Tag: academic credit

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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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The future of research communication is aggregation

Paper as aggregation
Image by cameronneylon via Flickr

“In the future everyone will be a journal editor for 15 minutes” – apologies to Andy Warhol

Suddenly it seems everyone wants to re-imagine scientific communication. From the ACS symposium a few weeks back to a PLoS Forum, via interesting conversations with a range of publishers, funders and scientists, it seems a lot of people are thinking much more seriously about how to make scientific communication more effective, more appropriate to the 21st century and above all, to take more advantage of the power of the web.

For me, the “paper” of the future has to encompass much more than just the narrative descriptions of processed results we have today. It needs to support a much more diverse range of publication types, data, software, processes, protocols, and ideas, as well provide a rich and interactive means of diving into the detail where the user in interested and skimming over the surface where they are not. It needs to provide re-visualisation and streaming under the users control and crucially it needs to provide the ability to repackage the content for new purposes; education, public engagement, even main stream media reporting.

I’ve got a lot of mileage recently out of thinking about how to organise data and records by ignoring the actual formats and thinking more about what the objects I’m dealing with are, what they represent, and what I want to do with them. So what do we get if we apply this thinking to the scholarly published article?

For me, a paper is an aggregation of objects. It contains, text, divided up into sections, often with references to other pieces of work. Some of these references are internal, to figures and tables, which are representations of data in some form or another. The paper world of journals has led us to think about these as images but a much better mental model for figures on the web is of an embedded object, perhaps a visualisation from a service like Many Eyes, Swivel, and Tableau Public. Why is this better? It is better because it maps more effectively onto what we want to do with the figure. We want to use it to absorb the data it represents, and to do this we might want to zoom, pan, re-colour, or re-draw the data. But we want to know if we do this that we are using the same underlying data, so the data needs a home, an address somewhere on the web, perhaps with the journal, or perhaps somewhere else entirely, that we can refer to with confidence.

If that data has an individual identity it can in turn refer back to the process used to generate it, perhaps in an online notebook or record, perhaps pointing to a workflow or software process based on another website. Maybe when I read the paper I want that included, maybe when you read it you don’t – it is a personal choice, but one that should be easy to make. Indeed, it is a choice that would be easy to make with today’s flexible web frameworks if the underlying pieces were available and represented in the right way.

The authors of the paper can also be included as a reference to a unique identifier. Perhaps the authors of the different segments are different. This is no problem, each piece can refer to the people that generated it. Funders and other supporting players might be included by reference. Again this solves a real problem of today, different players are interested in how people contributed to a piece of work, not just who wrote the paper. Providing a reference to a person where the link show what their contribution was can provide this much more detailed information. Finally the overall aggregation of pieces that is brought together and finally published also has a unique identifier, often in the form of the familiar DOI.

This view of the paper is interesting to me for two reasons. The first is that it natively supports a wide range of publication or communication types, including data papers, process papers, protocols, ideas and proposals. If we think of publication as the act of bringing a set of things together and providing them with a coherent identity then that publication can be many things with many possible uses. In a sense this is doing what a traditional paper should do, bringing all the relevant information into a single set of pages that can be found together, as opposed to what they usually do, tick a set of boxes about what a paper is supposed to look like. “Is this publishable?” is an almost meaningless question on the web. Of course it is. “Is it a paper?” is the question we are actually asking. By applying the principles of what the paper should be doing as opposed to the straightjacket of a paginated, print-based document, we get much more flexibility.

The second aspect which I find exciting revolves around the idea of citation as both internal and external references about the relationships between these individual objects. If the whole aggregation has an address on the web via a doi or a URL, and if its relationship both to the objects that make it up and to other available things on the web are made clear in a machine readable citation then we have the beginnings of a machine readable scientific web of knowledge. If we take this view of objects and aggregates that cite each other, and we provide details of what the citations mean (this was used in that, this process created that output, this paper is cited as an input to that one) then we are building the semantic web as a byproduct of what we want to do anyway. Instead of scaring people with angle brackets we are using a paradigm that researchers understand and respect, citation, to build up meaningful links between packages of knowledge. We need the authoring tools that help us build and aggregate these objects together and tools that make forming these citations easy and natural by using the existing ideas around linking and referencing but if we can build those we get the semantic web for science as a free side product – while also making it easier for humans to find the details they’re looking for.

Finally this view blows apart the monolithic role of the publisher and creates an implicit marketplace where anybody can offer aggregations that they have created to potential customers. This might range from a high school student putting their science library project on the web through to a large scale commercial publisher that provides a strong brand identity, quality filtering, and added value through their infrastructure or services. And everything in between. It would mean that large scale publishers would have to compete directly with the small scale on a value-for-money basis and that new types of communication could be rapidly prototyped and deployed.

There are a whole series of technical questions wrapped up in this view, in particular if we are aggregating things that are on the web, how did they get there in the first place, and what authoring tools will we need to pull them together. I’ll try to start on that in a follow-up post.

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A question of trust

I have long being sceptical of the costs and value delivered by our traditional methods of peer review. This is really on two fronts, firstly that the costs, where they have been estimated are extremely high, representing a multi-billion dollar subsidy by governments of the scholarly publishing industry. Secondly the value that is delivered through peer review, the critical analysis of claims, informed opinion on the quality of the experiments, is largely lost. At best it is wrapped up in the final version of the paper. At worst it is simply completely lost to the final end user. A part of this, which the more I think about the more I find bizarre is that the whole process is carried on under a shroud of secrecy. This means that as an end user, as I do not know who the peer reviewers are, and do not necessarily know what  process has been followed or even the basis of the editorial decision to publish. As a result I have no means of assessing the quality of peer review for any given journal, let alone any specific paper.

Those of us who see this as a problem have a responsibility to provide credible and workable alternatives to traditional peer review. So far despite many ideas we haven’t, to be honest, had very much success. Post-publication commenting, open peer review, and Digg like voting mechanisms have been explored but have yet to have any large success in scholarly publishing. PLoS is leading the charge on presenting article level metrics for all of its papers, but these remain papers that have also been through a traditional peer review process. Very little that is both radical with respect to the decision and means of publishing and successful in getting traction amongst scientists has been seen as yet.

Out on the real web it has taken non-academics to demonstrate the truly radical when it comes to publication. Whatever you may think of the accuracy of Wikipedia in your specific area, and I know it has some weaknesses in several of mine, it is the first location that most people find, and the first location that most people look for, when searching for factual information on the web. Roderic Page put up some interesting statistics when he looked this week at the top hits for over 5000 thousand mammal names in Google. Wikipedia took the top spot 48% of the time and was in the top 10 in virtually every case (97%). If you want to place factual information on the web Wikipedia should be your first port of call. Anything else is largely a waste of your time and effort. This doesn’t incidentally mean that other sources are not worthwhile or have a place, but that people need to work with the assumption that people’s first landing point will be Wikipedia.

“But”, I hear you say, “how do we know whether we can trust a given Wikipedia article, or specific statements in it?”

The traditional answer has been to say you need to look in the logs, check the discussion page, and click back the profiles of the people who made specific edits. However this in inaccessible to many people, simply because they do not know how to process the information. Very few universities have an “Effective Use of Wikipedia 101” course. Mostly because very few people would be able to teach it.

So I was very interested in an article on Mashable about marking up and colouring Wikipedia text according to its “trustworthiness”. Andrew Su kindly pointed me in the direction of the group doing the work and their papers and presentations. The system they are using, which can be added to any MediaWiki installation measures two things, how long a specific piece of text has stayed in situ, and who either edited it, or left it in place. People who write long lasting edits get higher status, and this in turn promotes the text that they have “approved” by editing around but not changing.

This to me is very exciting because it provides extra value and information for both users and editors without requiring anyone to do any more work than install a plugin. The editors and writers simply continue working as they have. The user can access an immediate view of the trustworthiness of the article with a high level of granularity, essentially at the level of single statements. And most importantly the editor gets a metric, a number that is consistently calculated across all editors, that they can put on a CV. Editors are peer reviewers, they are doing review, on a constantly evolving and dynamic article that can both change in response to the outside world and also be continuously improved. Not only does the Wikipedia process capture most of the valuable aspects of traditional peer review, it jettisons many of the problems. But without some sort of reward it was always going to be difficult to get professional scientists to be active editors. Trust metrics could provide that reward.

Now there are many questions to ask about the calculation of this “karma” metric, should it be subject biased so we know that highly ranked editors have relevant expertise, or should it be general so as to discourage highly ranked editors from modifying text that is outside of their expertise? What should the mathematics behind it be? It will take time clearly for such metrics to be respected as a scholarly contribution, but equally I can see the ground shifting very rapidly towards a situation where a lack of engagement, a lack of interest in contributing to the publicly accessible store of knowledge, is seen as a serious negative on a CV. However this particular initiative pans out it is to me this is one of the first and most natural replacements for peer review that could be effective within dynamic documents, solving most of the central problems without requiring significant additional work.

I look forward to the day when I see CVs with a Wikipedia Karma Rank on them. If you happen to be applying for a job with me in the future, consider it a worthwhile thing to include.

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Won’t someone please think of the policy wonks?

I wouldn’t normally try to pick a fight with Chad Orzel, and certainly not over a post which I mostly agree with, but I wanted to take some issue with the slant in his relatively recent post We are science (see also a good discussion in the comments).  Chad makes a cogent argument that there is a lot of whining about credit and rewards and that ‘Science’ or ‘The Powers That Be’ are blamed for a lot of these things. His point is that ‘We are science’ – and that it is the community of scientists or parts of it that makes these apparently barmy decisions. And as part of the community, if we want to see change, it is our responsibility to get on and change things. There is a strong case for more grass-roots approaches and in particular for those of us with some influence over appointments and promotions procedures to make the case in those committees for widening criteria. I certainly don’t have any problem with his exhortation to ‘be the change’ rather than complain about it. I would hope that I do reasonably good, though by no means perfect,  job of trying to live by the principles I am advocate.

Yet at the same time I think his apparent wholesale rejection of top-down approaches is a bit too much. There is a place for advocating changes in policy and standards. It is not always the right approach, either because it is not the right time or place, but sometimes it is. One reason for advocating changes in policy and standards is to provide a clear message about what the aspirations of a community or funding body are. This is particularly important in helping younger researchers assess the risks and benefits of taking more open approaches. Many of the most energetic advocates of open practice are actually in no position to act on their beliefs because as graduate students and junior postdocs they have no power to make the crucial decisions over where to publish and how (and whether) to distribute data and tools.

Articulations of policy such as the data sharing statements required by the UK BBSRC make it clear that there is an aspiration to move in this direction, that funding will be linked to delivering on these targets. This will both encourage young scientists to make the case to their PIs that this is the way forward and will also influence hiring committees. Chad makes the point that a similar mandate on public outreach for NSF grants has not been taken seriously by grantees. Here I would agree. There is no point having such policies if they are not taken seriously. But everything I have seen and heard so far suggests that the BBSRC does intend to take their policy and delivery on the data sharing statements very seriously indeed.

Top down initiatives are also sometimes needed to drive infrastructure development. The usability of the tools that will be needed to deliver on the potential of data and process sharing is currently woefully inadequate. Development is necessary and funding for this is required. Without the clear articulation from funders that this is the direction in which they wish to go, that they expect to see standards rising year on year, and without them backing that up with money, then nothing much will happen. Again BBSRC has done this, explicitly stating that it expects funding requestst to include support for data availability. The implication is that if people haven’t thought about the details of what they will do and its costs there will be questions asked. I wonder whether this was true of the NSF outreach scheme?

Finally, policy and top-down fiat has the potential, when judiciously applied to accelerate change. Funders, and indeed governments want to see better value for money on the research investment and see greater data availability as one way of achieving that. Chad actually provides an example of this working. The NIH deposition mandate has significantly increased the proportion of NIH funded papers available in PubMedCentral (I suspect Chad’s figure of 30% is actually taken from my vague recollection of the results of the Wellcome Trust Mandate – I think that current evidence suggests that the NIH mandate is getting about 50% now – just saw a graph of this somewhere but can’t find it now! Mark Siegal in the comments provided a link to the data in an article in Science (sorry behind paywall) – here). Clearly providing funding and policy incentives can move us further in that direction quicker. Anybody who doesn’t believe that funding requirements can drive the behaviour of research communities extremely effectively clearly hasn’t applied for any funding in the last decade.

But it remains the case that policy and funding is a blunt instrument. It is far more effective in the long term to bring the community with you by persuasion than by force. A community of successful scientists working together and changing the world is a more effective message in many ways than a fiat from the funding body. All I’m saying is that a combination of both is called for.

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Thinking about peer review of online material: The Peer Reviewed Journal of Open Science Online

I hold no particular candle for traditional peer review. I think it is inefficient, poorly selective, self reinforcing, often poorly done, and above all, far too slow. However I also agree that it is the least worst system we have available to us.  Thus far, no other approaches have worked terribly well, at least in the communication of science research. And as the incumbent for the past fifty years or so in the post of ‘generic filter’ it is owed some respect for seniority alone.

So I am considering writing a fellowship proposal that would be based around the idea of delivering on the Open Science Agenda via three independent projects, one focussed on policy and standards development, one on delivering a shareable data analysis pipeline for small angle scattering as an exemplar of how a data analysis process can be shared, and a third project based around building the infrastructure for embedding real science projects involving chemistry and drug discovery in educational and public outreach settings. I think I can write a pretty compelling case around these three themes and I think I would be well placed to deliver on them, particularly given the collaborative support networks we are already building in these areas.

The thing is I have no conventional track record in these areas. There are a bunch of papers currently being written but none that will be out in print by the time the application is supposed to go in. My recorded contribution in this area is in blog posts, blog comments, presentations and other material, all of which are available online. But none of which are peer-reviewed in the conventional sense.

One possibility is to make a virtue of this – stating that this is a rapidly moving field – that while papers are in hand and starting to come out that the natural medium for communication with the specific community is online through blogs and other media. There is an argument that conventional peer review simply does not map on to the web of data, tools, and knowledge that is starting to come together and that measuring a contribution in this area by conventional means is simply misguided.  All of which I agree with in many ways.

I just don’t think the referees will buy it.

Which got me thinking. It’s not just me, many of the seminal works for the Open Science community are not peer reviewed papers. Bill Hooker‘s three parter [1, 2, 3] at Three Quarks Daily comes to mind, as does Jean-Claude’s presentation on Nature Precedings on Open Notebook Science, Michael Nielsen’s essay The Future of Science, and Shirley Wu’s Envisioning the scientific community as One Big Lab (along with many others). It seems to me that these ought to have the status of peer reviewed papers which raises the question. We are a community of peers, we can referee, we can adopt some sort of standard of signficance and decide to apply that selectively to specific works online. So why can’t we make them peer reviewed?

What would be required? Well a stable citation obviously, so probably a DOI and some reasonably strong archival approach, probably using WebCite.  There would need to be a clear process of peer review, which need not be anonymous, but there would have to be a clear probity trail to show that an independent editor or group of referees made a decision and that appropriate revisions had been made and accepted. The bar for acceptance would also need to be set pretty high to avoid the charge of simply rubber stamping a bunch of online material. I don’t think open peer review is a problem for this community so many of the probity questions can be handled by simply having the whole process out in the open.

One model would be for an item to be submitted by posting a link on a new page on an independent Wiki . This would then be open to peer review. Once three (five?) independent reviewers had left comments and suggestions – and a version of the document created that satisfied them posted – then the new version could be re-posted at the author’s site, in a specified format which would include the DOI and arhival links, along with a badge that would be automatically aggregated to create the index a la researchblogging.org. There would need to be a charge, either for submission or acceptance – submission would keep volume down and (hopefully) quality up.

How does this differ from setting up a journal? Well two major things – one is that the author remains the publisher so the costs of publication per se are taken out of the equation. This is important as it keeps costs down – not zero, there is still the cost of the DOI and (even if it is donated) the time of editors and referees in managing the process and giving a stamp of authority. The main cost is in maintaining some sort of central index and server pointing out at the approved items. It would also be appropriate to support WebCite if that is the backstop archive. But the big costs for journals are in providing storage that is stable in the long term and managing peer review. If the costs of storage are offloaded and  the peer review process can be self organised then the costs drop significantly.

The second major advantage is that, as a community we already do a lot of this, looking over blog posts, linking to presentations, writing commentary or promoting them on FriendFeed. The reason why ArXiv worked was that there was already a culture of preprints amongst that community. The reason why commenting, rating,  and open peer review trials have not been as successful as people had hoped is because there is no pre-existing culture of doing these things. We already have a culture of open peer review in our community. Is it worth formalising it for the really high quality material that’s already out there?

I am aware that this goes against many of the principles of open and continuous review that many of you hold dear but I think it could serve two useful purposes. First it means that members of the community, particularly younger members, can bolster their CV with peer reviewed papers. Come the revolution this won’t matter but we’re not there yet. Making these contributions tangible for people could be quite powerful. Secondly it takes the important material out of the constant stream of objects flitting past on our screens and gives them a static (I won’t say permanent) priviledged place as part of the record of this field.  Many of them perhaps already have this but I think there is a value in formalising it. Is it worth considering? This proposal is out for review.

 

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Can post publication peer review work? The PLoS ONE report card

This post is an opinion piece and not a rigorous objective analysis. It is fair to say that I am on the record as and advocate of the principles behind PLoS ONE and am also in favour of post publication peer review and this should be read in that light. [ed I’ve also modified this slightly from the original version because I got myself mixed up in an Excel spreadsheet]

To me, anonymous peer review is, and always has been, broken. The central principle of the scientific method is that claims and data to support those claims are placed, publically, in the view of expert peers. They are examined, and re-examined on the basis of new data, considered and modified as necessary, and ultimately discarded in favour of an improved, or more sophisticated model. The strength of this process is that it is open, allowing for extended discussion on the validity of claims, theories, models, and data. It is a bearpit, but one in which actions are expected to take place in public (or at least community) view. To have as the first hurdle to placing new science in the view of the community a process which is confidential, anonymous, arbitrary, and closed, is an anachronism.

It is, to be fair, an anachronism that was necessary to cope with rising volumes of scientific material in the years after the second world war as the community increased radically in size. A limited number of referees was required to make the system manageable and anonymity was seen as necessary to protect the integrity of this limited number of referees. This was a good solution given the technology of the day. Today, it is neither a good system, nor an efficient system, and we have in principle the ability to do peer review differently, more effectively, and more efficiently. However, thus far most of the evidence suggests that the scientific community dosen’t want to change. There is, reasonably enough, a general attitude that if it isn’t broken it doesn’t need fixing. Nonetheless there is a constant stream of suggestions, complaints, and experimental projects looking at alternatives.

The last 12-24 months have seen some radical experiments in peer review. Nature Publishing Group trialled an open peer review process. PLoS ONE proposed a qualitatively different form of peer reivew, rejecting the idea of ‘importance’ as a criterion for publication. Frontiers have developed a tiered approach where a paper is submitted into the ‘system’ and will gradually rise to its level of importance based on multiple rounds of community review. Nature Precedings has expanded the role and discipline boundaries of pre-print archives and a white paper has been presented to EMBO Council suggesting that the majority of EMBO journals be scrapped in favour of retaining one flagship journal for which papers would be handpicked from a generic repository where authors would submit, along with referees’ reports and author’s response, on payment of a submission charge. Of all of these experiments, none could be said to be a runaway success so far with the possible exception of PLoS ONE. PLoS ONE, as I have written before, succeeded precisely because it managed to reposition the definition of ‘peer review’. The community have accepted this definition, primarily because it is indexed in PubMed. It will be interesting to see how this develops.

PLoS has also been aiming to develop ratings and comment systems for their papers as a way of moving towards some element of post publication peer review. I, along with some others (see full disclosure below) have been granted access to the full set of comments and some analytical data on these comments and ratings. This should be seen in the context of Euan Adie’s discussion of commenting frequency and practice in BioMedCentral journals which broadly speaking showed that around 2% of papers had comments and that these comments were mostly substantive and dealt with the science. How does PLoS ONE compare and what does this tell us about the merits or demerits of post publication peer review?

PLoS ONE has a range of commenting features, including a simple rating system (on a scale of 1-5) the ability to leave freetext notes, comments, and questions, and in keeping with a general Web 2.o feel the ability to add trackbacks, a mechanism for linking up citations from blogs. Broadly speaking a little more than 13% (380 of 2773) of all papers have ratings and around 23% have comments, notes, or replies to either (647 of 2773, not including any from PLoS ONE staff) . Probably unsurprisingly most papers that have ratings also have comments. There is a very weak positive correlation between the number of citations a paper has received (as determined from Google Scholar) and the number of comments (R^2 = 0.02, which is probably dominated by papers with both no citations and no comments, which are mostly recent, none of this is controlled for publication date).

Overall this is consistent with what we’d expect. The majority of papers don’t have either comments or ratings but a significant minority do. What is slightly suprising is that where there is arguably a higher barrier to adding something (click a button to rate versus write a text comment) there is actually more activity. This suggests to me that people are actively uncomfortable with rating papers versus leaving substantive comments. These numbers compare very favourably to those reported by Euan on comments in BioMedCentral but they are not yet moving into the realms of the majority. It should also be noted that there has been a consistent  programme at PLoS ONE with the aim of increasing the involvement of the community. Broadly speaking I would say that the data we have suggest that that programme has been a success in raising involvement.

So are these numbers ‘good’? In reality I don’t know. They seem to be an improvement on the BMC numbers arguing that as systems improve and evolve there is more involvement. However, one graph I received seems to indicate that there isn’t an increase in the frequency of comments within PLoS ONE over the past year or so which one would hope to see. Has this been a radical revision of how peer review works? Well not yet certainly, not until the vast majority of papers have ratings, but more importantly not until we have evidence that people are using those ratings. We are not yet in a position where we are about to see a stampede towards radically changed methods of peer review and this is not surprising. Tradition changes slowly – we are still only just becoming used to the idea of the ‘paper’ being something that goes beyond a pdf, embedding that within a wider culture of online rating and the use of those ratings will take some years yet.

So I have spent a number of posts recently discussing the details of how to make web services better for scientists. Have I got anything useful to offer to PLoS ONE? Well I think some of the criteria I suggested last week might be usefully considered. The problem with rating is that it lies outside the existing workflow for most people. I would guess that many users don’t even see the rating panel on the way into the paper. Why would people log into the system to look at a paper? What about making the rating implicit when people bookmark a paper in external services? Why not actually use that as the rating mechanism?

I emphasised the need for a service to be useful to the user before there are any ‘social effects’ present. What can be offered to make the process of rating a paper useful to the single user in isolation? I can’t really see why anyone would find this useful unless they are dealing with huge number of papers and can’t remember which one is which from day to day. It may be useful within groups or journal clubs but all of these require a group to sign up.  It seems to me that if we can’t frame it as a useful activity for a single person then it will be difficult to get the numbers required to make this work effectively on a community scale.

In that context, I think getting the numbers to around the 10-20% level for either comments or ratings has to be seen as an immense success. I think it shows how difficult it is to get scientists to change their workflows and adopt new services. I also think there will be a lot to learn about how to improve these tools and get more community involvement. I believe strongly that we need to develop better mechanisms for handling peer review and that it will be a very difficult process getting there. But the results will be seen in more efficient dissemination of information and more effective communication of the details of the scientific process. For this PLoS, the PLoS ONE team, as well as other publishers, including BioMedCentral, Nature Publishing Group, and others, that are working on developing new means of communication and improving the ones we have deserve applause. They may not hit on the right answer first off, but the current process of exploring the options is an important one, and not without its risks for any organisation.

Full disclosure: I was approached along with a number of other bloggers to look at the data provided by PLoS ONE and to coordinate the release of blog posts discussing that data. At the time of writing I am not aware of who the other bloggers are, nor have I read what they have written. The data that was provided included a list of all PLoS ONE papers up until 30 July 2008, the number of citations, citeulike bookmarks, trackbacks, comments, and ratings for each paper. I also received a table of all comments and a timeline with number of comments per month. I have been asked not to release the raw data and will honour that request as it is not my data to release. If you would like to see the underlying data please get in contact with Bora Zivkovic.

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The problem of academic credit and the value of diversity in the research community

This is the second in a series of posts (first one here) in which I am trying to process and collect ideas that came out of Scifoo. This post arises out of a discussion I had with Michael Eisen (UC Berkely) and Sean Eddy (HHMI Janelia Farm) at lunch on the Saturday. We had drifted from a discussion of the problem of attribution stacking and citing datasets (and datasets made up of datasets) into the problem of academic credit. I had trotted out the usual spiel about the need for giving credit for data sets and for tool development.

Michael made two interesting points. The first was that he felt people got too much credit for datasets already and that making them more widely citeable would actually devalue the contribution. The example he cited was genome sequences. This is a case where, for historical reasons, the publication of a dataset as a paper in a high ranking journal is considered appropriate.

In a sense I agree with this case. The problem here is that for this specific case it is allowable to push a dataset sized peg into a paper sized hole. This has arguably led to an over valuing of the sequence data itself and an undervaluing of the science it enables. Small molecule crystallography is similar in some regards with the publication of crystal structures in paper form bulking out the publication lists of many scientists. There is a real sense in which having a publication stream for data, making the data itself directly citeable, would lead to a devaluation of these contributions. On the other hand it would lead to a situation where you would cite what you used, rather than the paper in which it was, perhaps peripherally described. I think more broadly that the publication of data will lead to greater efficiency in research generally and more diversity in the streams to which people can contribute.

Michael’s comment on tool development was more telling though. As people at the bottom of the research tree (and I count myself amongst this group) it is easy to say ‘if only I got credit for developing this tool’, or ‘I ought to get more credit for writing my blog’, or anyone of a thousand other things we feel ‘ought to count’. The problem is that there is no such thing as ‘credit’. Hiring decisions and promotion decisions are made on the basis of perceived need. And the primary needs of any academic department are income and prestige. If we believe that people who develop tools should be more highly valued then there is little point in giving them ‘credit’ unless that ‘credit’ will be taken seriously in hiring decisions. We have this almost precisely backwards. If a department wanted tool developers then it would say so, and would look at CVs for evidence of this kind of work. If we believe that tool developers should get more support then we should be saying that at a higher, strategic level, not just trying to get it added as a standard section in academic CVs.

More widely there is a question as to why we might think that blogs, or public lectures, or code development, or more open sharing of protocols are something for which people should be given credit. There is often a case to be made for the contribution of a specific person in a non-traditional medium, but that doesn’t mean that every blog written by a scientists is a valuable contribution. In my view it isn’t the medium that is important, but the diversity of media and the concomitant diversity of contributions that they enable. In arguing for these contributions being significant what we are actually arguing for is diversity in the academic community.

So is diversity a good thing? The tightening and concentration of funding has, in my view, led to a decrease in diversity, both geographical and social, in the academy. In particular there is a tendency to large groups clustered together in major institutions, generally led by very smart people. There is a strong argument that these groups can be more productive, more effective, and crucially offer better value for money. Scifoo is a place where those of us who are less successful come face to face with the fact that there are many people a lot smarter than us and that these people are probably more successful for a reason. And you have to question whether your own small contribution with a small research group is worth the taxpayer’s money. In my view this is something you should question anyway as an academic researcher – there is far too much comfortable complacency and sense of entitlement, but that’s a story for another post.

So the question is; do I make a valid contribution? And does that provide value for money? And again for me Scifoo provides something of an answer. I don’t think I spoke to any person over the weekend without at least giving them something new to think about, a slightly different view on a situation, or just an introduction to something that hadn’t heard of before. These contributions were in very narrow areas, ones small enough for me to be expert, but my background and experience provided a different view. What does this mean for me? Probably that I should focus more on what makes my background and experience unique – that I should build out from that in the directions most likely to provide a complementary view.

But what does it mean more generally? I think that it means that a diverse set of experiences, contributions, and abilities will improve the quality of the research effort. At one session of Scifoo, on how to support ground breaking science, I made the tongue in cheek comment that I thought we needed more incremental science, more filling in of tables, of laying the foundations properly. The more I think about this the more I think it is important. If we don’t have proper foundations, filled out with good data and thought through in detail, then there are real risks in building new skyscrapers. Diversity adds reinforcement by providing better tools, better datasets, and different views from which to examine the current state of opinion and knowledge. There is an obvious tension between delivering radical new technologies and knowledge and the incremental process of filling in, backing up, and checking over the details. But too often the discussion is purely about how to achieve the first, with no attention given to the importance of the second. This is about balance not absolutes.

So to come back around to the original point, the value of different forms of contribution is not due to the fact that they are non-traditional or because of the medium per se, it is because they are different. If we value diversity at hiring committees, and I think we should, then looking at a diverse set of contributions, and the contribution that a given person is likely to make in the future based on their CVs, we can assess more effectively how they will differ from the people we already have. The tendency of ‘the academy’ to hire people in its own image is well established. No monoculture can ever be healthy; certainly not in a rapidly changing environment. So diversity is something we should value for its own sake, something we should try to encourage, and something that we should search CVs for evidence of. Then the credit for these activities will flow of its own accord.

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More on the science exchance – or building and capitalising a data commons

Image from Wikipedia via ZemantaBanknotes from all around the World donated by visitors to the British Museum, London

Following on from the discussion a few weeks back kicked off by Shirley at One Big Lab and continued here I’ve been thinking about how to actually turn what was a throwaway comment into reality:

What is being generated here is new science, and science isn’t paid for per se. The resources that generate science are supported by governments, charities, and industry but the actual production of science is not supported. The truly radical approach to this would be to turn the system on its head. Don’t fund the universities to do science, fund the journals to buy science; then the system would reward increased efficiency.

There is a problem at the core of this. For someone to pay for access to the results, there has to be a monetary benefit to them. This may be through increased efficiency of their research funding but that’s a rather vague benefit. For a serious charitable or commercial funder there has to be the potential to either make money, or at least see that the enterprise could become self sufficient. But surely this means monetizing the data somehow? Which would require restrictive licences, which is not at the end what we’re about.

The other story of the week has been the, in the end very useful, kerfuffle caused by ChemSpider moving to a CC-BY-SA licence, and the confusion that has been revealed regarding data, licencing, and the public domain. John Wilbanks, whose comments on the ChemSpider licence, sparked the discussion has written two posts [1, 2] which I found illuminating and have made things much clearer for me. His point is that data naturally belongs in the public domain and that the public domain and the freedom of the data itself needs to be protected from erosion, both legal, and conceptual that could be caused by our obsession with licences. What does this mean for making an effective data commons, and the Science Exchange that could arise from it, financially viable? Continue reading “More on the science exchance – or building and capitalising a data commons”

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Attribution for all! Mechanisms for citation are the key to changing the academic credit culture

A reviewer at the National Institutes of Health evaluates a grant proposal.Image via Wikipedia

Once again a range of conversations in different places have collided in my feed reader. Over on Nature Networks, Martin Fenner posted on Researcher ID which lead to a discussion about attribution and in particular Martin’s comment that there was a need to be able to link to comments and the necessity of timestamps. Then DrugMonkey posted a thoughtful blog about the issue of funding body staff introducing ideas from unsuccessful grant proposals they have handled to projects which they have a responsibility in guiding. Continue reading “Attribution for all! Mechanisms for citation are the key to changing the academic credit culture”

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Giving credit, filtering, and blogs versus traditional research papers

Another post prompted by an exchange of comments on Neil Saunder’s blog. The discussion here started about the somewhat arbitrary nature of what does and does not get counted as ‘worthy contributions’ in the research community. Neil was commenting on an article in Nature Biotech that had similar subject matter to some Blog posts, and he was reflecting on the fact that one would look convincing on a CV and the others wouldn’t. The conversation in the comments drifted somewhat into a discussion of peer review with Maxine (I am presuming Maxine Clarke from Nature?). You should read her comment  and the post and other comments in full but I wanted to pick out one bit. Continue reading “Giving credit, filtering, and blogs versus traditional research papers”