I read a good quote the other day:

“Bollocks to waiting 10 years for progress. I want people to know about it now, and then do something about it” – Dr Paul Fisher

So why do we wait? Why isn’t there immediate publication, analysis and dissemination of data? Publication of Scientific data as it stands is a broken business model…for the most part. The advent of journals like PLoS and their subsequent success shows that the scientific community is taking note of what steps need to be taken. In my short life as a scientist, there has always been one thing that really annoys me. The inefficiency of scientific publishing and subsequent global sharing of knowledge. In terms of making significant advances available to wide audiences as peer reviewed publications, PLoS has it covered. But what about the rest of your research?

What percentage of the figures that went into your undergrad, masters or doctorate thesis were ever published? The ones that you didnt publish were probably good basic science, or figures that didnt tell a complete story. As a PhD student, I became very aware of the fact that a large amount of my data, although good, would never be published as it did not show significant differences. I then began wondering how many times experiments had been repeated globally unnecessarily. And so FigShare started life as an idea for researchers to publish all of their data that would otherwise never leave their lab books. By categorising and tagging the research, it becomes very searchable and other scientists should not reproduce experiments and waste money when they have been conducted several times by other labs. Following the alpha release, FigShare received a lot of attention and a lot of feedback. This caused the site to develop and it now allows the upload of Figures, Datasets and most recently media (eg. videos).

This is not a new idea, and big data and data sharing projects have won several big JISC grants, but your average researcher needs this to be simple in order to adopt. The CKAN repository is a fantastic project which allows you to upload data from any field, such as government finances, weather forecasts and traffic reports. Where I feel this becomes inaccessible for scientists is the ease of uploads. A choice from 50 licenses for your data is intimidating enough to make most postdocs turn and run. For a project like FigShare, the more research that is uploaded, the more useful the site becomes. In order for this to happen, uploading research needs to be simple. This is what FigShare gives you. Give your bit of research, be it a figure, dataset or some other media format a name, hit upload, add details like your name, some tags and you have a nicely presented, citable, published figure.

FigShare now also serves as a repository for preprints figures. ie. Figures that will one day be published but feedback is requested on the prelimonary data. FigShare can be used as a platform to collaborate where users can contact one another and request to use figures in their publications etc. This means that previous unused figures, maybe from unfinished postdocs or PhD projects can be published, gaining the author more publications.

There is also the ability to easily share your figures, datasets and videos via a host of social media platforms through ‘share buttons’ on every page. This is a new way of bringing scientific research online and to a new audience. An example of how this can benefit science is already producing examples such as this one – A lot of scientists hear how social media can benefit research and yet there has been little evidence of how these tools can be exploited to make science more efficient. Imagine real time discussions about science you did yesterday, not last year when you first submitted your paper.

FigShare is a permanent database of your research. To further ensure this, FigShare is supported by Systems Institute. Systems Institute is a not for profit which is providing ongoing support for the hosting of FigShare as it expands. This also allows FigShare to make backups of all of your data each and every day.

So please, upload your data now and do your bit to help science progress in an efficient manner. It’ll probably do wonders for your academic career too!

Koblenz (Germany), 14-15 June 2011
An ACM Web Science Conference 2011 Workshop

Call for papers

The increasing quantity and velocity of scientific output is presenting scholars with a deluge of data. There is growing concern that scholarly output may be swamping traditional mechanisms for both pre-publication filtering (e.g peer review) and post-publication impact filtering (e.g. the Journal Impact Factor).

Increasing scholarly use of Web2.0 tools like CiteULike, Mendeley, Twitter, and blog-style article commenting presents an opportunity to create new filters. Metrics based on a diverse set of social sources could yield broader, richer, and more timely assessments of current and potential scholarly impact. Realizing this, many authors have begun to call for investigation of these “altmetrics.” (see altmetrics.org)

Despite the growing speculation and early exploratory investigation into the value of altmetrics, however, there remains little concrete, objective research into the properties of these metrics: their validity, their potential value and flaws, and their relationship to established measures. Nor has there been any large umbrella to bring these multiple perspectives together. The altmetrics11 workshop aims to  encourage both these. Submissions are invited from a variety of areas:

• New metrics based on social media
• Tracking science communication on the Web
• Relation between traditional metrics and altmetrics
• Peer-review and altmetrics
• Tools for gathering, analyzing, disseminating altmetrics

Important Dates

Submissions

Prospective authors should submit 2-page extended abstracts (max. 1000 words, not including references). If necessary, the workshop organizers will select the most relevant, original, and significant abstracts for presentation. Experimental results will be given preference, followed by technical reports on working altmetrics tools and position papers. All selected submissions will be published online for open peer review and discussion. Authors are encouraged to participate in the discussions of their work. Based on the presentations and online discussion, selected authors may be asked to submit full papers for peer-reviewed proceedings.

Location

The workshop is hosted by the ACM Web Science Conference 2011 (Koblenz, Germany). This interdisciplinary conference focuses on advances in studying the full range of social-technical relationships on the Web. Please visit the Web Science site for more information.

Organizers

• Paul Groth – VU University Amsterdam, NL
• Jason Priem –University of North Carolina at Chapel Hill, USA
• Dario Taraborelli – Wikimedia Foundation, USA

The organizers have an interdisciplinary background covering Sociology, Information and Library Science and Computer Science.

No one can read everything. We rely on filters to make sense of the scholarly literature, but the narrow, traditional filters are being swamped. However, the growth of new, online scholarly tools allows us to make new filters; these alt-metrics reflect the broad, rapid impact of scholarship in this burgeoning ecosystem. We call for more tools and research based on alt-metrics.

As the volume of academic literature explodes, scholars rely on filters to select the most relevant and significant sources from the rest.

Unfortunately, scholarship’s three main filters for importance are failing:

• Peer-review has served scholarship well, but is beginning to show its age. It is slow, encourages conventionality, and fails to hold reviewers accountable. Moreover, given that most papers are eventually published somewhere, peer-review fails to limit the volume of research.
• Citation counting measures are useful, but not sufficient. Metrics like the h-index are even slower than peer-review: a work’s first citation can take years. Citation measures are narrow; influential work may remain uncited. These metrics are narrow; they neglect impact outside the academy, and also ignore the context and reasons for citation.
• The JIF, which measures journals’ average citations per article, is often incorrectly used to assess the impact of individual articles. It’s troubling that the exact details of the JIF are a trade secret, and that significant gaming is relatively easy.

Tomorrow’s filters: alt-metrics

In growing numbers, scholars are moving their everyday work to the web. Online reference managers Zotero and Mendeley each claim to store over 40 million articles (making them substantially larger than PubMed); as many as a third of scholars are on Twitter, and a growing number tend scholarly blogs.

These new forms reflect and transmit scholarly impact: that dog-eared (but uncited) article that used to live on a shelf now lives in Mendeley, CiteULike, or Zotero–where we can see and count it. That hallway conversation about a recent finding has moved to blogs and social networks–now, we can listen in. The local genomics dataset has moved to an online repository–now, we can track it. This diverse group of activities forms a composite trace of impact far richer than any available before. We call the elements of this trace alt-metrics.

Alt-metrics expand our view of what impact looks like, but also of what’s making the impact. This matters because expressions of scholarship are becoming more diverse. Articles are increasingly joined by:

• The sharing of “raw science” like datasets, code, and experimental designs
• Semantic publishing or “nanopublication,” where the citeable unit is an argument or passage rather than entire article.
• Widespread self-publishing via blogging, microblogging, and comments or annotations on existing work.

Because alt-metrics are themselves diverse, they’re great for measuring impact in this diverse scholarly ecosystem. In fact, alt-metrics will be essential to sift these new forms, since they’re outside the scope of traditional filters. This diversity can also help in measuring the aggregate impact of the research enterprise itself.

Alt-metrics are fast, using public APIs to gather data in days or weeks. They’re open–not just the data, but the scripts and algorithms that collect and interpret it. Alt-metrics look beyond counting and emphasize semantic content like usernames, timestamps, and tags. Alt-metrics aren’t citations, nor are they webometrics; although these latter approaches are related to alt-metrics, they are relatively slow, unstructured, and closed.

How can alt-metrics improve existing filters?

With alt-metrics, we can crowdsource peer-review. Instead of waiting months for two opinions, an article’s impact might be assessed by thousands of conversations and bookmarks in a week. In the short term, this is likely to supplement traditional peer-review, perhaps augmenting rapid review in journals like PLoS ONE, BMC Research Notes, or BMJ Open. In the future, greater participation and better systems for identifying expert contributors may allow peer review to be performed entirely from alt-metrics. Unlike the JIF, alt-metrics reflect the impact of the article itself, not its venue. Unlike citation metrics, alt-metrics will track impact outside the academy, impact of influential but uncited work, and impact from sources that aren’t peer-reviewed. Some have suggested alt-metrics would be too easy to game; we argue the opposite. The JIF is appallingly open to manipulation; mature alt-metrics systems could be more robust, leveraging the diversity of of alt-metrics and statistical power of big data to algorithmically detect and correct for fraudulent activity. This approach already works for online advertisers, social news sites, Wikipedia, and search engines.

The speed of alt-metrics presents the opportunity to create real-time recommendation and collaborative filtering systems: instead of subscribing to dozens of tables-of-contents, a researcher could get a feed of this week’s most significant work in her field. This becomes especially powerful when combined with quick “alt-publications” like blogs or preprint servers, shrinking the communication cycle from years to weeks or days. Faster, broader impact metrics could also play a role in funding and promotion decisions.

Road map for alt-metrics

Speculation regarding alt-metrics (Taraborelli, 2008; Neylon and Wu, 2009; Priem and Hemminger, 2010) is beginning to yield to empirical investigation and working tools. Priem and Costello (2010) and Groth and Gurney (2010) find citation on Twitter and blogs respectively. ReaderMeter computes impact indicators from readership in reference management systems. Datacite promotes metrics for datasets. Future work must continue along these lines.

Researchers must ask if alt-metrics really reflect impact, or just empty buzz. Work should correlate between alt-metrics and existing measures, predict citations from alt-metrics, and compare alt-metrics with expert evaluation. Application designers should continue to build systems to display alt-metrics, develop methods to detect and repair gaming, and create metrics for use and reuse of data. Ultimately, our tools should use the rich semantic data from alt-metrics to ask “how and why?” as well as “how many?”

Alt-metrics are in their early stages; many questions are unanswered. But given the crisis facing existing filters and the rapid evolution of scholarly communication, the speed, richness, and breadth of alt-metrics make them worth investing in.

Jason Priem (University of North Carolina-Chapel Hill)
Dario Taraborelli (University of Surrey)
Paul Groth (VU University Amsterdam)
Cameron Neylon (Science and Technology Facilities Council)

Source: http://altmetrics.org/manifesto 

Readers of this blog are not new to my ramblings on soft peer review, social metrics and post-publication impact measures:

• can we measure the impact of scientific research based on usage data from collaborative annotation systems, social bookmarking services and social media?
• should we expect major discrepancies between citation-based and readership-based impact measures?
• are online reference management systems more robust a data source to measure scholarly readership than traditional usage factors (e.g. downloads, clickthrough rates etc.)?

These are some of the questions addressed in my COOP ’08 paper. Jason Priem also discusses the prospects of what he calls “scientometrics 2.0″ in a recent First Monday article and it is really exciting to see a growing interest in these ideas from both the scientific and the STM publishing community.

We now need to think of ways of putting these ideas into practice. Science Online London 2010 earlier this month offered a great chance to test a real-world application of these ideas in front of a tech-friendly audience and this post is meant as its official announcement.

ReaderMeter is a proof-of-concept application showcasing the potential of readership data obtained from reference management tools. Following the announcement of the Mendeley API, I decided to see what could be built on top of the data exposed by Mendeley and the first idea was to write a mashup aggregating author-level readership statistics based on the number of bookmarks scored by each of one’s publications. ReaderMeter queries the data provider’s API for articles matching a given author string. It parses the response and generates a report with several metrics that attempt to quantify the relative impact of an author’s scientific production based on its consumption by a population of readers (in this case the 500K-strong Mendeley user base):

The figure above shows a screenshot of ReaderMeter’s results for social scientist Duncan J Watts, displaying global bookmark statistics, the breakdown of readers by publication as well as two indices (the H_R index and the G_R index) which I compute using bookmarks as a variable by analogy to the two popular citation-based metrics. Clicking on a reference allows you to drill down to display readership statistics for a given publication, including the scientific discipline, academic status and geographic location of readers of an individual document:

A handy permanent URL is generated to link to ReaderMeter’s author reports (using the scheme: [SURNAME].[FORENAME+INITIALS]), e.g.:

http://readermeter.org/Watts.Duncan_J

I also included a JSON interface to render statistics in a machine-readable format, e.g.:

http://readermeter.org/Watts.Duncan_J/json

Below is a sample of the JSON output:

{
	"author": "Duncan J Watts",
	"author_metrics":
	{
		"hr_index": "15",
		"gr_index": "26",
		"single_most_read": "140",
		"publication_count": "57",
		"bookmark_count": "760",
		"data_source": "mendeley"
	},
	"source": "http://readermeter.org/Watts.Duncan_J",
	"timestamp": "2010-09-02T15:41:08+01:00"
}

Despite being just a proof of concept (it was hacked in a couple of nights!), ReaderMeter attracted a number of early testers who gave a try to its first release. Its goal is not to redefine the concept of research impact as we know it, but to complement this notion with usage data from new sources and help identify aspects of impact that may go unnoticed when we only focus on traditional, citation-based metrics. Before a mature version of ReaderMeter is available for public consumption and for integration with other services, though, several issues will need to be addressed.

1. Author name normalisation

The first issue to be tackled is the fact the same individual author may be mentioned in a bibliographic record under a variety of spelling alternates: Rod Page was among the first to spot and extensively discuss this issue, which will hopefully be addressed in the next major upgrade (unless a provision to fix this problem is directly offered by Mendeley in a future upgrade of their API).

2. Article deduplication

A similar issue affects individual bibliographic entries, as noted by Egon Willighagen among others. Given that publication metadata in reference management services can be extracted by a variety of sources, the uniqueness of a bibliographic record is far from given. As a matter of fact, several instances of the same publication can show up as distinct items, with the result of generating flawed statistics when individual publications and their relative impact need to be considered (as is the case when calculating the H- and G-index). To what extent crowdsourced bibliographic databases (such as those of Mendeley, CiteULike, Zotero, Connotea, and similar distributed reference management tools) can tackle the problem of article duplication as effectively as manually curated bibliographic databases, is an interesting issue that sparked a heated debate (see this post by Duncan Hull and the ensuing discussion).

3. Author disambiguation

A way more challenging problem consists in disambiguating real homonyms. At the moment, ReaderMeter is unable to tell the difference between two authors with an identical name. Considering that surnames like Wang appear to be shared by about 100M people on the planet, the problem of how to disambiguate authors with a common surname is not something that can be easily sorted out by a consumer service such as ReaderMeter. Global initiatives with a broad institutional support such as the ORCID project are trying to fix this problem for good by introducing a unique author identifier system, but precisely because of their scale and ambitious goal they are unlikely to provide a viable solution in the short run.

4. Reader segmentation and selection biases

You may wonder: how genuine is data extracted from Mendeley as an indicator of an author’s actual readership? Calculating author impact metrics based on the user population of a specific service will always by definition result in skewed results due to different adoption rates by different scientific communities or demographic segments (e.g. by academic status, language, gender) within the same community. And how about readers who just don’t use any reference management tools? Björn Brembs posted some thoughtful considerations on why any such attempt at measuring impact based on the specific user population of a given platform/service is doomed to fail. His proposed solution, however – a universal outlet where all scientific content consumption should happen–sounds not only like an unlikely scenario, but also in many ways an undesirable one. Diversity is one of the key features of the open source ecosystem, for one, and as long as interoperability is achieved (witness the example of the OAI protocol and its multiple software implementation), there is certainly no need for a single service to monopolise the research community’s attention for projects such as ReaderMeter to be realistically implemented. The next step on ReaderMeter’s roadmap will be to integrate data from a variety of content providers (such as CiteULike or Bibsonomy) that provide free access to article readership information: although not the ultimate solution to the enormous problem of user segmentation, data integration from multiple sources should hopefully help reduce biases introduced by the population of a specific service.

What’s next

I will be working in the coming days on an upgrade to address some of the most urgent issues, in the meantime feel free to test ReaderMeter, send me your feedback and feature requests, follow the latest news on the project via Twitter or just help spread the word!

After a few months of private testing, Mendeley announced the public release of their open API. This will allow developers and researchers to build applications and data analysis on top of a massive database of human-annotated scientific references.

We are excited to see our friends at Mendeley push forward on the open science front by making their database accessible to third parties and I look forward to seeing what developers will build on top of this data goldmine. In the meantime, check out the Mendeley Developer Portal or follow the dedicated twitter account for updates.

Using HTML5 features, this is the kind of obvious tool that makes symbol lookup faster than doing it by hand.

Just draw the symbol in the box and up comes the LaTeX code, and the package name that contains it.

I’m not sure how big of a problem selecting classes is for students, and how much it can be automated, but now there’s a tool specifically solving this problem. CourseRank tracks scheduling conflicts, together with some other Interesting features. For example, it gathers course/professor reviews, workload estimations and aggregates questions and answers.

Right now the selection of universities is not that great. It makes sense since the service is specifically tailored to each university, so I can imagine the implementation can take a while.

Judith Simon and Diego Ponte from the LiquidPub project are seeking participants for a a survey about scientific publishing and the Web 2.0.

The aim of the survey is to gauge the potential acceptance of a Web 2.0 inspired production and dissemination of scientific publications by different scientific communities and by practitioners. The survey is hence tailored for researchers in all domains as well as for people working in the publishing industry.

Judith promised to report back on the results of the survey

Citation copy-editing is one of those deceptively small burdens that have a way of taking over the working day. If left untended, the task of tidying up casually scribbled references can snowball to crisis proportions as a submission deadline approaches. Similarly, when a submission to one publisher is unsuccessful, significant effort may be required to recast its citations in the format required by another. Collaboration outside of one’s own field can bring with it an unwelcome tangle of fresh style-guide quandaries to ponder and fight through. These are things that the machines, if they want to make themselves useful, should be doing for us.

There is plenty of collective experience in this line, and as fate would have it, there are also plenty of collective solutions. In the TeX/LaTeX world, authors and their editors can today choose between BibTeX and BibLaTeX — both of them excellent utilities — with the several variants of the former supported by no fewer than four separate versions of the BibTeX program. [1] Users of WYSIWYG word processors can look to the bibliographic support built into Word or Open Office, or they can turn to an external solution such as EndNote ™, ProCite ™, Reference Manager ™, or more recently Zotero or Mendeley. Migrating data between these environments is a process fraught with uncertainty, but it is sometimes unavoidable when you need this kind of output, and it can only be produced on that kind of system …

… with so many solutions to choose from, it’s hard to go right. [2]

The citeproc-js citation processor is a Javascript implementation of the Citation Style Language (CSL), an XML schema for describing citation styles that aspires to strike this problem at its root. CSL is a general, open standard that enables fully modular control over bibliographic formatting. This means that CSL is capable of accurately describing styles used in many disciplines, from the sciences, through the humanities to law. It also means that a CSL style description can be used with any other application that understands the CSL language. And it means that the style description is separated to the extent possible from the target document; you can switch styles at any time, even after the writing process is complete. Generality, a comprehensive pooling of community resources, user-centric ease of use: all areas where, collectively, our current menagerie of productivity tools could do better.

CSL first saw wide application in the Zotero project. [3] Citeproc-js has been developed in the first instance for use in Zotero, [4] but it runs as a separate module via a (relatively) simple API, and with appropriate wrappers, it can be deployed pretty much anywhere. Potentially, any application that generates dynamic content — text processors, word processors, weblog environments, and dynamic websites — can use CSL and citeproc-js to provide publisher-correct citation and bibliography facilities without exceptional programming effort. [5]

Development of the CSL language has been spearheaded by Bruce D’Arcus. The citeproc-js processor adheres to version 1.0 of the CSL specification, [6] which has been engineered and documented during the past year primarily by Bruce and Rintze Zelle, with incidental contributions by myself and others. It will debut, together with the new processor, in Zotero 2.1, which should begin to emerge, if all goes well, during this calendar year.

Meanwhile, the processor itself is complete, documented, and more or less ready to go. [7] Here is a short run-down of some of the highlights:

Disambiguation

In author-date citation styles, works by the same author must be distinguished from one another in some way. The current Zotero processor performs name and cite disambiguation as required by the Chicago Manual of Style. There are in fact at least six other disambiguation methods in general use. CSL 1.0 and the new processor will support all of them.

Sorting

The AGU journals, in particular, impose extremely demanding sorting rules in the bibliography. [8] CSL 1.0 and the new processor support multiple sort keys with arbitrary sort order for each key. A wide variety of sorting schemes can be implemented, including the AGU sort.

Parallel citation support

Many legal styles, including the Bluebook style common in American law journals, require that law cases appearing in multiple reporters be cited to each reporter, with the case name in front, and the court and year of decision at the end. [9] The new processor supports this behavior.

On-the-fly document updates

The API of the new processor supports targeted context-sensitive updates of citations in a document that are affected by an insertion, deletion or edit, for efficient transactions with a word-processor or weblog plugin.

Localization of dates

CSL version 0.8 currently supports the use of localized terms for style-supplied labels and the like. CSL 1.0 will add sophisticated localization of dates; both the language of month names and the ordering and formatting of elements will adjust appropriately when the language of a citation style is changed.

Sophisticated names handling

A great deal of work has gone into enhancing the handling of names in CSL 1.0. European conventions on the handling of particles such as “von”, “van”, “di” and the like can be accounted for appropriately both in the sorting and in the rendering of individual names.

In-field formatting

For scientific publishing, the new processor recognizes a limited subset of HTML as markup within titles, enabling superscript, subscript, small capitals, italics, boldface. The processor also implements the flip-flopping of italic and boldface, and of quotation marks, to avoid ambiguity in rendered citations. The HTML used in markup is transformed by the processor into the selected output format (HTML, RTF, LaTeX, or whatever) during rendering.

Multi-lingual citation support

The new processor implements experimental support for multi-lingual citations, providing a flexible mechanism for the transliteration of names and titles, for the supplementary translation of titles, and for the use of alternative sort strings needed for Asian languages.

As it leaves my laptop, citeproc-js is just a bare Javascript module with some test suite wrappers to check that it actually performs as advertised. But with the widening availability and increasing efficiency of Javascript runtime tools, I do hope that it has some prospect of escaping from its cage and wreaking order on the world of bibliography management. If you’re an integrator or site administrator, the fine citeproc-js manual is your first point of reference. If you’re an end user, keep an eye out for the CSL mark, coming soon (maybe) to an application near you!

Peter Sefton wrote a series of posts on wave. He has published on Scholarly HTML so I read attentively what he has to say. What follows is some highlights of his posts, and my thinking about where things are going. There are at least four things that bother me about wave –as it is today:

1- It’s not really HTML

I thought that waves being XML documents would be a good thing because it’d separate content and formatting. But it seems that they made some strange decisions about how to represent formatting with “very tenuous relationship to HTML”. For example

While there is talk of ‘XML documents’ in the whitepapers etc, a wave document in the current implementation is apparently a series of lines of text. All formatting and what you might think of as structure, such as whether something is a heading or not, is considered an annotation.

This is important right now because the only way to get the resulting doc is to dump the html to a file, or ‘copy-paste’. So, in a way, losing formatting like this would completely incapacitate wave for serious paper writing. I have had some success just copy-pasting and keeping most formatting, but I cannot risk to write a long paper and lose all formatting. Which won’t happen, because…

2- It doesn’t work too well for long, structured documents

Having a large blip for the entire paper with many people editing it seems to perform poorly. And having each person write their own blip-per-paragraph is not very pretty. It’s in fact distracting. I don’t discard wave’s usability to go through the roof once people start making things with it (robots). But it all depends on how well the API is designed. For example, following the mailing list, it seems that there’s no easy way to reorder blips programatically. This sounds like bad design to me.

3- It doesn’t integrate well with citation tools

It may be a matter of time before all the bibliographic tools we like get integrated. For example, Igor does offer some basic integration but this is far from satisfactory.

4-Formatting is simplistic

Wave has no table support. Figures are also not what you would expect, even for a notetaker. Captions are not implemented, nor footnotes. finally, LaTeXy is not the most convenient way to get equations done, I’m afraid, and it doesn’t go both ways.

Clearly, the content/form separation in wave is not designed for academic collaboration, and it shows. The questions is whether we can make this happen by writing robots[1]. Whether wave is the open platform that would make academic writing 2.0 happen.

Wave is just a tool. Why does this matter so much?

You may think that thinking too much about tools is counterproductive. But the way things are, it looks like tools are more and more important. Right now, we are stuck with the paper metaphor. Authors can produce pdfs, and publishers too. A publisher may make a prettier one, but that adds little value. We are equaled in terms of tools. However, publishers such as Elsevier want to get away from the paper metaphor (which is a good thing). As a consequence, authors will not be able to produce HTML as rich as the publisher’s. Here, the difference in tools matter.

In this case, Wave does look like an easy way to craft an interactive experience with little effort. So, even if you discard wave’s usefulness as a collaboration tool, it has quite a lot of value.

But it could very well be that wave doesn’t fulfill its promise. Microsoft Office 2010 offers similar functionality (close to real time edits). And of course, word has unparalleled features such as track changes, integration with endNote, etc. It could be that people adopt this new way of collaborative writing in real time without using wave. What worries me is that openOffice looks seriously left behind now. If it looks like a half-assed implementation of word 2003 features now, imagine when real-time hits mainstream. You need a serious server infrastructure to support that, which is possible for Google or MS, but not for a –smallish- open source foundation. I hope they find a way to jump in the train before it’s too late. Wave has a lot potential, because it is open. If openOffice could support that wave protocol, it could be a big achievement for open source.

If you have had any experience drafting a long doc in wave, please post it in the comments.

[1] For more on this, see my Wave intro for RuPy 2009.