To appear in: In: Jacobs, N., Eds. Open Access: Key Strategic, Technical and Economic

Aspects. Chandos. Note, final versions of figures and captions still to come, but their content and positions will be as shown here. The reference list will also be pruned, to cite or remove uncited works


The Open Research Web: A Preview of the Optimal and the Inevitable


Nigel Shadbolt, Tim Brody, Les Carr, and Stevan Harnad

University of Southampton


Most of this book has been about the past and the present of Open Access (OA). Let’s now take a brief glimpse at its future, for it is already within reach and almost within sight: Once the optimal and inevitable outcome for the research literature has became actual , namely, all 2.5 million of the annual articles appearing in the planet’s 24,000 peer-reviewed research journals are freely accessible online to all would-be users, then:


(1)  All their OAI metadata and full-texts will be harvested and reverse-indexed by services such as Google, OAIster (and new OAI/OA services), making it possible to search all and only the research literature in all disciplines using Boolean full-text search (and, or not, etc.).

(2)  Boolean full-text search will be augmented by Artificial Intelligence (AI) based text-analysis and classification techniques superior to human pre-classification, infinitely less time-consuming, and applied automatically to the entire OA full-text corpus.

(3)  Articles and portions of articles will also be classified, tagged and annotated in terms of “ontologies” (lists of the kinds of things of interest in a subject domain, their characteristics, and their relations to other things) as provided by authors, users, other authorities, or automatic AI techniques, creating the OA research subset of the “semantic web” (Berners-Lee et al. 2001).

(4)  The OA corpus will be fully citation interlinked – every article forward-linked to every article it cites and backward-linked to every article that cites it -- making it possible to navigate all and only the research journal literature in all disciplines via citation-surfing instead of just ordinary link-surfing.

(5)  A CiteRank analog of Google’s PageRank algorithm will allow hits to be rank-ordered by weighted citation counts instead of just ordinary links (not all citations are equal: a citation by a much-cited author/article weighs more than a citation by a little-cited author/article; Page et al. 1999).

(6)  In addition to ranking hits by author/article/topic citation counts, it will also be possible to rank them by author/article/topic download counts (consolidated from multiple sites, caches, mirrors, versions) (Bollen et al. 2005; Moed 2005b).

(7)  Ranking and download/citation counts will not just be usable for searching but also (by individuals and institutions) for prediction, evaluation and other forms of analysis, on- and off-line (Moed 2005a).

(8)  Correlations between earlier download counts and later citation counts will be available online, and usable for extrapolation, prediction and eventually even evaluation (Brody et al. 2005).

(9)  Searching, analysis, prediction and evaluation will also be augmented by co-citation analysis (who/what co-cited or was co-cited by whom/what?), co-authorship analysis, and eventually also co-download analysis (who/what co-downloaded or was co-downloaded by whom/what? [user identification will of course require user permission]).

(10)                 Co-text analysis (with AI techniques, including latent semantic analysis [what text and text-patterns co-occur with what? Landauer et al. 1998], semantic web analysis, and other forms of “semiometrics”; MacRae & Shadbolt 2006) will complement online and off-line citation, co-citation, download and co-download analysis (what texts have similar or related content or topics or users?).

(11)                 Time-based (chronometric) analyses will be used to extrapolate early download, citation, co-download and co-citation trends, as well as correlations between downloads and citations, to predict research impact, research direction and research influences.

(12)                 Authors, articles, journals, institutions and topics will also have “endogamy/exogamy” scores: how much do they cite themselves? in-cite within the same “family” cluster? out-cite across an entire field? across multiple fields? across disciplines?

(13)                 Authors, articles, journals, institutions and topics will also have latency and longevity scores for both downloads and citations: how quickly do citations/downloads grow? how long before they peak? how long-lived are they?

(14)                 “Hub/authority” analysis will make it easier to do literature reviews, identifying review articles citing many articles (“hubs”) or key articles/authors (“authorities”) cited by many articles.

(15)                 “Silent” or “unsung” authors or articles, uncited but important influences, will be identified (and credited) by co-citation and co-text analysis and through interpolation and extrapolation of semantic lines of influence.

(16)                 Similarly, generic terms that are implicit in ontologies (but so basic that they are not explicitly tagged by anyone) -- as well as other “silent” influences, intermediating effects, trends and turning points -- can be discovered, extracted, interpolated and extrapolated from the patterns among the explicit properties such as citations and co-authorships, explicitly tagged features and relationships, and latent semantics.

(17)                 Author names, institutions, URLs, addresses and email addresses will also be linked and disambiguated by this kind or triangulation.

(18)                 Resource Description Framework (RDF) graphs (who is related to what, how?) will link objects in domain “ontologies”. For example, Social Network Analyses (SNA) on co-authors will be extended to other important relations and influences (projects directed, PhD students supervised etc.)

(19)                 Co-text and semantic analysis will identify plagiarism as well as unnoticed parallelism and potential convergence.

(20)                 A “degree-of-content-overlap” metric will be calculable between any two articles, authors, groups or topics.

(21)                 Co-authorship, co-citation/co-download, co-text and chronometric path analyses will allow a composite “heritability” analysis of individual articles, indexing the amount and source of their inherited content, their original contribution, their lineage, and their likely future direction.

(22)                 Cluster analyses and chronograms will allow connections and trajectories to be visualized, analyzed and navigated iconically.

(23)                 User-generated tagging services (allowing users to both classify and evaluate articles they have used by adding tags anarchically) will complement systematic citation-based ranking and evaluation and author-based, AI-based, or authority-based semantic-web tagging, both at the article/author level and at the level of specific points in the text.

(24)                 Commentaries -- peer-reviewed, moderated, and unmoderated -- will be linked to and from their target articles, forming a special, amplified class of annotated tags.

(25)                 Referee-selection (for the peer reviewing of both articles and research proposals) will be greatly facilitated by the availability of the full citation-interlinked, semantically tagged corpus.

(26)                 Deposit date-stamping will allow priority to be established.

(27)                 Research articles will be linked to tagged research data, allowing independent re-analysis and replication.

(28)                 The Research Web will facilitate much richer and more diverse and distributed collaborations, across institutions, nations, languages and disciplines (e-science, collaboratories).


Many of these future powers of the Open Access Research Web revolve around research impact: predicting it, measuring it, tracing it, navigating it, evaluating it, enhancing it. What is research impact?


Research Impact. The reason the employers and funders of scholarly and scientific researchers mandate that they should publish their findings (“publish or perish”) is that if research findings are kept in a desk drawer instead of being published then the research may as well not have been done at all. The impact of a piece of research is the degree to which it has been useful to other researchers and users in generating further research and applications: how much the work has been read, used, built-upon, applied and cited in other research as well as in educational, technological, cultural, social and practical applications (Moed 2005a).

The first approximation to a metric of research impact is the publication itself. Research that has not yielded any publishable findings has no impact. A second approximation metric of research impact is where it is published: To be accepted for publication, a research report must first be peer-reviewed, i.e., evaluated by qualified specialists who advise a journal editor on whether or not the paper can potentially meet that journal’s quality standards, and what revision needs to be done to make it do so. There is a hierarchy of journals in most fields, the top ones exercising the greatest selectivity, with the highest quality standards. So the second approximation impact metric for a research paper is the level in the journal quality hierarchy of the journal that accepts it. But even if published in a high-quality journal, a paper that no one goes on to read has no impact. So a third approximation impact metric comes from a paper’s usage level. This was hard to calculate in print days, but in the online era, downloads can be counted (Kurtz et al 2002, 2004; Harnad & Brody 2004; Brody et al. 2005; Bollen et al. 2005; Moed 2005b). Yet even if a paper is downloaded and read, it may not be used -- not taken up, applied and built upon in further research and applications. The fourth metric and currently the closest approximation to a paper’s research impact is accordingly whether it is not only published and read, but cited, which indicates that it has been used (by users other than the original author), as an acknowledged building block in further published work.

Being cited does not guarantee that a piece of work was important, influential and useful, and some papers are no doubt cited only to discredit them; but, on average, the more a work is cited, the more likely that it has indeed been used and useful (Garfield 1955, 1973, 1998, 1999; Brookes 1980/81; Wolfram 2003). Other estimates of the importance and productivity of research have proved to be correlated with its citation frequency. For example, every four years for two decades now, the UK Research Assessment Exercise (RAE) has been evaluating the research output of every department of every UK university, assigning each a rank along a 5-point scale on the basis of many different performance indicators, some consisting of peer judgments of the quality of published work, some consisting of objective metrics (such as prior research grant income, number of research students, etc.). A panel decides each department’s rank and then each is funded proportionately. In many fields the ranking turns out to be most highly correlated with prior grant income, but it is almost as highly correlated with another metric: the total citation counts of each department’s research output (Eysenck & Smith 2002, Harnad 2003; Harnad et al. 2003) even though citations – unlike grant income -- are not counted explicitly in the RAE evaluation. Because of the high correlation of the overall RAE outcome with metrics, it has now been decided, two decades after the inception of the RAE, to drop the peer-re-evaluation component entirely, and to rely entirely on research impact metrics (UK Office of Science and Technology 2006).


Measuring and Monitoring Article, Author and Group Research Impact.


ISI first provided the means of counting citations for articles, authors, or groups (see Garfield citations). We have used the same method – of linking citing articles to cited articles via their reference lists – to create Citebase (Brody 2003, 2004; Hitchcock et al 2003), a search engine like google, but based on citation links rather than arbitrary hyperlinks, and derived from the OA database instead of the ISI database. Citebase already embodies a number of the futuristic features we listed earlier. It currently ranks articles and authors by citation impact, co-citation impact or download impact and can be extended to incorporate multiple online measures (metrics) of research impact.


With only 15% of journal articles being spontaneously self-archived overall today, this is still too sparse a database to test and analyze the power of a scientometric engine like Citebase, but %OA is near 100% in a few areas of physics, and this is where Citebase has been focused. Boolean search query results (using content words plus “and,” “or,” “not” etc.) can currently be quantified by Citebase and ranked in terms of article or author download counts, article/author citation counts, article/author co-citedness counts (how often is a sample of articles co-cited with – or by -- a given article or author?), hub/authority counts (an article is an “authority” the more it is cited by other authorities; this is similar to google’s PageRank algorithm, which does not count each of a cited article’s incoming citations as equal, but weights them by the citation counts of the article that is doing the citing; an article is a “hub” the more it cites authorities; Page et al. 1999). Citebase also has a download/citation correlator which correlates downloads and citations across an adjustable time window. Natural future extensions of these metrics include download growth-rate, latency-to-peak, and longevity indices and citation growth-rate, latency-to-peak, and longevity indices.

So far, these metrics are only being used to rank-order the results of Citebase searches, as google is used. But they have the power to do a great deal more, and will gain still more power as %OA approaches 100%. The citation and download counts can be used to compare research impact, ranking articles, authors or groups; they can also be used to compare an individual’s own research impact with itself across time. The download and citation counts have also been found to be positively correlated with one another, so that early downloads, within 6 months of publication, can predict citations after 18 months or more (Brody et al. 2005). This opens up the possibility of time-series analyses, not only on articles’, authors’ or groups’ impact trajectories over time, but the impact trajectories of entire lines of research, when the citation/download analysis is augmented by similarity/relatedness scores derived from semantic analysis of text, for example, word and pattern co-occurrence, as in latent semantic analysis (Landauer et al 1998).

The natural objective is to develop a scientometric multiple regression equation for analyzing research performance and predicting research direction based on an OA database, beginning with the existing metrics. Such an equation of course needs to be validated against other metrics. The fourteen candidate predictors so far -- [1-4] article/author citation counts, growth rates, peak latencies, longevity; [5-8] the same metrics for downloads; [ix] download/citation correlation-based predicted citations; [10-12] hub/authority scores; [12-13] co-citation (with and by) scores; [14] co-text scores) -- can be made available open-endedly via tools like citebase, so that apart from users using them to rank search query results for navigation, individuals and institutions can begin using them to rank articles, authors or groups, validating them against whatever metrics they are currently using, or simply testing them open-endedly.

The method is essentially the same for navigation as well as analysis and evaluation: A search output -- or an otherwise selected set of candidates for ranking and analysis -- could each have the potential regression scores, whose weights could be set to 0 or a range from minimum to maximum, with an adjustable weight scale for each, normalizing to one across all the non-zero weights used. Students and researchers could use such an experimental battery of metrics as different ways of ranking literature search results; editors could use them for ranking potential referees; peer-reviewers could use them to rank the relevance of references; research assessors could use them to rank institutions, departments or research groups; institutional performance evaluators could use them to rank staff for annual review; hiring committees could use them to rank candidates; authors could use them to rank themselves against their competition.

It is important to stress that at this point all of this would not only be an unvalidated regression equation, to be used only experimentally, but that even after being validated against an external criterion or criteria, it would still need to be used in conjunction with human evaluation and judgment, and the regression weights would no doubt have to be set differently for different purposes, and always open for tweaking and updating. But it will begin ushering in the era of online, interactive scientometrics based on an Open Access corpus and in the hands of all users.

The software we have already developed and will develop, together with the growing webwide database of OA articles, and the data we will collect and analyse from it, will allow us to do several things for which the unique historic moment has arrived: (1) motivate more researchers to provide OA by self-archiving; (2) map the growth of OA across disciplines, countries and languages; (3) navigate the OA literature using citation-linking and impact ranking; (4) measure, extrapolate and predict the research impact of individuals, groups, institutions, disciplines, languages and countries; (5) measure research performance and productivity, (6) assess candidates for research funding; (7) assess the outcome of research funding, (8) map the course of prior research lines, in terms of individuals, institutions, journals, fields, nations; (9) analyze and predict the direction of current and future research trajectories;(10) provide teaching and learning resources that guide students (via impact navigation) through the large and growing OA research literature in a way that navigating the web via google alone cannot come close to doing.

At the forefront in the critical developments in OA across the past decade, our research team at Southampton University, UK:

(i) hosts one of the first OA journals (since1994),

(ii) hosts the first journal OA preprint archive (since 1994)

(iii) formulated the first OA self-archiving proposal (Okerson & O’Donnell 1995)

(iv) founded one of the first central OA Archives (1997)

(v) founded the American Scientist Open Access Forum (1998)

(vi) created the first (and now the most widely used) institutional OAI-compliant Archive-creating software (Sponsler & Van de Velde 2001, adopted by over 150 universities worldwide)

(vii) co-drafted the BOAI (Budapest Open Access Initiative) self-archiving FAQ (2001)

(viii) created the first citation impact-measuring search engine (Hitchcock et al. 2003)

(ix) created the first citation-seeking tool (to trawl the web for the full text of a cited reference) (2002)

(x) designed the first OAI standardized CV (2002)

(xi) designed the first demonstration tool for predicting later citation impact

from earlier download impact (Brody et al. 2005)

(xii) compiled the BOAI (Budapest Open Access Initiative) Eprints software Handbook (2003)

(xiii) formulated the model self-archiving policy for departments and institutions (2003)

(xiv) created and maintain ROAR, the Registry of Open Access Repositories worldwide (2003)

(xv) collaborated in the creation and maintenance of the ROMEO directory of journals’ self-archiving policies (2004: of the top 9,000 journals across all fields, 92% already endorse author self-archiving)

(xvi) created and maintain ROARMAP, a registry of institutions’ self-archiving policies (2004)

(xvii) piloted the paradigm of collecting, analyzing and disseminating data on the magnitude of the OA impact advantage and the growth of OA across all disciplines worldwide (Harnad et al. 2004)

The multiple online research impact metrics we are developing will allow the rich new database , the Research Web, to be navigated, analyzed, mined and evaluated in powerful new ways that were not even conceivable in the paper era – nor even in the online era, until the database and the tools became openly accessible for online use by all: by researchers, research institutions, research funders, teachers, students, and even by the general public that funds the research and for whose benefit it is being conducted: Which research is being used most? By whom? Which research is growing most quickly? In what direction? under whose influence? Which research is showing immediate short-term usefulness, which shows delayed, longer term usefulness, and which has sustained long-lasting impact? Which research and researchers are the most authoritative? Whose research is most using this authoritative research, and whose research is the authoritative research using? Which are the best pointers (“hubs”) to the authoritative research? Is there any way to predict what research will have later citation impact (based on its earlier download impact), so junior researchers can be given resources before their work has had a chance to make itself felt through citations? Can research trends and directions be predicted from the online database? Can text content be used to find and compare related research, for influence, overlap, direction? Can a layman, unfamiliar with the specialized content of a field, be guided to the most relevant and important work? These are just a sample of the new online-age questions that the Open Research Web will begin to answer.



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