I’ve been watching the reflection on the Science Blogs diaspora and the wider conversation on what next for the Science Blogosphere with some interest because I remain both hopeful and sceptical that someone somewhere is really going crack the problem of effectively using the social web for advancing science. I don’t really have anything to add to Bora’s masterful summary of the larger picture but I wanted to pick out something that was interesting to me and that I haven’t seen anyone else mention.

Much of the reflection has focussed around what ScienceBlogs, and indeed Nature Network is, or was, good for as a place to blog. Most have mentioned the importance of the platform in helping to get started and many have mentioned the crucial role that the linking from more prominent blogs played in getting them an audience. What I think no-one has noted is how much the world of online writing has changed since many of these people started blogging. There has been consolidation in the form of networks and the growth of the internet as a credible media platform with credible and well known writers. At the same time, the expectations of those writers, in terms of their ability to express themselves through multimedia, campaigns, widgets, and other services has outstripped the ability of those providing networks to keep up. I don’t think it’s an accident that many of the criticisms of ScienceBlogs seem to be similar to those of Nature Network when it comes to technical issues.

What strikes me is a distinct parallel between the networks and scientific journals (and indeed newspapers). One of the great attractions of the networks, even two or three years ago, was that the technical details of providing a good quality user experience were taken care of for you. The publication process was still technically a bit difficult for many people who just wanted to get on and write. Someone else was taking care of this for you. Equally the credibility provided by ScienceBlogs or the Nature name were and still are a big draw. The same way a journal provides credibility, the assumption that there is a process back there that is assuring quality in some way.

The world, and certainly the web, has moved on. The publication step is easy – as it is now much easier on the wider web. The key thing that remains is the link economy. Good writing on the web lives and dies by the links, the eyeballs that come to it, the expert attention that is brought there by trusted curators. Scientists still largely trust journals to apportion up their valuable attention, and people will still trust the front page of ScienceBlogs and others to deliver quality content. But what the web teaches us over and over again is that a single criterion for authority, to quality curation, to editing is not enough. In the same way that a journal’s impact factor cannot tell you anything about the quality of an individual paper, a blog collective or network doesn’t tell you anything much about an individual author, blog, or piece of writing.

The future of writing on the web will be more diverse, more federated, and more driven by trusted and selective editors and discoverers who will bring specific types of quality content into my attention stream. Those looking for “the next science blogging network” will be waiting a while because there won’t be one, at least not one that is successful. There will be consolidation, there will larger numbers of people writing for commercial media outlets, both old and new, but there won’t be a network because the network will the web. What there will be, somehow, sometime, and I hope soon will be a framework in which we can build social relationships that help us discover content of interest from any source, and that supports people to act as editors and curators to republish and aggregate that content in new and interesting ways. That won’t just change the way people blog about science, but will change the way people communicate, discover, critique, and actually do science.

Like Paulo Nuin said, the future of scientific blogging is what it has always been. It’s just writing. It’s always just been writing. That’s not the interesting bit. The interesting bit is that how we find what we want to read is changing radically…again. That’s where the next big thing is. If someone figures out please tell me. I promise I’ll link to you.

Title of this post is liberally borrowed from some of Richard Grant’s of which the most recent was the final push it took for me to actually write it.

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The following is my contribution to a collection prepared by the British Library and released today at the Wellcome Trust, called “Driving UK Research. Is copyright a help or a hindrance?”  - Press Release – Document[pdf] – which is being released under a CC-BY-NC license. The British Library kindly allowed authors to retain copyright on their contributions so I am here releasing the text into the public domain via a CCZero waiver. I would also like to acknowledge the contribution of Chris Morrison in editing and improving the piece.

If I want to be confident that this text will be used to its full extent  I am going to have to republish it separately to this collection. Not because the collection uses  restrictive rights management or licences, it actually uses a relatively liberal copyright licence. No, the problem is copyright itself and the way it interacts with how we create knowledge in the 21^st century.

Until recently we would use texts or data by reading, taking notes, making photocopies, and then writing down new insights. We would refer to the originals by citing them. A person making limited copies or taking notes (perhaps quoting the text) does not breach copyright because of the notion of “fair dealing”. Making copies of reasonable portions of a work is explicitly not a violation of copyright. If it were we wouldn’t be able to do any useful work at all.

Today, scholarship and research cannot effectively proceed via manual human processes. There is simply too much for us to handle. On the other hand we have excellent computer systems that can, to some extent at least, take these notes for us. Automated assistants that can read the text for us, that can do text mining, data aggregation and indexing allowing us to cope with the volume of information. As these tools improve we have an opportunity to radically increase the speed of the innovation cycle, using the human brain for what it is best at: insight and creative thinking; and using machines for what they are best at: indexing, checking, collecting.

The problem is that to do this those machines need to take a copy of the whole of the text and in doing so they trigger copyright. Even though the collection you are reading is released under a Creative Commons licence that allows non-commercial use, no-one can take a copy, find an interesting sentence, and then index it if they are going to make money. Google are not allowed to check what is here and index it for us.

Or perhaps they are. Perhaps this does come under “fair use” in the US. Or maybe it does, but not in the UK. What about Australia? Or Brazil? All with slightly different copyright law and a slightly different relationship between copyright and contract law. Even if current legal opinion says it is allowed a future court case could change that. The only way I can be sure that my text is available into the future is to give up the copyright altogether.

To build effectively on the scientific and cultural data being generated today we need computers. If a human were doing the job it would clearly be covered by fair dealing. What we need is a clear and explicit statement that machine based analysis for the purpose of indexing, mining, or collecting references is a fair dealing exception, even where a full copy is taken. There clearly need to be boundaries. The entire work should not be kept or distributed. As with existing fair dealing we could have guidelines on amounts kept or quoted: perhaps no more than 5% of a work. These could easily be developed and be compatible with existing fair dealing guidance.

We risk stifling the development of new tools, both commercial and academic, and new knowledge under the weight of a legal regime that was designed to cope with the printing press. At the same time a simple statement that this kind of analysis is fair dealing will provide certainty without damaging the interests of copyright holders or complicating copyright law. These new uses will ultimately bring more traffic, and perhaps more customers, to the primary documents. By taking the simple and easy step of making automated analysis an allowable fair dealing exception everyone wins.

Clay Shirky’s famous soundbite has helped to focus on minds on the way information on the web needs to be tackled and a move towards managing the process of selecting and prioritising information. But in the research space I’m getting a sense that it is fuelling a focus on preventing publication in a way that is analogous to the conventional filtering process involved in peer reviewed publication.

Most recently this surfaced at Chronicle of Higher Education, to which there were many responses, Derek Lowe’s being one of the most thought out. But this is not isolated.

@JISC_RSC_YH: How can we provide access to online resources and maintain quality of content?  #rscrc10 [twitter via@branwenhide]

Me: @branwenhide @JISC_RSC_YH isn’t the point of the web that we can decouple the issues of access and quality from each other? [twitter]

There is a widely held assumption that putting more research onto the web makes it harder to find the research you are looking for. Publishing more makes discovery easier.

The great strength of the web is that you can allow publication of anything at very low marginal cost without limiting the ability of people to find what they are interested in, at least in principle. Discovery mechanisms are good enough, while being a long way from perfect, to make it possible to mostly find what you’re looking for while avoiding what you’re not looking for.  Search acts as a remarkable filter over the whole web through making discovery possible for large classes of problem. And high quality search algorithms depend on having a lot of data.

It is very easy to say there is too much academic literature – and I do. But the solution which seems to be becoming popular is to argue for an expansion of the traditional peer review process. To prevent stuff getting onto the web in the first place. This is misguided for two important reasons. Firstly it takes the highly inefficient and expensive process of manual curation and attempts to apply it to every piece of research output created. This doesn’t work today and won’t scale as the diversity and sheer number of research outputs increases tomorrow. Secondly it doesn’t take advantage of the nature of the web. They way to do this efficiently is to publish everything at the lowest cost possible, and then enhance the discoverability of work that you think is important. We don’t need publication filters, we need enhanced discovery engines. Publishing is cheap, curation is expensive whether it is applied to filtering or to markup and search enhancement.

Filtering before publication worked and was probably the most efficient place to apply the curation effort when the major bottleneck was publication. Value was extracted from the curation process of peer review by using it reduce the costs of layout, editing, and printing through simple printing less.  But it created new costs, and invisible opportunity costs where a key piece of information was not made available. Today the major bottleneck is discovery. Of the 500 papers a week I could read, which ones should I read, and which ones just contain a single nugget of information which is all I need? In the Research Information Network study of costs of scholarly communication the largest component of publication creation and use cycle was peer review, followed by the cost of finding the articles to read which represented some 30% of total costs. On the web, the place to put in the curation effort is in enhancing discoverability, in providing me the tools that will identify what I need to read in detail, what I just need to scrape for data, and what I need to bookmark for my methods folder.

The problem we have in scholarly publishing is an insistence on applying this print paradigm publication filtering to the web alongside an unhealthy obsession with a publication form, the paper, which is almost designed to make discovery difficult. If I want to understand the whole argument of a paper I need to read it. But if I just want one figure, one number, the details of the methodology then I don’t need to read it, but I still need to be able to find it, and to do so efficiently, and at the right time.

Currently scholarly publishers vie for the position of biggest barrier to communication. The stronger the filter the higher the notional quality. But being a pure filter play doesn’t add value because the costs of publication are now low. The value lies in presenting, enhancing, curating the material that is published. If publishers instead vied to identify, markup, and make it easy for the right people to find the right information they would be working with the natural flow of the web. Make it easy for me to find the piece of information, feature work that is particularly interesting or important, re-intepret it so I can understand it coming from a different field, preserve it so that when a technique becomes useful in 20 years the right people can find it. The brand differentiator then becomes which articles you choose to enhance, what kind of markup you do, and how well you do it.

All of these are things that publishers already do. And they are services that authors and readers will be willing to pay for. But at the moment the whole business and marketing model is built around filtering, and selling that filter. By impressing people with how much you are throwing away. Trying to stop stuff getting onto the web is futile, inefficient, and expensive. Saving people time and money by helping them find stuff on the web is an established and successful business model both at scale, and in niche areas. Providing credible and respected quality measures is a viable business model.

We don’t need more filters or better filters in scholarly communications – we don’t need to block publication at all. Ever. What we need are tools for curation and annotation and re-integration of what is published. And a framework that enables discovery of the right thing at the right time. And the data that will help us to build these. The more data, the more reseach published, the better. Which is actually what Shirky was saying all along…

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Image by cameronneylon via Flickr

Jon Eisen asked a question on Friendfeed last week that sparked a really interesting discussion of what an electronic research record should look like. The conversation is worth a look as it illustrates different perspectives and views on what is important. In particular I thought Jon’s statement of what he wanted was very interesting:

I want a system where people record EVERYTHING they are doing in their research with links to all data, analyses, output, etc [CN - my italics]. And I want access to it from anywhere. And I want to be able to search it intelligently. Dropbox won’t cut it.

This is interesting to me because it maps onto my own desires. Simple systems that make it very easy to capture digital research objects as they are created and easy-to-use tools that make it straightforward to connect these objects up. This is in many ways the complement of the Reseach Communication as Aggregation idea that I described previously. By collecting all the pieces and connecting them up correctly we create a Research Record as Aggregation, making it easy to wrap pieces of this up and connect them to communications. It also provides a route towards bridging the divide between research objects that are born digital and those that are physical objects that need to be represented by digital records.

Ok. So so much handwaving – what about building something? What about building something this weekend at ScienceHackDay? My idea is that we can use three pieces that have recently come together to build a demonstrator of how such a system might work. Firstly the DropBox API is now available (and I have a developer key). DropBox is a great tool that delivers on the promise of doing one thing well. It sits on your computer and synchronises directories with the cloud and any other device you put it on. Just works. This makes it a very useful entry point for the capture of digital research objects. So Step One:

Build a web service on the DropBox API that enables users (or instruments) to subscribe and captures new digital objects, creating an exposed feed of resources.

This will enable us to captures and surface research objects with users simply dropping files into directories on local computers. Using DropBox means these can be natively synchronised across multiple user computers which is nice. But then we need to connect these objects up, ideally in an automatic way. To do this we need a robust and general way of describing relationships between them. As part of the OREChem project, a collaboration between Cambridge, Southampton, Indiana, Penn State and Cornell Universities and PubChem, supported by Microsoft, Mark Borkum has developed an ontology that describes experiments (unfortunately there is nothing available on the web as yet – but I am promised there will be soon!). Nothing so new there, been done before. What is new here is that the OREChem vocabulary describes both plans and instances of carrying out those plans. It is very simple, essentially describing each part of a process as a “stage” which takes in inputs and emits outputs. The detailed description of these inputs and outputs is left for other vocabularies. The plan and the record can have a one to one correspondence but don’t need to. It is possible to ask whether a record satisfies a plan and alternately given evidence that a plan has been carried out that all the required inputs must have existed at some point.

Why does this matter? It matters because for a particular experiment we can describe a plan. For instance a UV-Vis spectrophotometer measurement requires a sample, a specific instrument, and emits a digital file, usually in a specific format. If our webservice above knows that a particular DropBox account is associated with a UV-Vis instrument and it sees a new file of the right type it knows that the plan of a UV-Vis measurement must have been carried out. It also knows which instrument was used (based on the DropBox account) and might know who it was who did the measurement (based on the specific folder the file appeared in). The web service is therefore able to infer that there must exist (or have existed) a sample. Knowing this it can attempt to discover a record of this sample from known resources, the public web, or even by emailing the user, asking them for it, and then creating a record for them.

A quick and dirty way of building a data model and linking it to objects on the web is to use Freebase and the Freebase API. This also has the advantage that we can leverage Freebase Gridworks to add records from spreadsheets (e.g. sample lists) into the same data model. So Step Two:

Implement OREChem experiment ontology in Freebase. Describe a small set of plans as examples of particular experimental procedures.

And then Step Three:

Expand the web service built in Step One to annotate digital research objects captured in Freebase and connect them to plans. Attempt to build in automatic discovery of inferred resources from known and unknown resources, and a system to failover to ask the user directly.

Freebase and DropBox may not be the best way to do this but both provide a documented API that could enable something to be lashed up quickly. I’m equally happy to be told that SugarSync, Open Calais, or Talis Connected Commons might be better ways to do this, especially if someone will be at ScienceHackDay with expertise in this. Demonstrating something like this could be extremely valuable as it would actually leverage semantic web technology to do something useful for researchers, linking their data into a wider web, while not actually bothering them with the details of angle brackets

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Last Thursday night I was privileged to be invited to the 10th anniversary celebrations for BioMedCentral and to help announce and give the first BMC Open Data Prize. Peter Murray-Rust has written about the night and the contribution of Vitek Tracz to the Open Access movement. Here I want to focus on the prize we gave, the rationale behind it, and the (difficult!) process we went through to select a winner.

Prizes motivate behaviour in researchers. There is no question that being able to put a prize down on your CV is a useful thing. I have long felt, originally following a suggestion from Jeremiah Faith, that a prize for Open Research would be a valuable motivator and publicity aid to support those who are making an effort. I was very happy therefore to be asked to help judge the prize, supported by Microsoft, to be awarded at the BMC celebration for the paper in a BMC journal that was an oustanding example of Open Data. Iain Hrynaszkiewicz and Matt Cockerill from BMC, Lee Dirks from Microsoft Research, along with myself, Rufus Pollock, John Wilbanks, and Peter Murray-Rust tried to select from a very strong list of contenders a shortlist and a prize winner.

Early on we decided to focus on papers that made data available rather than software frameworks or approaches that supported data availability. We really wanted to focus attention on conventional scientists in traditional disciplines that were going beyond the basic requirements. This meant in turn that a whole range of very important contributions from developers, policy experts, and others were left out. Particularly noteable examples were “Taxonomic information exchange and copyright: the Plazi approach” and “The FANTOM web resource: from mammalian transcriptional landscape to its dynamic regulation“.

This still left a wide field of papers making significant amounts of data available. To cut down at this point we looked at the licences (or lack thereof) under which resources were being made available. Anything that wasn’t broadly speaking “open” was rejected at this point. This included code that wasn’t open source, data that was only available via a login, or that had non-commercial terms. None of the data provided was explicitly placed in the public domain, as recommended by Science Commons and the Panton Principles, but a reasonable amount was made available in an accessible form with no restrictions beyond a request for citation. This is an area where we expect best practice to improve and we see the prize as a way to achieve that. To be considered any external resource will have to be compliant ideally with all of Science Commons Protocols, the Open Knowledge Definition, and the Panton Principles. This means an explicit dedication of data to the public domain via PDDL or ccZero.

Much of the data that we looked at was provided in the form of Excel files. This is not ideal but in terms of accessibility it’s actually not so bad. While many of us might prefer XML, RDF, or at any rate CSV files the bottom line is that it is possible to open most Excel files with freely available open source software, which means the data is accessible to anyone. Note that “most” though. It is very easy to create Excel files that make data very hard to extract. Column headings are crucial (and were missing or difficult to understand in many cases) and merging and formatting cells is an absolute disaster. I don’t want to point to examples but a plea to those who are trying to make data available: if you must use Excel just put column headings and row headings. No merging, no formatting, no graphs. And ideally export it as CSV as well. It isn’t as pretty but useful data isn’t about being pretty. The figures and tables in your paper are for the human readers, for supplementary data to be useful it needs to be in a form that computers can easily access.

We finally reduced our shortlist to only about ten papers where we felt people had gone above and beyond the average. “Large-scale insertional mutagenesis of a coleopteran stored grain pest, the red flour beetle Tribolium castaneum, identifies embryonic lethal mutations and enhancer traps” received particular plaudits for making not just data but the actual beetles available. “Assessment of methods and analysis of outcomes for comprehensive optimization of nucleofection” and “An Open Access Database of Genome-wide Association Results” were both well received as efforts to make a comprehensive data resource available.

In the end though we were required to pick just one winner. The winning paper got everyone’s attention right from the beginning as it came from an area of science not necessarily known for widespread data publication. It simply provided all of the pieces of information, almost without comment, in the form of clearly set out tables. They are in Excel and there are some issues with formatting and presentation, multiple sheets, inconsistent tabulation. It would have been nice to see more of the analysis code used as well. But what appealed most was that the data were simply provided above and beyond what appeared in the main figures as a natural part of the presentation and that the data were in a form that could be used beyond the specific study. So it was a great pleasure to present the prize to Yoosuk Lee on behalf of the authors of “Ecological and genetic relationships of the Forest-M form among chromosomal and molecular forms of the malaria vector Anopheles gambiae sensu stricto“.

Many challenges remain, making this data discoverable, and improving the licensing and accessibility all round. Given that it is early days, we were impressed by the range of scientists making an effort to make data available. Next year we will hope to be much stricter on the requirements and we also hope to see many more nominations. In a sense for me, the message of the evening was that the debate on Open Access publishing is over, its only a question of where the balance ends up. Our challenge for the future is to move on and solve the problems of making data, process, and materials more available and accessible so as to drive more science.