Tag: open research

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Replication, reproduction, confirmation. What is the optimal mix?

Issues surrounding the relationship of Open Research and replication seems to be the meme of the week. Abhishek Tiwari provided notes on a debate describing concerns about how open research could damage replication and Sabine Hossenfelder explored the same issue in a blog post. The concern fundamentally is that by providing more of the details of our research we may actually be damaging the research effort by reducing the motivation to reproduce published findings or worse, as Sabine suggests, encouraging group think and a lack of creative questioning.

I have to admit that even at a naive level I find this argument peculiar. There is no question that in aiming to reproduce or confirm experimental findings it may be helpful to carry out that process in isolation, or with some portion of the available information withheld. This can obviously increase the quality and power of the confirmation, making it more general. Indeed the question of how and when to do this most effectively is very interesting and bears some thought. The optimization of these descisions in specific cases will be important part of improving research quality. What I find peculiar is the apparent belief in many quarters (but not necessarily Sabine who I think has a much more sophisticated view) that this optimization is best encouraged by not bothering to make information available. We can always choose not to access information if it is available but if it is not we cannot choose to look at it. Indeed to allow the optimization of the confirmation process it is crucial that we could have access to the information if we so decided.

But I think there is a deeper problem than the optimization issue. I think that the argument also involves two category errors. Firstly we need to distinguish between different types of confirmation. There is pure mechanical replication, perhaps just to improve statistical power or to re-use a technique for a different experiment. In this case you want as much detail as possible about how the original process was carried out because there is no point in changing the details. The whole point of the exercise is to keep things as similar as possible. I would suggest the use of the term “reproduction” to mean a slightly different case. Here the process or experiment “looks the same” and is intended to produce the same result but the details of the process are not tightly controlled. The purpose of the exercise is to determine how robust the result or output is to modified conditions. Here withholding, or not using, some information could be very useful. Finally there is the process of actually doing something quite different with the intention of testing an idea or a claim from a different direction with an entirely different experiment or process. I would refer to this as “confirmation”. The concerns of those arguing against providing detailed information lie primarily with confirmation, but the data and process sharing we are talking about relates more to replication and reproduction. The main efficiency gains lie in simply re-using shared process to get down a scientific path more rapidly rather than situations where the process itself is the subject of the scientific investigation.

The second category error is somewhat related in as much as the concerns around “group-think” refer to claims and ideas whereas the objects we are trying to encourage sharing when we talk about open research are more likely to be tools and data. Again, it seems peculiar to argue that the process of thinking independently about research claims is aided by reducing  the amount of data available. There is a more subtle argument that Sabine is making and possibly Harry Collins would make a similar one, that the expression of tools and data may be inseparable from the ideas that drove their creation and collection. I would still argue however that it is better to actively choose to omit information from creative or critical thinking rather than be forced to work in the dark. I agree that we may need to think carefully about how we can effectively do this and I think that would be an interesting discussion to have with people like Harry.

But the argument that we shouldn’t share because it makes life “too easy” seems dangerous to me. Taking that argument to its extreme we should remove the methods section from papers altogether. In many cases it feels like we already have and I have to say that in day to day research that certainly doesn’t feel helpful.

Sabine also makes a good point, that Michael Nielsen also has from time to time, that these discussions are very focussed on experimental and specifically hypothesis driven research. It bears some thinking about but I don’t really know enough about theoretical research to have anything useful to add. But it is the reason that some of the language in this post may seem a bit tortured.

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Open Data, Open Source, Open Process: Open Research

There has been a lot of recent discussion about the relative importance of Open Source and Open Data (Friendfeed, Egon Willighagen, Ian Davis). I don’t fancy recapitulating the whole argument but following a discussion on Twitter with Glyn Moody this morning [1, 2, 3, 4, 5, 6, 7, 8] I think there is a way of looking at this with a slightly different perspective. But first a short digression.

I attended a workshop late last year on Open Science run by the Open Knowledge Foundation. I spent a significant part of the time arguing with Rufus Pollock about data licences, an argument that is still going on. One of Rufus’ challenges to me was to commit to working towards using only Open Source software. His argument was that there wasn’t really any excuses any more. Open Office could do the job of MS Office, Python with SciPy was up to the same level as MatLab, and anything specialist needed to be written anyway so should be open source from the off.

I took this to heart and I have tried, I really have tried. I needed a new computer and, although I got a Mac (not really ready for Linux yet), I loaded it up with Open Office, I haven’t yet put my favourite data analysis package on the computer (Igor if you must know), and have been working in Python to try to get some stuff up to speed. But I have to ask whether this is the best use of my time. As is often the case with my arguments this is a return on investment question. I am paid by the taxpayer to do a job. At what point is the extra effort I am putting into learning to use, or in some cases fight with, new tools cost more than the benefit that is gained, by making my outputs freely available?

Sometimes the problems are imposed from outside. I spent a good part of yesterday battling with an appalling, password protected, macroed-to-the-eyeballs Excel document that was the required format for me to fill in a form for an application. The file crashed Open Office and only barely functioned in Mac Excel at all. Yet it was required, in that format, before I could complete the application. Sometimes the software is just not up to scratch. Open Office Writer is fine, but the presentation and spreadsheet modules are, to be honest, a bit ropey compared to the commercial competitors. And with a Mac I now have Keynote which is just so vastly superior that I have now transferred wholesale to that. And sometimes it is just a question of time. Is it really worth me learning Python to do data analysis that I could knock in Igor in a tenth of the time?

In this case the answer is, probably yes. Because it means I can do more with it. There is the potential to build something that logs process the way I want to , the potential to convert it to run as a web service. I could do these things with other OSS projects as well in a way that I can’t with a closed product. And even better because there is a big open community I can ask for help when I run into problems.

It is easy to lose sight of the fact that for most researchers software is a means to an end. For the Open Researcher what is important is the ability to reproduce results, to criticize and to examine. Ideally this would include every step of the process, including the software. But for most issues you don’t need, or even want, to be replicating the work right down to the metal. You wouldn’t after all expect a researcher to be forced to run their software on an open source computer, with an open source chipset. You aren’t necessarily worried what operating system they are running. What you are worried about is whether it is possible read their data files and reproduce their analysis. If I take this just one step further, it doesn’t matter if the analysis is done in MatLab or Excel, as long as the files are readable in Open Office and the analysis is described in sufficient detail that it can be reproduced or re-implemented.

Lets be clear about this: it would be better if the analysis were done in an OSS environment. If you have the option to work in an OSS environment you can also save yourself time and effort in describing the process and others have a much better chances of identifying the sources of problems. It is not good enough to just generate an Excel file, you have to generate an Excel file that is readable by other software (and here I am looking at the increasing number of instrument manufacturers providing software that generates so called Excel files that often aren’t even readable in Excel). In many cases it might be easier to work with OSS so as to make it easier to generate an appropriate file. But there is another important point; if OSS generates a file type that is undocumented or worse, obfuscated, then that is also unacceptable.

Open Data is crucial to Open Research. If we don’t have the data we have nothing to discuss. Open Process is crucial to Open Research. If we don’t understand how something has been produced, or we can’t reproduce it, then it is worthless. Open Source is not necessary, but, if it is done properly, it can come close to being sufficient to satisfy the other two requirements. However it can’t do that without Open Standards supporting it for documenting both file types and the software that uses them.

The point that came out of the conversation with Glyn Moody for me was that it may be more productive to focus on our ability to re-implement rather than to simply replicate. Re-implementability, while an awful word, is closer to what we mean by replication in the experimental world anyway. Open Source is probably the best way to do this in the long term, and in a perfect world the software and support would be there to make this possible, but until we get there, for many researchers, it is a better use of their time, and the taxpayer’s money that pays for that time, to do that line fitting in Excel. And the damage is minimal as long as source data and parameters for the fit are made public. If we push forward on all three fronts, Open Data, Open Process, and Open Source then I think we will get there eventually because it is a more effective way of doing research, but in the meantime, sometimes, in the bigger picture, I think a shortcut should be acceptable.

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A personal view of Open Science – Part II – Tools

The second installment of the paper (first part here) where I discuss building tools for Open (or indeed any) Science.

Tools for open science – building around the needs of scientists

It is the rapid expansion and development of tools that are loosely categorised under the banner of ‘Web2.0’ or ‘Read-write web’ that makes the sharing of research material available. Many of the generic tools, particularly those that provide general document authoring capabilities, have been adopted and used by a wide range of researchers. Online office tools can enable collaborative development of papers and proposals without the need for emailing documents to multiple recipients and the resultant headaches associated with which version is which. Storing spreadsheets, databases, or data online means that collaborators have easy access to the most recent versions and can see how these are changing. More generally the use of RSS feed readers and bookmarking sites to share papers of interest and, to some extent, to distribute the task of triaging the literature are catching in in some communities. The use of microblogging platforms such as Twitter and aggregation and conversational tools such as Friendfeed have recently been used very effectively to provide coverage of conferences in progress, including collaborative note-taking. In combination with streamed or recorded video as well as screencasts and sharing of presentations online the idea of a dstributed conference, while not an everyday reality, is becoming feasible.

However it is often the case that,while useful, generic web based services do not provide desired functionality or do not fit well into the existing workflows of researchers. Here there is the opportunity, and sometime necessity, to build specialised or adapated tools. Collaborative preparation of papers is a good example of this. Conventional web bookmarking services, such as del.icio.us provide a great way of sharing the literature or resources that a paper builds on with other authors but they do not automatically capture and recognise the necessary metadata associated with published papers (journal, date, author, volume, page numbers). Specialised services such as citeulke and Connotea have been developed to enable one click bookmarking of published literature and these have been used effectively by for example using a specific tag for references associated with a specific paper in progress. The problem with these services as they exist at the moment is that they don’t provide the crucial element in the workflow that scientists want to aggregate the references for, the formatting of the references in the finalised paper. Indeed the lack of formatting functionality in GoogleDocs, the most widely used collaborative writing tool, means that in practice the finalised document is usually cut and pasted into Word and the references formatted using proprietary software such as Endnote.The available tools do not provide the required functionality.

A number of groups and organisations have investigated the use of Blogs and Wikis as collaborative and shareable laboratory notebooks. However few of these systems offer good functionality ‘out of the box’. While there are many electronic laboratory notebook systems sold by commercial interests most are actually designed around securing data rather than sharing it so are not of interesthere. While the group of Jean-Claude Bradley has used the freely hosted WikiSpaces as a laboratory notebook without further modification, much of the data and analysis is hosted on other services, including YouTube, FlickR, and GoogleDocs. The OpenWetWare group has made extensive modifications to the MediaWiki system to provide laboratory notebook functionality whereas Garret Lisi has adapted the TiddlyWiki framework as a way of presenting his notebook. The Chemtools collaboration at the University of Southampton has developed a specialised Blog platform . Commercial offerings in the area of web based lab notebooks are also starting to appear. All of these different systems have developed because of the specialised needs of recording the laboratory work of the scientists they were designed for. The different systems make different assumptions about where they fit in the workflow of the research scientist, and what that workflow looks like. They are all, however, built around the idea that they need to satisfy the needs of the user.

This creates a tension in tool building. General tools, that can be used across a range of disciplines, are extremely challenging to design, because workflows, and the perception of how they work, are different in different disciplines. Specialist tools can be built for specific fields but often struggle to translate into new areas. Because the market is small in any field the natural desire for designers is to make tools as general as possible. However in the process of trying to build for a sufficiently general workflow it is often the case that applicability to specific workflows is lost. There is a strong argument based on this for building interoperable modules, rather than complete systems, that will allow domain specialists to stich together specific solutions for specific fields or even specific experiments. Interoperability of systems and standards that enable it is a criteria that is sometimes lost in the development process, but is absolutely essential to making tools and processes shareable. The use of workflow management tools, such as Taverna, Kepler, and VisTrails have an important role to play here.

While not yet at a stage where they are widely configurable by end users the vision behind them has the potential both to make data analysis much more straightforward for experimental scientist but also to solve many of the problems involved in sharing process, as opposed to data. The idea of visually wiring up online or local analysis tools to enable data processing pipelines is compelling. The reason most experimental scientists use spreadsheets for data analysis is that they do not wish to learn programming languages. Providing visual programming tools along with services with clearly defined inputs and outputs will make it possible for a much wider range of scientists to use more sophisticated and poweful analysis tools. What is more the ability to share, version, and attribute, workflows will go some significant distance towards solving the problem of sharing process. Services like MyExperiment which provide an environment for sharing and versioning Taverna workflows provide a natural way of sharing the details of exactly how a specific analysis is carried out. Along with an electronic notebook to record each specific use of a given workflow or analysis procedure (which can be achieved automatically though an API) the full details of the raw data, analysis procedure, and any specific parameters used, can be recorded. This combination offers a potential route out of the serious problem of sharing research processes if the appropriate support infrastructure can be built up.

Also critical to successful sharing is a shared language or vocabulary. The development of ontologies, controlled vocabularies, and design standards are all important in sharing knowledge and crucial to achieving the ulitmate goals of making this knowledge machine readable. While there are divisions in the technical development and user communities over the development and use of controlled vocabularies there is little disagreement over the fact that good vocabularies combined with good tools are useful. The disagreements tend to lie in how they are best developed, when they should be applied, and whether they are superior to or complementary to other approaches such as text mining and social tagging. An integrated and mixed approach to the use of controlled vocabularies and standards is the most likely to be successful. In particular it is important to match the degree of structure in the description to the natural degree of structure in the object or objects being described. Highly structured and consistent data types, such as crystal structures and DNA sequences, can benefit greatly from highly structured descriptions which are relatively straightforward to create, and in many cases are the standard outputs of an analysis process. For large scale experimental efforts the scale of the data and sample management problem makes an investment in detailed and structured desriptions worth while. In a small laboratory doing unique work, however, there may be a strong case for using local descriptions and vocabularies that are less rigorous but easier to apply and able to grow to fit the changing situation on the ground. Ideally designed in such a way that mapping onto an external vocabulary is feasible if it is required or useful in the future.

Making all of this work requires that researchers adopt these tools and that a community develops that is big enough to provide the added value that these tools might deliver. For a broad enough community to adopt these approaches the tools must fit well in their existing workflow and help to deliver the things that researchers are already motivated to produce. For most researchers, published papers are the measure of their career success and the basis of their reward structures. Therefore tools that make it easier to write papers, or that help researchers to write better papers, are likely to get traction. As the expectations of the quality and completeness of supporting data increase for published papers, tools that make it easier for the researcher to collate and curate the record of their research will become important. It is the process of linking the record of what happened in the laboratory, or study, to the first pass intepretation and analysis of data, through further rounds of analysis until a completed version is submitted for review, that is currently poorly supported by available tools, and it is this need that will drive the development of improved tools. These tools will enable the disparate elements of the record of research, currently scattered between paper notebooks, various data files on multiple hard drives, and unconnected electronic documents, to be chained together. Once this record is primarily electronic, and probably stored online in a web based system, the choice to make the record public at any stage from the moment the record is made to the point of publication, will be available. The reason to link this to publication is to tie it into an existing workflow in the first instance. Once the idea is embedded the steps involved in making the record even more open are easily taken.

Part III covers social issues around Open Science.

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The Southampton Open Science Workshop – a brief report

On Monday 1 September we had a one day workshop in Southampton discussing the issues that surround ‘Open Science’. This was very free form and informal and I had the explicit aim of getting a range of people with different perspectives into the room to discuss a wide range of issues, including tool development, the social and career structure issues, as well as ideas about standards and finally, what concrete actions could actually be taken. You can find live blogging and other commentary in the associated Friendfeed room and information on who attended as well as links to many of the presentations on the conference wiki.

Broadly speaking the day was divided into three chunks, the first was focussed on tools and services and included presentations on MyExperiment, Mendeley, Chemtools, and Inkspot Science. Branwen Hide of Research Information Network has written more on this part. Given that the room contained more than the usual suspects the conversation focussed on usability and interfaces rather than technical aspects although there was a fair bit of that as well.

The second portion of the day revolved more around social challenges and issues. Richard Grant presented his experience of blogging on an official university sanctioned site and the value of that for both outreach and education. One point he made was that the ‘lack of adoption problem’ seen in science just doesn’t seem to exist in the humanities. Perhaps this is because scientists don’t generally see ‘writing’ as a valuable thing in its own right. Certainly there is a preponderance of scientists who happen also to see themselves as writers on Nature Network.

Jennifer Rohn followed on from Richard, and objected to my characterising her presentation as “the skeptic’s view”. A more accurate characterisation would have been “I’d love to be open but at the moment I can’t: This is what has to change to make it work”. She presented a great summary of the proble, particularly from the biological scientist’s point of view as well as potential solutions. Essentially the problem is that of the ‘Minimum Publishable Unit’ or research quantum as well as what ‘counts’ as publication. Her main point was that for people to be prepared to publish material that falls short of a full paper they need to get some proportional credit for that. This folds closely into the discussion of what can be cited and what should be cited in particular contexts. I have used the phrase ‘data sized peg into a paper shaped hole’ to describe this in the past.

After lunch Liz Lyon from UKOLN talked about curation and long term archival storage which lead into an interesting discussion about the archiving of blogs and other material. Is it worth keeping? One answer to this was to look at the real interest today in diaries from the second world war and earlier from ‘normal people’. You don’t necessarily need to be a great scientist, or even a great blogger, for the material to be of potential interest to historians in 50-100 years time. But doing this properly is hard – in the same way that maintaining and indexing data is hard. Disparate sites, file formats, places of storage, and in the end whose blog is it actually? Particularly if you are blogging for, or recording work done at, a research institution.

The final session was about standards or ‘brands’. Yaroslav Nikolaev talked about semantic representations of experiments. While important it was probably a shame in the end we did this at the end of the day because it would have been helpful to get more of the non-techie people into that discussion to iron out both the communication issues around semantic web as well as describing the real potential benefits. This remains a serious gap – the experimental scientists who could really use semantic tools don’t really get the point, and the people developing the tools don’t communicate well what the benefits are, or in some cases (not all I hasten to add!) actually build the tools the experimentalists want.

I talked about the possibility of a ‘certificate’ or standard for Open Science, and the idea of an organisation to police this. It would be safe to say that, while people agreed that clear definitions would be hepful, the enhusiasm level for a standards organisation was pretty much zero. There are more fundamental issues of actually building up enough examples of good practice, and working towards identifying best practice in open science, that need to be dealt with before we can really talk about standards.

On the other hand the idea of ‘the fully supported’ paper got immediate and enthusiastic support. The idea here is deceptively simple, and has been discussed elsewhere; simply that all the relevant supporting information for a paper (data, detailed methodology, software tools, parameters, database versions etc. as well as access to required materials at reasonable cost) should be available for any published paper. The challenge here lies in actually recording experiments in such a way that this information can be provided. But if all of the record is available in this form then it can be made available whenever the researcher chooses. Thus by providing the tools that enable the fully supported paper you are also providing tools that enable open science.

Finally we discussed what we could actually do: Jean-Claude Bradley discussed the idea of an Open Notebook Science challenge to raise the profile of ONS (this is now setup – more on this to follow). Essentially a competition type approach where individuals or groups can contribute to a larger scientific problem by collecting data – where the teams get judged on how well they describe what they have done and how quickly they make it available.

The most specific action proposed was to draft a ‘Letter to Nature’ proposing the idea of the fully supported paper as a submission standard. The idea would be to get a large number of high profile signatories on a document which describes  a concrete step by step plan to work towards the final goal, and to send that as correspondence to a high profile journal. I have been having some discussions about how to frame such a document and hope to be getting a draft up for discussion reasonably soon.

Overall there was much enthusiasm for things Open and a sense that many elements of the puzzle are falling into place. What is missing is effective coordinated action, communication across the whole community of interested and sympathetic scientsts, and critically the high profile success stories that will start to shift opinion. These ought to, in my opinion, be the targets for the next 6-12 months.

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Q&A in this week’s Nature – one or two (minor) clarifications

So a bit of a first for me. I can vaguely claim to have contributed to two things into the print version of Nature this week. Strictly speaking my involvement in the first, the ‘From the Blogosphere‘ piece on the Science Blogging Challenge, was really restricted to discussing the idea (originally from Richard Grant I believe) and now a bit of cheerleading and ultimately some judging. The second item though I can claim some credit for in as much as it is a Q&A with myself and Jean-Claude Bradley that was done when we visited Nature Publishing Group in London a few weeks back.

It is great that a journal like Nature views the ideas of data publication, open notebook science, and open science in general as worthy of featuring. This is not an isolated instance either, as we can point to the good work of the Web Publishing Group, in developing useful resources such as Nature Precedings, as well as previous features in the print edition such as the Horizons article (there is also another version on Nature Precedings) written by Peter Murray-Rust. One thing I have heard said many times in recent months is that while people who advocate open science may not agree with everything NPG with respect to copyright and access, people are impressed and encouraged by the degree of engagement that they maintain with the community.

I did however just want to clarify one or two of the things I apparently said. I am not claiming that I didn’t say those things – the interview was recorded after all – but just that on paper they don’t really quite match what I think I meant to say. Quoting from the article:

CN-Most publishers regard what we do as the equivalent of presenting at a conference, or a preprint. That hasn’t been tested across a wide range of publishers, and there’s at least one — the American Chemical Society — that doesn’t allow prepublication in any form whatsoever.

That sounds a little more extreme than what I meant to say – there are a number of publishers that don’t allow submission of material that has appeared online as a pre-print and the ACS has said that they regard online publication as equivalent to a pre-print. I don’t have any particular sympathy for the ACS but I think they probably do allow publication of material that was presented at ACS conferences.

CN-Open notebooks are practical but tough at the moment. My feeling is that the tools are not yet easy enough to use. But I would say that a larger proportion of people will be publishing electronically and openly in ten years.

Here I think what I said is too conservative on one point and possibly not conservative enough on the other. I did put my neck out and say that I think the majority of scientists will be using electronic lab notebooks of one sort or another in ten years. Funder data sharing policies will drive a much greater volume of material online post publication (hopefully with higher quality description) and this may become the majority of all research data. I think that more people will be making more material available openly as it is produced as well but I doubt that this will be a majority of people in ten years – I hope for a sizeable and significant minority and that’s what we will continue to work towards.

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How I got into open science – a tale of opportunism and serendipity

So Michael Nielsen, one morning at breakfast at Scifoo asked one of those questions which never has a short answer; ‘So how did you get into this open science thing?’ and I realised that although I have told the story to many people I haven’t ever written it down. Perhaps this is a meme worth exploring more generally but I thought others might be interested in my story, partly because it illustrates how funding drives scientists, and partly because it shows how the combination of opportunism and serendipity can make for successful bedfellows.

In late 2004 I was spending a lot of my time on the management of a large collaborative research project and had had a run of my own grant proposals rejected. I had a student interested in doing a PhD but no direct access to funds to support the consumables cost of the proposed project. Jeremy Frey had been on at me for a while to look at implementing the electronic lab notebook system that he had lead the development of and at the critical moment he pointed out to me a special call from the BBSRC for small projects to prototype, develop, or implement e-science technologies in the biological sciences. It was a light touch review process and a relatively short application. More to the point it was a way of funding some consumables.

So the grant was written. I wrote the majority of it, which makes somewhat interesting reading in retrospect. I didn’t really know what I was talking about at the time (which seems to be a theme with my successful grants). The original plan was to use the existing, fully semantic, rdf backed electronic lab notebook and develop models for use in a standard biochemistry lab. We would then develop systems to enable a relational database to be extracted from the rdf representation and present this on the web.

The grant was successful but the start was delayed due to shenanigans over the studentship that was going to support the grant and the movement of some of the large project to another institution with one of the investigators. Partly due to the resulting mess I applied for the job I ultimately accepted at RAL and after some negotiation organised an 80:20 split between RAL and Southampton.

By the time we had a student in place and had got the grant started it was clear that the existing semantic ELN was not in a state that would enable us to implement new models for our experiments. However at this stage there was a blog system that had been developed in Jeremy’s group and it was thought it would be an interesting experiment to use this as a notebook. This would be almost the precise opposite of the rdf backed ELN. Looking back at it now I would describe it as taking the opportunity to look at a Web 2.0 approach to the notebook as compared to a Web 3.0 approach but bear in mind that at the time I had little or no idea of what these terms meant, let alone the care with which they need to be used.

The blog based system was great for me as it meant I could follow the student’s work online and doing this I gradually became aware of blogs in general and the use of feed readers. The RSS feed of the LaBLog was a great help as it made following the details of experiments remotely straightforward. This was important as by now I was spending three or four days a week at RAL while the student was based in Southampton. As we started to use the blog, at first in a very naïve way we found problems and issues which ultimately led to us thinking about and designing the organisational approach I have written about elsewhere [1, 2]. By this stage I had started to look at other services online and was playing around with OpenWetWare and a few other services, becoming vaguely aware of Creative Commons licenses and getting a grip on the Web 2.0 versus Web 3.0 debate.

To implement our newly designed approach to organising the LaBLog we decided the student would start afresh with a clean slate in a new blog. By this stage I was playing with using the blog for other things and had started to discover that there were issues that meant the ID authentication we were using didn’t always work through the RAL firewall. I ended up having complicated VPN setups, particularly working from home, where I couldn’t log on to the blog and I have my email online at the same time. This, obviously, was a pain and as we were moving to a new blog which could have new security settings I said, ‘stuff it, let’s just make it completely visible and be done with it’.

So there you go. The critical decision to move to an Open Notebook status was taken as the result of a firewall. So serendipity, or at least the effect of outside pressures, was what made it happen.  I would like to say it was a carefully thought out philosophical decision but, although the fact that I was aware of the open access movement, creative commons, OpenWetWare, and others no doubt prepared the background that led me to think down that route, it was essentially the result of frustration.

So, so far, opportunism and serendipity, which brings us back to opportunism again, or at least seizing an opportunity. Having made the decision to ‘go open’ two things clicked in my mind. Firstly the fact that this was rather radical. Secondly, the fact that all of these Web 2.0 tools combined with an open approach could lead to a marked improvement in the efficiency of collaborative science, a kind of ‘Science 2.0’ [yes, I know, don’t laugh, this would have been around March 2007]. Here was an opportunity to get my name on a really novel and revolutionary concept! A quick Google search revealed that, funnily enough, I wasn’t the first person to think of this (yes! I’d been scooped!), but more importantly it led to what I think ought to be three of the Standard Works of Open Science, Bill Hooker’s three part series on Open Science at 3 Quarks Daily [1, 2, 3], Jean-Claude Bradley’s presentation on Open Notebook Science at Nature Precedings (and the associated original blog post coining the term), and Deepak Singh’s talk on Open Science at Ignite Seattle. From there I was inspired to seize the opportunity, get a blog of my own, and get involved. The rest of my story story, so far, is more or less available online here and via the usual sources.

Which leads me to ask. What got you involved in the ‘open’ movement? What, for you, were the ‘primary texts’ of open science and open research? There is a value in recording this, or at least our memories of it, for ourselves, to learn from our mistakes and perhaps discern the direction going forward. Perhaps it isn’t even too self serving to think of it as history in the making. Or perhaps, more in line with our own aims as ‘open scientists’, that we would be doing a poor job if we didn’t record what brought us to where we are and what is influencing our thinking going forward. I think the blogosphere does a pretty good job of the latter, but perhaps a little more recording of the former would be helpful.