Posted by Rory on December 12th, 2011 @ 12:30 pm

Social networks as the new vehicle for brown nosing and sucking up

There is an interesting piece in today’s Financial Times by Lucy Kellaway (for those who don’t know she is the UK’s most famous ‘corporate agony aunt’) called Social Networks Upend Office Etiquette.  In the piece Kellaway laments the rise of ‘brown nosing’ and and ‘sucking up’ to bosses on Facebook and LinkedIn.

Twitter:  just a forum for flattery?

She then turns to Twitter:

“Above all, people flatter each other on Twitter. Indeed, this seems to be the main function of the site: it’s a great big, instant electronic support group. You tweet someone, they do it back to you.”

Twitter in the office versus Twitter for groups with particular interests

Kellaway’s observations center on people using Twitter in an office context, i.e. on the horizontal and vertical relationships that drive the office dynamic.  The people I am engaged with on Twitter are primarily either  academics and/or’ independent agents’ – software developers, people working in small organizations, etc.  So their Twitter activity centers around mutual interests — scientific topics, data sharing, science and social media, etc. — rather than office dynamics.  How does the charge of ‘Twitter is just for flattery’ stand up in this quite different context?

I am pleased to say that in my experience brown nosing and sucking up are relatively uncommon.  They do exist, but are certainly not the main drivers of conversations.

Simple flattery is more common, but still far from being the driver of most conversations.

Self-congratulation, another feature that Kellaway highlights, is, however, fairly common, and I have to say it’s not something I like.  In fact I find it an unfortunate aspect of the groups whose conversations I follow and participate in.  I would draw a distinction here between reporting something that you have done or participated in, like a presentation or blog post —  to me it’s a good thing to let others know about those kinds of things — and blowing your own horn — to me that’s usually not necessary and it does not enhance my impression of the person doing the cheerleading.

Another aspect of the conversations among the groups I follow I find somewhat troubling — because it is limiting — is that most of the people have basically similar views about the core topics that are discussed, and come from similar work backgrounds.  I suppose that is inevitable, because if they didn’t they wouldn’t want to be part of the conversations.  There is a positive aspect of this, too, in precisely the ‘support group’ aspect of Twitter that Kellaway points to.  The Twitter groups I follow usefully serve to support people who participate in them because they take heart from the fact that there are others with similar interests and largely similar perspectives.

Still, it would be nice if, for example, people who disagree with ‘open science’ participated more in discussions about open science, and if more scientists working in commercial backgrounds, and more people working in companies that provide services and tools for conducting scientific research, engaged with academic scientists about the use of social media in science.

Enriching the debate through widening the group of participants

For the groups I follow, Twitter is already a lot more than just a ‘great big electronic support group”, but diversifying the  participant  base by the addition of  people from these other backgrounds would certainly enrich the debate.  And that’s important because beyond providing support for groups with common interests Twitter also has the potential to become a forum for even more meaningful discussion and debate about the issues that interest those groups.

Article has no comments. Click To Read/Write Comments

Posted by Rory on November 28th, 2011 @ 6:13 pm

I recently came across a useful guide to electronic lab notebooks on the University of Utah website http://campusguides.lib.utah.edu/content.php?pid=126157&sid=2131670.

Two things interested me about the guide.

Categorization of electronic lab notebooks

The first is the breakdown of electronic lab notebooks into three categories (each entry is further categorized as being focused on biology, chemistry, general use, etc.):

1. Vendor ELNs (36 entries, which came from Atrium Research)
2. Open source ELNs (7 entries)
3. General note taking and management (14 entries)

This strikes me as a sensible breakdown which accurately captures the three different kinds of electronic lab notebooks scientists are likely to come across.

ELNs have two layers

The second is the characterization of ELNs as having two layers.  The first is a Calculations and Data Manipulation layer where researchers access and work with data:

The second is an ‘IP protection or people layer’ where researchers collaborate around the research data:

I found this characterization to be quite intriguing.  First you engage with the data, then you engage with people — collaborators, your PI, etc., about the data.  That’s quite a neat way of thinking about ELNs.  And it’s simply put and presented.

Thanks and well done to the University of Utah for making such a useful, and accessible, overview of electronic lab notebooks available!

Article has no comments. Click To Read/Write Comments

Posted by Rory on November 14th, 2011 @ 12:25 pm

The Encyclopedia of Research: an exciting concept

The Encyclopedia of Original Research (EOR) is being proposed by Daniel Mietchen as a GitHub open repository of scientific articles that evolve along with the topic they cover through a series of reviews. You can read more about this concept/project, and the philosophy behind it, here and here.  And Daniel has just written a post called How would you fund research:  An Open Science Perspective, where he explores among other things the relationship between EOR and various open funding models, including the SciFund Challenge.

Daniel and I have had a couple of stimulating email exchanges about EOR.  Our exchanges covered various issues, such as how to incentivize people to contribute to and use EOR, and how EOR will distinguish itself from services in adjacent areas like Mendeley. I thought it would be interesting to build on those exchanges with a post focusing on a particular issue EOR will have to face, namely the interface between ‘evolving publications’ in EOR and the data that lies behind the publications.

How to deal with data used in the research leading to publications and reviews?

Will the data used in the original publication be presented along with the publication? If so, will this be optional, or a condition of having the publication included in EOR?  If the data is presented, will a certain format be required and therefore necessarily supported?  These issues will also arise in relation to reviews, because in some cases the reviewer may want to present their own data.

Data presentation: mandatory or optional?

Behind these simple questions lie a couple of difficult decisions or tradeoffs for EOR. A key objective of EOR is to encourage ongoing discussion of publications.  This objective will be significantly enhanced by making the data used in the research that led to publications available for readers and reviewers.  But, as we all know and many have pointed out, getting people to provide their data is difficult, for a variety of reasons probably foremost among them that the data is often (a) scattered among a variety of things like a paper lab notebook, spreadsheets, wikis, etc., and (b) in multiple formats, so that making it available in a single place or repository is not simple or straightforward.  So, the first decision or tradeoff is between requiring data to be provided, thereby enhancing the quality of the project, and making that optional, thereby encouraging more people to submit publications and review them because it will be easier to do that without having to present the data.

The difficulty of developing an interface for data presentation

My understanding is that EOR is planning to make it possible to present data related to publications, and probably to reviews.  So, EOR will face a secondary, technical issue: how to make that possible for contributors?  I.e. in what format(s) will it be possible to upload data, and what kind of interface will be presented to contributors to enable them to upload data?  Again there is a tradeoff:  a simple interface is easy to use, which should stimulate greater interest in contributing, but if the interface is too simple it won’t be able to accommodate the diverse kinds of data that people will need to include.

This isn’t just EOR’s problem

These issues EOR will face in determining how to interface with data used in research is of course not just EOR’s problem, and they are not issues that EOR will be able to ‘solve’ on its own.  They reflect the broader fact that the interface between data and publication is (a) crucial, but (b) generally ignored by both data collection/sharing apps like electronic notebooks, and publications-focussed apps and services.

As I discussed in last week’s post, data is currently collected in such diverse formats and structures — from paper notebooks to spreadsheets to wikis to blogs to electronic lab notebooks to note taking apps like Evernote to databases — that it is not yet possible to develop publication practices that facilitate anything close to convenient and comprehensive replication and verification of results by including data and code along with the publication.

Towards better presentation of data in EOR, and generally in connection with publications

For things to improve, in my view, there needs to be movement from both ends, i.e. from both the ‘publications aggregators’ (i.e. both existing publishers and innovators like EOR) and from the data collection end, i.e. tools, apps and services that scientists use to collect and organize their data (‘data aggregators’).

What publications aggregators can do

For publications aggregators, the first step is to acknowledge the importance of making data available along with publications in a useful and usable form.  EOR, it appears, has taken this first step.  The second step is to develop structures that facilitate easy inclusion of data alongside publications, in preparation for the day when data aggregators have made it easier for scientists to export their data from the data aggregation tools.  Again, EOR seems to be interested in developing along these lines, and I would say that an opportunity exists for EOR to take the lead in this area.

What data aggregators can do

At their end, data aggregators need to make it far easier to export data from their tools and services. It is difficult and in many cases practically impossible to export data from most electronic lab notebooks, and even generic tools like Evernote do not support export very well.  Google is better, and is serving as a model.  Google has a Data Liberation Front team, whose goal is to make it easier to move data in and out of Google products.  Earlier this year Google introduced Google Takeout, a service which lets you take data from multiple Google products at once. Only a few products, including Buzz, are included now, but the plan is to expand Google Takeout to other products going forward.

An offer to collaborate with EOR

In December Axiope will be releasing a new version of our electronic lab notebook and sample management system eCAT.  With this new version, for the first time, export (other than exporting the XML, which is already possible) will be specifically supported.  With the new version it will be possible to export from both the Notebook and the Inventory (i.e. sample management) sides of eCAT.

eCAT Notebook records will be exportable to ODF (Open Document Format = the format for Open Office).  We have chosen that format for two reasons.   The first is that eCAT is platform-agnostic; it runs on Windows, OS X and Linux, and so does ODF.  The second is that ODF supports retention of links and embedded images, so after export the formatting will be retained.

It will be possible to export eCAT Inventory records to CSV.  We have chosen to start with CSV because in our experience scientists like to see sample data in spreadsheets.

We see this initial  export capability as a modest but important starting point for making data from eCAT ‘portable’. We plan a series of future releases with additional kinds of export capabilities.  This means that over the same period that EOR is taking shape, export from eCAT will be developing.  To us, this seems like a great opportunity to explore better publication/data interfaces from both the data aggregation end and the publications aggregation end.  So, here is an open invitation to Daniel and others involved with EOR:

If you keep us advised on the data formats and methods for including data you think EOR will be needing, we will do our best to ensure that it’s possible to export data from eCAT in those formats and using these methods.

Article has no comments. Click To Read/Write Comments

Posted by Rory on November 9th, 2011 @ 11:24 am

The importance of reproducibility

In a post yesterday, “Disrupt science, but not how you’d think“, Victoria Stodden writes, “I am not necessarily in favor of greater openness during the process of scientific collaboration. I am however necessarily in favor of openness in communication of the discoveries at the time of publication.”  To enable this, she goes on to argue, “we need to establish the routine inclusion of the data and code with publication, such that the results can be conveniently replicated and verified.”

Two examples of making data available using currently available tools

This seems to me to be a very important point, and one that few would dispute.  But how to make reproducibility happen is not so obvious.  As the following two examples make clear, even when the will is there, the tools do not yet exist to make reproducibility convenient and therefore widespread.

Example 1:  Append an entire paper lab notebook to the publication

Gregory I. Lang and David Botstein recently published a paper (A Test of the Coordinated Expression Hypothesis for the Origin and Maintenance of the GAL Cluster in Yeast) to which an entire 101 page lab book containing all the notes, strain construction, methods and raw data that went into producing the paper was included as supplementary data.  I was so struck by this that I wrote a post about it, pointing out that the authors would have (a) saved themselves a lot of time and (b) made it easier for themselves and others to make use of the data they generated if they had recorded their data electronically.

So how does the inclusion of Greg Lang’s paper notebook stand up to the reproducibility test?  It gets top marks for openness; all the experiments, results, materials used, observations, questions asked, thoughts, etc.,the ‘actual scholarship’ in the words of Victoria Stodden’s thesis advisor, are there for all to see.  But, and it is a big but, notwithstanding the openness this scholarship is actually pretty inaccessible in practical terms.  It’s impossible to search on key terms, there is no linking, internally or with external sources of information, and all the benefits of electronic recording are absent.  It would take a huge effort to plough through the notebook, understand how it all fits together, and pick out relevant bits, and the magnitude of this effort surely acts as a major barrier to anyone trying to reproduce the research.

Example 2: Document and publish online everything that happens in the lab as it happens

The Roberts lab at the University of Washington focuses on characterizing physiological responses of marine organisms to environmental change.   The lab has adopted a highly innovative way of organizing and communicating its research.  The lab uses a wiki as the home for its research activities and results.  Protocols, the lab calendar, and image and data repositories are housed in the wiki. That in itself is not particularly novel.  What is more interesting is that the wiki is also home to each individual lab member’s online lab notebook.

And this organizational platform is just the starting point for the more radical part of the lab’s innovation, which is the way it presents and communicates its research, in real time.  The lab uses facebook, tumblr, youtube, and flickr to post developments related to the lab’s research and activities.

The lab members also use their twitter account, @genefish, to push out an auto-feed of all or virtually all notebook entries, blog posts, calendar modifications, photo uploads, etc.  @genefish currently has more than 13,000 tweets!  The core of @genefish is the tweets about the research being documented in each individual’s lab notebook.  Each notebook + the tweets = a step by step account of each lab member’s research as it takes place, and since all the notebooks are included in the lab’s wiki, the lab notebooks + the tweets = a complete record of the collective research in the lab as it takes place.

Like Greg Lang, the Roberts lab gets high marks for openness.  Indeed they have extended the openness to include (a) collaboration between lab members, and (b) open publication of the research as it happens. It’s hard to imagine a more thorough, or well-organized, example of open science than this.  And in terms of adoption of available online resources — Twitter, Facebook, etc., the Roberts lab is on the bleeding edge.

What about reproducibility?  Everything (absolutely everything, it seems!) is there for others to see and access electronically, and there is a record of the process as well as of the results.  These are big plusses in terms of facilitating reproducibility.  There are, however, also some negatives, relating to information overload. There is so much information there that it could be difficult for someone wanting to reproduce the research to zero in on the important and relevant bits.  Another aspect of this is the use of so many different platforms to capture and communicate information — the wiki, Twitter, Facebook, etc.  Although these are all electronic, searching for a term or a key component of the research would probably be even more difficult here than with the paper lab book.

Even with the will, the way is not there yet . . . but it’s coming

Victoria Stodden concludes her post by saying   “. . . It is of primary importance to establish publication practices that facilitate the replication and verification of results by including data and code . . .”  The two examples discussed above demonstrate that currently available tools are nowhere close to being convenient enough or sufficiently fit for purpose to support the development of the kinds of publication practices Victoria Stodden would like to see.   On a brighter note, the examples also reflect the widespread interest in making reproducibility possible, and the wide range of experimentation going on.

Article has no comments. Click To Read/Write Comments

Posted by Rory on October 26th, 2011 @ 4:48 pm

Two radically different kinds of note taking?

This week I came across ButterflyNet — a mobile capture and access system for field biology research developed by the Stanford HCI group.  I discovered ButterflyNet in a footnote to Finders/Keepers:  a Longitudinal Study of People Managing Information Scraps in a Micro-note Tool on a ‘micro-note taking tool by David Karger and others at MIT and the University of Southampton.  These are two examples of note taking at the extreme poles of the spectrum: notetaking in the field is complex, with entry of heterogeneous kinds of data, over extended periods of time, often in difficult conditions, whereas micro-notes are scraps of information — like  ’to do’ items and web urls — that people want to note down as simply and as quickly as possible.  As you’d expect, there are some differences between what note takers want from tools in the two different situations, but what I found more interesting was the number of underlying similarities.

Differences

Field biology

Field biology is a complex activity.  It is carried out in extended sessions, involves observations of multiple things and activities, some of which are changing while the observation takes place, often involve taking photographs and/or collecting physical samples, and requiring recording location information about the subjects of the study.  This results in collection and production of often large amounts of heterogeneous forms of data.  The collection of data in the field, moreover, is only one step in an extended research process.  After the data has been collected in the field, it will be organized, analyzed and often tested back in the lab, with results subsequently discussed and reported in meetings, presentations, and publications.

The paper presenting the results from a trial of ButterflyNet points out that this results in some key requirements:

1. Capturing and accessing heterogeneous data
2. Transforming and integrating this data
3. Robust tools

Micro notetaking

Micro notetaking refers to taking quick notes or recording scraps of information. The kind of thing you put down on a Post-it note or its many electronic wannabe equivalents.  The Finders/Keepers authors found that people wanting to take quick electronic notes have three requirements for notetaking tools; the tools must be:

1. Easy to use.
2. Organized to let people arbitrarily capture small bits of information quickly and easily.
3. Able to keep these information items readily available in visible locations.

Similarities

So much for the differences in the two note taking environments, and the resulting differences in the requirements of field biologists and micro notetakers.  But underlying these differences are some fundamental similarities.  These are reflected in the following feedback from the biologists involved in trialling ButterflyNet:

1. The in-field focus — when time is expensive — is on documentation, rather than interface manipulation.
2. The top advantage of ButterflyNet was that it would help participants to capture and transcribe more data
3. Participants would rather save field time, even if it resulted in more work later.

So it turns out that for field biologists, just like micro note takers, the primary concern is to make data entry as quick, simple and easy as possible, even if this results in more work later on.

Consequences for tool design

These conclusions lead to some important, if difficult, consequences for those of us designing tools for biologists.  The main ones, it seems to me, are:

• Data entry must be ultra simple and easy — a major challenge given the heterogeneity of the data being entered.
• This inevitably increases the complexity of dealing with and organizing the data after it has been entered, so it’s a double complexity — heterogeneous data with little structure captured at the time of entry.
• Users still want the ability to structure data for the purpose of  analyzing and testing it in the lab, and subsequently discussing and reporting data and results in meetings, presentations, and publications.
• The second challenge is thus building support for organizing and dealing with the data in ways which (a) appear simple to the user, but (b) allow the user to organize, manipulate and present the data, and integrate it with other data (from databases, the web, etc.) in a variety of sophisticated ways.

What kind of field biology notetaking tool would this mean in practice?

ButterlyNet was produced in 2005.  It’s safe to describe it as (a) a brave attempt using the technology then available, and (b) way ahead of its time.  The main curiousity, from the perspective of 2011, is ButterflyNet’s creative attempts to integrate paper note taking with online data capture and retrieval.  This was highly innovative in 2005, but in the age of the iPad, it makes ButterflyNet look extremely clunky.  The question that leaps to mind today is:  why not just get rid of the paper and do it all (a) online and (b) on a tablet?

What would that take?  A tool that

1. (Like Butterfly Net) Supports entry of heterogeneous kinds of data including notes, photos, audio, video, GPS data, and data relating to physical samples.
2. Allows simple and quick entry of all these kinds of data, on tablets and offline if necessary (hence dispensing with the need for paper).
3. Facilitates easy association of the different kinds of data; automatically, or by the user in ways the user determines.
4. Syncs between tablets and pcs or macs.
5. Is designed to be collaborative, i.e. has support for controlled sharing of data.

If a tool like that sounds interesting to you, watch this space!

Article has no comments. Click To Read/Write Comments

Posted by Rory on October 10th, 2011 @ 12:57 pm

Article has no comments. Click To Read/Write Comments

Posted by Rory on July 6th, 2011 @ 4:48 pm

The rise of apps for science

Last week I wrote a post discussing whether apps will overtake websites in scientific research as they now have done for general usage.  I later discovered that the previous day Antony Williams and Sean Ekins had launched Scientific Mobile Applications, a “community resource for developers and users to share information about the various science apps that are available”.  There is a useful presentation on Slideshare discussing the rise of mobile apps for science and providing additional background to the establishment of Scientific Mobile Applications, which is laid out as a editable wiki.

I wanted to mention that before going on, as I promised to do last week, to discuss how the rise of apps in scientific research is likely to change the way research is done.  First, it’s a nice coincidence that my post and the launch of Scientific Mobile Applications happened on almost the same day!   Second, this coincidence affirms the theme that both Antony and Sean, and I, are stressing, namely the coming onset of a slew of apps for science.  Third, I would like to thank Antony and Sean for creating what has the potential to become a very useful resource.

Now, onto the main topic — how the rise of apps in science is likely to impact the way science is done.  Here are a few changes that are likely to accompany the rise of apps for research — they are pretty fundamental.

Where you find resources

Currently you probably find new ‘resources’ — services, publications, people, tools, materials, etc., primarily through (a) Google, and (b) websites, e.g. PubMed, Mendeley, discipline-specific forums and databases, and possibly (c) social media like Twitter and Facebook.  As more and more apps for science become available, people will begin to spend more time looking for resources in a third ‘space’, namely appstores.  At first this may not seem like a big change since it will just feel like an additional  ’resource space’.  But if science goes the way of general usage and apps overtake websites as the place where scientists spend a majority of their time online, then apps will become the primary place people go to find resources.  When they want to find a new resource their first instinct will be to look for it in the apps they are already using, and if they can’t find it there they will look next in the app store.  Apps will become the primary resource metaphor, and things that used to seem fundamental, like Google and websites, will be seen as just other apps.

Where you get information

The primacy of apps will also impact where you find information.  Currently people think of the  web as the window or portal through which you look or search for information generally, not just resources.    But if the interface to your mobile device has replaced your desktop as the screen you see most often in the lab or the office, then you will be looking at something like this:

When that happens, you are likely to organize your thinking about where to find a bit of information around which app will help you in the quickest and most effective way. And, as with resources, if you don’t have an app that is good at finding whatever it is you are looking for, your next step is likely, again, to be an app store where you can find one that is.

How you work

In future your research is likely to center around a series of apps. Already there are an increasing number of apps for consuming information related to research — for reading and for search.  But as data entry on tablets becomes easier, and more complex forms of data entry become possible, apps for producing and manipulating information will also proliferate, and hence you will find that you are actively ‘producing’ on apps as well as just consuming.  For example, you can currently find apps which allow you to read barcodes, and these can be applied in some cases to barcodes on inventory items used in the lab.  But in future you will be able to enter data into an app about particular samples, and that data entry will be quick and convenient.  So, you will be producing data through the app as well as consuming it.

How you share informaton

Since most of the information you consume and produce will be through an app, information that you share with others will also be through an app.   Since the ability to share with others, or with others who don’t have the app, is in many cases limited, this will lead to limitations on the ability to share data.

Next week: the implications, good and bad

So there are some of the changes the rise of apps in research are likely to stimulate. These changes have far reaching implications, some good and and some not so good. Next week I’ll take a look at those implications.

Article has no comments. Click To Read/Write Comments

Posted by Rory on June 28th, 2011 @ 3:50 pm

Apps are overtaking websites

A Gilmor Gang video last week contains a fascinating discussion around the theme that apps are replacing websites as ‘the default architecture of the planet’.

If that’s true, in future the window to your research could look like

rather than

Before looking at what the implications of this change  for research could be, here are a few of the key points (assertions in some cases) I noted from the video:

1. Ease of acquisition.  It’s easier to purchase an app from the appstore than something from most websites (except on Amazon), because Apple already has your credit card information.
2. Virality.  It’s easier to build viral loops into apps than into websites and so popular apps get adopted more quickly.
3. Ease of access. Many people never felt comfortable using the browser and an address bar to access websites. Accessing an app is more natural and appealing — you just click on it.
4. Mobility.  The iPad is replacing laptops for people who are travelling or moving around, and use of native apps is higher than use of webapps on the iPad (and the iPhone).

The Gilmor Gang’s speculations are back up by facts: it was also reported last week that people in the US are spending more time using apps on smartphones than browsing the internet on a desktop computer or mobile. Drilling down, it’s also interesting to note what apps people are actually spending time on:  47 % of time is spent on games apps, 32 % using social media apps,  9% using news apps, and 7% using entertainment apps.

Apps for scientific research:  the current state of play favors ‘consumption’ of information rather than ‘production’ of information

That’s a total of 95% of time spent using apps on things which, it is safe to assume, are for the most part not research-related (although a small part of the time spent on social media apps and news apps could be related to research).  If you took a look a the percentage of research time scientists spend on apps as opposed to browsing the web, it would certainly be far lower than 95%, and doubtless a small fraction of the overall amount of time they spend on smartphones and browsing the internet.  In large measure this is due to the fact there there are (relatively speaking) so few apps aimed at scientific research.   There are a few for biology and for chemistry, for example, but they are only able to help with a tiny fraction of the activities biologists and chemists engage in in connection with their research.

In contrast to the paucity of research-related apps for science, there has been a veritable explosion of  apps for use in science ‘teaching’ and ‘education’, some of which are covered in a recent article.  What accounts for the difference?   One probable factor is that most of the teaching/education apps primarily provide content that is intended to be consumed by the users, or at most requires a little simple input from them, whereas most of the research apps require the user to ‘use’ the app in some way by inputting new information or manipulating information or features of the app.  With the current state of mobile technology, it is easy to get information onto a mobile platform like a smartphone or a tablet, but it is still relatively difficult to input information.  So ‘active’ research requiring ‘production’ of information is still much easier to carry out on a computer using websites and web apps.

The future: will research go the way of general usage?

In the near future — within the next 12 months — inputting information onto tablets is going to become much easier, because of improvements in (a) typing interfaces, (b) writing using a stylus, and (c) input via touch.  This will remove the most apparent barrier to using tablets for research in science.   As that happens, the compelling advantages of apps noted by the Gilmor Gang:  ease of acquisition, virality, ease of access and mobility — are likely to come to the fore with developers of tools for scientific research, resulting in an upsurge in apps produced for  use in scientific research.

Next week I’ll take a look at the implications of the likely growth in research apps for individual scientists and scientific communities.

Article has no comments. Click To Read/Write Comments

Posted by Rory on June 15th, 2011 @ 10:44 am

It just works

A couple of months back a great quote flashed across my Twitter feed:

‘Researchers don’t want to manage their data, they want to USE it!’.

That quote has been rolling around in my head ever since.   We’ve adopted it as our mantra at Axiope, because it so nicely sums up what we’ve learned about how scientists think about their data.  When  Steve Jobs kept repeating the phrase, ‘It just works‘, in the recent launch of the iCloud, I was reminded of the quote again, and I thought, wow, that will appeal to scientists.  The purpose of this post is to look in a bit more detail about what it is in iCloud that ‘just works’, and how that could be relevant to scientists working with their data.

Data/stuff is wirelessly pushed between devices

As Jobs said, “iCloud stores your content and wirelessly pushes it to all your devices – phone, iPAD and mac/pc”.  So, if like most scientists you use your mac/pc and your phone in your research, and a small but growing number of scientists you use or are thinking about using an iPAD in your research, content entered on any device automatically appears on the other devices.

Apps are integrated

Apple’s calendar, mail and contacts apps are integrated with the iCloud so again, when something is entered into one of the apps on one device it is there in all devices.  Useful for scientists who already use these apps; will it help persuade others to adopt them?

Documents and file management and sharing

I suspect for that for scientists how documents and files get handled is more important than calendar/contacts/mail integration.   After all documents (which start out as documents and then of course also become files) are the key tool scientists use for recording and analyzing data, so like files they are an absolutely critical element that scientists will evaluate in iCloud. Here’s what one observer had to say about documents in iCloud:

“Documents in the iCloud directly attacks Google Docs with everything but collaborative editing in a richer UI that feels more directly competitive with Office”

But there’s a rub — you can’t share your documents with others.

Files are just as important as documents — here again the iCloud takes a big step forward.  MG Siegler pointed out in this post:

“Apple has been going out of their way to avoid using the word “syncing” with regard to iCloud. That implies that files exist in one place and need to be moved. But again, even that’s too technical for the story Apple is weaving. With iPad/iPhone and now OS X Lion, you don’t save documents anymore. They save automatically — but an easier way to think about it is that they just exist, as is, in realtime on all your devices.”

Dropbox–which Jobs mentioned in a disparaging aside–is already very popular with scientists.   Why?  One of the main reasons is, ‘it just works’.    The iCloud’s ‘automatic saving’ takes this a step further, a step scientists are likely to view as helping them to ‘use their data, not manage it’, and hence embrace.  But here’s the rub, as pointed out by someone who compared the Dropbox and iCloud experiences:

“I’d rather use Dropbox vs iCloud at this time. With Dropbox it gives you total control and the ability to use anywhere and everywhere. When Photostream, via iCloud, sends the pics I took on my iPhone to my iPad, it’s a broadcast stream. I can’t delete or change them for example.

And iCloud feels like a seprate universe from everything else in my life. As far as I can tell, I can’t point my Boxee to iCloud. I can’t send songs from iCloud to things like turntable.fm. I don’t think i can share my iCloud data with my friends or family.”

So you will pay a price for files being taken care of automatically, and that price is a limitation on the ability to share files.

The bottom line

So what’s the bottom line?  iCloud will help scientists ‘use, not manage, their data’ by automatically saving files and pushing them between iPhone, mac or pc, and iPAD, and that is a massive reduction in the overhead of data management.  However, those who take advantage of this new option will find that it comes at the cost of making it more difficult to share their data — in its various forms — with others.  In other words, the overhead of dealing with your own ‘stuff’ is reduced, but the overhead of sharing this ‘stuff’ with others is increased.

Article has no comments. Click To Read/Write Comments

Posted by Rory on May 13th, 2011 @ 4:53 pm

The news from Skype has been coming thick and fast recently.  In late March the launch of Skype in the classroom, a new Skype service aimed at teachers.  Then earlier this week the blockbuster:  Skype purchased by Microsoft.

I this post I’d like to look  how Skype already is being used as a collaborative tool by researchers, and look ahead to ways in which Skype could be integrated with other tools to put it at the center of research collaboration.

Stage One:  Skype for calls about research

Lots and lots of people are using Skype to discuss research these days.  For one to one calls, and also for calls with multiple collaborators to discuss ongoing collaborations and grant proposals.  Michael Mitzenmacher tried to capture the appeal:

“Yesterday, I used Skype on three different research projects to synch up with my collaborators .  . .  . Most of what I do with Skype I could do as well on the phone. [but] Somehow, the real value of Skype is that it’s running on my computer, where I’m doing the research work as well. I can look through relevant old e-mails or read relevant documents while Skype continues managing the conversation. Somehow, it all works more conveniently than using the phone and the computer.”

In this current usage paradigm, Skype is just being used as a cheap (free) and convenient calling channel.  Its wider potential is hinted at in Michael’s comment that the real value of Skype is that it leaves him free to access and play around with his research materials during the call.

Skype in the classroom points the way to Stage Two:  Skype as a tool for finding new collaborators

Skype in the classroom is basically  a ‘platform’ or directory, that allows teachers and students to find others with similar interests, and then, using Skype, make contact with each other and speak to each other.   So it adds a discovery mechanism to the basic communication capability.  Or, as Skype explains it:

“Teachers create a profile that sets out their interests, specialties and location, they can create projects. Projects are a way for teachers to find partner classes, partner teachers or guest speakers for a specific learning activity. You can browse through projects or even search by keyword, which makes it easy for teachers to share expertise and collaborate on projects even when they don’t already know each other . . . Once teachers find someone they’d like to connect with, they can add that person as a Skype contact. “

If Skype has created this platform for teachers, there is no reason it couldn’t create a similar platform for the much larger community of researchers engaged in science.

Stage 3: Skype as the spoke of a collaborative hub

Discovery and communication are two essential elements of collaboration, but they are just scratching the surface of what’s needed for a rounded collaborative environment.  The real win would come if/when Skype was integrated into the research materials that Michael Mitzenmacher refers to.  That is a  realistic possibility because scientists are now beginning to use tools — Google Docs and electronic lab notebooks, for example — in which these materials are collected under one roof and accessible not only by individuals but by groups.  So it’s possible to imagine a group of researchers in different locations talking about their research on Skype, and all looking at — or even jointly editing — the document they are discussing, and for that conversation to be captured on Skype and/or the collaborative tool in which the document is kept, so that it is searchable along with the contents of the document itself.

Will Microsoft take Skype in this direction?  Presumably not in the direction of easy integration with Google Docs!  A bolder strategy would be to use Skype integration as a backward way of making Office Web Apps more of a serious competitor for Google Docs.    But as long as Microsoft maintains Skype’s platform neutrality, it may not matter too much because the door will be open for third parties to produce market driven integrations with Skype.

Article has no comments. Click To Read/Write Comments