O'Really?

June 29, 2012

Impact Factor Boxing 2012

Rocky Balboa  Philadelphia, PA

Rocky Balboa, Philadelphia, PA. Creative Commons licensed picture by seng1011 (steve eng) on Flickr.

[This post is part of an ongoing series about impact factors]

In the world of abused performance metrics, the impact factor is the undisputed heavyweight champion of the (publishing) world.

It has been an eventful year in the boxing ring of scientific publishing since the last set of figures were published by Thomson-Reuters. A brand new journal called PeerJ launched with a radical publish ’til you perish business model [1]. There’s another new journal on the way too in the shape of eLifeSciences – with it’s own significant differences from current publishing models. Then there was the Finch report on Open Access. If that wasn’t enough fun, there’s been the Alternative metrics “Altmetrics” movement gathering pace [2], alongside suggestions that the impact factor may be losing its grip on the supposed “title” [3].

The impact factors below are the most recent, published June 28th 2012, covering data from 2011. Love them or loathe them, use them or abuse them, game them or shame them … here is a tiny selection of impact factors for the 10,675 journals that are tracked in Journal Citation Reports (JCR) ordered by increasing punch power.

WARNING: Abusing these figures can seriously damage your Science – you have been warned! Normal caveats apply, see nature.com/metrics.

Journal 2011 data from isiknowledge.com/JCR Eigenfactor™ Metrics
Total Cites Impact Factor 5-Year Impact Factor Immediacy Index Articles Cited Half-life Eigenfactor™ Score Article Influence™ Score
Russian Journal of Cardiology* 3 0.005 0.000 75 0.00000
BMC Bioinformatics 14268 2.751 3.493 0.293 557 4.2 0.07757 1.314
PLoS ONE 75544 4.092 4.537 0.437 13781 2.4 0.50216 1.797
Briefings in Bioinformatics 2859 5.202 7.749 0.692 65 4.3 0.01129 2.857
PLoS Computational Biology 8924 5.215 5.844 0.710 407 3.1 0.06968 2.722
OUP Bioinformatics 43380 5.468 6.051 0.666 707 6.2 0.15922 2.606
Nucleic Acids Research 106520 8.026 7.417 2.016 1230 7.4 0.30497 3.003
Genome Biology 15556 9.036 7.896 1.550 151 5.2 0.08221 4.124
PLoS Biology 20579 11.452 13.630 2.461 180 4.6 0.14975 7.830
Science 480836 31.201 32.452 6.075 871 9.4 1.41282 17.508
Nature 526505 36.280 36.235 9.690 841 9.4 1.65658 20.353
New England Journal of Medicine 232068 53.298 50.075 11.484 349 7.8 0.66466 21.293
CA – A Cancer Journal for Clinicians** 10976 101.780 67.410 21.263 19 3.8 0.04502 24.502

* The Russian Journal of Cardiology is included here for reference as it has the lowest non-zero impact factor of any science journal. A rather dubious honour…

** The Cancer Journal for Clinicians is the highest ranked journal in science, it is included here for reference. Could it be the first journal to have an impact factor of more than 100?

References

  1. Richard Van Noorden (2012). Journal offers flat fee for ‘all you can publish’, Nature, 486 (7402) 166. DOI: 10.1038/486166a
  2. Jason Priem, Heather Piwowar and Bradley Hemminger (2012).  Altmetrics in the wild: Using social media to explore scholarly impact arxiv.org/abs/1203.4745
  3. George Lozano, Vincent Lariviere and Yves Gingras (2012). The weakening relationship between the Impact Factor and papers’ citations in the digital age arxiv.org/abs/1205.4328

May 18, 2012

Web analytics: Numbers speak louder than words

Two hundo! by B. Rosen

Two hundred light painting by B. Rosen, via  Flickr available by Creative Commons license

According to the software which runs this site, this is the 200th post here at O’Really To mark the occasion, here are some stats via WordPress with thoughts and general navel-gazing analysis paralysis [1] on web analytics. It all started just over six years ago at nodalpoint with help from Greg Tyrelle, the last four years have been WordPressed with help from Matt Mullenweg. WordPress stats are unfortunately very primitive compared to the likes of Google Analytics and don’t give you access to the server log files either. WordPress probably flatters to deceive by exaggerating page views and encouraging users to post more content, but it doesn’t count self-visits to the blog. Despite all the usual limitations of the murky underworld of web analytics and SEO, here are the stats, warts and all.

As of May 2012, this blog is just shy of 200,000 page views in total with 500+ comments (genuine) comments and 100,000+ spam comments nuked by the Akismet filter. The busiest day so far was the 15th February 2012 with 931 views of a post in a single day which got linked to by the Wall Street Journal. The regular traffic is pretty steady around the 1,000 views per week (~4000 views per month) mark. Most readers come from the United States, United Kingdom and Germany (jawohl! in that order) which breaks down as follows:

Top posts: What people read when they get here

The most popular pages here are as follows:

Page Views
Home page / Archives 33,977
Impact Factor Boxing 2010 17,267
Impact Factor Boxing 2009 10,652
How many journal articles have been published? 7,181
Impact Factor Boxing 2011 6,635

Are we obsessed with dodgy performance metrics like journal impact factors? I’m not, honest guv’, but lots of people on t’interwebs clearly are.

Top search terms: How people get here

The search engines send traffic here through the following search terms:

Search terms Views
plos biology impact factor 2010 3,175
impact factor 2010 1,631
impact factor 1,589
plos biology impact factor 1,566
impact factor 2009 1,333

Is there a correlation between Obsessive Compulsive Disorder (OCD) and Impact Factor (IF)? Probably. Will it ever stop? Probably not.

Referrals: Spread the link love

It’s not just search engines that send you traffic…

Referrer Views
Search Engines 16,339
cs.man.ac.uk 4,654
Twitter 2,334
friendfeed.com 2,262
flickr.com 2,077
researchblogging.org 1,904
en.wordpress.com 1.037

… social media (twitter, friendfeed, flickr, researchblogging and wordpress etc) refers nearly as much traffic as the search engines do. I fit the demographic of bloggers previously described [1]: male, educated and a life scientist.

Top five clicks: How people leave

This is what people are clicking on:

URL Clicks
isiknowledge.com/JCR 914
feeds2.feedburner.com/oreally 407
en.wikipedia.org/wiki/Dead_on_arrival 396
aps.org/publications/apsnews/200811/zero-gravity.cfm 363
plosbiology.org 305

Dear Thomson Reuters, you should have an associates scheme like Amazon. I’m advertising your commercial product (Journal Citation Reports) for free! I’m far too kind, please send me a generous cheque immediately for my troubles or I will remove all links to your product.

Lots of people looking for the lyrics of the Friends sitcom jingle don’t know what “Your love life’s D.O.A.” means. Glad to be of service.

Conclusions

Traffic here is fairly modest compared to some blogs, but is still significant and to my mind justifies the time spent blogging. It is great fun to blog, and like most things in life, it can be very time consuming to do well. There is a long way to go before reaching the 10,000 hours milestone, maybe one day.

What people are actually interested in reading, and what you think they will be interested in reading are often two completely different things. Solo blogging has disadvantages and it’s been very tempting to try and join one of the many excellent blogging collectives like PLoS Blogs, Occam’s Typewriter or the Guardian science blogs. For the meantime though, going it alone on a personal domain name has it’s advantages too.

So, if you’ve read, commented or linked to this site, thank you very much. I hope you enjoy reading these posts as much as I enjoy writing them. Like smartphones and wifi, it’s hard to imagine life without blogs and bloggers.

References

  1. Shema, H., Bar-Ilan, J., & Thelwall, M. (2012). Research Blogs and the Discussion of Scholarly Information PLoS ONE, 7 (5) DOI: 10.1371/journal.pone.0035869

June 28, 2011

Impact Factor Boxing 2011

Khmer Boxing by  lecercle, on Flickr[This post is part of an ongoing series about impact factors. See Impact Factor Boxing 2012 for the latest figures.]

Well it’s that time again. The annual sweaty fist-fight for supremacy between the scientific journals, as measured by impact factors, is upon us. Much ink (virtual and actual) has been spilt on the subject of impact factors, which we won’t add to here, other than to say:

Hey look, the “European” journals might be catching up with the “American” ones. [1]

So, love them, loathe them, use them, abuse them, ignore them or obsess over them… here’s a tiny selection of the 10,196 journals that are tracked in Journal Citation Reports (JCR) ordered by increasing impact.

WARNING: Abusing these figures can seriously damage your Science – you have been warned! (normal caveats apply)

Journal 2010 data from isiknowledge.com/JCR Eigenfactor™ Metrics
Total Cites Impact Factor 5-Year Impact Factor Immediacy Index Articles Cited Half-life Eigenfactor™ Score Article Influence™ Score
The Naval Architect* 16 0.005 0.004 0.005 189 0.00002 0.001
BMC Bioinformatics 12653 3.028 3.786 0.475 690 3.9 0.08086 1.495
PLoS ONE 42795 4.411 4.610 0.515 6714 2.1 0.32121 1.943
OUP Bioinformatics 40659 4.877 6.325 0.707 700 5.7 0.17973 2.649
PLoS Computational Biololgy 6849 5.515 6.251 0.727 406 2.8 0.06075 2.984
Genome Biology 14194 6.885 7.353 1.295 173 4.9 0.07688 3.585
Nucleic Acids Research 100444 7.836 7.314 1.755 1101 7.0 0.32867 3.016
Briefings in Bioinformatics 2886 9.283 7.395 1.204 49 5.8 0.01013 2.737
PLoS Biology 18453 12.469 14.375 2.706 214 4.1 0.16084 8.225
Science 469704 31.364 31.769 6.789 862 9.0 1.46485 16.859
Nature 511145 36.101 35.241 8.791 862 9.1 1.74466 19.334
New England Journal of Medicine 227674 53.484 52.362 10.675 345 7.5 0.69167 21.366
CA – A Cancer Journal for Clinicians ** 9801 94.262 70.216 8.667 18 3.8 0.04923 24.782

* The Naval Architect is included here for reference as it has the lowest non-zero impact factor of any science journal. A rather dubious honour…

** The Cancer Journal for Clinicians is the highest ranked journal in science, is included here for reference.

[Creative Commons licensed picture of Khmer boxing picture by lecercle]

References

  1. Karageorgopoulos, D., Lamnatou, V., Sardi, T., Gkegkes, I., & Falagas, M. (2011). Temporal Trends in the Impact Factor of European versus USA Biomedical Journals PLoS ONE, 6 (2) DOI: 10.1371/journal.pone.0016300

June 22, 2010

Impact Factor Boxing 2010

Golden Gloves Prelim Bouts by Kate Gardiner[This post is part of an ongoing series about impact factors. See this post for the latest impact factors published in 2012.]

Roll up, roll up, ladies and gentlemen, Impact Factor Boxing is here again. As with last year (2009), the metrics used in this combat sport are already a year out of date. But this doesn’t stop many people from writing about impact factors and it’s been an interesting year [1] for the metrics used by many to judge the relative value of scientific work. The Public Library of Science (PLoS) launched their article level metrics within the last year following the example of BioMedCentral’s “most viewed” articles feature. Next to these new style metrics, the traditional impact factors live on, despite their limitations. Critics like Harold Varmus have recently pointed out that (quote):

“The impact factor is a completely flawed metric and it’s a source of a lot of unhappiness in the scientific community. Evaluating someone’s scientific productivity by looking at the number of papers they published in journals with impact factors over a certain level is poisonous to the system. A couple of folks are acting as gatekeepers to the distribution of information, and this is a very bad system. It really slows progress by keeping ideas and experiments out of the public domain until reviewers have been satisfied and authors are allowed to get their paper into the journal that they feel will advance their career.”

To be fair though, it’s not the metric that is flawed, more the way it is used (and abused) – a subject covered in much detail in a special issue of Nature at http://nature.com/metrics [2,3,4,5]. It’s much harder than it should be to get hold of these metrics, so I’ve reproduced some data below (fair use? I don’t know I am not a lawyer…) to minimise the considerable frustrations of using Journal Citation Reports (JCR).

Love them, loathe them, use them, abuse them, ignore them or obsess over them … here’s a small selection of the 7347 journals that are tracked in JCR  ordered by increasing impact.

Journal Title 2009 data from isiknowledge.com/JCR Eigenfactor™ Metrics
Total Cites Impact Factor 5-Year Impact Factor Immediacy Index Articles Cited Half-life Eigenfactor™  Score Article Influence™ Score
RSC Integrative Biology 34 0.596 57 0.00000
Communications of the ACM 13853 2.346 3.050 0.350 177 >10.0 0.01411 0.866
IEEE Intelligent Systems 2214 3.144 3.594 0.333 33 6.5 0.00447 0.763
Journal of Web Semantics 651 3.412 0.107 28 4.6 0.00222
BMC Bionformatics 10850 3.428 4.108 0.581 651 3.4 0.07335 1.516
Journal of Molecular Biology 69710 3.871 4.303 0.993 916 9.2 0.21679 2.051
Journal of Chemical Information and Modeling 8973 3.882 3.631 0.695 266 5.9 0.01943 0.772
Journal of the American Medical Informatics Association (JAMIA) 4183 3.974 5.199 0.705 105 5.7 0.01366 1.585
PLoS ONE 20466 4.351 4.383 0.582 4263 1.7 0.16373 1.918
OUP Bioinformatics 36932 4.926 6.271 0.733 677 5.2 0.16661 2.370
Biochemical Journal 50632 5.155 4.365 1.262 455 >10.0 0.10896 1.787
BMC Biology 1152 5.636 0.702 84 2.7 0.00997
PLoS Computational Biology 4674 5.759 6.429 0.786 365 2.5 0.04369 3.080
Genome Biology 12688 6.626 7.593 1.075 186 4.8 0.08005 3.586
Trends in Biotechnology 8118 6.909 8.588 1.407 81 6.4 0.02402 2.665
Briefings in Bioinformatics 2898 7.329 16.146 1.109 55 5.3 0.01928 5.887
Nucleic Acids Research 95799 7.479 7.279 1.635 1070 6.5 0.37108 2.963
PNAS 451386 9.432 10.312 1.805 3765 7.6 1.68111 4.857
PLoS Biology 15699 12.916 14.798 2.692 195 3.5 0.17630 8.623
Nature Biotechnology 31564 29.495 27.620 5.408 103 5.7 0.14503 11.803
Science 444643 29.747 31.052 6.531 897 8.8 1.52580 16.570
Cell 153972 31.152 32.628 6.825 359 8.7 0.70117 20.150
Nature 483039 34.480 32.906 8.209 866 8.9 1.74951 18.054
New England Journal of Medicine 216752 47.050 51.410 14.557 352 7.5 0.67401 19.870

Maybe next year Thomson Reuters, who publish this data, could start attaching large government health warnings (like on cigarette packets) and long disclaimers to this data? WARNING: Abusing these figures can seriously damage your Science – you have been warned!

References

  1. Rizkallah, J., & Sin, D. (2010). Integrative Approach to Quality Assessment of Medical Journals Using Impact Factor, Eigenfactor, and Article Influence Scores PLoS ONE, 5 (4) DOI: 10.1371/journal.pone.0010204
  2. Abbott, A., Cyranoski, D., Jones, N., Maher, B., Schiermeier, Q., & Van Noorden, R. (2010). Metrics: Do metrics matter? Nature, 465 (7300), 860-862 DOI: 10.1038/465860a
  3. Van Noorden, R. (2010). Metrics: A profusion of measures Nature, 465 (7300), 864-866 DOI: 10.1038/465864a
  4. Braun, T., Osterloh, M., West, J., Rohn, J., Pendlebury, D., Bergstrom, C., & Frey, B. (2010). How to improve the use of metrics Nature, 465 (7300), 870-872 DOI: 10.1038/465870a
  5. Lane, J. (2010). Let’s make science metrics more scientific Nature, 464 (7288), 488-489 DOI: 10.1038/464488a

[Creative Commons licensed picture of Golden Gloves Prelim Bouts by Kate Gardiner ]

September 18, 2009

Popular, personal and public data: Article-level metrics at PLoS

PLoS: The Public Library of ScienceThe Public Library of Science (PLoS) is a non-profit organisation committed to making the world’s scientific and medical literature freely accessible to everyone via open access publishing. As recently announced they have just published the first article-level metrics (e.g. web server logs and related information) for all articles in their library. This is novel, interesting and potentially useful data, not currently made publicly available by other publishers. Here is a  selection of some of the data, taken from the full dataset here (large file), which includes the “top ten” papers by viewing statistics.

Article level metrics for some papers published in PLoS (August 2009)

Rank* Article Journal Views Citations**
1 Why Most Published Research Findings Are False (including this one?) [1] PLoS Medicine 232847 52
2 Initial Severity and Antidepressant Benefits: A Meta-Analysis of Data Submitted to the Food and Drug Administration [2] PLoS Medicine 182305 15
3 Serotonin and Depression: A Disconnect between the Advertisements and the Scientific Literature [3] PLoS Medicine 105498 16
4 The Diploid Genome Sequence of an Individual Human [4] PLoS Biology 88271 54
5 Ultrasonic Songs of Male Mice [5] PLoS Biology 81331 8
6 Complete Primate Skeleton from the Middle Eocene of Messel in Germany: Morphology and Paleobiology [6] PLoS ONE 62449 0
7 The Impact Factor Game: It is time to find a better way to assess the scientific literature [7] PLoS Medicine 61353 13
8 A Map of Recent Positive Selection in the Human Genome [8] PLoS Biology 59512 94
9 Mapping the Structural Core of Human Cerebral Cortex [9] PLoS Biology 58151 8
10 Ten Simple Rules for Getting Published [10] PLoS Computational Biology 57312 1
11 Men, Women, and Ghosts in Science [11] PLoS Biology 56982 0
120 Defrosting the Digital Library: Bibliographic Tools for the Next Generation Web [12] (w00t!) PLoS Computational Biology 16295 3
1500 Specificity and evolvability in eukaryotic protein interaction networks [13] PLoS Computational Biology 4270 7
1632 Comparative genomics and disorder prediction identify biologically relevant SH3 protein interactions [14] PLoS Computational Biology 4063 10
1755 Folding Very Short Peptides Using Molecular Dynamics [15] PLoS Computational Biology 3876 2
2535 Microblogging the ISMB: A New Approach to Conference Reporting [16] PLoS Computational Biology 3055 1
7521 Probing the Flexibility of Large Conformational Changes in Protein Structures through Local Perturbations [17] PLoS Computational Biology 1024 0
12549 Deciphering Proteomic Signatures of Early Diapause in Nasonia [18] PLoS ONE 0 0

*The rank is based on the 12,549 papers for which viewing data (combined usage of HTML + PDF + XML) are available.

**Citation counts are via PubMedCentral (data from CrossRef and Scopus is also provided, see Bora’s comments and commentary at Blue Lab Coats.)

Science is not a popularity contest but…

Analysing this data is not straightforward. Some highly-viewed articles are never cited (reviews, editorial, essays, opinion, etc). Likewise, popularity and importance are not the same thing. Some articles get lots of citations but few views, which suggests that people are not actually reading the papers them before citing them. As described on the PLoS website article-level-metrics.plos.org:

“When looking at Article-Level Metrics for the first time bear the following points in mind:

  • Online usage is dependent on the article type, the age of the article, and the subject area(s) it is in. Therefore you should be aware of these effects when considering the performance of any given article.
  • Older articles normally have higher usage than younger ones simply because the usage has had longer to accumulate. Articles typically have a peak in their usage in the first 3 months and usage then levels off after that.
  • Spikes of usage can be caused by media coverage, usage by large numbers of people, out of control download scripts or any number of other reasons. Without a detailed look at the raw usage logs it is often impossible to tell what the reason is and so we encourage you to regard usage data as indicative of trends, rather than as an absolute measure for any given article.
  • We currently have missing usage data for some of our articles, but we are working to fill the gaps. Primarily this affects those articles published before June 17th, 2005.
  • Newly published articles do not accumulate usage data instantaneously but require a day or two before data are shown.
  • Article citations as recorded by the Scopus database are sometimes undercounted because there are two records in the database for the same article. We’re working with Scopus to correct this issue.
  • All metrics will accrue over time (and some, such as citations, will take several years to accrue). Therefore, recent articles may not show many metrics (other than online usage, which accrues from day one). ”

So all the usual caveats apply when using this bibliometric data. Despite the limitations, it is more revealing than the useful (but simplistic) “highly accesssed” papers at BioMedCentral, which doesn’t always give full information on what “highly” actually means next to each published article. It will be interesting to see if other publishers now follow the lead of PLoS and BioMed Central and also publish their usage data combined with other bibliometric indicators such as blog coverage. For authors publishing with PLoS, this data has an added personal dimension too, it is handy to see how many views your paper has.

As paying customers of the services that commercial publishers provide, should scientists and their funders be demanding more of this kind of information in the future? I reckon they should. You have to wonder, why these kind of innovations have taken so long to happen, but they are a welcome addition.

[More commentary on this post over at friendfeed.]

References

  1. Ioannidis, J. (2005). Why Most Published Research Findings Are False PLoS Medicine, 2 (8) DOI: 10.1371/journal.pmed.0020124
  2. Kirsch, I., Deacon, B., Huedo-Medina, T., Scoboria, A., Moore, T., & Johnson, B. (2008). Initial Severity and Antidepressant Benefits: A Meta-Analysis of Data Submitted to the Food and Drug Administration PLoS Medicine, 5 (2) DOI: 10.1371/journal.pmed.0050045
  3. Lacasse, J., & Leo, J. (2005). Serotonin and Depression: A Disconnect between the Advertisements and the Scientific Literature PLoS Medicine, 2 (12) DOI: 10.1371/journal.pmed.0020392
  4. Levy, S., Sutton, G., Ng, P., Feuk, L., Halpern, A., Walenz, B., Axelrod, N., Huang, J., Kirkness, E., Denisov, G., Lin, Y., MacDonald, J., Pang, A., Shago, M., Stockwell, T., Tsiamouri, A., Bafna, V., Bansal, V., Kravitz, S., Busam, D., Beeson, K., McIntosh, T., Remington, K., Abril, J., Gill, J., Borman, J., Rogers, Y., Frazier, M., Scherer, S., Strausberg, R., & Venter, J. (2007). The Diploid Genome Sequence of an Individual Human PLoS Biology, 5 (10) DOI: 10.1371/journal.pbio.0050254
  5. Holy, T., & Guo, Z. (2005). Ultrasonic Songs of Male Mice PLoS Biology, 3 (12) DOI: 10.1371/journal.pbio.0030386
  6. Franzen, J., Gingerich, P., Habersetzer, J., Hurum, J., von Koenigswald, W., & Smith, B. (2009). Complete Primate Skeleton from the Middle Eocene of Messel in Germany: Morphology and Paleobiology PLoS ONE, 4 (5) DOI: 10.1371/journal.pone.0005723
  7. The PLoS Medicine Editors (2006). The Impact Factor Game PLoS Medicine, 3 (6) DOI: 10.1371/journal.pmed.0030291
  8. Voight, B., Kudaravalli, S., Wen, X., & Pritchard, J. (2006). A Map of Recent Positive Selection in the Human Genome PLoS Biology, 4 (3) DOI: 10.1371/journal.pbio.0040072
  9. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C., Wedeen, V., & Sporns, O. (2008). Mapping the Structural Core of Human Cerebral Cortex PLoS Biology, 6 (7) DOI: 10.1371/journal.pbio.0060159
  10. Bourne, P. (2005). Ten Simple Rules for Getting Published PLoS Computational Biology, 1 (5) DOI: 10.1371/journal.pcbi.0010057
  11. Lawrence, P. (2006). Men, Women, and Ghosts in Science PLoS Biology, 4 (1) DOI: 10.1371/journal.pbio.0040019
  12. Hull, D., Pettifer, S., & Kell, D. (2008). Defrosting the Digital Library: Bibliographic Tools for the Next Generation Web PLoS Computational Biology, 4 (10) DOI: 10.1371/journal.pcbi.1000204
  13. Beltrao, P., & Serrano, L. (2007). Specificity and Evolvability in Eukaryotic Protein Interaction Networks PLoS Computational Biology, 3 (2) DOI: 10.1371/journal.pcbi.0030025
  14. Beltrao, P., & Serrano, L. (2005). Comparative Genomics and Disorder Prediction Identify Biologically Relevant SH3 Protein Interactions PLoS Computational Biology, 1 (3) DOI: 10.1371/journal.pcbi.0010026
  15. Ho, B., & Dill, K. (2006). Folding Very Short Peptides Using Molecular Dynamics PLoS Computational Biology, 2 (4) DOI: 10.1371/journal.pcbi.0020027
  16. Saunders, N., Beltrão, P., Jensen, L., Jurczak, D., Krause, R., Kuhn, M., & Wu, S. (2009). Microblogging the ISMB: A New Approach to Conference Reporting PLoS Computational Biology, 5 (1) DOI: 10.1371/journal.pcbi.1000263
  17. Ho, B., & Agard, D. (2009). Probing the Flexibility of Large Conformational Changes in Protein Structures through Local Perturbations PLoS Computational Biology, 5 (4) DOI: 10.1371/journal.pcbi.1000343
  18. Wolschin, F., & Gadau, J. (2009). Deciphering Proteomic Signatures of Early Diapause in Nasonia PLoS ONE, 4 (7) DOI: 10.1371/journal.pone.0006394

July 24, 2009

Escape from the impact factor: The Great Escape?

The Great Escape with Steve McQueenQuite by chance, I stumbled on this interesting paper [1] yesterday by Philip Campbell who is the Editor-in-Chief of the scientific über-journal Nature [2]. Here is the abstract:

As Editor-in-Chief of the journal Nature, I am concerned by the tendency within academic administrations to focus on a journal’s impact factor when judging the worth of scientific contributions by researchers, affecting promotions, recruitment and, in some countries, financial bonuses for each paper. Our own internal research demonstrates how a high journal impact factor can be the skewed result of many citations of a few papers rather than the average level of the majority, reducing its value as an objective measure of an individual paper. Proposed alternative indices have their own drawbacks. Many researchers say that their important work has been published in low-impact journals. Focusing on the citations of individual papers is a more reliable indicator of an individual’s impact. A positive development is the increasing ability to track the contributions of individuals by means of author-contribution statements and perhaps, in the future, citability of components of papers rather than the whole. There are attempts to escape the hierarchy of high-impact-factor journals by means of undifferentiated databases of peer-reviewed papers such as PLoS One. It remains to be seen whether that model will help outstanding work to rise to due recognition regardless of editorial selectivity. Although the current system may be effective at measuring merit on national and institutional scales, the most effective and fair analysis of a person’s contribution derives from a direct assessment of individual papers, regardless of where they were published.

It’s well worth reading the views of the editor of an important closed-access journal like Nature, a world champion heavyweight of Impact Factor Boxing. So their view on article-level bibliometrics and novel models of scientific publishing on the Web like PLoS ONE is enlightening. There are some interesting papers in the same issue, which has a special theme on the use and misuse of bibliometric indices in evaluating scholarly performance. Oh, and the article is published in an Open Access Journal too. Is it just me, or is there a strong smell of irony in here?

References

  1. Philip Campbell (2008). Escape from the impact factor Ethics in Science and Environmental Politics, 8, 5-7 DOI: 10.3354/esep00078
  2. Philip Campbell (1995). Postscript from a new hand Nature, 378 (6558), 649-649 DOI: 10.1038/378649b0
  3. John Sturges (1963) The Great Escape

June 23, 2009

Impact Factor Boxing 2009

Fight Night Punch Test by djclear904[This post is part of an ongoing series about impact factors]

The latest results from the annual impact factor boxing world championship contest are out. This is a combat sport where scientific journals are scored according to their supposed influence and impact in Science. This years competition rankings include the first-ever update to the newly introduced Five Year Impact Factor and Eigenfactor™ Metrics [1,2] in Journal Citation Reports (JCR) on the Web (see www.isiknowledge.com/JCR warning: clunky website requires subscription*), presumably in response to widespread criticism of impact factors. The Eigenfactor™ seems to correlate quite closely with the impact factor scores, both of which work at the level of the journal, although they use different methods for measuring a given journals impact. However, what many authors are often more interested in is the impact of an individual article, not the journal where it was published. So it would be interesting to see how the figures below tally with Google Scholar, see also comments by Abhishek Tiwari. I’ve included a table below of bioinformatics impact factors, updated for June 2009. Of course, when I say 2009 (today), I mean 2008 (these are the latest figures available based on data from 2007) – so this shiny new information published this week is already out of date [3] and flawed [4,5] but here is a selection of the data anyway: [update: see figures published in June 2010.]

Journal Title 2008 data from isiknowledge.com/JCR Eigenfactor™ Metrics
Total Cites Impact Factor 5-Year Impact Factor Immediacy Index Articles Cited Half-life Eigenfactor™ Score Article Influence™ Score
BMC Bionformatics 8141 3.781 4.246 0.664 607 2.8 0.06649 1.730
OUP Bioinformatics 30344 4.328 6.481 0.566 643 4.8 0.18204 2.593
Briefings in Bioinformatics 2908 4.627 1.273 44 4.5 0.02188
PLoS Computational Biology 2730 5.895 6.144 0.826 253 2.1 0.03063 3.370
Genome Biology 9875 6.153 7.812 0.961 229 4.4 0.07930 3.858
Nucleic Acids Research 86787 6.878 6.968 1.635 1070 6.5 0.37108 2.963
PNAS 416018 9.380 10.228 1.635 3508 7.4 1.69893 4.847
Science 409290 28.103 30.268 6.261 862 8.4 1.58344 16.283
Nature 443967 31.434 31.210 8.194 899 8.5 1.76407 17.278

The internet is radically changing the way we communicate and this includes scientific publishing, as media mogul Rupert Murdoch once pointed out big will not beat small any more – it will be the fast beating the slow.  An interesting question for publishers and scientists is, how can the Web help the faster flyweight and featherweight boxers (smaller journals) compete and punch-above-their-weight with the reigning world champion heavyweights (Nature, Science and PNAS)? Will the heavyweight publishers always have the killer knockout punches? If you’ve got access to the internet, then you already have a ringside seat from which to watch all the action. This fight should be entertaining viewing and there is an awful lot of money riding on the outcome [6-11].

Seconds away, round two…

References

  1. Fersht, A. (2009). The most influential journals: Impact Factor and Eigenfactor Proceedings of the National Academy of Sciences, 106 (17), 6883-6884 DOI: 10.1073/pnas.0903307106
  2. Bergstrom, C., & West, J. (2008). Assessing citations with the Eigenfactor Metrics Neurology, 71 (23), 1850-1851 DOI: 10.1212/01.wnl.0000338904.37585.66
  3. Cockerill, M. (2004). Delayed impact: ISI’s citation tracking choices are keeping scientists in the dark. BMC Bioinformatics, 5 (1) DOI: 10.1186/1471-2105-5-93
  4. Allen, L., Jones, C., Dolby, K., Lynn, D., & Walport, M. (2009). Looking for Landmarks: The Role of Expert Review and Bibliometric Analysis in Evaluating Scientific Publication Outputs PLoS ONE, 4 (6) DOI: 10.1371/journal.pone.0005910
  5. Grant, R.P. (2009) On article-level metrics and other animals Nature Network
  6. Corbyn, Z. (2009) Do academic journals pose a threat to the advancement of Science? Times Higher Education
  7. Fenner, M. (2009) PLoS ONE: Interview with Peter Binfield Gobbledygook blog at Nature Network
  8. Hoyt, J. (2009) Who is killing science on the Web? Publishers or Scientists? Mendeley Blog
  9. Hull, D. (2009) Escape from the Impact Factor: The Great Escape? O’Really? blog
  10. Murray-Rust, P. (2009) THE article: Do academic journals pose a threat to the advancement of science? Peter Murray-Rust’s blog: A Scientist and the Web
  11. Wu, S. (2009) The evolution of Scientific Impact shirleywho.wordpress.com

* This important data should be freely available (e.g. no subscription), since crucial decisions about the allocation of public money depend on it, but that’s another story.

[More commentary on this post over at friendfeed. CC-licensed Fight Night Punch Test by djclear904]

February 20, 2009

Mistaken Identity: Google thinks I’m Maurice Wilkins

Who's afraid of Google?In a curious case of mistaken identity, Google seems to think I’m Maurice Wilkins. Here is how. If you Google the words DNA and mania (google.com/search?q=dna+mania) one of the first results is a tongue-in-cheek article I wrote two years ago about our obsession with Deoxyribonucleic Acid. Now Google (or more precisely Googlebot) seems to think this article is written by one M Wilkins. That’s M Wilkins as in the physicist Maurice Wilkins, the third man of the double helix (after Watson and Crick) and Nobel prize winner back in ’62. How could such a silly (but amusing) mistake be made? Because the article is about what Wilkins once said, but not actually by Wilkins. Computers can’t tell the difference between these two things. Consequently, it has been known for some time that Google Scholar has many other mistaken identities for authors like this. Scholar even thinks there is an author called Professor Forgotten Password (a prolific author who has been widely cited in many fields)!

The other curiosity is this, the original post on nodalpoint.org is also counted as a citation in Google Scholar too. It’s a bit of a mystery how scholar actually works, what it includes (and excludes) and how big it is, but you’ll find the article counted as a proper citation for a book about genes. Scientific spammers must be licking their lips with the opportunity to influence results and citation counts, with humble blog posts, rather than more kosher articles in peer-reviewed scientific journals.

So what does this all this curious interweb mischief tell us?

  1. Identifying people on the web is a tricky business, more complex than most people think
  2. Googlebot needs to have its algowithms tweaked by those Google Scholars at the Googleplex. Not really surprising, what else did you expect from Beta software? (P.S. Googlebot, when you read this, I’m not Maurice Wilkins, that’s not my name. I haven’t won a Nobel prize either.  I’m sort of flattered that you’ve mistaken me for such a distinguished scientist, so I’ll enjoy my alternative identity while it lasts.)
  3. Blogs are increasingly part of the scientific conversation, counted in various bibliometrics, will Google Scholar (and the rest) start indexing other blogs too? Where will this trend leave more conventional bibliometrics like the impact factor?

(Note: These search results were correct at the time of writing, but may change over time, results preserved for posterity on flickr)

References

  1. Maurice Wilkins (2003) The Third Man of the Double Helix: The Autobiography of Maurice Wilkins isbn:0198606656
  2. Péter Jacsó (2008) Savvy searching – Google Scholar revisited. Online Information Review 32: 102-11 DOI:10.1108/14684520810866010 (see also Defrosting the Digital Library)
  3. Douglas Kell (2008) What’s in a name? Guest, ghost and indeed quite imaginary authorships BBSRC blogs
  4. Neil R. Smalheiser and Vetle I. Torvik Author Name Disambiguation (This is a preprint version of a chapter published in Volume 43 (2009) of the Annual Review of Information Science and Technology (ARIST) (B. Cronin, Ed.) which is available from the publisher Information Today, Inc (http://books.infotoday.com/asist/#arist).
  5. Duncan Hull (2007) DNA mania. Nodalpoint.org
  6. Jules De Martino and Katie White (2008) That’s not my name (video)

August 22, 2008

If Science was an Olympic Sport…

Olympic Rings by JL08A fictional scene from the future: The Olympic games, London 2012. A new candidate sport is on trial, joining skateboarding, rugby and golf at their debut Olympic games. It is challenging discipline called Science, a sport more ancient than Olympia itself. The crowd awaits eagerly in the all new Boris Johnson Olympic stadium. It has taken more than 2000 years just to convince the International Olympic Committee that Science is worthy of being an Olympic sport. The big day has finally arrived but the judges are still arguing about how to award the medals to scientists. Despite all the metrics involved, it’s all very very subjective. The games go ahead anyway, and there are lots of exciting new events: (more…)

November 1, 2006

Bioinformatics Impact Factors

B of the Bang (in Big Bangchester)There are all sorts of flaws with using impact factors for judging the quality of biomedical research. Love them or hate them, just getting hold of impact factors for journals in bioinformatics and related fields is much harder than it should be, so I thought I’d reproduce some statistics I gathered here. The rankings, which you should use with caution [1,2], are correct as of June 2006 (and apply to citations in 2005) courtesy of Journal Citation Reports®, part of Thomson ISI Web of Knowledge. JCR has a pretty horrible clunky web interface when compared to some of its rivals [3,4], maybe one day they’ll make it better. Anyway, this is not a comprehensive list, just a fairly random selection of bioinformatics and computer science journals that publish articles I’ve been reading the last few years.

Journal ISI impact factor
Science 30.927
Cell 29.431
Nature Reviews Molecular Cell Biology 29.852
Nature 29.273
Nature Genetics 25.797
Nature Biotechnology 22.378
Nature Reviews Drug Discovery 18.775
PLOS Biology 14.672
PNAS 10.231
Genome Research 10.139
Genome Biology 9.712
Drug Discovery Today 7.755
Nucleic Acids Research 7.552
Bioessays 6.787
Plant Physiology 6.114
Bioinformatics (OUP) 6.019
BMC Bioinformatics 4.958
BMC Genomics 4.092
Proteins: structure, function and bioinformatics 4.684
IEEE Intelligent Systems 2.560
Journal of Computational Biology 2.446
Journal of Biomedical Informatics 2.388
IEEE Internet Computing 2.304
Artificial Intelligence in Medicine 1.882
Comparative and Functional Genomics 0.992
Concurrency and Computation: Practice and experience 0.535
Briefings in Bioinformatics (OUP) not listed
PLOS Computational Biology not listed
Journal of Web Semantics not listed

One point of interest, cheeky young upstart BioMed Central Bioinformatics (going since 2000) seems to be catching up on traditional old-school favourite OUP Bioinformatics (going since 1985), which as mentioned on nodalpoint, has been publishing some dodgy parser papers lately.

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