Using the glossaries package in Latex and Linux, Kile

I was recently frustrated by the limitations of the acronym and glossary packages: I wanted to have something that joined the functionality of both together. Luckily, I found that with the glossaries package, which actually states that it is the replacement for the now-obsolete glossary package.

In order to make this tutorial, I have used the following resources, which you may also find useful: the CTAN glossaries page; the glossaries INSTALL file; (one, two) links from the latex community pages; and a page from the Cambridge Uni Engineering Department. These instructions work for Ubuntu Karmic Koala: please modify where necessary for your system.

Installing glossaries

Note for Windows users: While the makeglossaries command is a perl script for Unix users, there is also a .bat version of the file for Windows users. However, I don’t know how to set up MIKTex or equivalent to use this package. Feel free to add a comment if you can add information about this step.

1. Get and unzip the glossaries package. I downloaded it from here. Though you can download the source and compile, I found it much easier to simply download the tex directory structure (tds) zip file.  Unfortunately, the texlive-latex-extra package available on ubuntu or kubuntu does not contain the glossaries package – it only contains glossary and acronym. I unzipped the contents of the zip file into a directory called “texmf” in my home directory. You’ll also want to run “texhash ~/texmf/” to update the latex database, according to the INSTALL instructions.
2. (Optionally) get the xfor package. If your system is like mine, after you’ve installed the glossaries package latex will complain that it doesn’t have the xfor package (which also is not available via apt-get in Ubuntu). Download this package from here.
3. Open the glossaries zip as root in a nautilus window, terminal window, or equivalent. You’ll be copying the contents to various locations in the root directory structure, and will need root access to do this.
4. Find the location of your root texmf directory. In Karmic, this is /usr/share/texmf/, though it may be in another location on your system.
5. Copy the contents of the tex and doc directories from the glossaries zip into the matching directory structure in your texmf directory. For me, this meant copying the “doc/latex/glossaries” subdirectory in the zip file to “/usr/share/texmf/doc/latex/”, and the same for the tex directory (copy “tex/latex/glossaries” subdirectory in the zip file to “/usr/share/texmf/tex/latex/”). In theory, you can also copy the scripts/ directory in the same way, but I did step 6 instead, as this is what was suggested in the INSTALL document.
6. Update the master latex database. Simply run the command “sudo mktexlsr”
7. Add the location of your scripts/glossaries directory to your $PATH. This gives programs access to makeglossaries, the perl script you will be using (if you’re in linux/unix). If you followed my default instructions in step 1, this location will be “/home/yourname/texmf/scripts/glossaries”.
8. Test the installation. Change into the directory you created in step 1, into the “doc/latex/glossaries/samples/” subdirectory. There, run “latex minimalgls”. If you get an error about xfor, please see step 9. Otherwise, run “makeglossaries” and then “latex minimalgls” again. If everything works, the package is set up for command-line use. You may wish to modify your Kile setup to use glossaries – go to step 10 if this is the case.
9. Set up the xfor package. Run steps 3-6 again, but with the xfor.tds.zip file instead of the glossaries zip file. This package is simpler than glossaries, and does not contain a scripts/ subdirectory, so you will not need to do step 7. After installation, try running step 8 again: everything should work.
10. Setting up Kile. Though I’m using Ubuntu, I find the Kubuntu latex editor Kile to be my favourite (just “sudo apt-get install kile” on Ubuntu). To set up Kile for using glossaries, you need to add another build tool that runs makeglossaries.
    1. Go to Settings -> Configure Kile
    2. Select the “Build” choice, which is a submenu of “Tools” on the left-hand menu.
    3. This brings up a “Select a Tool” pane and a “Choose a configuration for the tool …” pane.
    4. Click on the “New” button at the bottom of the “Select a Tool” pane.
    5. Type in a descriptive name for the tool such as “MakeGlossaries”, and click “Next”.
    6. The next prompt will be to select the default behaviour (or class) of the tool. I based MakeGlossaries on MakeIndex, as they both run in similar ways. Click “Finish” to finish.
    7. For some reason for me, Kile wasn’t initially picking up my changes in my $PATH, so in the General tab of the “Choose a configuration for the tool MakeGlossaries” pane, I put the full path plus the name of the “makeglossaries” script in the “Command” field. You may only need to put in “makeglossaries”.
    8. In the “Options” field of the same tab and pane as step 7, just put in ‘%S’.
    9. Change the selected tab from “General” to “Menu”. In the “Add tool to Build menu:” field, select “Compile” from the pull-down menu. This allows it to appear in the quick compile menu in the main editor window.
    10. I didn’t change any other options. Press “OK” in the main Settings window.
    11. You should now be able to access MakeGlossaries within Kile. Remember, you have to run latex (e.g. PDFLatex) as normal first, to generate the initial file; then run MakeGlossaries; then run PDFLatex or similar again.

Good luck, and I hope this helps people!

Tips on using glossaries

I usually keep all of my acronyms/glossary entries read by the glossaries file in a glossaries-file.tex or similar, and use “\include” to pass it to my main tex file. The links I posted at the top of this tutorial contain a number of useful examples, and included below are my favorites from those locations as well as a few of my own.

Note on usage within your document: Please note that to reference these entries, use \gls{entrylabel} for both referencing an acronym or a glossary entry. Further, to access the plural version of either, use \glspl{entrylabel}. By default, you do NOT need to put in a plural form of an acronym: latex will add an “s” to the expanded form and to the short form when you reference the acronym with \glspl{TLA} rather than \gls{TLA}.

A plain glossary entry that is not also an acronym. The first “sample” is the label used to reference this entry. The second “name={sample}” is the name of the glossary entry, as viewed once the glossary is compiled. The description is the actual definition for the glossary entry:

\newglossaryentry{sample}{name={sample},description={a sample entry}}

A plain acronym entry that is not also a glossary entry. The TLA acronym below illustrates the very basic acronym form. The “aca” example after it illustrates how to add non-normal plurals to the short and long form of the acronym. Then, again, the first instance of “aca” is the label with which to reference the acronym, and the second instance is the name as viewed in the compiled document. The final {} section is the expanded form of the acronym:

\newacronym[]{TLA}{TLA}{Three-letter acronym}
\newacronym[\glsshortpluralkey=cas,\glslongpluralkey=contrivedacronyms]{aca}{aca}{a contrived acronym}

Using an acronym within the glossary definition of a glossary entry. If you wish to make use of an acronym within the glossary definition, and have that acronym indexed properly within the glossary as well as the main text, here is what you do. First, make the acronym. Note that there is nothing special about this acronym:

\newacronym[]{DL}{DL}{Description Logics}

Second, make a normal glossary entry, and reference the acronym as normal. No special work necessary! Please also note that you can put in \cite references within a glossary entry with no problem at all:

\newglossaryentry{TBox}{name={TBox},description={This component of a \gls{DL}-based ontology describes
"intensional knowledge", or knowledge about the problem domain in general. The "T" in TBox could,
therefore, mean "Terminology" or "Taxonomy". It is considered to be unchanging
knowledge~\cite[pg. 12]{2003Description}. Deductions, or \textit{logical implications},
for TBoxes can be done by verifying that a generic relationship follows logically
from TBox declarations~\cite[pg. 14]{2003Description}.}}

Using an acronym as the name of a glossary entry. You sometimes want to use a defined acronym as the name for a glossary entry – this allows you to create a definition for an acronym. In this case, build your acronym as follows. Note that you need to add a “description” field to the square brackets:

\newacronym[description={\glslink{pos}{Part of Speech}}]{POS}{POS}{Part Of Speech}

Then, reference the acronym in the glossary entry as follows (notice the different label for this entry):

\newglossaryentry{pos}{name=\glslink{POS}{Part Of Speech},text=Part of Speech,
description={``Part of Speech''Description}}

Good luck, and have fun.

Inspiring Science Autumn Newsletter

I’ve been meaning to link to this Autumn’s Inspiring Science newsletter, put out by Claire Willis and others at the Science Learning Centre North-East. Not only does it have interesting articles on the science outreach they’ve been involved with recently and what’s coming up in the near future, but it also has a short article on me and my partnered teacher, Louise, as part of the Teacher Scientist Network. Find more about the programme on the Inspiring Science website. Enjoy!

Short Tutorial on using Pellet + OWLAPI + SWRL Rules

I’ve been looking through Pellet and OWLAPI documentation over the past few days, looking for a good example of running existing SWRL rules via the OWLAPI using Pellet’s built-in DL-safe SWRL support. SWRL is used in ontology mappping, and is a powerful tool. Up until now, I’ve just used the SWRLTab, but needed to start running my rules via plain Java programs, and so needed to code the running of the mapping rules in the OWLAPI (which I’m more familiar with than Jena). Once I clean up the test code, I’ll link it from here so others can take a look if they feel like it.

This example uses the following versions of the software:

• Pellet 2.0.0
• OWLAPI 1.1 (that is, the 1_1 part of the Subverison repository). I forsee no problems with using the new OWLAPI for OWL2, but I haven’t installed that yet.

Pre-existing Examples

Pellet provides a SWRL rule example (RulesExample.java in the Pellet download), but only for Jena, and not the OWLAPI. The OWLAPI Example3.java covers the creation of SWRL rules, but not their running. Therefore, to help others who may be walking the same path as I, a short example of OWLAPI + Pellet + SWRL follows.

New Example

This example assumes that you already have the classes, individuals, and rules mentioned below in an OWL file or files. Here is how the test ontology looks, before running the rule (you can use reasoner.getKB().printClassTree() to get this sort of output):


owl:Thing
source:SourceA - (source:indvSourceA)
source:SourceB - (source:indvSourceB)
target:TargetA
target:TargetB


The example SWRL rule is this (the rule.toString() method prints this kind of output, while iterating over ontology.getRules()):


Rule( antecedent(SourceA(?x)) consequent(TargetA(?x)) )


Please note that if you want to modularise your OWL files, as I do (I have different files for the source classes, the target classes, the source individuals, the target individuals, and the rules) then make sure your owl:imports in the primary OWL ontology are correct, and that you’ve mapped them correctly with the SimpleURIMapper class and the manager.addURIMapper(mapper)method. I will update this post with some unit tests of this setup once I’ve cleaned up the code for public consumption.

Once you have your ontology properly loaded into an OWLAPI OWLOntology class, you should simply realize the ontology with the following command to run the SWRL rules:


getReasoner().getKB().realize();

After this command, all that’s left to do is save the new inferences. In this simple case, one individual is asserted to also be a child of the TargetA class, as follows:


owl:Thing
source:SourceA - (source:indvSourceA)
source:SourceB - (source:indvSourceB)
target:TargetA - (source:indvSourceA)
target:TargetB

You can do this by using the following code to explicitly save the new inferences to a separate ontology file. You can modify InferredOntologyGenerator to just save a subset of the inferences, if you like. Have a look in the OWLAPI code or javadoc for more information. Alternatively, you could just iterate over the ABox and just save the new individuals to a file. Here’s the code for saving the ontology to a new location:


OWLOntology exportedOntology = manager.createOntology( URI.create( outputLogicalUri ) );
InferredOntologyGenerator generator = new InferredOntologyGenerator( reasoner );
generator.fillOntology( manager, exportedOntology );
manager.saveOntology( exportedOntology, new RDFXMLOntologyFormat(), URI.create( outputPhysicalUri ) );


I hope this helps some people!

Science Commons provide a list of considerations for researchers looking to license their ontology

Back in March, I wrote a blog post about my experiences trying to find out a) if ontologies should be licensed, b) if ontologies could be licensed, and c) what sort of license would be appropriate. After all, it isn’t clear what sort of thing an ontology is: is it software, or is it a document, or is it something else completely? In this post, I included a response I had received from the nice folks over at Science Commons, giving their perspective on the situation.

Today, I came across a Science Commons blog post by Kaitlin Thaney announcing OWL 2. In it, she also mentions that Science Commons now have a Reading Room article on Ontology Copyright Licensing Considerations which is well worth a read. It updates the information contained in my March post, and provides some useful thoughts on how we should go about licensing ontologies. The section below was the part that particularly caught my eye:

For sharing ontologies in a community or publicly, it would be prudent to think about copyright and licensing. For example, the ontology creator could say that “to the extent I may have copyright in my ontology, I license it in the following way.” In that way, she can reassure the community that even in the event copyright is later found to exist, they may rely upon her offer of a license. This provides an important “safety net” for the community of users, given the uncertainty about whether a given ontology may be copyrightable.

The above section seems to be the biggest new point compared with their earlier statement. While they primarily recommend CC0, they do acknowledge that many researchers may wish to choose an attribution-based licences such as the CC Attribution license.

If you create ontologies, then you should read this article: it’s short, easy to understand, and gives you the information you need to make your own decisions.

Live blogging with Wave: not so live when you can’t make the Wave public

I live blogged Cameron Neylon’s talk today at Newcastle University, and I did it in a Wave. There were a few pluses, and a number of minuses. Still, it’s early days yet and I’m willing to take a few hits and see if things get better (perhaps by trying to write my own robots, who knows?). In effect, today was just an exercise, and what I wrote in the Wave could have equally well been written directly in this blog.

(You’ll get the context of this post if you read my previous post on trying to play around with Google Wave. Others, since, have had a similar experience to mine. Even so, I’m still smiling – most of the time )

Pluses: The Wave was easy to write in, and easy to create. It was a very similar experience to my normal WordPress blogging experience.

Minuses: I wanted to make the Wave public from the start, but have yet to succeed in this. Adding public@a.googlewave.com or public@a.gwave.com just didn’t work: nothing I tried was effective. Also, the copying and pasting simply failed to work when copying the content of the Wave from Iron into my WordPress post in Firefox: while I could copy into other windows and editors, I simply couldn’t copy into WordPress. When I logged into Wave via Firefox, the copy-and-paste worked, but automatically included the highlighting that occurred due to my selecting the text, and then I couldn’t un-highlight the wave! What follows is a very colorful copy of my notes. I’ve removed the highlighting now, to make it more readable.

I’d like to embed the Wave here directly. In theory, I can do this with the following command:

[wave id="googlewave.com!w%252BtZ-uDfrYA.2"]

Unfortunately, it seems this Wavr plugin is not available via the wordpress.com setup. So, I’ll just post the content of the Wave below, so you can all read about Cameron Neylon’s fantastic presentation today, even if my first experiment in Wave wasn’t quite what I expected. Use the Wave id above to add this Wave to your inbox, if you’d like to discuss his presentation or fix any mistakes of mine. It should be public, but I’m having some issues with that, too!

Cameron Neylon’s talk on Capturing Process and Science Online. Newcastle University, 15 October 2009.

Please note that all the mistakes are mine, and no-one else’s. I’m happy to fix anything people spot!

We’re either on top of a dam about to burst, or under it about to get flooded. He showed a graph of data entering GenBank. Interestingly, the graph is no longer exponential, and this is because most of the sequence data isn’t goinginto GenBank, but is being put elsehwere.

The human scientist does not scale. But the web does scale! The scientist needs help with their data, with their analysis etc. They’ll go to a computer scientist to help them out. The CS person gives them a load of technological mumbo jumbo that they are suspicious of. What they need is someone to interpolate the computer stuff and the biologist. They may try an ontologist, however, that also isn’t always too productive: the message they’re getting is that they’re being told how to do stuff, which doesn’t go down very well. People are shouting, but not communicating. This is because all the people might want different things (scientists want to record what’s happening in the lab, the ontologist wants to ensure that communication works, and the CS person wants to be able to take the data and do cool stuff with it).

Scientists are worried that other people might want to use their work. Let’s just assume they think that sharing data is exciting. Science wants to capture first and communicate second, ontologists want to communicate, and CS wants to process. There are lots of ways to publish on the web, in an appropriate way. However, useful sharing is harder than publishing. We need the agreed structure to do the communication, because machines need structure. However, that’s not the way humans work: humans tell stories. We’ve created a disconnect between these two things. The journal article is the story, but isn’t necessarily providing access to all the science.

So, we need to capture research objects, publish those objects, and capture the structure through the storytelling. Use the MyTea project as a example/story: a fully semantic (RDF-backed) laboratory record for synthetic chemistry. This is a structured discipline which has very consistent workflows. This system was tablet-based. It is effective and is still being used. However, what it didn’t work for was molecular biology / bioengineering etc — a much wider range of things than just chemistry. So Cameron and others got some money to modify the system: take MyTea (highly structured and specific system) and extend it into molecular biology. Could they make it more general, more unstructured? One thing that immediately stands out for unstructured/flexible is blogs. So, they thought that they could make a blog into a lab notebook. Blogs already have time stamps and authors, but there isn’t much revision history therefore that got built into the new system.

However, was this unstructured system a recipe for disaster? Well, yes it is — to start with. What warrants a post, for example? Should a day be one post? An experiment? There was little in the way of context or links. People who also kept a physical lab book ended up having huge lists of lab book references. So, even though there was a decent amount of good things (google indexing etc) it was still too messy. However, as more information was added, help came from an unexpected source: post metadata. They found that pull-down menus for templates were being populated by the titles of the posts. They used the metadata from the posts and used that to generate the pull-down menu. In the act of choosing that post, a link is created from that post to the new page made by the template. The templates depend on the metadata, and because the templates are labor saving, users will put in metadata! Templates feed on metadata, which feed the templates, and so on: a reinforcing system.

An ontology was “self-assembled” out of this research work and the metadata used for the templates. Their terms were compared to the Sequence Ontology and found some exact matches and some places where they identified some possible errors in the sequence ontology (e.g. conflation of purpose into one term). They’re capturing first, and then the structure gets added afterwards. They can then map their process and ontologies onto agreed vocabularies for the purpose of a particular story. They do this because we want to communicate to other communities and researchers that are interested in their work.

So, you need tools to do this. Luckily, there are tools available that exploit structure where it already exists (like they’ve done in their templates, aka workflows). You can imagine instruments as bloggers (take the human out of the loop). However, we also need tools to tell stories: to wire up the research objects into particular stories / journal articles. This allows people who are telling different stories to connect to the same objects. You could aggregate a set of web objects into one feed, and link them together with specific predicates such as vocabs, relationships, etc. This isn’t very narrative, though. So, we need tools that interact with people while they’re doing things – hence Google Wave.

An example is Igor, the Google Wave citation robot. You’re having a “conversation” with this Robot: it’s offering you links, choices, etc while having it look and feel like you’re writing a document. Also is the ChemSpider Robot, written by Cameron. Here, you can create linked data without knowing you’ve done it. The Robots will automatically link your story to the research objects behind it. Robots can work off of each other, even if they aren’t intended to work together. Example: Janey-robot plus Graphy. If you pull the result from a series of robots into a new Wave, the entire provenance from the original wave is retained, and is retained over time. Workflows, data, or workflows+data can be shared.

Where does this take us? Let’s say we type “the new rt-pcr sample”. The system could check for previous rt-pcr samples, and choose the most recent one to link to in the text (after asking them if they’re sure). As a result of typing this (and agreeing with the robot), another robot will talk to a MIBBI standard to get the required minimum information checklist and create a table based on that checklist. And always, adding links as you type. Capture the structure – it’s coming from the knowledge that you’re talking about a rt-pcr reaction. This is easier than writing out by hand. As you get a primer, you drop it into your database of primers (which is also a Wave), and then it can be automatically linked in your text. Allows you to tell a structured story.

Natural user interaction: easy user interaction with web services and databases. You have to be careful: you don’t want to be going back to the chemical database every time you type He, is, etc. In the Wave, you could somehow state that you’re NOT doing arsenic chemistry (the robot could learn and save your preferences on a per-user, per-wave basis. There are problems about Wave: one is the client interface, another is user understanding. In the client, some strange decisions have been made – it seems to have been made the way that people in Google think. However, the client is just a client. Specialized clients, or just better clients, will be some of the first useful tools. In terms of user understanding, all of us don’t quite understand yet what Wave is.

We’re not getting any smarter. Experimentalists need help, and many recognize this and are hoping to use these new technologies. To provide help, we need structure so machines can understand things. However, we need to recognize and leverage the fact that humans tell stories. We need to have structure, but we need to use that structure in a narrative. Try to remember that capturing and communication are two different things.

The sound of two hands Waving

I got a Google Wave account (grin) via Cameron Neylon on Monday morning (thanks, Cameron!). I’m trying not to get caught up in all the hype, but I can’t help grinning when I’m using it, even though I don’t really know what I’m doing, and even after seeing the Science Online Demo and a couple Google videos.

But where and how will we get the benefit of the Wave?

I’ve read a few articles, and played around a little, and chatted with people, but I’m still a complete novice. So, I’m not going to talk about technical aspects of waving here. However, even now I can see that the power of Wave will not be in what’s available by default (as was the case with Gmail – you got an account, started using it, and that was pretty much it). It will be in the new applications, interfaces and most especially the Robots that will be riding the Wave with us where the most value will be. OK, so I’ve only had an account for one day, but I think even as a beginner, I can see it is in what we will create for ourselves and our communities to use that will make or break this new thing. And, as ‘we‘ are so much a requirement for this to work, my next point becomes pretty important.

What it will really take to get the best out of Wave for us researchers and scientists?

It will take many, many scientists participating. Social networking needs to get a lot more important to people who currently may just make use of e-mail and web browsing. This is exciting, but we’ll need their help. A very good slideshow by Sacha Chua about this can be found on Slideshare. Use it to convince your friends!

First steps.

As for me, I’ll be waving with both hands this Thursday at 2pm, when Cameron Neylon comes to talk about open science, Google Wave, and more. Unless Cameron is a fantastic multitasker, I may be the only one with an account at the presentation. Not sure how interesting it will be if I am the only one waving. I’ll keep you updated, and post my experience with live blogging with Wave here, and let you know how it goes.

I’m also hoping that I can get some of my research out there into the wider world via Wave robots. I have an interest in structured information (ontologies, data standards etc) and think this may lead to some interesting things.

So, the sound of two hands waving? Pretty quiet, I think. But add another few hundred pairs of hands, and things may get a lot louder.

Inspiring Science Autumn Newsletter

I recently attended an open day at the Science Learning Centre North-East (SLCNE) in my role as half of a Teacher Scientist Network (TSN) partnership. There Louise, my partnered teacher, and I gave a short presentation on how the TSN works, and more specifically about our efforts last year. I enjoyed talking about what a positive experience it was, and also enjoyed seeing the other initiatives (such as Science in the Spotlight and Scientists@Work) that the SLCNE manages.

As an extra bonus, the newsletter for this Centre for Autumn had an article on my TSN partnership with Louise (hence the categorization of this post into the “Self Reference” section). Not only can you read the interview with me and Louise, but you can also read about:

• ‘Liquid Science’ in March 2010 at Newcastle’s Liquid and Diva Nightclub
• How you can get funding from the Royal Society (up to £3000!) for “teachers and scientists or engineers to work together on creative investigations involving 5–16 year olds”. The funding goes straight to the school, and the closing date is November 6th. More information: www.royalsoc.ac.uk/education/partnership.htm.
• Details on the 2009 SLCNE Christmas Lecture from Dr. Laura Grant. She’ll be giving a ‘Cool Science’ presentation “which looks at some of the strange things that happen at low temperatures. The lectures will be performed at four venues across the North East during the first week of December and are suitable for Year 6/7 pupils.” More information: www.slcne.org.uk/christmas.

I strongly encourage you all to join in with your local SLC or branch of TSN, and to have a look at this season’s newsletter!

Science Online London 09: Thoughts, not Transcript

First off, I’d like to thank the many people who re-tweeted my blog posts throughout Science Online London this past Saturday. With your help, Saturday was my best day ever for visits to the site. I hope people enjoyed my posts, and perhaps stayed long enough to find out what I blog about when I’m not at conferences (those I’m most proud of include a day I spent at a primary school last year, and a co-authored post with Frank Gibson on attribution versus citation).

The Royal Institute

Those solo09 posts I wrote on Saturday were intended mainly as notes, as a transcript of what went on. It helps me concentrate to take notes, and due to my fabulous parents talking me into taking typing classes in high school, I am able to (mostly) keep up with presentations! But I wasn’t the only one blogging, and many people since Saturday have been writing up and posting their thoughts: Martin Fenner has been keeping track of what seem to be all blog posts about solo09, so please visit his post to find out what everyone thought of the day.

My blog posts on the day were a record of the day’s presentations, from my point of view. Today’s post is more personal – it was my first time at a Science Online conference, and this is a record of my impressions.

The day started very early for me, though I was not alone in this. I was on a 6am train, and managed to find my way to the Royal Institue (my first visit) before 8:30am. Luckily, they had already laid out the name badges of people whose first name began with “A”, and I grabbed my badge and went to see how many people were around. After geeking out way too much when I met Cameron Neylon for the first time in the physical world (when discussing online avatars with him I tried a bad pun referencing the recent Guild music video about avatars which fell a bit flat), I went for a wander around the building. In one of the libraries I found this book, which amused me:

A book in one of the libraries at the Royal Institute. Memoirs of Libraries, in a library!

Then I wandered upstairs and had a look at the Faraday Theatre, with its surprisingly uncomfortable seating but beautiful fittings and fantastic ambience. Just a tip though – watch out for the Ambulatory Displays up there on the first floor. The British Library had a table set up in a prime position opposite the Faraday Theatre, and at that table I met some BL people as well as Stewart Wills, an Editor for Science. I had never spoken with a Science editor before, and I had a really enjoyable conversation with him and the BL people about wildflowers and ontologies for 20 minutes or so, until it was time for the conference to start.

I won’t go heavily into the presentations, as I have already covered them. Suffice to say I thought they were all very interesting, often entertaining, and definitely educational. While I would have loved to have much more time for open discussion at the end of each presentation, that didn’t spoil my enjoyment. I had my first experience with Second Life, and watching the odd behaviors of the avatars in it was almost hypnotic. One seemed to be playing the spoons or typing on an invisible keyboard or something. Many others seemed to be hanging off an invisible wire in their back, and others flounced, tilted alarmingly, or even looked attentive.

I will choose a favorite presentation though: I loved the theatrics and the content of John Gilbey. He presented a number of speculations about the far future, and said that we could all vote for our favorite by emailing him in the next week. Then, he’ll do his best to write about it in the context of the University of Rural England and get it into print Fun! You can email him at gilbey@bcs.org.uk.

I had a number of good conversations with Sara Fletcher of Diamond Light Source about power cables, last year’s Science Online, and meeting people in the real world who you’ve gotten to know only through the (unreal?) world of the Internet. We were the ones sitting near the annoying ringing iPhone during the metrics/statistics talk by Richard Grant and others. No, it was NOT our phone, and yes, we tried to find it to turn it off but were unsuccessful.

It was great seeing bloggers made flesh: Petra Boynton, Jack of Kent, Cameron Neylon and Peter Murray-Rust were just a few of the people I either listened to or spoke with for the first time. Peter, Phil Lord and I had a great conversation about ontologies OWL ontologies – well, about semantics.

I left London that evening, this time on a full train of tired people wanting to get home that was in stark contrast to the quiet, empty train and the beautiful sunrise that began the day. I had a great experience and my thanks goes out to all the organizers and people who helped make Science Online London work. I am now more interested in Google Wave, still want a single unifying identifier for me and my online personas (one identifier per persona, or one per person?)  and am more aware of the legal implications of blogging. I feel like I’ve increased not just my knowledge of all things science and online, but also the size of my online science community, which is a community that has enriched my research environment and work life more in the past year than I ever thought possible. The Life Scientists, Science 2.0, Twitter and my good friend Google Reader keep me in touch with all of the other blogs of science of friends and colleagues, and I’m following many more after Science Online. I am a better scientist and researcher because of my connections to this community – Thank you all!

Breakout 3: Author identity – Creating a new kind of reputation online (Science Online London 2009)

Duncan Hull, Geoffrey Bilder, Michael Habib, Reynold Guida

ResearcherID, Contributor ID, Scopus Author ID, etc. help to connect your scientific record. How do these tools connect to your online identity, and how can OpenID and other tools be integrated? How can we build an online reputation and when should we worry about our privacy?

Geoff Bilder:

Almost every aspect of a person can change without the person themselves changing. So, you want to have an identifier that is a hook to you, and which is better than a name (which is changeable). What about retinal scans? Fingerprints? OpenID? Where does your profile come in? A profile is a collection of attributes that you use to describe who you are. With author identity, what we want is the ability to get at the profile of a person in an unambiguous manner. Until we have such a thing, how do you tell people what your canonical profile is? To complicate matters even more, each user will want multiple personas, each with their own profiles.

When talking about identity, two issues are often conflated: identity authentication and knowledge discovery identity system. That is, you must be more rigorous in determining swho someone is (logging into your identity) than in figuring out who wrote a paper. Further complications occur in the lossy conversion between languages of authors’ names.

Whatever is done, has to be done on an international scale, must be interdisciplinary, and must be interinstitutional. The oldest content cited thus far in CrossRef (with a DOI) is from the 1600s. What happens when you die to your identifier? A final issue is scale: there are about 200K new DOIs per month, and even if we guess at 5 authors per DOI, then there could be between 5-21K failures of identification per month if you estimate a 96-97% success rate for author identification.

Duncan Hull:

He spoke about openID is science, among other things. Currently, authentication of people is very different in most online applications, and is generally only done with a simple username and password combination. Simon Willison (The Guardian) estimates that the average online user has at least 18 user accounts and 3.49 passwords. OpenID is trying to end up with a situation where there are fewer usernames AND passwords.

OpenID works by redirecting you to your openID provider to log in, then sends you back to the location you started at. However, having a URL as a username is not very intuitive. Further, logging in via redirection can be confusing. Therefore while adoption of openId is growing, it may not properly take off until browsers and other vendors support it better. Mentioned myExperiment as something which accepts openId.

Michael Habib:

Michael presented a nice diagram: a square divided into 4 parts, with “about me” and “not about me” across the top, and “by me” and “not by me” down the side. It is the “not” category for both where the disambiguation of people is the most important. He used the example of Einstein and the LC Authority Files to figure out what all of the different versions of his name are.

Completely different from the LC Authority files, which is manually and carefully checked by only certain people, is ClaimID. ClaimID is a way to collect all aspects of your identity in one place. However, it is dependent upon each individual being truthful about what they have ownership over.

Another approach is the Scopus Author ID, which is completely machine aggregated. It is validated by publications, and scales well. It has 99% precision and 95% recall. The cons is that it is impersonal, and those precision and recall values really aren’t very good when you consider that this is about ownership of an article, and that there are a very large number of people.

There is also 2collab, where you can combine author ids (that you know about) into one identity. Then, you can add any other item on the web that is about you.

Reynold Guida (from Thomson Reuters):

They’ve built software to try to address author identity and attribution. If you look at the literature since 2000, communication and scientific collaboration has really changed. What we notice is that the number of multi-author papers has started to increase, while single-author papers have decreased. A google search for common surnames really highlights the problems associated with identity. Name ambiguity is a real problem. The connection between the reseacher and the institution and the community is a real problem. Two of the most important parts in this discussion are who do I know, and who do I want to know? The connections a person makes affects all aspects of their career.

Therefore they have created researcherId (free, secure, open). Privacy options are controlled by the user, even if the institution created the record. There is integration with EndNote, Web of Knowledge, and other systems to help build publication lists. You can link to / visualize your researcher id profile really easily from your own websites.

Discussion:

Question: Has anyone thought through the security implications of these single ID systems: one slip-up and your entire identity has been hacked? GB: Multiple identities encourages poor behaviour, as the thought of changing your password everywhere is so overwhelming that people don’t do it. But yes, these problems exist. However, the tradeoffs make it worthwhile to their minds. You should NOT conflate knowledge issues with security issues. This is because information for your scholarly profile is, by definition, public anyway.

Question: Do different openId providers and author id and researcher id know about each other in the computational sense? Not really yet.

Question: What about just making the markup of the web more semantically friendly? DH: The Google approach is a good one. RG: It’s all about getting the information into the workflow.

Question (Phil Lord): What worries me is that there has been a big land grab for author identity space: for example, you cannot log into Yahoo with any other open id than a Yahoo open id. There’s a lot of value in being in control of someone’s id. Therefore there is a big potential danger. GB: For every distributed system, you need a centralized indexing thing to get it to work correctly. Therefore we need to make sure that if a centralized system appears, there should be accountability.

FriendFeed Discussion

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

Real-time statistics in science (Science Online London 2009)

Victor Henning, Richard Grant, Virginia Barbour

Academic prestige, setting research trends, getting jobs and tenure, grant funding – they are largely based on publishing in high-Impact Factor journals and getting citations. Not only are these measures flawed and widely critized: “You could write the entire history of science in the last 50 years in terms of papers rejected by Science or Nature”, said Nobel laureate Paul Lauterbur. Citation measures are also subject to a considerable time-lag. If you write a paper today, it takes a year to get it published, and another year passes by until citations of it appear. What if there were alternative measures of scientific impact? What if these measures were available in real-time, letting you track the trends in your discipline as they develop? That’s what we’ll discuss in this session.

Richard Grant:

Employers like metrics to discover if they’re spending money in the right places. Researchers want to see that what they’re doing is relevant. This is why we want metrics. But what can metrics do, and what can’t they do? Impact factors: doesn’t actually tell you how good research is in a given journal. He is involved in the qualitative assessment of articles. More like a FriendFeed method of assessment. Corporate bit: http:/f1000.com. The crucial thing they want to have is quality. What they do at f1000 is pretty slow, by necessity. There is also, though, a tying-in with the community.

Virginia Barbour:

She’d like to reclaim the word “impact” from “impact factor”. How do you assess quality: usage, media coverage, blog coverage, expert ratings, discussion thread activity, who is reading it, who is citing it, where the research was done, effect on public policy? No single one is one you should rely on. Traditional measures are often not the most important. Many feel that the way papers are being evaluated is actually detrimental to the research process. Most users of journal sites are not coming via the home page – they’re coming via Google and other methods: people just don’t start at the first page of a journal and read through.

NEJM is changing the way their front pages look and the Journal of Vision is changing the way the metrics are displayed. At PLoS, in Phase 1 they want to have data that isn’t owned by someone else – that we can actually use and verify. In Phase 2, they also want to have the number of downloads of the article. This data will be broken down by the type of views. They also want to make the metrics more sophisticated, with more sources for each data type, more sophisticated web usage data, provide tools for analysis, and more.

Victor Henning:

Used last.fm as an analogy for article metrics, and as an introduction to Mendeley. In this way, you can track article pervasiveness in reference manager libraries, track article reading time in PDF viewers, and track user tags and ratings. One key difference with Mendeley and last.fm is privacy: they believe that some scientists don’t want others to know what literature they find interesting.

They have synchronization with citeulike, and will shortly have synchronization with Zotero. The goal of all this is to aggregate statistics for their users. All of the information is available by academic discipline, geographic region, and more. Once we’re at the point where there are true article metrics, this can be the basis for individualized recommendations.

Discussion:

Question: It seems we’re replacing a single impact factor with a large number of new ones. How do you forsee people managing and understanding all of those metrics? RG: we’re not in the business of replacing the impact factor – just providing more information to the researcher.

VB: I can imagine that people will be able to go to grant funding agencies and tell them how much coverage in all sorts of media your paper received.

Question (Phil Lord): I worry about reading times as a measure of quality. In music the listeners and musicians are largely disjoint. In science this is definitely not true. Many of the metrics mentioned are very much open to fiddling and self-citation. What do you say about this? VH: We’re not advocating replacing the impact factor. However, it is always better to have more data, more metrics.

Question: I print out my articles. How will that affect things?

Missed most of the rest of the discussion because of a phone that wouldn’t stop ringing – see the Twitter hashtag #solo09 for all the gory details.

FriendFeed Discussion

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!