Some brief thoughts on the end of FriendFeed

There was a time, around 2009 or so, when almost every post at this blog was tagged “friendfeed”. So with the announcement (which frankly I expected 5 years ago) that it is to be shut down, I guess a few words are in order.

I’m thankful to FriendFeed for facilitating many of my current online friendships. It was uniquely successful in creating communities composed of people with an interest in how to do science online, not just talk about (i.e. communicate) science online. It was justly famous for bringing together research scientists with other communities: librarians in particular, people from the “tech world”, patient advocates, educators – all under the umbrella of a common interest in “open science”. We even got a publication or two out of it.

To this day I am not sure why it worked so well. One key feature was that it allowed people to coalesce around pieces of information. In contrast to other networks it was the information, presented via a sparse, functional interface, that initially brought people together, as opposed to the user profile. There was probably also a strong element of “right people in the right place at the right time.”

It’s touching that people are name-checking me on Twitter regarding the news of the shutdown, given that no trace of my FriendFeed activity remains online. Realising that my activity was getting more and more difficult to retrieve for archiving and that bugs were never going to be fixed, I opted several years ago to delete my account. The loss of my content pains me to this day, but inaccurate public representation of my activities due to poor technical implementation pains me more.

I’ve seen a few reactions along the lines of “what is all the fuss about.” How short is our collective memory. To those people: look at Facebook, Yammer or even Twitter and ask yourself where the idea of a stream of items with associated discussion came from.

Farewell then FriendFeed, pioneer tool of the online open science community. We never did find a tool quite as good as you.

Academic Karma: a case study in how not to use open data

Update: in response to my feedback, auto-generated profiles without accounts are no longer displayed at Academic Karma. Well done and thanks to them for the rapid response.

A news story in Nature last year caused considerable mirth and consternation in my social networks by claiming that ResearchGate, a “Facebook for scientists”, is widely-used and visited by scientists. Since this is true of nobody that we know, we can only assume that there is a whole “other” sub-network of scientists defined by both usage of ResearchGate and willingness to take Nature surveys seriously.

You might be forgiven, however, for assuming that I have a profile at ResearchGate because here it is. Except: it is not. That page was generated automatically by ResearchGate, using what they could glean about me from bits of public data on the Web. Since they have only discovered about one-third of my professional publications, it’s a gross misrepresentation of my achievements and activity. I could claim the profile, log in and improve the data, but I don’t want to expose myself and everyone I know to marketing spam until the end of time.

One issue with providing open data about yourself online is that you can’t predict how it might be used. Which brings me to Academic Karma.
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Farewell FriendFeed. It’s been fun.

I’ve been a strong proponent of FriendFeed since its launch. Its technology, clean interface and “data first, then conversations” approach have made it a highly-successful experiment in social networking for scientists (and other groups). So you may be surprised to hear that from today, I will no longer be importing items into FriendFeed, or participating in the conversations at other feeds.

Here’s a brief explanation and some thoughts on my online activity in the coming months.
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APIs have let me down part 2/2: FriendFeed

In part 1, I described some frustrations arising out of a work project, using the Array Express API. I find that one way to deal mentally with these situations is to spend some time on a fun project, using similar programming techniques. A potential downside of this approach is that if your fun project goes bad, you’re really frustrated. That’s when it’s time to abandon the digital world, go outside and enjoy nature.

Here then, is why I decided to build another small project around FriendFeed, how its failure has led me to question the value of FriendFeed for the first time and why my time as a FriendFeed user might be up.
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APIs have let me down part 1/2: ArrayExpress

The API – Application Programming Interface – is, in principle, a wonderful thing. You make a request to a server using a URL and back come lovely, structured data, ready to parse and analyse. We’ve begun to demand that all online data sources offer an API and lament the fact that so few online biological databases do so.

Better though, to have no API at all than one which is poorly implemented and leads to frustration? I’m beginning to think so, after recent experiences on both a work project and one of my “fun side projects”. Let’s start with the work project, an attempt to mine a subset of the ArrayExpress microarray database.
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Does your LinkedIn Map say anything useful?

LinkedIn, the “professional” career-oriented social network, is one of those places on the Web where I maintain a profile for visibility. I’m yet to gain any practical value whatsoever from it. That said, I know plenty of people who do find it useful – mostly, it seems, those living near the north-east or west coast of the USA.

inmap

My LinkedIn Network


LinkedIn have something of a reputation for innovation – see LinkedIn Labs, their small demonstration products, for example. The latest of these is named InMaps. It’s been popping up on blogs and Twitter for several days. Essentially, it creates a graph of your LinkedIn network, applies some community detection algorithm to cluster the members and displays the results as a pretty, interactive graphic that you can share.

What seems to have captured the imagination is that the graphs indicate communities that are instantly recognisable to the user. There’s mine on the right (click for full-size version). It’s not a large, complex or especially interesting network but when I “eyeballed” it, I was immediately able to classify the three sub-graphs:

  • Orange – mostly people with whom I have worked or currently work, plus a few “random” contacts: note that this group is hardly interconnected at all
  • Green – people who work in bioinformatics or computational biology, particularly genomics: two major hubs connect me with this group
  • Blue – the largest, densest network is composed largely of what I’d call the “BioGang”: people that I interact with on Twitter and FriendFeed, many of whom I haven’t met in person

This confirms what I’ve long suspected: I prefer to network with smart strangers than my immediate peers and colleagues. Or as Bill Joy said, “no matter who you are, most of the smartest people work for someone else.” I’ve seen this misquoted as “where you are”, which makes more sense to me.

Dear Google

I think you’re a pretty good company. I like many of your products and use them daily, for work and at home. I admire many of your innovations and technical solutions.

But this Buzz thing. You’ve really messed up. Two points:

  1. Social networks should always be opt-in. Never, never opt-out. I choose whether to join in the first place. If I do join, I choose who to connect with, what to share and who can see it. And I expect complete control over the entire process, from the outset.
  2. My list of email contacts is not a social network. It’s a list of people with whom I’ve corresponded by email at least once. That’s all they have in common. Furthermore, there’s a big difference between them exposing their public profiles and me exposing their presence in my address book.

I am normally an enthusiastic, early-adopter of new web tools and a pretty “tech-savvy” individual. Yet Buzz has me confused, annoyed and eager to disable it as fast as I can. It’s not me, it’s you.

I hope that you put more thought into how your next release might impact your users.

Wikification: thinking in public

Over the last 3 years, I’ve stored many small snippets of information in a set of Google Notebooks. Sample topics: notes for blog posts, programming skills that I’d like to learn and preliminary (or half-baked) ideas for research or software projects. I’ve learned that:

  • Whilst Google Notebook is great for scraping information from web pages, it leaves a lot to be desired in terms of editing and presentation
  • Ideas left in private notebooks quickly become dead ideas

Yes you can publicise, tag and collaborate at a Google Notebook, but this doesn’t fit with my workflow – or that of many others, I suspect. So, I’ve taken as much of the material as I want to make public and dumped it on a wiki at Wikidot.com. By the way, if you’re looking for a free hosted wiki with plenty of features, you could do a lot worse.

If anything there interests you enough to add material, let me know and I’ll invite you as an editor (you’ll need to create a wikidot account if you don’t have one).

What I learned from Clay Shirky about science online

The “science online” community has somehow compiled a required reading list (thanks John!), from which many ideas and quotes are mined. I recently finished reading an entry on the list: Here Comes Everybody, by Clay Shirky.

I enjoyed the book – much of it was familiar to me, but it makes good use of specific examples to convey general principles. Of more interest to me is the application of these ideas to science online. Here’s what I think we can extrapolate from the book – and this is purely my personal interpretation.
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