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Submission to the Royal Society Enquiry

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The Royal Society is running a public consultation exercise on Science as a Public Enterprise. Submissions are requested to answer a set of questions. Here are my answers.

1. What ethical and legal principles should govern access to research results and data? How can ethics and law assist in simultaneously protecting and promoting both public and private interests?

There are broadly two principles that govern the ethics of access to research results and data. Firstly there is the simple position that publicly funded research should by default be accessible to the public (with certain limited exceptions, see below). Secondly claims that impinge on public policy, health, safety, or the environment, that are based on research should be supported by public access to the data. See more detail in answer to Q2.

2 a) How should principles apply to publicly-funded research conducted in the public interest?

By default research outputs from publicly funded research should be made publicly accessible and re-usable in as timely a manner as possible. In an ideal world the default would be immediate release, however this is not a practically accessible goal in the near future. Cultural barriers and community inertia prevent the exploitation of technological tools that demonstrably have the potential enable research to move faster and more effectively. Research communication mechanisms are currently shackled to the requirements of the research community to monitor career progression and not optimised for effective communication.

In the near term it is practical to move towards an expectation that research outputs that support published research should be accessible and re-usable. Reasonable exceptions to this include data that is personally identifiable, that may place cultural or environmental heritage at risk, that places researchers at risk, or that might affect the integrity of ongoing data collection. The key point is that while there are reasonable exceptions to the principle of public access to public research outputs that these are exceptions and not the general rule.

What is not reasonable is to withhold or limit the re-use of data, materials, or other research outputs from public research for the purpose of personal advancement, including the “squeezing out of a few more papers”. If these outputs can be more effectively exploited elsewhere then this a more efficient use of public resources to further our public research agenda. The community has placed the importance of our own career advancement ahead of the public interest in achieving outcomes from public research for far too long.

What is also politically naive is to believe or even to create the perception that it is acceptable to withhold data on the basis that “the public won’t understand” or “it might be misused”. The web has radically changed the economics of information transfer but it has perhaps more importantly changed the public perception on access to data. The wider community is rightly suspicious of any situation where public information is withheld. This applies equally to publicly funded research as it does to government data.

2 b) How should principles apply to privately-funded research involving data collected about or from individuals and/or organisations (e.g. clinical trials)?

Increasingly public advocacy groups are becoming involved in contributing to a range of research activities including patient advocacy groups supporting clinical trials, environmental advocacy groups supporting data collection, as well as a wider public involvement in, for instance, citizen science projects.

In the case where individuals or organisations are contributing to research they have a right for that contribution to be recognised and a right to participate on their own terms (or to choose not to participate where those terms are unacceptable).

Organised groups (particularly patient groups) are of growing importance to a range of research. Researchers should expect to negotiate with such groups as to the ultimate publication of data. Such groups should have the ability to demand greater public release and to waive rights to privacy. Equally contributors have a right to expect a default right to privacy where personally identifiable information is involved.

Privacy trumps the expectation of data release and the question of what is personally identifiable information is a vexed question which as a society we are working through. Researchers will need to explore these issues with participants and to work to ensure that data generated can be anonymised in a way that enables the released data to effectively support the claims made from it. This is a challenging area which requires significant further technical, policy, and ethics work.

2 c) How should principles apply to research that is entirely privately-funded but with possible public implications?

It is clear that public funded research is a public good. By contrast privately funded research is properly a private good and the decision to release or not release research outputs lies with the funder.

It is worth noting that much of the privately funded research in UK universities is significantly subsidised through the provision of public infrastructure and this should be taken into consideration when defining publicly and privately funded research. Here I consider research that is 100% privately funded.

Where claims are made on the basis of privately funded research (e.g. of environmental impact or the efficacy of health treatments) then such claims SHOULD be fully supported by provision of the underlying evidence and data if they are to be credible. Where such claims are intended to influence public policy such evidence and data MUST be made available. That is, evidence based public policy must be supported by the publication of the full evidence regardless of the source of that evidence. Claims made to influence public policy that are not supported by provision of evidence must be discounted for the purposes of making public policy.

2 d) How should principles apply to research or communication of data that involves the promotion of the public interest but which might have implications from the privacy interests of citizens?

See above: the right to privacy trumps any requirement to release raw data. Nonetheless research should be structured and appropriate consent obtained to ensure that claims made on the basis of the research can be supported by an adequate, publicly accessible, evidence base.

3. What activities are currently under way that could improve the sharing and communication of scientific information?

A wide variety of technical initiatives are underway to enable the wider collection, capture, archival and distribution of research outputs including narrative, data, materials, and other elements of the research process. It is technically possible for us today to immediately publish the entire research record if we so choose. Such an extreme approach is resource intensive, challenging, and probably not ultimately a sensible use of resources. However it is clear that more complete and rapid sharing has the potential to increase the effectiveness and efficiency of research.

The challenges in exploiting these opportunities are fundamentally cultural. The research community is focussed almost entirely on assessment through the extremely narrow lens of publication of extended narratives in high profile peer reviewed journals. This cultural bias must be at least partially reversed before we can realise the opportunities that technology affords us. This involves advocacy work, policy development, the addressing of incentives for researchers and above all the slow and arduous process of returning the research culture to one which takes responsibility for the return on the public investment, including economic, health, social, education, and research returns and one that takes responsibility for effective communication of research outputs.

4. How do/should new media, including the blogosphere, change how scientists conduct and communicate their research?

New media (not really new any more and increasingly part of the mainstream) democratise access to communications and increase the pace of communication. This is not entirely a good thing and en masse the quality of the discourse is not always high. High quality depends on the good will, expertise, and experience of those taking part.There is a vast quantity of high quality, rapid response discourse that occurs around research on the web today even if it occurs in many places. The most effective means of determining whether a recent high profile communication stands up to criticism is to turn to discussion on blogs and news sites, not to wait months for a possible technical criticism to appear in a journal. In many ways this is nothing new, it is return to the traditional approaches of communication seen at the birth of the Royal Society itself of direct and immediate communication between researchers by the most efficient means possible; letters in the 17C and the web today.

Alongside the potential for more effective communication of researchers with each other there is also an enormous potential for more effective engagement with the wider community, not merely through “news and views” pieces but through active conversation, and indeed active contributions from outside the academy. A group of computer consultants are working to contribute their expertise in software development to improving legacy climate science software. This is a real contribution to the research effort. Equally the right question at the right time may come from an unexpected source but lead to new insights. We need to be open to this.

At the same time there is a technical deficiency in the current web and that is the management of the sheer quantity of potential connections that can be made. Our most valuable resource in research is expert attention. This attention may come from inside or outside the academy but it is a resource that needs to be efficiently directed to where it can have the most impact. This will include the necessary development of mechanisms that assist in choosing which potential contacts and information to follow up. These are currently in their infancy. Their development is in any case a necessity to deal with the explosion of traditional information sources.

5. What additional challenges are there in making data usable by scientists in the same field, scientists in other fields, ‘citizen scientists’ and the general public?

Effective sharing of data and indeed most research outputs remains a significant challenge. The problem is two-fold, first of ensuring sufficient contextual information that an expert can understand the potential uses of the research output. Secondly the placing of that contextual information in a narrative that is understandable to the widest possible range of users. These are both significant challenges that are being tackled by a large number of skilled people. Progress is being made but a great deal of work remains in developing the tools, techniques, and processes that will enable the cost effective sharing of research outputs.

A key point however is that in a world where publication is extremely cheap then simply releasing whatever outputs exist in their current form can still have a positive effect. Firstly where the cost of release is effectively zero even if there is only a small chance of those data being discovered and re-used this will still lead to positive outcomes in aggregate. Secondly the presence of this underexploited resource of released, but insufficiently marked up and contextualised, data will drive the development of real systems that will make them more useful.

6 a) What might be the benefits of more widespread sharing of data for the productivity and efficiency of scientific research?

Fundamentally more efficient, more effective, and more publicly engaging research. Less repetition and needless rediscovery of negative results and ideally more effective replication and critiquing of positive results are enabled by more widespread data sharing. As noted above another important outcome is that even suboptimal sharing will help to drive the development of tools that will help to optimise the effective release of data.

6 b) What might be the benefits of more widespread sharing of data for new sorts of science?

The widespread sharing of data has historically always lead to entirely new forms of science. The modern science of crystallography is based largely on the availability of crystal structures, bioinformatics would simply not exist without genbank, the PDB, and other biological databases and the astronomy of today would be unrecognizable to someone whose career ended prior to the availability of the Sloan Digital Sky Survey. Citizen science projects of the type of Galaxy Zoo, Fold-IT and many others are inconceivable without the data to support them. Extrapolating from this evidence provides an exciting view of the possibilities. Indeed one which it would be negligent not to exploit.

6 c) What might be the benefits of more widespread sharing of data for public policy?

Policy making that is supported by more effective evidence is something that appeals to most scientists. Of course public policy making is never that simple. Nonetheless it is hard to see how a more effective and comprehensive evidence base could fail to support better evidence based policy making. Indeed it is to be hoped that a wide evidence base, and the contradictions it will necessarily contain, could lead to a more sophisticated understanding of the scope and critique of evidence sources.

6 d) What might be the benefits of more widespread sharing of data for other social benefits?

The potential for wider public involvement in science is a major potential benefit. As in e) above a deeper understanding of how to treat and parse evidence and data throughout society can only be positive.

6 e) What might be the benefits of more widespread sharing of data for innovation and economic growth?

Every study of the release of government data has shown that it leads to a nett economic benefit. This is true even when such data has traditionally been charged for. The national economy benefits to a much greater extent than any potential loss of revenue. While this is not necessarily sufficient incentive for private investors to release data in this case of public investment the object is to maximise national ROI. Therefore release in a fully open form is the rational economic approach.

The costs of lack of acces to publicly funded research outputs by SMEs is well established. Improved access will remove the barriers that currently stifle innovation and economic growth.

6 f) What might be the benefits of more widespread sharing of data for public trust in the processes of science?

There is both a negative and a positive side to this question. On the positive greater transparency, more potential for direct involvement, and a greater understanding of the process by which research proceeds will lead to greater public confidence. On the negative, doing nothing is simply not an option. Recent events have shown not so much that the public has lost confidence in science and scientists but that there is deep shock at the lack of transparency and the lack of availability of data.

If the research community does not wish to be perceived in the same way as MPs and other recent targets of public derision then we need to move rapidly to improve the degree of transparency and accessibility of the outputs of public research.

7. How should concerns about privacy, security and intellectual property be balanced against the proposed benefits of openness?

There is little evidence that the protection of IP supports a nett increase on the return on the public investment in research. While there may be cases where it is locally optimal to pursue IP protection to exploit research outputs and maximise ROI this is not generally the case. The presumption that everything should be patented is both draining resources and stifling British research. There should always be an avenue for taking this route to exploitation but there should be a presumption of open communication of research outputs and the need for IP protection should be justified on a case by case basis. It should be unacceptable for the pursuit of IP protection to damage the communication and downstream exploitation of research.

Privacy issues and concerns around the personal security of researchers have been discussed above. National security issues will in many cases fall under a justifiable exception to the presumption of openness although it is clear that this needs care and probably oversight to retain public confidence.

8. What should be expected and/or required of scientists (in companies, universities or elsewhere), research funders, regulators, scientific publishers, research institutions, international organisations and other bodies?

British research could benefit from a statement of values, something that has the cultural significance of the Haldane principle (although perhaps better understood) or the Hippocratic oath. A shared cultural statement that captures a commitment to efficiently discharging the public trust invested in us, to open processes as a default, and to specific approaches where appropriate would act as a strong centre around which policy and tools could be developed. Leadership is crucial here in setting values and embedding these within our culture. Organisations such as the Royal Society have an important role to play.

Researchers and the research community need to take these responsibilities on ourselves in a serious and considered manner. Funders and regulators need to provide a policy framework, and where appropriate community sanctions for transgression of important principles. Research institutions are for the most part tied into current incentive systems that are tightly coupled to funding arrangements and have limited freedom of movement. Nonetheless a serious consideration of the ROI of technology transfer arrangements and of how non-traditional outputs, including data, contribute to the work of the institution and its standing are required. In the current economic climate successful institutions will diversify in their approach. Those that do not are unlikely to survive in their current form.

Other comments

This is not the first time that the research community has faced this issue. Indeed it is not even the first time the Royal Society has played a central role. Several hundred years ago it was a challenge to persuade researchers to share information at all. Results were hidden. Sharing was partial, only within tight circles, and usually limited in scope. The precursors of the Royal Society played a key role in persuading the community that effective sharing of their research outputs would improve research. Many of the same concerns were raised; concerns about the misuse of those outputs, concerns about others stealing ideas, concerns about personal prestige and the embarrassment potential of getting things wrong.

The development of journals and the development of a values system that demanded that results be made public took time, it took leadership, and with the technology of the day the best possible system was developed over an extended period. With a new technology now available we face the same issues and challenges. It is to be hoped that we tackle those challenges and opportunities with the same sense of purpose.

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(S)low impact research and the importance of open in maximising re-use

Open
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This is an edited version of the text that I spoke from at the Altmetrics Workshop in Koblenz in June. There is also an audio recording of the talk I gave available as well as the submitted abstract for the workshop.

I developed an interest in research evaluation as an advocate of open research process. It is clear that researchers are not going to change themselves so someone is going to have to change them and it is funders who wield the biggest stick. The only question, I thought,  was how to persuade them to use it

Of course it’s not that simple. It turns out that funders are highly constrained as well. They can lead from the front but not too far out in front if they want to retain the confidence of their community. And the actual decision making processes remain dominated by senior researchers. Successful senior researchers with little interest in rocking the boat too much.

The thing you realize as you dig deeper into this as that the key lies in finding motivations that work across the interests of different stakeholders. The challenge lies in finding the shared objectives. What it is that unites both researchers and funders, as well as government and the wider community. So what can we find that is shared?

I’d like to suggest that one answer to that is Impact. The research community as a whole has stake in convincing government that research funding is well invested. Government also has a stake in understanding how to maximize the return on its investment. Researchers do want to make a difference, even if that difference is a long way off. You need a scattergun approach to get the big results, but that means supporting a diverse range of research in the knowledge that some of it will go nowhere but some of it will pay off.

Impact has a bad name but if we step aside from the gut reactions and look at what we actually want out of research then we start to see a need to raise some challenging questions. What is research for?  What is its role in our society really? What outcomes would we like to see from it, and over what timeframes? What would we want to evaluate those outcomes against? Economic impact yes, as well as social, health, policy, and environmental impact. This is called the ‘triple bottom line’ in Australia. But alongside these there is also research impact.

All these have something in common. Re-use. What we mean by impact is re-use. Re-use in industry, re-use in public health and education, re-use in policy development and enactment, and re-use in research.

And this frame brings some interesting possibilities. We can measure some types of re-use. Citation, retweets, re-use of data or materials, or methods or software. We can think about gathering evidence of other types of re-use, and of improving the systems that acknowledge re-use. If we can expand the culture of citation and linking to new objects and new forms of re-use, particularly for objects on the web, where there is some good low hanging fruit, then we can gather a much stronger and more comprehensive evidence base to support all sorts of decision making.

There are also problems and challenges. The same ones that any social metrics bring. Concentration and community effects, the Matthew effect of the rich getting richer. We need to understand these feedback effects much better and I am very glad there are significant projects addressing this.

But there is also something more compelling for me in this view. It let’s us reframe the debate around basic research. The argument goes we need basic research to support future breakthroughs. We know neither what we will need nor where it will come from. But we know that its very hard to predict – that’s why we support curiosity driven research as an important part of the portfolio of projects. Yet the dissemination of this investment in the future is amongst the weakest in our research portfolio. At best a few papers are released then hidden in journals that most of the world has no access to and in many cases without the data, or other products either being indexed or even made available. And this lack of effective dissemination is often because the work is perceived as low, or perhaps better, slow impact.

We may not be able to demonstrate or to measure significant re-use of the outputs of this research for many years. But what we can do is focus on optimizing the capacity, the potential, for future exploitation. Where we can’t demonstrate re-use and impact we should demand that researchers demonstrate that they have optimized their outputs to enable future re-use and impact.

And this brings me full circle. My belief is that the way to ensure the best opportunities for downstream re-use, over all timeframes, is that the research outputs are open, in the Budapest Declaration sense. But we don’t have to take my word for it, we can gather evidence. Making everything naively open will not always be the best answer, but we need to understand where that is and how best to deal with it. We need to gather evidence of re-use over time to understand how to optimize our outputs to maximize their impact.

But if we choose to value re-use, to value the downstream impact that our research or have, or could have, then we can make this debate not about politics or ideology but how about how best to take the public investment in research and to invest it for the outcomes that we need as a society.

 

 

 

 

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Wears the passion? Yes it does rather…

Leukemia cells.
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Quite some months ago an article in Cancer Therapy and Biology by Scott Kern of Johns Hopkins kicked up an almighty online stink. The article entitled “Where’s the passion” bemoaned the lack of hard core dedication amongst the younger researchers that the author saw around him…starting with:

It is Sunday afternoon on a sunny, spring day.

I’m walking the halls—all of them—in a modern $59 million building dedicated to cancer research. A half hour ago, I completed a stroll through another, identical building. You see, I’m doing a survey. And the two buildings are largely empty.

The point being that if they really cared, those young researchers would be there day in-day out working their hearts out to get to the key finding. At one level this is risible, expecting everyone to work 24×7 is not a good or efficient way to get results. Furthermore you have to wonder why these younger researchers have “lost their passion”. Why doesn’t the environment create that naturally, what messages are the tenured staff sending through their actions. But I’d be being dishonest if there wasn’t a twinge of sympathy for me as well. Anyone who’s run a group has had that thought that the back of their mind; “if only they’d work harder/smarter/longer we’d be that much further ahead…”.

But all of that has been covered by others. What jumped out of the piece for me at the time were some other passages, ones that really got me angry.

When the mothers of the Mothers March collected dimes, they KNEW that teams, at that minute, were performing difficult, even dangerous, research in the supported labs. Modern cancer advocates walk for a cure down the city streets on Saturday mornings across the land. They can comfortably know that, uh…let’s see here…, some of their donations might receive similar passion. Anyway, the effort should be up to full force by 10 a.m. or so the following Monday.

[…]

During the survey period, off-site laypersons offer comments on my observations. “Don’t the people with families have a right to a career in cancer research also?” I choose not to answer. How would I? Do the patients have a duty to provide this “right”, perhaps by entering suspended animation?

Now these are all worthy statements. We’d all like to see faster development of cures and I’ve no doubt that the people out there pounding the streets are driven to do all they can to see those cures advance. But is the real problem here whether the postdocs are here on a Sunday afternoon or are there things we could do to advance this? Maybe there are other parts of the research enterprise that could be made more efficient…like I don’t know making the results of research widely available and ensuring that others are in the best position possible to build on their results?

It would be easy to pick on Kern’s record on publishing open access papers. Has he made all the efforts that would enable patients and doctors to make the best decisions they can on the basis of his research? His lab generates cell lines that can support further research. Are those freely available for others to use and build on? But to pick on Kern personally is to completely miss the point.

No, the problem is that this is systemic. Researchers across the board seem to have no interest whatsoever in looking closely at how we might deliver outcomes faster. No-one is prepared to think about how the system could be improved so as to deliver more because everyone is too focussed on climbing up the greasy pole; writing the next big paper and landing the next big grant. What is worse is that it is precisely in those areas where there is most public effort to raise money, where there is a desperate need, that attitudes towards making research outputs available are at their worse.

What made me absolutely incandescent about this piece was a small piece of data that some of use have known about for a while but has only just been published. Heather Piwowar, who has done a lot of work on how and where people share, took a close look at the sharing of microarray data. What kind of things are correlated with data sharing. The paper bears close reading (Full disclosure: I was the academic editor for PLoS ONE on this paper) but one thing has stood out from me as shocking since the first time I heard Heather discuss it: microarray data linked to studies of cancer is systematically less shared.

This is not an isolated case. Across the board there are serious questions to be asked about why it seems so difficult to get the data from studies that relate to cancer. I don’t want to speculate on the reasons because whatever they are, they are unnacceptable. I know I’ve recommended this video of Josh Sommer speaking many times before, but watch it again. Then read Heather’s paper. And then decide what you think we need to do about it. Because this can not go on.

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How to waste public money in one easy step…

Oleic Acid
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Peter Murray-Rust has sparked off another round in the discussion of the value that publishers bring to the scholarly communication game and told a particular story of woe and pain inflicted by the incumbent publishers. On the day he posted that I had my own experience of just how inefficient and ineffective our communication systems are by wasting the better part of the day trying to find some information. I thought it might be fun to encourage people to post their own stories of problems and frustrations with access to the literature and the downstream issues that creates, so here is mine.

I am by no means a skilled organic chemist but I’ve done a bit of synthesis in my time and I certainly know enough to be able to read synthetic chemistry papers and decide whether a particular synthesis is accessible. So on this particular day I was interested in deciding whether it was easy or difficult to make deuterated mono-olein. This molecule can be made by connecting glycerol to oleic acid. Glycerol is cheap and I should have in my hands some deuterated oleic acid in the next month or so. The chemistry for connecting acids to alcohols is straightforward, I’ve even done it myself, but this is a slightly special case. Firstly the standard methods tend to be wasteful of the acid, which in my case is the expensive bit. The second issue is that glycerol has three alcohol groups. I only want to modify one, leaving the other two unchanged, so it is important to find a method that gives me mostly what I want and only a little of what I don’t.

So the question for me is: is there a high yielding reaction that will give me mostly what I want, while wasting as little as possible of the oleic acid? And if there is a good technique is it accessible given the equipment I have in the lab? Simple question, quick trip to Google Scholar, to find reams of likely looking papers, not one of which I had full text access to. The abstracts are nearly useless in this case because I need to know details of yields and methodology so I had several hundred papers, and no means of figuring out which might be worth an inter-library loan. I spent hours trying to parse the abstracts to figure out which were the most promising and in the end I broke…I asked someone to email me a couple of pdfs because I knew they had access. Bear in mind what I wanted to do was spend a quick 30 minutes or so to decide whether this was pursuing in detail. What is took was about three hours, which at full economic cost of my time comes to about £250. That’s about £200 of UK taxpayers money down the toilet because, on the site of the UKs premiere physical and biological research facilities I don’t have access to those papers. Yes I could have asked someone else to look but that would have taken up their time.

But you know what’s really infuriating. I shouldn’t even have been looking at the papers at all when I’m doing my initial search. What I should have been able to do was ask the question:

Show me all syntheses of mono-olein ranked first by purity of the product and secondly by the yield with respect to oleic acid.

There should be a database where I can get this information. In fact there is. But we can’t afford access to the ACS’ information services here. These are incredibly expensive because it used to be necessary for this information to be culled from papers by hand. But today that’s not necessary. It could be done cheaply and rapidly. In fact I’ve seen it done cheaply and rapidly by tools developed in Peter’s group that get around ~95% accuracy and ~80% recall over synthetic organic chemistry. Those are hit rates that would have solved my problem easily and effectively.

Unfortunately despite the fact those tools exist, despite the fact that they could be deployed easily and cheaply, and that they could save researchers vast amounts of time research is being held back by a lack of access to the literature, and where there is access by contracts that prevent us collating, aggregating, and analysing our own work. The public pays for the research to be done, the public pays for researchers to be able to read it, and in most cases the public has to pay again if they should want to read it. But what is most infuriating is the way the public pays yet again when I and a million other scientists waste our time, the public’s time, because the tools that exist and work cannot be deployed.

How many researchers in the UK or world wide are losing hours or even days every week because of these inefficiencies. How many new tools or techniques are never developed because they can’t legally be deployed? And how many hundreds of millions of dollars of public money does that add up to?

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A new sustainability model: Major funders to support OA journal

Open Access logo and text
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”The Howard Hughes Medical Institute, the Max Planck Society and the Wellcome Trust announced today that they are to support a new, top-tier, open access journal for biomedical and life sciences research. The three organisations aim to establish a new journal that will attract and define the very best research publications from across these fields. All research published in the journal will make highly significant contributions that will extend the boundaries of scientific knowledge.” [Press Release]

It has been clear for some time that the slowness of the adoption of open access publication models by researchers is in large part down to terror that we have of stepping out of line and publishing in the ‘wrong’ journals. More radical approaches to publication will clearly lag even further behind while this inherent conservatism is dominant. Publishers like PLoS and BMC have tackled this head on by aiming to create prestigous journals but the top of the pile has remained the traditional clutch of Nature, Science, and Cell.

The incumbent publishers have simultaneously been able to sit back due to a lack of apparent demand from researchers. As the demand from funders has increased they have held back, complaining the business models to support Open Access publication are not clear. I’ve always found the ‘business model’ argument slightly specious. Sustainability is important but scholarly publishing has never really had a viable business model, it has had a subsidy from funders. Part of the problem has been the multiple layers and channels that subsidy has gone through but essentially funders, through indirect funding of academic libraries, have been footing the bill.

Some funders, and the Wellcome Trust has lead on this, have demanded that their researchers make their outputs accessible while simultaneously requiring publishers comply with their requirements on access and re-use rights. But progress has been slow, particularly in opening up what is perceived as the top of the market. Despite major inroads made by PLoS Biology and PLos Medicine those journals perceived as the most prestigious have remain resolutely closed.

Government funders are mostly constrained in their freedom to act but the Wellcome Trust, HHMI, and Max Planck Society have the independence to take the logical step. They are already paying for publication, why not actively support the formation of a new journal, properly open access, and at the same time lend the prestige that their names can bring?

This will send a very strong message, both to researchers and publishers, about what these funders value, and where they see value for money. It is difficult to imagine this will not lead to a seismic shift in the publishing landscape, at least from a political and financial perspective. I don’t believe this journal will be as technically radical as I would like, but it is unlikely it could be while achieving the aims that it has. I do hope the platform it is built on enables innovation both in terms of what is published and the process by which it is selected.

But in a sense that doesn’t matter. This venture can remain incredibly conservative and still have a huge impact on taking the research communication space forward. What it means is that three of the worlds key funders have made an unequivocal statement that they want to see Open Access, full open access on publication without restrictions on commercial use, or text-mining, or re-use in any form, across the whole of the publication spectrum. And if they don’t get it from the incumbent publishers they’re prepared to make it happen themselves.

Full Disclosure: I was present at a meeting at Janelia Farm in 2010 where the proposal to form a journal was discussed by members of the Wellcome, HHMI, and MPG communities.

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Cultural Infrastructure: Or why the ‘Big Society’ and post-pub peer review will fail in their current forms

As a wooly headed social liberal with a strong belief in the power of the internet to connect people it is probably natural for to me have some sympathy for the ideas behind the “Big Society” espoused by the UK Prime Minister. The concept here is that many of the support roles traditionally taken by or at least mediated by government can be more effectively and efficiently provided in the community by direct interactions between members of the public. The potential of the web to inform, support critical debate, and above all connect people is enormous. There is a real potential to create markets for exchange of resources, primarily time and money, between those who have and those who need, while naturally tailoring these services to what is really needed at a local level. The efficiency of markets can be combined with a socialist vision of community responsibility combining the best of both political visions and jettisoning the failures of each.

There is of course trouble in utopia. What actually happens as government withdraws as the guarantor or service provision and hands over the reins to local community groups is a mixture of triumph and farce. A few communities are strong enough to engage their members and deliver services competently and effectively but these are usually the wealthy areas that are already well served. More widely a mixture of incompetence (in a purely technical sense) and a simple lack of people prepared or able to carry this load leads to those areas with poor service provision to receive less. There are groups that will step in to take up the slack but they tend to fall into two groups, either wealthy benefactors or groups with a specific agendas, sometimes with extreme viewpoints. Regardless of motivation, these groups come into communities from the outside. In that sense they are just like government, only with less regulation, probably less experience of service provision, and above all, not part of the community.

Building the technical infrastructure to support the Big Society is easy. But just because technical infrastructure is there doesn’t mean that community members have the time, the resources, or most importantly the motivation to use it. The cultural infrastructure, the assumptions that would motivate people to get involved, the support for people to take time off work (or work shorter hours) so as to spend time in the community, and the trust in systems for pooling the necessary resources simply doesn’t exist.  This cultural infrastructure will take a generation to build and will require a focused effort from government, communities, and industry. And it will require money.

So what does this have to do with peer review? Researchers contribute to traditional peer review because it something we expect to do. It is often claimed that we do peer review because we expect the favour of high quality review back in return. I just don’t buy it. If this were true we’d find it much easier to get people to share data (because they’d get data shared back in the future) or materials (similarly). None of this happens because we don’t have a cultural infrastructure that supports and values these behaviours. We do however have an existing cultural infrastructure which supports the need for traditional peer review to be done.

Naturally it follows that those things that aren’t seen as an assumed part of the job don’t get done. Post-publication peer review, despite its potential is one of these things. Data and process sharing are others. Data sharing plans and funder mandates are really just scaffolding. They support the structure that we can see that we want but they aren’t the foundations and pillars that will support a robust community. The moral debate on these issues has fundamentally been won, it is merely a question of implementation and managing the transition. But we now need to look beyond the technical problems and the application of big sticks to understand how we can build the cultural infrastructure that supports a world where reproducibility, effective communication of all research outputs, and ongoing critique of the outputs of others are assumed to be part of the job.

 

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Evidence to the European Commission Hearing on Access to Scientific Information

European Commission
Image by tiseb via Flickr

On Monday 30 May I gave evidence at a European Commission hearing on Access to Scientific Information. This is the text that I spoke from. Just to re-inforce my usual disclaimer I was not speaking on behalf of my employer but as an independent researcher.

We live in a world where there is more information available at the tips of our fingers than even existed 10 or 20 years ago. Much of what we use to evaluate research today was built in a world where the underlying data was difficult and expensive to collect. Companies were built, massive data sets collected and curated and our whole edifice of reputation building and assessment grew up based on what was available. As the systems became more sophisticated new measures became incorporated but the fundamental basis of our systems weren’t questioned. Somewhere along the line we forgot that we had never actually been measuring what mattered, just what we could.

Today we can track, measure, and aggregate much more, and much more detailed information. It’s not just that we can ask how much a dataset is being downloaded but that we can ask who is downloading it, academics or school children, and more, we can ask who was the person who wrote the blog post or posted it to Facebook that led to that spike in downloads.

This is technically feasible today. And make no mistake it will happen. And this provides enormous potential benefits. But in my view it should also give us pause. It gives us a real opportunity to ask why it is that we are measuring these things. The richness of the answers available to us means we should spend some time working out what the right questions are.

There are many reasons for evaluating research and researchers. I want to touch on just three. The first is researchers evaluating themselves against their peers. While this is informed by data it will always be highly subjective and vary discipline by discipline. It is worthy of study but not I think something that is subject to policy interventions.

The second area is in attempting to make objective decisions about the distribution of research resources. This is clearly a contentious issue. Formulaic approaches can be made more transparent and less easy to legal attack but are relatively easy to game. A deeper challenge is that by their nature all metrics are backwards looking. They can only report on things that have happened. Indicators are generally lagging (true of most of the measures in wide current use) but what we need are leading indicators. It is likely that human opinion will continue to beat naive metrics in this area for some time.

Finally there is the question of using evidence to design the optimal architecture for the whole research enterprise. Evidence based policy making in research policy has historically been sadly lacking. We have an opportunity to change that through building a strong, transparent, and useful evidence base but only if we simultaneously work to understand the social context of that evidence. How does collecting information change researcher behavior? How are these measures gamed? What outcomes are important? How does all of this differ cross national and disciplinary boundaries, or amongst age groups?

It is my belief, shared with many that will speak today, that open approaches will lead to faster, more efficient, and more cost effective research. Other groups and organizations have concerns around business models, quality assurance, and sustainability of these newer approaches. We don’t need to argue about this in a vacuum. We can collect evidence, debate what the most important measures are, and come to an informed and nuanced inclusion based on real data and real understanding.

To do this we need to take action in a number areas:

1. We need data on evaluation and we need to able to share it.

Research organizations must be encouraged to maintain records of the downstream usage of their published artifacts. Where there is a mandate for data availability this should include mandated public access to data on usage.

The commission and national funders should clearly articulate that that provision of usage data is a key service for publishers of articles, data, and software to provide, and that where a direct payment is made for publication provision for such data should be included. Such data must be technically and legally reusable.

The commission and national funders should support work towards standardizing vocabularies and formats for this data as well critiquing it’s quality and usefulness. This work will necessarily be diverse with disciplinary, national, and object type differences but there is value in coordinating actions. At a recent workshop where funders, service providers, developers and researchers convened we made significant progress towards agreeing routes towards standardization of the vocabularies to describe research outputs.

2. We need to integrate our systems of recognition and attribution into the way the web works through identifying research objects and linking them together in standard ways.

The effectiveness of the web lies in its framework of addressable items connected by links. Researchers have a strong culture of making links and recognizing contributions through attribution and citation of scholarly articles and books but this has only recently being surfaced in a way that consumer web tools can view and use. And practice is patchy and inconsistent for new forms of scholarly output such as data, software and online writing.

The commission should support efforts to open up scholarly bibliography to the mechanics of the web through policy and technical actions. The recent Hargreaves report explicitly notes limitations on text mining and information retrieval as an area where the EU should act to modernize copyright law.

The commission should act to support efforts to develop and gain wide community support for unique identifiers for research outputs, and for researchers. Again these efforts are diverse and it will be community adoption which determines their usefulness but coordination and communication actions will be useful here. Where there is critical mass, such as may be the case for ORCID and DataCite, this crucial cultural infrastructure should merit direct support.

Similarly the commission should support actions to develop standardized expressions of links, through developing citation and linking standards for scholarly material. Again the work of DataCite, CoData, Dryad and other initiatives as well as technical standards development is crucial here.

3. Finally we must closely study the context in which our data collection and indicator assessment develops. Social systems cannot be measured without perturbing them and we can do no good with data or evidence if we do not understand and respect both the systems being measured and the effects of implementing any policy decision.

We need to understand the measures we might develop, what forms of evaluation they are useful for and how change can be effected where appropriate. This will require significant work as well as an appreciation of the close coupling of the whole system.
We have a generational opportunity to make our research infrastructure better through effective evaluation and evidence based policy making and architecture development. But we will squander this opportunity if we either take a utopian view of what might technically feasible, or fail to act for a fear of a dystopian future. The way to approach this is through a careful, timely, transparent and thoughtful approach to understanding ourselves and the system we work within.

The commission should act to ensure that current nascent efforts work efficiently towards delivering the technical, cultural, and legal infrastructure that will support an informed debate through a combination of communication, coordination, and policy actions.

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Michael Nielsen, the credit economy, and open science

No credit cards please.......

Michael Nielsen is a good friend as well as being an inspiration to many of us in the Open Science community. I’ve been privileged to watch and in a small way to contribute to the development of his arguments over the years and I found the distillation of these years of effort into the talk that he recently gave at TEDxWaterloo entirely successful. Here is a widely accesible and entertaining talk that really pins down the arguments, the history, the successes and the failures of recent efforts to open up science practice.

Professional scientific credit is the central issue

I’ve been involved in many discussions around why the potential of opening up research practice hasn’t lead to wider adoption of these approaches. The answer is simple, and as Michael says very clearly in the opening section of the talk, the problem is that innovative approaches to doing science are not going to be adopted while those that use them don’t get conventional scientific credit. I therefore have to admit to being somewhat nonplussed by GrrlScientist’s assessment of the talk that “Dr Nielsen has missed — he certainly has not emphasised — the most obvious reason why the Open Science movement will not work: credit.”

For me, the entire talk is about credit. He frames the discussion of why the Qwiki wasn’t a huge success, compared to the Polymath project, in terms of the production of conventional papers, he discusses the transition from Galileo’s anagrams to the development of the scientific journal in terms of ensuring priority and credit. Finally he explicitly asks the non-scientist members of the audience to do something that even more closely speaks to the issue of credit, to ask their scientist friends and family what they are doing to make their results more widely available. Remember this talk is aimed at a wider audience, the TEDxWaterloo attendees and the larger audience for the video online (nearly 6,000 when I wrote this post). What happens when taxpayers start asking their friends, their family, and their legislative representatives how scientific results are being made available? You’d better believe that this has an affect on the credit economy.

Do we just need the celebrities to back us?

Grrl suggests that the answer to pushing the agenda forward is to enlist Nobelists to drive projects in the same way that Tim Gowers pushed the Polymath project. While I can see the logic and there is certainly value in moral support from successful scientists we already have a lot of this. Sulston, Varmus, Michael and Jon Eisen, and indeed Michael himself just to name a few are already pushing this agenda. But moral support and single projects are not enough. What we need to do is hack the underlying credit economy, provide proper citations for data and software, exploit the obsession with impact factors.

The key to success in my view is a pincer movement. First, showing that more (if not always completely) open approaches can outcompete closed approaches on traditional assessment measures, something demonstrated successfully by Galaxy Zoo, the Alzeimers Disease Neuroimaging Initiative, and the Polymath Projects. Secondly changing assessment policy and culture itself, both explicitly by changing the measures by which researchers are ranked, and implicitly by raising the public expectation that research should be open.

The pendulum is swinging and we’re pushing it just about every which-way we can

I guess what really gets my back up is that Grrl sets off with the statement that “Open Science will never work” but then does on to put her finger on exactly the point where we can push to make it work. Professional and public credit is absolutely at the centre of the challenge. Michael’s talk is part of a concerted, even quite carefully coordinated, campaign to tackle this issue at a wide range of levels. Michael’s tour of his talk, funded by the Open Society Institute seeks to raise awareness. My recent focus on research assessment (and a project also funded by OSI) is tackling the same problem from another angle. It is not entirely a coincidence that I’m writing this in a hotel room in Washington DC and it is not at all accidental that I’m very interested in progress towards widely accepted researcher identifiers. The development of Open Research Computation is a deliberate attempt to build a journal that exploits the nature of journal rankings to make software development more highly valued. 

All of these are part of a push to hack, reconfigure, and re-assess the outputs and outcomes that researchers get credit for and the the outputs and outcomes that are valued by tenure committees and grant panels. And from where I stand we’re making enough progress that Grrl’s argument seems a bit tired and outdated. I’m seeing enough examples of people getting credit and reward for being open and simply doing and enabling better science as a result that I’m confident the pendulum is shifting. Would I advise a young scientist that being open will lead to certain glory? No, it’s far from certain, but you need to distinguish yourself from the crowd one way or another and this is one way to do it. It’s still high risk but show me something in a research career that is low risk and I’ll show something that isn’t worth doing.

What can you do?

If you believe that a move towards more open research practice is a good thing then what can you do to make this happen? Well follow what Michael says, give credit to those who share, explicitly acknowledge the support and ideas you get from others. Ask researchers how they go about ensuring that their research is widely available and above all used. The thing is, in the end changing the credit economy itself isn’t enough, we actually have to change the culture that underlies that economy. This is hard but it is done by embedding the issues and assumptions in the everyday discourse about research. “How useable are your research outputs really?” is the question that gets to the heart of the problem. “How easily can people access, re-use, and improve on your research? And how open are you to getting the benefit of other people’s contribution?” are the questions that I hope will become embedded in the assumptions around how we do research. You can make that happen by asking them.

 

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Best practice in Science and Coding. Holding up a mirror.

The following is the text from which I spoke today at the .Astronomy conference. I think there is some video available on the .Astronomy UStream account and I also have audio which I will put up somewhere soon.

There’s a funny thing about the science and coding communities. Each seems to think that the other has all the answers. Maybe the grass is just greener…For many years as an experimental scientist I looked jealously at both computational scientists and coders in general.  Wouldn’t it be so much less stressfull, I naively thought, to have systems that would do what they were told, to be easily able to re-run experiments and to be able to rely on getting the same answer.  Above all, I thought, imagine the convenience of just being able to take someone else’s work and being able to easily and quickly apply it to my own problems.

There is something of a mythology around code, and perhaps more so around open source, that it can be relied on, that there is a toolkit out there already for every problem. That there is a Ruby Gem, or an R library for every problem, or most memorably that I can sit on a python command line and just demand antigravity by importing it. Sometimes these things are true, but I’m guessing that everyone has experience of it not being true. Of the python library that looks as though it is using dictionaries but is actually using some bizarre custom data type, the badly documented ruby gem, or the perl…well, just the perl really. The mythology doesn’t quite live up to the hype. Or at least not as often as we might like.

But if us experimental scientists have an overoptimistic view of how repeatable and reliable computational tools are then computer scientists have an equally unrealistic view of how experimental science works. Greg Wilson, one of the great innovators in computer science education once said, while criticizing documentation and testing standards of scientific code “An experimental scientist would never get away with not providing their data, not providing their working. Experimental science is expected to be reproducible from the detailed methodology given….”….data provided…detailed methodology…reproducible…this doesn’t really sound like the experimental science literature that I know.

Ted Pedersen in an article with the wonderful title “Empiricism is not a matter of faith” excoriates computational linguistics by holding it up to what he sees as the much higher standards of reproducibility and detailed description of methodology in experimental science. Yet I’ve never been able to reproduce an experiment based only on a paper in my life.

What is interesting about both of these view points is that we are projecting our very real desire to raise standards against a mythology of someone else’s practice. There seems to be a need to view some other community’s practice as the example rather than finding examples within our own. This is odd because it is precisely the best examples, within each community, that inspire the other. There are experimental scientists that give detailed step by step instructions to enable others to repeat their work, who make the details of the protocols available online, and who work within their groups to the highest standards of reproducibility that are possible in the physical world.  Equally there are open source libraries and programmes with documentation that are both succinct and detailed, that just works when you import the library, that is fully tested and comes with everything you need to make sure it will work with your systems. Or that breaks in an informative way, making it clear what you need to do with your own code to get it working.

If we think about what makes science work; effective communication, continual testing and refinement, public criticism of claims and ideas; the things that make up good science, and mean that I had a laptop to write this talk on this morning, that meant the train and taxi I caught actually run, that, more seriously a significant portion of the people in this room did not in fact die in childhood. If we look at these things then we see a very strong correspondence with good practice in software development. High quality and useful documentation is key to good software libraries.  You can be as open source as you like but if no-one can understand your code they’re not going to use it. Controls, positive and negative, statistical and analytical are basically unit tests. Critique of any experimental result comes down to asking whether each aspect of the experiment is behaving the way it should, has each process been tested that a standard input gives the expected output. In a very real sense experiment is an API layer we use to interact with the underlying principles of nature.

So this is a nice analogy, but I think we can take this further, in fact I think that code and experiment are actually linked at a deeper level. Both are an instantiation of process that take inputs and generate outputs. These are (to a first approximation – good enough for this discussion) deterministic in any given instance. But they are meaningless without context. Useless without the meaning that documentation and testing provide.

Let me give you an example. Ana Nelson has written a lovely documentation tool called Dexy. This builds on concepts of literate programming in a beautifully elegant and simple way. Take a look for the details but in essence it enables you to directly incorporate the results of arbitrary running code into your documentation. As you document what your code does you provide examples, parts of the process that are actively running, and testing the code as you go. If you break the method you break your documentation. It is also not an accident that if you are thinking about documentation as you build your code then it helps to create good modular structures that are easy to understand and therefore both easy to use and easy to communicate. They may be a little more work to write but the value you are creating by thinking about the documentation up front means you are motivated to capture this up front. Design by contract and test driven development are tough, Documentation Driven Development can really help drive good process.

Too often when we write a scientific paper it’s the last part of the process. We fabricate a story that makes sense so that we can fit in the bits we want to. Now there’s nothing wrong with this. Humans are narrative processing systems, we need stories to make sense of the world. But its not the whole story. What if, as we collect and capture the events that we ultimately use to tell our story, that we also collect and structure the story of what actually happened? Of the experiments that didn’t work, of the statistical spread of good and bad results. There’s a sarcastic term in synthetic organic chemistry, the “American Yield” in which we imagine that 20 PhD students have been tasked with making a compound and the one who manages to get the highest overall yield gets to be first author. This isn’t actually a particularly useful number. Much more useful to the chemist who wants to use this prep is the spread of values, information that is generally thrown away. The difference between actually incorporating the running of the code into the documentation, and just showing one log file, cut and pasted, from when it worked well. You lose the information about when it doesn’t work.

Other tools from coding can also provide inspiration. Tools like Hudson for continuous integration. Everytime the code base is changed everything gets re-built, dependencies are tested, unit tests run, and a record of what gets broken. If you want to do X you do want to use this version of that library.  This isn’t a problem. In any large codebase things are going to get broken as changes are made, you change something, see what is broken, then go back and gradually fix those things until you’re ready to create commit to the main branch (at which point someone else has broken something…)

Science is continuous integration. This is what we do, we make changes , we check what they break, see if the dependencies still hold and if necessary go back and fix them. This is after all where the interesting science is. Or it would be if we did it properly. David Shotton and others have spoken about the question of “citation creep” or “hedging erosion” [see for example this presentation by Anita de Waard]. This is where something initially reported in one paper as a possibility, or even just a speculation gets converted into fact by a process of citation. What starts as “…it seems possible that…” can get turned into “…as we know that X causes Y (Bloggs et al, 2009)…” within 18 months or a couple of citations. Scientists are actually not very good at checking their dependencies. And they have a tendency of coming back to bite us in exactly the same way as a quick patch that wasn’t properly tested can.

Just imagine if we could do this. If everytime a new paper was added to the literature we could run a test against the rest. Check all the dependencies…if this isn’t true then all of these other papers in doubt as well…indeed if we could unit test papers would it be worth peer reviewing them? There is good evidence that pair coding works, and little evidence that traditional peer review does. What can we learn from this to make the QA processes in science and software development better?

I could multiply examples. What would an agile lab look like? What would be needed to make it work? What can successful library development communities tell us about sharing samples, and what can the best data repositories tell us about building the sites for sharing code? How can we apply the lessons of StackOverflow to a new generation of textbooks and how can we best package up descriptions of experimental protocols in a way that provides the same functionality as sharing an Amazon Machine Image.

Best practice in coding mirrors best practice in science. Documentation, testing, integration are at the core. Best practice is also a long way ahead of common practice in both science and coding. Both, perhaps are driven increasingly by a celebrity culture that is more dependent on what your outputs look like (and where they get published) than whether anyone uses them. Testing and documentation are hardly glamorous activities.

So what can we do about it? Improving practice is an arduous task. Many people are doing good work here with training programmes, tools, standards development and calls to action online and in the scientific literature. Too many people and organizations for me to call out and none of them getting the credit they deserve.

One of the things I have been involved with is to try and provide a venue, a prestigious venue, where people can present code that has been developed to the highest standards. Open Research Computation, a new Open Access journal from BioMedCentral, will publish papers that describe software for research. Our selection criteria don’t depend on how important the research problem is, but on the availability, documentation, and testing of the code. We expect the examples given in these papers to be reproducible, by which we mean that the software, the source code, the data, and the methodology are provided and described well enough that it is possible to reproduce those examples.  By applying high standards, and by working with authors to help them reach those standards we aim to provide a venue which is both useful and prestigious. Think about it, a journal that contains papers describing the most useful and useable tools and libraries is going to get a few citations and (whisper it) ought to get a pretty good impact factor. I don’t care about impact factors but I know the reality on the ground is that that those of you looking for jobs or trying to keep them do need to worry about them.

In the end, the problem with a journal, or with code, or with science is that we want everyone else to provide the best documentation, the best tested code and procedures, but its hard to justify doing it ourselves. I mean I just need something that works; yesterday. I don’t have time to write the tests in advance, think about the architecture, re-read all of that literature to check what it really said. Tools that make this easier will help, tools like Dexy and Hudson, or lab notebooks that capture what we’re doing and what we are creating, rather than what we said we would do, or what we imagine we did in retrospect.

But it’s motivation that is key here. How do you motivate people do the work up front? You can tell them that they have to of course but really these things work best when people want to make the effort. The rewards for making your work re-usable can be enormous but they are usually further down the road than the moment where you make the choice not to bother. And those rewards are less important to most people than getting to the Nature paper, or getting mentioned in Tim O’Reilly’s tweet stream.

It has to be clear that making things re-usable is the highest contribution that you can make, and for it to be rewarded accordingly.  I don’t even really care what forms of re-use are counted, re-use in research, re-use in education, in commerce, in industry, in policy development. ORC is deliberately – very deliberately – intended to hack the impact factor system by featuring highly re-usable tools that will gain lots of citations. We need more of these hacks.

I think this shift is occurring. It’s not widely know just how close UK science funding went to being slashed in the comprehensive spending review. That it wasn’t was due to a highly coherent and well organized campaign that convinced ministers and treasury that the re-use of UK research outputs generated enormous value, both economic, social and educational for the country and indeed globally. That the Sloan Digital Sky Survey was available in a form that could be re-used to support the development of something like Galaxy Zoo played a part in this. The headlong rush of governments worldwide to release their data is a massive effort to realize the potential value of the re-use of that data.

This change in focus is coming. It will no longer be enough in science to just publish. As David Willetts said in [answer to a question] in his first policy speech, “I’m very much in favour of peer review, but I worry when the only form of review is for journals”.  Government wants evidence of wider use. They call it impact, but its basically re-use. The policy changes are coming, the data sharing policies, the public engagement policies, the impact assessments. Just showing outputs will no be enough, showing that you’ve configured those outputs so that the potential for re-use is maximized will be an assumption of receiving funding.

William Gibson said the future is already here, its just unevenly distributed. They Might Be Giants asked, not quite in response, “but where’s my jetpack?”  The jetpacks, the tools, are around us and being developed if you know where to look. Best practice is unevenly distributed both in science and in software development but it’s out there if you want to go looking. The motivation to adopt it? The world around us is changing. The expectations of the people who fund us are changing. Best practice in code and in science have an awful lot in common. If you can master one you will have to tools to help you with the other. And if you have both then you’ll be well positioned to ride the wave of change as it sweeps by.


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A return to “bursty work”

Parris Island, S.C., barrage balloon (LOC)
Image by The Library of Congress via Flickr

What seems like an age ago a group of us discussed a different way of doing scientific research. One partly inspired by the modular building blocks approach of some of the best open source software projects but also by a view that there were tremendous efficiency gains to be found in enabling specialisation of researchers, groups, even institutes, while encouraging a shared technical and social infrastructure that would help people identify the right partners for the very specific tasks that they needed doing today.

“Bursty work” is a term first used by Chris Messina but introduced to the online community of scientists by Deepak Singh. At the time it seemed obvious that with enough human and financial capital that a loose network of specialist groups could do much better science, and arguably much more effective exploitation of that science, than isolated groups perpetually re-inventing the wheel.

The problem of course is that science funding is not configured that way, a problem that is that bane of any core-facility manager’s existence. Maintaining a permanent expert staff via a hand to mouth existence of short term grants is tough. Some succeed but more probably fail, and there is very little glory in this approach. Once again it is prestige that gets promotion, not effective and efficient use of resources.

But the world is changing, a few weeks ago I got a query from a commercial partner interested in whether I could solve a specific problem. This is a small “virtual company” that aims to target the small scale, but potentially high value, innovations that larger players don’t have the flexibility to handle.  Everything is outsourced, samples prepared and passed from contractor to contractor. Turns out I think we can solve their problem and it will be exciting to see this work applied. What is even more gratifying is that the company came across this work in an Open Access journal which made it easier both to assess how useful it was and whether to get in touch. In the words of my contact:

“The fact that your work was in an open access journal certainly made it easier for me to access. I guess the same google search would have found it in a different journal, but it might have required a subscription for access. In that case I would have used the free info available (corresponding authors, university addresses etc) to try and get in touch based on the abstract.”

The same problems of course remain. How do I reasonably cost this work? What is the value of being involved vs just being a contractor. And of course, where will I find the time, or the pair of hands, to get the work done. People with the right expertise don’t grow on trees, and it’s virtually impossible to get people on short contracts at the moment. Again, in the words of our collaborator:

“Bursty work” sounds a little like how [our company] is trying to operate. One problem is moving from an investment environment where investors invest in companies to one where they invest in projects. Has any work been done to identify investors who like the idea of bursty work?

Nonetheless, its exciting to me that some elements of what was beginning to seem like a pipe dream are coming to pass. It takes time for the world to catch up, but where there is a demand for innovation, and an effective market, the opportunities are there for the people who can make them work.

[It won’t escape anyone’s notice that I’ve given no details of either the project or the company. We are doing this under an NDA and as this is someone else’s project I’m not going to be difficult about it. We make progress one small step at a time]

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