase
1720 EBSCO:CINAHL with Full Text
17119 mendeley.com:mendeley
16885 mendeley.com/mendeley:
1529 EBSCO:cmedm
1505 OVID:ovftdb

I actually was alerted to this oops when looking to see how many referrals were from mendeley, by using grep on the counts file (if grep complains about a “Binary file”, just use the -a switch…):

17119 beat the “top count” 9181 from OVID:medline – obviously I’d done something wrong!

Specifically, the sort had sorted by character not by numerical value… (17119 and 16885 are numerically grater than 1720, but 171 and 168 are less (in string sorting terms) than 172. The reasoning is the same as why we’d index aardman before aardvark).

To force sort to sort using numerical values, rather than string values, we need to use th -n switch (so now I know!):

cut -f 40 L2_2011-04.csv | sort | uniq -c | sort -k 1 -r -n > uniqueSID.csv

Here’s what we get now:

74740 www.isinet.com:WoK:UA
34186
20900 www.isinet.com:WoK:WOS
17119 mendeley.com:mendeley
16885 mendeley.com/mendeley:
9181 OVID:medline
9006 Elsevier:Scopus
7929 EBSCO:CINAHL
6720 EBSCO:jlh
...

To compare the referrals from the actual sources (e.g. the aggregated EBSCO sources, rather than EBSCO:CINAHL, EBSCO:jlh and so on), we can split on the “:” character, to create a two columns from one: the first containing the bit before the ‘:’, the second column containing the bit after:

sed s/:/'ctrl-v<TAB>'/ uniqueSD.csv | sort -k > uniquerootSID.csv

(Some versions of sed may let you identify the tab character as \t; I had to explicitly put in a tab by using ctrl-V then tab.)

What this does is retain the number of lines, but sort the file so all the EBSCO referrals are next to each other, all the Elsevier referrals are next to each other, and so on.

Via an answer on Stack Overflow, I found this bit of voodoo that would then sum the contributions from the same root referrers:

cat uniquerootSID.csv | awk '{a[$2]+=$1}END{for(i in a ) print i,a[i] }' | sort -k 2 -r -n > uniquerootsumSID.csv

Using data from the file uniquerootSID.csv, the awk command sets up an array (a) that has indices corresponding to the different sources (EBSCO, Elsevier, and so on). It then runs an accumulator that sums the contributions from each unique source. After processing all the rows (END), the routine then loops through all the unique sources in the a array, and emits the source and the total. The sort command then sorts the output by total for each source and puts the list into the file uniquerootsumSID.csv.

Here are the top 15:

www.isinet.com 99453
EBSCO 44870
OVID 27545
mendeley.com 17119
mendeley.com/mendeley 16885
Elsevier 9446
CSA 6938
EI 6180
Ovid 4353
wiley.com 3399
jstor 2558
mimas.ac.uk 2553
summon.serialssolutions.com 2175
Dialog 2070
Refworks 1034

If we add the two Mendeley referral counts that gives ~34,000 referrals. How much are the referrals from commercial databases costing, I wonder, by comparison? Of course, it may be that the distribution of referrals from different institutions is different. Some institutions may see all their traffic through EBSCO, or Ovid, or indeed Mendeley… If nothing else though, this report suggests that Mendeley is generating a fair amount of EDINA openurl traffic…

Let’s use the cut command again to see how much traffic is coming from each unique insititution (not that I know how to decode these identifiers…); column 4 is the one we want (remember, we use the uniq command to count the occurrences of each identifier):

cut -f 4 L2_2011-04.csv | sort | uniq -c | sort -k 1 -r -n > uniqueInstID.csv

Here are the top 10 referrer institutions (columns are: no. of referrals, institution ID):

41268 553329
31999 592498
31168 687369
29442 117143
24144 290257
23645 502487
18912 305037
18450 570035
11138 446861
10318 400091

How about column 5, the routerRedirectIdentifier:

195499 athens
39381 wayf
29904 ukfed
24766 no identifier
140 ip

How about the publication year of requests (column 17):

45867
26400 2010
16284 2009
13425 2011
13134 2008
10731 2007
8922 2006
8088 2005
7288 2004

It seems to roughly follow year?!

How about unique journal title (column 15):

258740
277 Journal of World Business
263 Journal of Financial Economics
263 Annual Review of Nuclear and Particle Science
252 Communications in Computer and Information Science
212 Journal of the Medical Association of Thailand Chotmaihet thangphaet
208 Scandinavian Journal of Medicine & Science in Sports
204 Paleomammalia
194 Astronomy & Astrophysics
193 American Family Physician

How about books (column 29 gives ISBN):

278817
1695 9780470669303
750 9780470102497
151 0761901515
102 9781874400394

And so it goes..

What’s maybe worth remembering is that I haven’t had to use any tools other than command line tools to start exploring this data, notwithstanding the fact that the source file may be too large to open in some everyday applications…

The quick investigation I was able to carry out on the EDINA openurl data also built directly on what I’d learned in doing the Postcards post (except for the voodoo awk script to sum similarly headed rows, and the sort switches to reverse the order of the sort, and force a numerical rather than string based sort). Also bear in mind that three days ago, I didn’t know how to do any of this…

…but what I do suspect is that it’s the sort of thing that Unix sys admins play around with all the time, e.g. in the context of log file hacking…

PS so what else can we do…? It strikes me that by using the date and timestamp, as well as the institutional ID and referrer ID, we can probably identify searches that are taking place: a) within a particular session, b) maybe by the same person over several days (e.g. in the case of someone coming in from the same place within a short window of time (1-2 hours), or around about the same time on the same day of the week, from the same IDs and searching around a similar topic).

Assuming my projects haven’t been cut out at the final acceptance stage because I haven’t yet submitted a revised project plan,

Preamble
As OU courses are increasingly presented through the VLE, many of them opt to have one or more “Library Resources” pages that contain links to course related resources either hosted on the OU Library website or made available through a Library operated web service. Links to Library hosted or moderated resources may also appear inline in course content on the VLE. However, at the current time, it is difficult to get much idea about the extent to which any of these resources are ever accessed, or how students on a course make use of other Library resources.

With the state of the collection and reporting of activity data from the VLE still evolving, this project will explore the extent to which we can make use of data I do know exists, and to which I do have access, specifically Google Analytics data for the library.open.ac.uk domain.

The intention is to produce a three-way reporting framework using Google Analytics for visitors to the OU Library website and Library managed resources from the VLE. The reports will be targeted at: subject librarians who liaise with course teams; course teams; subscription managers.

Google Analytics (to which I have access) are already running on the library website and the matter just(?!) arises now of:

1) Identifying appropriate filters and segments to capture visits from different courses;

2) development of Google Analytics API wrapper calls to capture data by course or resource based segments and enable analysis, visualisation and reporting not supported within the Google Analytics environment.

3) Providing a meaningful reporting format for the three audience types. (note: we might also explore whether a view over the activity data may be appropriate for presenting back to students on a course.)

The Project
The OU Library has been running Google Analytics for several year, but to my knowledge has not started to exploit the data being collected as part of a reporting strategy on the usage of library resources resulting from referrals from the VLE. (Whenever a user clicks on a link in the VLE that leads to the Library website, the Google Analytics on the Library website can capture that fact.)

At the moment, we do not tend to work on optimising our online courses as websites so that they deliver the sorts of behaviour we want to encourage. If we were a web company, we would regularly analyse user behaviour on our course websites and modify them as a result.

This project represents the first step in a web analytics approach to understanding how our students access Library resources from the VLE: reporting. The project will then provide the basis for a follow on project that can look at how we can take insight from those reports and make them actionable, for example in the redesign of the way links to library resources are presented or used in the VLE, or how visitors from the VLE are handled when they hit the Library website.

The project complements work that has just started in the Library on a JISC funded project to making journal recommendations to students based on previous user actions.

The first outcome will be a set of Google Analytics filters and advanced segments tuned to the VLE visitor traffic and resource usage on the Library website. The second will be a set of Google analytics API wrappers that allow us to export this data and use it outside the Google Analytics environment.

The final deliverables are three report types in two possible flavours:

1) a report to subject librarians about the usage of library resources from visitors referred from the VLE for courses they look after

2) a report to librarians responsible for particular subscription databases showing how that resource is accessed by visitors referred from the VLE, broken down by course

3) a report to course teams showing how library resources linked to from the VLE for their course are used by visitors referred to those resources from the VLE.

The two flavours are:

a) Google analytics reports

b) custom dashboard with data accessed via the Google Analytics API

Recommendations will also be made based on the extent to which Library website usage by anonymous students on particular OU courses may be tracked by other means, such as affinity strings in the SAMS cookie, and the benefits that may accrue from this more comprehensive form of tracking.

If course team members on any OU courses presenting over the next 9 months are interested in how students are using the library website following a referral from the VLE, please get in touch. If academics on courses outside the OU would like to discuss the use of Google Analytics in an educational context, I’d love to hear from you too:-)

eSTEeM is joint initiative between the Open University’s Faculty of Science and Faculty of Maths, Computing and Technology to develop new approaches to teaching and learning both within existing and new programmes.

I got a question from @liwazi last week wondering why a SRU request to the Cambridge Library catalogue wasn’t being handled properly in Yahoo pipes… I think it’s because the Yahoo Pipes XML parser is sometimes like that!

Anyway, here was my fix – to use YQL as a proxy, based around a SRU request URL of the form:
http://search.lib.cam.ac.uk/sru.ashx?version=1.1&recordSchema=dc&query=SEARCH+TERMS
&operation=searchRetrieve&maximumRecords=10

Here’s the the form of YQL query (try it in YQL developer console):

select * from xml where url='http://search.lib.cam.ac.uk/sru.ashx?version=1.1&recordSchema=dc&query=SEARCH+TERMSsearchRetrieve&maximumRecords=10'

You can find a copy of the pipe here: SRU demo pipe

Note that as well as accessing the data via the pipe, you can also pull the results of a search into a web page directly from YQL as a JSON feed:

If you’re really keen, you might also define a YQL data table that would allow you to make a request of the form “select * from camsru where q=’learning perl’”, and then set up a short alias for the query so you could run it using a construction of the form http://query.yahooapis.com/v1/public/yql/EXAMPLEUSER/camsru?q=learning%20perl based on a YQL query of the form select * from camsru where q=@q

PS tomorrow is Mashed Library day at Lincoln – Pancakes and Mash. Be sure to follow #mashlib and chip in if you can ;-)

If you’re a regular listener of BBC Radio 4, you will almost certainly have come across In Our Time, a weekly, single topic discussion programme (with a longstanding archive of listen again material) hosted by Melvyn Bragg on matters scientific, philosophical, historical and cultural. In certain respects, In Our Time may be thought of as discussion based audio encyclopedia. The format sees a panel of three experts (made up of academics, commentators and critics knowledgeable on the topic for that week) teaching the host about the topic. A diligent student, he will of course have done some background reading, and posted links to the references consulted on the programme’s web page.

I’ve already had a quick play with the In Our Time data, looking to see how easy it is to relate programmes to expert academics from various UK universities (Visualising OU Academic Participation with the BBC’s “In Our Time”), but I also wondered whether it would be possible to do anything with the book references, such as using them to identify courses that may be related to a particular programme; (this is reminiscent of a couple of MOSAIC competition entries that looked at ways of recommending books based on courses, and courses based on books using @daveyp’s data from Huddersfield University library that associated course codes with the books borrowed by students taking those courses).

Being a lazy sort, I posted an idea to the OKF Ideas Incubator suggesting that it might be worth considering extracting references from In Our Time programme pages and then reconciling them with Linked Data representations of the corresponding book data.

And then, as if by magic, a solution appeared, from Orangeaurochs: “In Our Time” booklist which describes a method for parsing out the book data and then getting a Linked Data resource reference back from Bibliographica.

The original recipe suggested screenscraping the raw book references from the page HTML, but I posted a comment (at the time of writing, still in the moderation queue) which suggests:

Hi
Great to see you taking this challenge on. Re your step 2 – obtaining the reading list – a possibly more structured way of doing this is to get the appropriate section out of the xml or json representation of the programme page (eg http://www.bbc.co.uk/programmes/b00xhz8d.xml or http://www.bbc.co.uk/programmes/b00xhz8d.json).

I wonder if the BBC will start to structure the data even more – for example by adding explicitly marked up biblio data to book references?

Anyway, you can see an example of the results at pages with URLs of the form http://www.aurochs.org/inourtime_booklist/inourtime_booklist_v1.php?http://www.bbc.co.uk/programmes/b00xhz8d – just add the appropriate IOT programme page URL to extract the data from it.

There are a few hit and misses, but it’s a great start, and something that can be used as a starting point for thinking about how to annotate programme related booklists with structured bibliographic data and exploring what that might mean in a world of linked educational resources that can also reference linked BBC content… :-)

PS Hmmm, I wonder what other programmes are associated with books? A Good Read and Desert Island Discs certainly…

How many UK Higher Education Library websites are running Google Analytics, and how many of them are actually using them to report anything other than sitewide pageviews and visitor numbers?

A couple of years ago, I ran a series of posts on Library Analytics where I started to explore some of the ways in which Google Analytics (as it was then) could be used to help us start to understand how a library website was being used by its different sorts of visitors.

Two years on, and I’ve started looking again at Googalytics in the Library, and will hopefully get round to publishing a few posts at least about what I’ve learned about using as it currently stands for making sense of Library website usage, and for what we may be able to report back to course teams about library website activity of users referred from course pages on the OU VLE.

One thing I thought I’d like to try to do is come up with custom reports, segments and goal recipes that might be transferable, or useful to other HE Library websites, as well as identify “best practice” approaches that are used by other HE libraries running Google Analytics… But which libraries are running Google Analytics?

Using a list of HE Library websites grabbed from a November, 2009 dump of a scrape of the Sconul website (by @ostephens, I think?), I ran a quick python script to sniff library websites for evidence of Google Analytics tracking codes (results).

Total number of websites checked 181
Number with Google Analytics code detected 110 Percentage: 0.60773480663
Number without Google Analytics code detected 67 Percentage: 0.370165745856
Number of pages failed to load 4 Percentage: 0.0220994475138

So, it seems like a fair few folk are running Google Analytics… but I wonder: what are they reporting, what segments and custom reports do they find most useful, what goals have they defined (and do they carry a meaningful “financial” conversion value? If so, defined how?), are they in any sense “actionable” (that is, have they been used to prompt interventions to increase traffic, influence on-site behaviour, feed in to website design changes, feed in to subscription or book acquisition policies, improve links with course academics, update reading lists, contribute to VLE content or structure, schedule and staff online help services, influence opening hours etc. etc.)

If you are working in an HE library, running Google Analytics, and can provide even fragmentary answers to any of the above questions, please reply in a comment below, or feel free to email me (in confidence, if required) at: a.j.hirst@open.ac.uk

PS I’m even going to start looking to the literature, too… So for example, this is next on my reading list: Turner, S. J. (2010). Website Statistics 2.0: Using Google Analytics to Measure Library Website Effectiveness. Technical Services Quarterly, 27(3), 261-278. doi:10.1080/07317131003765910

PPS I thought I’d follow the single citation to that paper too, but it seems I can’t unless I pay for it…

This is interesting, methinks. Not only is the content of the paper kept behind a paywall, but so is its incoming link context…

A reminder that folk music is a living tradition, and often a voice of protest…

“Child of the Library” : taken from – http://soundcloud.com/pdcawley/child-of-the-library/
Author: pdcawley

http://creativecommons.org/licenses/by-nc-sa/3.0/

Full words and more description are at Save Our Libraries

PS see also:

It’s been some time since I last had a look at the “My Library” service in Google Books, but with the announcement of Google eBooks store (currently US only, except for out-of-copyright free downloads) I popped over to my Google Books account to see whether anything else had changed…

One of the little known (I think?) features of Google Books is the “My Library” personalisation which allows you to create a collection of books and search over them. Searching your library finds all the books in your library collection that contain the search phrase; if a preview of the book is available returns deep links into the book to the point(s) at which the search terms appear:

I’ve previously commented on the My Library aspect of Google Books in the context of its possible use by libraries for providing a full-text search option over books in their collection (e.g. Complementing the OPAC With a Full Text Search Book Catalogue where I describe the use of the service by Wiltshire Heritage Library (example) and the Penn State University Press booksearch (example)).

(At the moment I don’t think you can get statistics back on the searches carried out on a My Library profile, though Google books can do stats for publishers e.g. Google Books for Publishers).

Anyway – one of the problems I originally had with My Library was that you could only maintain a single collection. But it seems that it’s now possible to create separate collections by tagging books in your Library onto “shelves”:

(Shelves appeared at the start of 2010, it seems: Updated Books Home Page and My Library.)

So what immediately comes to mind is that if you’re running several courses, you could add the books used in the course to a My Library shelf, and then publish a link to a search context for that shelf to give a full text searchable version of the books on the list (assuming they’ve been scanned by the Goog, of course). Where previews are available, deep links into books will be available as part of the search results.

I haven’t really populated any shelves yet, but here’s the idea:

I haven’t explored the Book Search Data API yet, bit it does seem to offer the ability to search over a particular user’s public library, as well as retrieve lists of books from the library. API options also exist for adding books to a library, though the API seems to only support adding labels, rather than updating shelves (or maybe legacy handlers map labelled books onto shelves?). With a bit of digging, it might be possible to find a route to automate the creation of a library shelf from a list of books. (Hmmm, maybe I should try this with the OU Set books list?!;-)

Google Books shelves thus seem to provide a way of creating different lists of books within a single user library, although I’m not sure if there is a limit on the number of books contained within a shelf, or in the library as a whole. Another nice feature is that it’s possible to select a shelf based filter to just display books from a similar shelf (click on the label in the left-hand sidebar to filter by shelf); this search facet also seems to be passed through to a bookmarkable URL for the filtered search via the as_coll argument (I think?). (Which is to say: you can share a link for a search within a particular shelf in a particular user’s library.)

I’m not sure if Google Books is available through Google Apps for Education, but it could be a useful component of a full text book search context around books on a reading list?

PS As Google Scholar appears to be improving its coverage, it strikes me that the Goog still doesn’t offer a Google service for building searchable reference lists, although it does let you customise the addition of links that will bookmark a reference to a service for you:

Here’s how the links are displayed:

Given you can build weblink search contexts using Google custom search engines, full text book search contexts using the Books My Library service, search over content from bundled feeds in Google Reader and even run things like video search by user on Youtube*, the Goog must surely be looking to offer a collection building and searching over service for Google Scholar? So I wonder… could Google end up taking over a service like CiteULike or Mendeley to complement and bootstrap personalisation of their Google Scholar offering? Or would they just build their own (cut down) version of these services?

* Hmm… I wonder if there’s a Youtube API switch that lets you search playlists? It’s definitely possible to get a playlist feed out…

PPS the Goog is also lacking a way of exposing all these personal search contexts to a logged in user through the same interface. If it were down to me, I’d start to expose them in the left hand sidebar of Google websearch, so I’m guessing this will be a labs/experimental service in the new year, if it isn’t already so…

…maybe…?;-)

One of the many things that I suppose most people never think about is what makes up the raw ingredients of a software application. The answer is code, of course, and code that’s distributed over hundreds, if not thousands, of files; and not just static files, but files that may get edited repeatedly. By different people. At different times.

Keeping track of all these files, including which are the current ones, and also which are the previous versions (because it’s generally considered good practice to keep copies of all the old versions of your files (and the versions of the files around them that were current at the time!) in case you need to go back to them…) can be a nightmare-ish task, which is why software projects tend to use version control systems or code repositories to manage the various files.

Recently, I’ve come across a couple of visualisation tools that show how various software projects have evolved by virtue of the check-ins to a repository over time…

Firstly, CodeSwarm:

And today, gource:

So I’m wondering…could we do similar things for:
- the life of a wiki? It has page creations, deletions and updates…
- the growth of citation tree from an academic paper (so plot the original paper, the papers that cite it, the papers that cite the papers that cite the original, and so on?)
- the submissions to an open publication repository, such as eprints, with submissions as checkins, and maybe links between nodes when one paper cites another in the repository?
- the evolution of tweets around a hashtag (nodes are tweets containing the hashtag, forks are to other hashtags mentioned in the same tweet)?
- what else? (ideas in the comments, please;-)

Maybe visualisations for these exist (though I’m thinking more of animated tree based representations than things like Heavy Metal Umlaut+;-)?

Maybe there’s a common representation possible that would let us to use the tools developed e.g. for visualising code repository checkins on other sorts of tree (because the structure of the code in a repository, or on-citations from a publication) are trees, right?

PS See also: GLtrail server log visualisation.

PPS Visualising Google Analytics using gource via googalytics api and python [via @aneesha]