Four Reasons to Oppose the Use of Elsevier’s Services for the Medical Journal of Australia

Elsevier has a history of unethical behaviour:
  1. Elsevier created fake medical journals to promote Merck products.
  2. Elsevier sponsored arms fairs for the international sale of weapons.
  3. Elsevier sponsored a bill that would have eliminated the NIH mandate that medical research be make freely available within 12 months of publication.
  4. Elsevier requires university and medical libraries to sign agreements that prevent them from reporting the exorbitant prices the libraries pay to subscribe to Elsevier’s journals.
Thanks to such practices, Elsevier makes an outrageous level of profit, 36% of revenue- higher than BMW and higher than the mining giant Rio TintoprofitChart. While researchers and research funders are attempting to transition medical and science publishing to an open access model, Elsevier seeks to hinder this transition. It is their corporate mandate to preserve the high level of profits they make by charging subscription fees for the articles that describe taxpayer-funded research.

 


Researchers ought to be using other providers, not channeling more money into Elsevier.

 

A “tell” for researcher innumeracy?

Evaluating scientists is hard work. Assessing quality requires digging deep into a researcher’s papers, scrutinising methodological details and the numbers behind the narrative. That’s why people look for shortcuts such as the number of papers a scientist has published or the impact factor of the journals published in.

When reading a job or grant application, I frequently wonder: Does this person really take their data seriously and listen to what it’s telling them, or are they just trying to churn out papers? It can be hard to tell. But I’ve noticed an unintentional tell in the use of numbers. Some people, when reporting numbers, habitually report far more decimal places than are warranted.

For example, Thomson/ISI reports its much-derided journal impact factors to three decimal places. This is unwarranted, an example of false precision, both because of the low counts of article numbers and citations typically involved, and because their variability year to year is high. One decimal place is plenty (and given how poor a metric impact factor is, I’d prefer that impact factor simply not be used). So when a researcher reports the impact factor of every journal they’ve published in to three decimal places, to me this suggests the researcher’s numeracy is not top-notch. Impact factor is useful after all! Just not in the way intended.

I don’t necessarily expect all researchers to fully understand the sizes and variability of the numbers that go into impact factor, so I’m more concerned by how they report their own numbers. When to report all the decimal places calculated can be a subtle issue however, as full reporting of some numbers is important for reproducibility.

Bottom line, researchers should understand how summaries of data behave. Reporting numbers with faux precision is a bad sign.


For references, I’ve found some published papers on the issue of the third decimal place of impact factor:

Bar-Ilan, J. (2012). Journal report card. Scientometrics, 92, 249–260.

Mutz, R., & Daniel, H. D. (2012). The generalized propensity score methodology for estimating unbiased journal impact factors. Scientometrics, 92, 377–390.

old (from 2011) fast-track fee protest letter

There has been renewed interest in fast-track fees, after Nature Scientific Reports began piloting their use. Back in 2011, we wrote a protest letter to seven journals that were using fast-track fees at that time (some have since discontinued). The original website where we posted the letter is defunct, so I am re-posting here.


We write to ask that you discontinue the policy of fast-tracking submissions for a fee.

We have two objections to the policy. First is that we are against any form of preferential treatment for those who can pay. Fast-tracking for a fee creates a two-tier system, wherein the well-funded have an unfair advantage over the less well-to-do; in particular, it exacerbates the differences between developed and developing nations. The fast-track policy at the least allows faster publication by those with funds, improving the chance for the funded to win subsequent grants and to publish before other labs working on the same topic.

Our second objection to the policy stems from our concern that fast-tracked manuscripts will receive an advantage above and beyond just faster publication. Your policy requires that reviewers review more rapidly and editors make their decision in a shorter time than for non-fast-tracked manuscripts. There are three possible negative effects of this. First is that the reduced time for reviewers to spend on their work may lead to more superficial and less stringent reviews. Second is that the editor may sometimes have to complete their action letter on the basis of fewer reviews, when the reviewers do not finish by the deadline. The consequence is that at least some fast-tracked articles will receive less critical reviewing than those by author teams who do not pay for fast-track. The third possible negative effect reflects the linkage between fast-tracked articles and the journals finances. Your journal would receive more money if it evaluates fast-tracked articles less stringently, and even if it does not succumb to this incentive the readers may always have that perception.

Overall, the association of author fees with preferential treatment may eventually imperil sciences reputation among governments and the public. Science traditionally has been something of a refuge from the injustice of rich vs. poor, and previously in publishing there has always been the expectation that publication of an article is a mark of the quality of the work, not the depth of the pockets behind it.

Superficially, the policy of fees for fast-tracking seems similar to the Gold Open Access model, in which authors pay a fee to have their article published if it passes peer review. In most of those journals, however, the policy is set so that authors who pay are treated the same as those who dont. Most Gold OA journals offer a waiver for authors who cannot afford the usual fee, and reviewers and editors do not know whose fees are waived and whose are not. And in those unfortunate cases of journals that require a fee for all, at least there is no difference within the journal with some articles receiving preferential treatment.

We, the undersigned, will not submit work to a journal which offers competitive advantages at a financial premium; nor will we review for any such journal.

Alex O. Holcombe, PhD, Senior Lecturer, School of Psychology, University of Sydney (alex.holcombe@sydney.edu.au)
Claudia Koltzenburg, Managing editor, Cellular Therapy and Transplantation (an open access journal in Western/Russian cooperation), University Medical Center Hamburg-Eppendorf, Germany (managingeditor@ctt-journal.com)
Kaan Цztьrk, Dept. of Information Systems and Technologies, Yeditepe University, Istanbul, Turkey. (kaan.ozturk@yeditepe.edu.tr)
Ayşe Karasu, METU, Dept. of Physics, Ankara, Turkey (akarasu@metu.edu.tr)
Arman Abrahamyan, PhD, Postdoctoral Research Fellow, School of Psychology, University of Sydney (arman.abrahamyan@sydney.edu.au)
Bill Hooker, Portland, OR (cwhooker@fastmail.fm)
William Gunn, San Diego CA
Daniel Mietchen, PhD, Jena, Germany (daniel.mietchen@science3point0.com)
Daniel Linares, PhD, Generalitat de Catalunya, Spain (danilinares@gmail.com)
Barton L. Anderson, School of Psychology, University of Sydney
Kiley Seymour, PhD, Alexander von Humboldt postdoctoral fellow, Berlin, Germany
Bjorn Brembs, PhD, Heisenberg Fellow, Freie Universitдt Berlin, Germany
M Fabiana Kubke, PhD, University of Auckland, New Zealand
Graham Steel, Glasgow, Scotland ( graham at science3point0.com )
Matthew Davidson, Psychology Dept, Columbia University (matthew@psych.columbia.edu)
Richard Badge, PhD, Lecturer, Department of Genetics, University of Leicester, UK (rmb19@leicester.ac.uk)
Pedro Mendes, PhD, Professor, School of Computer Science, The University of Manchester, UK (mendes.uniman@googlemail.com)
R. Steven Kurti, PhD, Director Biomaterials and Photonics Laboratory, Loma Linda University School of Dentistry, California (skurti@llu.edu)

The above are the original authors and signatories. The link [now dead] will reveal new (post 25 April 2011) signatories.

Reporting items from a stream, and mixture modeling to reveal buffering and a bottleneck

In our basic task, one or two streams of stimuli are rapidly presented. The target(s) to be reported are highlighted with cues that encircle them. On half of trials, participants are first queried about the left target, and in half they are first queried about the right target. This has no significant effect on the main result- a substantial disadvantage in reporting the right target, if the left target must also be reported.

In our basic task, one or two streams of stimuli are rapidly presented. The target(s) to be reported are highlighted with cues that encircle them. On half of trials, participants are first queried about the left target, and in half they are first queried about the right target. This has no significant effect on the main result- a substantial disadvantage in reporting the right target, if the left target must also be reported.

My collaborators and I have started using a new behavioural technique to better understand attentional selection from a rapid stream of stimuli. We have applied this to gain insights into the effect of naps on learning (Cellini et al., in press), the nature of the attentional blink (Goodbourn et al., in preparation), and function in parietal patients.

Here I explain the technique in the context of our study of a particular attentional phenomenon called pseudoextinction (Goodbourn & Holcombe, 2015).

The technique dissociates time of sampling visual information from the nature of subsequent processing. Stimuli are presented rapidly in series (a “stream”), shown here with one stream of letters on the left and a second stream on the right.

On an unpredictable frame in the sequence, the stimuli on that frame are cued by two circles, which enclose the stimuli. The participants’ task is to report the cued stimuli, letters in this case.

Accuracy is much poorer for the cued stimulus on the right than for the cued stimulus on the left. But if only one of the streams is cued, accuracy is equally high whether the cue is on the left or the right. This deficit specific to two-target conditions is pseudoextinction. The deficit is unaffected by which stream the participant is asked to report first. It likely reflects a severe capacity limit.

a. Each response of the participant corresponds to a particular item in the stream (because all items are presented on each trial). The distribution of the positions of these items is usually centred around the time of the cue, denoted as zero. b.  Mixture modelling fits the data with a combination of two distributions, the guessing distribution shown in light grey and a Gaussian, shown in dark grey. This fit yields the latency (mean) and temporal precision (standard deviation) of the Gaussian as well as the proportion of guessing trials.

a. Each response of the participant corresponds to a particular item in the stream (because all items are presented on each trial). The distribution of the positions of these items is usually centred around the time of the cue, denoted as zero. b. Mixture modelling fits the data with a combination of two distributions, the guessing distribution shown in light grey and a Gaussian, shown in dark grey. This fit yields the latency (mean) and temporal precision (standard deviation) of the Gaussian as well as the proportion of guessing trials.

Participants’ responses were coded in terms of the serial position of the corresponding item in the stream. For example, if a participant reports the letter ‘A’ for the left stream and it was presented not at the time of the cue but two frames later, that response is coded as +2. If their report corresponds to the item immediately preceding the cued stimulus, it is coded as -1, and a report of the cued item is coded as 0. Random guesses thus will contribute an approximately uniform distribution to the histogram of serial position errors . This is quantified by mixture modelling, which determines the relative proportion of guesses and cue-related reports that best fit the data. We model the cue-related responses as a Gaussian distribution. The mixture modeling procedure yields its latency (position of the peak of the distribution relative to the time of the cue) and precision (standard deviation). It also estimates the proportion of trials that participants guessed or misperceived the letter versus the complementary proportion, which we call efficacy, of trials that participants reported a letter from around the time of the cue.

In cuing experiments, researchers typically conceive of the appearance of the cue as triggering attention to begin sampling from the scene. However, we have consistently observed that the distribution is symmetric and centred near the time of the cue. This indicates that rather than the cue triggering the intake of information from the letter stream, the letters are taken into a buffer before the cue is even presented. If letters were not already in a buffer at the time of the cue, responses from the left (earlier) side of the distribution would be relatively uncommon, skewing the distribution towards later responses.

When two streams are presented, participants perform much better for the stream on the left (if the streams are in a horizontal configuration) or much better for the stream on the top (if the streams are vertically arrayed). If only one stream is presented, participants perform approximately equally in all four positions (data not shown).

When two streams are presented, participants perform much better for the stream on the left (if the streams are in a horizontal configuration) or much better for the stream on the top (if the streams are vertically arrayed). If only one stream is presented, participants perform approximately equally in all four positions (data not shown).

The pseudoextinction phenomenon, a right-side deficit when both streams are cued, manifests both in raw accuracy and also in the accuracy-related parameter of the mixture modelling. This is the efficacy parameter – the proportion of trials captured by the cue-related Gaussian distribution. Whereas efficacy when only one stream is presented or cued is similar on both the left and the right of fixation, and above and below fixation (not shown), when two streams are presented one stream suffers. The right stream suffers in a horizontal arrangement and in a vertical arrangement the inferior stream suffers, consistent with preferred reading order.

The decrease in efficacy for the extinguished stream is not accompanied by a change in latency or standard deviation of the Gaussian distribution of cue-related responses. Moreover, the correlogram of the serial position error for the two streams reveals that the two streams are sampled independently, indicating that the items are buffered independently, without regard to reading order or which hemisphere they are processed by. Together these results suggest that items are always sampled from the stream in the same way, but a subsequent processing limitation results in pseudoextinction if two targets must be processed.

Related patterns of performance have arisen in previous literature, and typically have been attributed to a difference between the left and right hemisphere (e.g. Scalf, Banich, Kramer, Narechania, & Simon, 2007). That however cannot explain the superior/inferior difference, so researchers sometimes then appeal to a difference in dorsal vs. ventral cerebral functioning. We suspect it instead reflects attentional prioritisation of the left item for serial high-level processing, for tokenisation or memory consolidation.

References

Cellini, N., Goodbourn, P.T., McDevitt, E.A., Martini, P., Holcombe, A.O., & Mednick, S.C. (in press). A daytime nap reduces the attentional blink. Attention, Perception, & Psychophysics.

Goodbourn, P.T. & Holcombe, A.O. (2015). ‘Pseudoextinction': Asymmetries in simultaneous attentional selectionJournal of Experimental Psychology: Human Perception and Performance, 41(2), 364–84.

Martini, P. (2013) “Sources of bias and uncertainty in a visual temporal individuation task.” Attention, Perception, & Psychophysics 75: 168-181.

Scalf, P. E., Banich, M. T., Kramer, A. F., Narechania, K., & Simon, C. D. (2007). Double take: parallel processing by the cerebral hemispheres reduces attentional blink. Journal of Experimental Psychology. Human Perception and Performance, 33(2), 298–329. doi:10.1037/0096-1523.33.2.298

Nature Scientific Reports. Fast-tracking fees history and concerns.

Nature Scientific Reports has adopted is piloting fast-tracking for a fee.

Four years ago, I noticed that several journals had adopted such a policy. I raised a number of concerns, such as

  • What happens if the fast-tracking period elapses and a reviewer hasn’t gotten their review in yet? Will the decision about the manuscript be made without that review?
  • How is the additional money used? Does any go to reviewers?
  • Does the action editor know when a particular manuscript is being fast-tracked? Do the reviewers? To avoid monetary influence, both should be blind to this, but that seems impossible if these things are to be expedited.
  • Will articles which benefited from fast-tracking be indicated in a note associated with those articles? Without such a policy, all articles in the journal may be sullied, at least in the minds of cynics.
  • Are the fees worth risking the appearance of favoritism for money, the disadvantage in speed to scientists with fewer resources, and the possible loss of public trust in science?

We started a petition against the policy, and our complaints seem to have led to the demise of the policy at a few journals. For details, see my previous posts on the topic.

I suggest that the tag #fastTrackFee be used on social media to discuss this.

Animal visual arcana, tweeted

I started tweeting under the name VisFact, in addition to my normal account.

To make vision science more interesting to undergrads, I frequently contrast human visual abilities with those of various animals.

Vision is so important that some animals have evolved eyes bigger than their brain. So vision is definitely important enough to deserve this twitter account!

open letter to Society for Neuroscience regarding their new open-access journal

The below letter was spearheaded by Erin McKiernan and requests that the Society for Neuroscience bring its new open-access policy in line with emerging best practices for sharing of data and ability to re-use content. 

Dear Society for Neuroscience,

This is an open letter concerning the recent launch of the new open access journal, eNeuro.

We welcome the diversification of journal choices for authors looking for open access venues, as well as the willingness of eNeuro to accept negative results and study replications, its membership in the Neuroscience Peer Review Consortium, the publication of peer review syntheses alongside articles, and the requirement that molecular data be publicly available.

As strong supporters of open access, we welcome the commitment of the Society to making the works it publishes freely and openly available. However, we are concerned with several aspects of the specific approach, and outline herein a number of suggestions that would allow eNeuro to provide the full benefits of open access to the communities the journal aims to serve. 

Our first concern relates to the specific choice of license. The purpose of open access is to promote not just access to published content, but, equally important, its reuse. The default use of a CC BY-NC license places unreasonable restrictions on the reuse of articles published in eNeuro, and is incompatible with the standards of open access as set out by the Budapest Open Access Initiative (BOAI). NC restrictions have significant negative impact, limiting the ability to reuse material for educational purposes and advocacy to the detriment of scholarly communication. NC-encumbered materials, for example, cannot be used on Wikipedia or easily incorporated into Open Educational Resources. The NC clause also creates ambiguities and uncertainties (see for example, NC Licenses Considered Harmful) and there is little evidence on benefits of the clause to justify its use. In contrast, the value of the CC BY license is outlined in detail by the Open Access Scholarly Publishers Association. How will authors or the broader community benefit from restrictions on the commercial reuse of eNeuro content? The eNeuro fees policy acknowledges CC BY-NC is incompatible with the requirement of funders such as Research Councils UKand Wellcome Trust, and offers their authors the solution to upgrade to CC BY for a $500 surcharge. This penalizes authors funded by such agencies, as well others who choose to adhere to BOAI principles. We believe that the only way for eNeuro to deliver on its open access commitment is to make all articles CC-BY, and to set the fees to an appropriate level to support this choice.

Our second concern relates to data access. We commend the journal’s requirement that all molecular data be publicly available, but we believe the policy on sharing other types of data should be improved. The current language does not guarantee data will be made available, does not speak to the terms of data licensing, nor describes a course of action if a request for data is not fulfilled. The criterion of “appropriate scientific use” is also vague:  Would reuse of data for educational purposes, for example, meet that criterion, and who would make that decision? Open data aids in verification and replication of results, creation of new analysis tools, and can “fuel new discoveries”. The value of open data has been recognized by the Allen Institute for Brain Science, the BRAIN Initiative, and the Human Brain Project. Immediate sharing of all data types in an open repository (preferably underCC0) should be a requirement, unless prohibited by law (e.g., privacy laws). Several flexible outlets, such as Figshareand DataDryad, are available that make this easy and cost-effective.

Finally, while we commend eNeuro’s commitment to transparent peer review, we worry that only publishing a synthesis may sacrifice the richness inherent to the review process. We believe the neuroscience community would be better-served by having access to the complete reports from reviewers, as offered by PeerJ, several Biomed Central journals, and others. Reviews should also be licensed CC BY to allow for reuse in teaching materials, for example. Reviewers can be provided a mechanism to communicate confidentially with editors, removing the risk associated with making the full reviews publicly available. Reviewers should also be given the opportunity to sign their reviews for added transparency and to receive due credit for their work (e.g., through Publons).

Based on the above points, we recommend that eNeuro:

  • Makes CC BY the default license and provides equal pricing for all CC licenses;
  • Provides a transparent calculation of its article processing charges based on the publishing practices of the Society for Neuroscience and explains how additional value created by the journal will measure against the prices paid by the authors;
  • Considers offering full waivers to authors, especially those from low-income countries, who are unable to afford any publication fees;
  • Requires authors to deposit their data in a public repository (preferably under CC0), unless there are legal or ethical reasons not to do so;
  • Publishes full individual reviewer reports (CC BY licensed) alongside each article.

We hope the Society for Neuroscience will collaborate with the academic community to facilitate the dissemination of scientific knowledge through a journal committed to fully embracing the principles of open access.

We kindly request that you allow your response(s) to be made public along with this letter, and look forward to hearing your response soon.

Signatories –

Please note that the views expressed here represent those of the individuals and not the institutions or organizations with which they are affiliated.

  1. Erin C. McKiernan, independent scientist, SfN member
  2. Marco Arieli Herrera-Valdez, Universidad Nacional Autónoma de México
  3. Christopher R. Madan, University of Alberta
  4. Philippe Desjardins-Proulx, Ph.D. student
  5. Anders Eklund, Linköping University, Sweden
  6. M Fabiana Kubke, University of Auckland
  7. Alex O. Holcombe, University of Sydney
  8. Graham Steel, Open Science, Scotland
  9. Diano F. Marrone, Wilfrid Laurier University
  10. Charles Oppenheim, Professor, independent
  11. Zen Faulkes, The University of Texas-Pan American
  12. Jonathan P. Tennant, Imperial College London
  13. Nicholas M. Gardner, Marshall University
  14. Avinash Thirumalai, East Tennessee State University
  15. Travis Park, Monash University & Museum Victoria, Melbourne, Australia
  16. Ben Meghreblian, criticalscience.com, London, UK
  17. Sean Kaplan, University of the Witwatersrand, Johannesburg, South Africa
  18. Chris Chambers, Professor of cognitive neuroscience, Cardiff University, SfN member
  19. Joshua M. Nicholson, Founder of thewinnower.com, PhD Student Virginia Tech
  20. Jan Velterop, BOAI signatory and past Director of BioMed Central
  21. Timothée Poisot, University of Canterbury
  22. Jérémy Anquetin, Section d’archéologie et paléontologie, Switzerland
  23. Liz Allen, ScienceOpen
  24. Johannes Björk, Institute of Marine Sciences, Barcelona, Spain
  25. Ross Mounce, University of Bath
  26. Scott Edmunds, GigaScience, BGI Hong Kong
  27. Mayteé Cruz-Aponte, Universidad de Puerto Rico – Cayey
  28. Joseph R. Hancock, Montana State University-Bozeman
  29. Nazeefa Fatima, University of Huddersfield, UK
  30. Nitika Pant Pai, McGill University, Montreal
  31. Elizabeth Silva, San Francisco, CA
  32. Björn Brembs, University of Regensburg, Germany
  33. Gerard Ridgway, University of Oxford, UK
  34. Pietro Gatti-Lafranconi, University of Cambridge, UK
  35. Xianwen Chen, Norwegian University of Life Sciences, Norway
  36. Jacinto Dávila, Universidad de Los Andes
  37. Benjamin de Bivort, Harvard University
  38. Stephen Beckett, Ph.D. student, University of Exeter
  39. Mythili Menon, University of Southern California
  40. Adam Choraziak, behavioural strategist at RedJelly marketing
  41. Graham Triggs, Symplectic
  42. Guillaume Dumas, Institut Pasteur, FR
  43. Jeffrey W. Hollister, University of Rhode Island (adjunct)
  44. Célya Gruson-Daniel, Centre Virchow-Villermé, Université Paris Descartes, FR
  45. Gary S. McDowell, Tufts University, USA
  46. Pierre-Alexandre Klein, Institute of Neuroscience, Université de Louvain
  47. Julien Laroche, Akoustic Arts R&D Lab, Paris
  48. Alex Thome, University of Rochester
  49. Nicolas Guyon, Karolinska Institutet
  50. Sibele Fausto, University of São Paulo, Brazil
  51. Nonie Finlayson, The Ohio State University, SfN member
  52. Dalmeet Singh Chawla, Imperial College
  53. John Wilbanks, Chief Commons Officer, Sage Bionetworks
  54. David Carroll, Medical Student, Queen’s University Belfast
  55. Noelia Martínez-Molina, Brain Cognition and Plasticity Lab, Barcelona University
  56. Maximilian Sloan,  Laboratory of Molecular Neurodegeneration and Gene Therapy, University of Oxford
  57. Stephen Eglen, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, SfN member