Guidelines for archiving data AND code

The following is a cross-post from the Editor's blog of The American Naturalist, developed with input from various volunteers (credited below).

A CHECKLIST FOR REPRODUCIBLE ARCHIVING DATA AND CODE IN ECOLOGY, EVOLUTION, AND BEHAVIOR

 

December 1, 2021

Daniel I. Bolnick (daniel.bolnick@uconn.edu), Roger Schürch (rschurch@vt.edu), Daniel Vedder (daniel.vedder@idiv.de), Max Reuter (m.reuter@ucl.ac.uk), Leron Perez (leron@stanford.edu), Robert Montgomerie (mont@queensu.ca)

Starting January 1, 2022, The American Naturalist will require that any analysis and simulation code (R scripts, Matlab scripts, Mathematica notebooks) used to generate reported results be archived in a public repository (we specifically prefer Dryad, see below). This has been our recommendation for a couple of years, and author compliance has been very common. As part of our commitment to Open and Reproducible Science, we are transitioning to make this a requirement. The following document, developed with input from a variety of volunteers, is intended to be a relatively basic guide to help authors comply with this new requirement.

 

 

RATIONALE

The fundamental question you should ask yourself is, “If a reader downloads my data and code, will my scripts be comprehensible, and will they run to completion and yield the same results on their computer?” Any computer code used to generate scientific results should be readily usable by reviewers or readers. Sharing this information is vital for several reasons as it promotes: (i) the appropriate interpretation of results, (ii) checking the validity of analyses and conclusions, (iii) future data synthesis, (iv) replication, and (v) their use as a teaching tool for anyone learning to do analyses themselves. Shared code provides greater confidence in results. 

The recommendations below are designed to help authors conduct a final check when finishing a research project, before submitting a manuscript for publication. In our experience, you will find it easier to build reusable code and data if you adhere to these recommendations from the start of your research project. We separately list requirements, and recommendations in each category below.

 

1. CLEAR DOCUMENTATION

 

➤  Great template available here: https://github.com/gchure/reproducible_research

 

REQUIRED:

 

➤ Prepare a single README file with important information about your data repository as a whole (code and data files). Text (.txt or .rtf) and Markdown (.md) README files are readable by a wider variety of free and open source software tools, so have greater longevity. The README file should simply be called README.txt (or .rtf or .md). That file should contain, in the following order:

Citation to the publication associated with the datasets and code 

Author names, contact details

A brief summary of what the study is about 

 Identify who is responsible for collecting data and writing code.

List of all folders and files by name, and a brief description of their contents. For each data file, list all variables (e.g., columns) with a clear description of each variable (e.g., units)

Information about versions of packages and software used (including operating system) and dependencies (if these are not installed by the script itself). An easy way to get this information is to use sessionInfo() in R, or 'pip list --format freeze' in Python.

 

RECOMMENDED (for inclusion in the README file):

➤  Provide workflow instructions for users to run the software (e.g., explain the project workflow, and any configuration parameters of your software)

➤ Use informative names for folders and files (e.g., “code”, “data”, “outputs”)

➤  Provide license information, such as Creative Commons open source license language granting readers the right to reuse code. For more information on how to choose and write a license, see choosealicense.com. This is not necessary for DRYAD repositories, as you choose licensing standards when submitting your files.

➤ If applicable, list funding sources used to generate the archived data, and include information about permits (collection, animal care, human research). This is not necessary for DRYAD repositories, as it is also recoded when submitting your files.

➤  Link to Protocols.io or equivalent methods repositories where applicable

2. CLEAN CODE

REQUIRED:

➤  Scripts should start by loading required packages, then importing raw data from files archived in your data repository.

➤  Use relative paths to files and folders (e.g. avoid setwd() with an absolute path in R), so other users can replicate your data input steps on their own computers. 

➤ Annotate your code with comments indicating what the purpose of each set of commands is (i.e., “why?”). If the functioning of the code (i.e., “how”) is unclear, strongly consider re-writing it to be clearer/simpler.  In-line comments can provide specific details about a particular command

➤ Annotate code to indicate how commands correspond to figure numbers, table numbers, or subheadings of results within the manuscript.

➤  If you are adapting other researcher’s published code for your own purposes, acknowledge and cite the sources you are using. Likewise, cite the authors of packages that you use in your published article.

 

RECOMMENDED:

➤  Test code before uploading to your repository, ideally on a pristine machine without any packages installed, but at least using a new session.

➤  Use informative names for input files, variables, and functions (and describe them in the README file).

➤  Any data manipulations (merging, sorting, transforming, filtering) should be done in your script, for fully transparent documentation of any changes to the data.

➤  Organise your code by splitting it into logical sections, such as importing and cleaning data, transformations, analysis and graphics and tables. Sections can be separate script files run in order (as explained in your README) or blocks of code within one script that are separated by clear breaks (e.g., comment lines, #--------------), or a series of function calls (which can facilitate reuse of code).

➤  Label code sections with headers that match the figure number, table number, or text subheading of the paper.

➤  Omit extraneous code not used for generating the results of your publication, or place any such code in a Coda at the end of your script.

➤  Where useful, save and deposit intermediate steps as their own files. Particularly, if your scripts include computationally intensive steps, it can be helpful to provide their output as an extra file as an alternative entry point to re-running your code. 

➤  If your code contains any stochastic process (e.g., random number generation, bootstrap re-sampling), set a random number seed at least once at the start of the script or, better, for each random sampling task. This will allow other users to reproduce your exact results.

➤  Include clear error messages as annotations in your code that explain what might go wrong (e.g., if the user gave a text input where a numeric input was expected) and what the effect of the error or warning is.

 

3. CLEAN DATA

Checklist for preparing data to upload to DRYAD (or other repository)

 

Repository contents 

REQUIRED: 

 ➤ All data used to generate a published result should be included in the repository. For papers with multiple experiments or sets of observations, this may mean more than one data file.

➤ Save each file with a short, meaningful file name and extension (see DRYAD recommendations here).

➤ Prepare a README text file to accompany the data. Our recommendation is to put this in the single README file described above. For complex repositories where this readme becomes unmanageably long, you may opt to create a set of separate README files for the overall repository, with one master README and more specific README files for code and for data. But, our preference is one README. The README file(s) should provide a brief overall description of each data file’s contents, and a list of all variable names with explanation (e.g. units). This should allow a new reader to understand what the entries in each column mean and relate this information to the Methods and Results of your paper. Alternatively, this may be a “Codebook” file in a table format with each variable as a row and column providing variable names (in the file), descriptions (e.g. for axis labels), units, etc. 

➤ Save the README files as a text (.txt) or Markdown (.md) files and all of the data files as comma-separated variable (.csv) files. 

➤ Save all of the data files as comma-separated variable (.csv) files. If your data are in EXCEL spreadsheets you are welcome to submit those as well (to be able to use colour coding and provide additional information, such as formulae) but each worksheet of data should also be saved as a separate .csv file.

RECOMMENDED:

➤ We recommend also archiving any digital material used to generate data (e.g., photos, sound recordings, videos, etc), but this may require too much storage space for some repository sites. At a minimum, upload a few example files illustrating the nature of the material and a range of outcomes. We recognize that some projects entail too much raw data to archive all the photos / videos / etc in their original state.

Data file contents and formatting  

REQUIREMENTS: 

➤ Archived files should include the raw data that you used when you first began analyses, not group means or other summary statistics; for convenience, summary statistics can be provided in a separate file, or generated by code archived with the data.

➤ Identify each variable (column names) with a short name. Variable names should preferably be <10 characters long and not contain any spaces or special characters that could interfere with reading the data and running analysis code. Use an underline (e.g., wing_length) or camel case (e.g., WingLength) to distinguish words if you think that is needed.

RECOMMENDATIONS: 

➤ Omit variables not analyzed in your code.

➤ A common data structure is to ensure that every observation is a row and every variable is a column.

➤ Each column should contain only one data type (e.g., do not mix numerical values and comments or categorical scores in a single column).

➤  Use “NA” or equivalent to indicate missing data (and specify what you use in the README file)

4. COMPLETING YOUR ARCHIVE FOR UPLOAD TO DRYAD

REQUIREMENTS:

➤ Upload your data and code to a curated, version-controlled repository (e.g., DRYAD, zenodo). Your own GitHub account (or other privately or agency controlled website) does not qualify as a public archive because you control access and might take down the data at a later date.

➤ Provide all the metadata and information requested by the repository, even if this is optional and redundant with information contained in the README files. Metadata makes your archived material easier to find and understand.

➤ From the repository, get a private URL and provide this on submission of your manuscript so that editors and reviewers can access your archive before your data are made public.

 

RECOMMENDED:
➤ Prepare your data, code, and README files, before or during manuscript preparation (analysis and writing).

 

5. FOR MORE INFORMATION

More detailed guides to reproducible code principles can be found here:

Documenting Python Code: A Complete Guide - https://realpython.com/documenting-python-code/

A guide to reproducible code in Ecology and Evolution, British Ecological Society: https://www.britishecologicalsociety.org/wp-content/uploads/2019/06/BES-Guide-Reproducible-Code-2019.pdf?utm_source=web&utm_medium=web&utm_campaign=better_science 

Dokta tools for building code repositories:  https://github.com/stencila/dockta#readme

Version management for python projects:  https://python-poetry.org/

Principles of Software Development - an Introduction for Computational Scientists (https://doi.org/10.5281/zenodo.5721380), with an associated code inspection checklist (https://doi.org/10.5281/zenodo.5284377).

Style Guide for Data Files

 See the Google R style guide (https://google.github.io/styleguide/Rguide.html) and the Tidyverse style guide(https://style.tidyverse.org/syntax.html#object-names) for more information

Google style guide for Python: https://google.github.io/styleguide/pyguide.html

 

6. WHY DRYAD OR ZENODO?

 

The American Naturalist requests that authors use DRYAD or zenodo for their archives when possible. 

·                DRYAD/zenodo are curated and this means that there is some initial checking by DRYAD for completeness and consistency in both the data files and the metadata. DRYAD requires some compliance before they will allow a submission.

·                We are finding it much easier and more convenient to download repositories from DRYAD/zenodo rather than searching the ms etc for the files or repository

·                Files on DRYAD/zenodo cannot be arbitrarily deleted or changed by authors or others after publication. DRYAD will allow changes if a good case can be made—all changes are documented and all versions are retained.

·                DRYAD is free for Am Nat authors and we have a good working relationship with them and they take seriously our suggestions for improvement etc.

·                editors, reviewers, and authors will all become familiar with the workings of DRYAD

·                DRYAD and zenodo are now linked. DRYAD is for data files and zenodo for everything else (code, PDFs, etc). You need only upload all files to DRYAD and they will separate your archive into the appropriate parts. As you will see your DRYAD repository provides a link to the files on zenodo and vice versa

Towards a new path for academic societies and the scientific discourse

 Guest Post by

Wolf Blanckenhorn, Evolutionary Biology & Environmental Studies, University of Zürich,

Parting Editor-in-Chief of the Journal of Evolutionary Biology, September 2021

My term as Editor-in-Chief (EiC) of the Journal of Evolutionary Biology (JEB) is coming to a close. As I am also reaching retirement age, I am herewith using my farewell opportunity to reflect upon my experiences as EiC of our by now ‘traditional’ society journal. JEB was launched in Switzerland, in close link with the foundation of the European Society of Evolutionary Biology (ESEB), around the time when I undertook my PhD in the mid 1980s (see Stearns, 1987). Admittedly, compared to some considerably older societies and journals in our field of research, such as Evolution or American Naturalist, this tradition is not longstanding – and I fear may not last as long. This is because scientific journals, particularly society journals, are in peril as they have largely lost their function. Scientific articles are no longer printed and physically stored in libraries to peruse; instead, they are anchored somewhere in the World Wide Web for everybody to openly access in principle. Although the publishers and editors of JEB, like many other journals, have been working hard in recent years to ease manuscript submission and reviewing procedures for the benefit of authors (see Blanckenhorn,2021), we are in the midst of a paradigm shift in scientific publishing towards ‘Open Science’, fostered by a technological shift that may well make scientific journals superfluous in the long term. This drive towards Open Science particularly threatens the traditional copyright business model of scientific publishing, but also the established mode of operation of most scientific societies, many of which, as a rule and among other things, have established and run flagship journals to disseminate and communicate their scientific disciplines, not least also to co-finance their activities (see this article for a historical summary). So it is perhaps unsurprising that academic societies and editorial boards of their journals are keen on holding on to our traditional modus operandi in the face of this ongoing transformation, while at the same time seeing their journals wilt. Nevertheless, during my 4-year tenure as JEB EiC it has become clear at least to me, but also many others, that many aspects of the production of science must and will change, and that we should facilitate rather than resist these developments.

The Open Science practices demanded by politics and the public have by now been discussed widely for some time (see e.g. Stern & O’Shea, 2019); they require drastic modifications affecting the operation of society journals such as JEB and their societies (e.g. ESEB). Rather than discussing cosmetic changes to the function of the journal or the society to keep up the system, as done e.g. in Blanckenhorn,2021, I here more generally analyse the situation and how I envision scientific exchange and society activities taking place in the future. This in principle should pertain, more or less, to any scientific journals and academic societies, largely irrespective of their (sub)discipline.

A remaining role for scientific journals ?

As already stated, scientific journals have lost their primary function: the dissemination of narrated scientific results via printed media through (mainly university) libraries. Instead, science dissemination, and scientific discourse as a whole, now primarily occurs via the internet (WWW). As papers now can be found easily using various search engines, and cited directly via their individual DOIs, the journal they appear in becomes largely irrelevant. In the past, journals have topically ordered or classified science, which was clearly helpful for orienting and finding the important papers within a (sub)field, their editorial board of topical experts acting as gatekeepers and referees. With the proliferation of journals, and scientists worldwide, this function has become blurred. Supra-topical (i.e. general) journals such as Science, Scientific American or Nature, which cover all disciplines of the natural sciences and therefore have no clear profile other than that of a ‘Science Gazette’, never really had this function, but were of benefit in a world where few academics wanted to stay up-to-date with developments in other fields. This is no longer possible, nor necessary. Instead, these general journals have adopted an outreach (if not lobbying) function, purportedly delivering cutting-edge science at all levels and in all fields, thus aspiring to be the main liaison between the scientific community, the public, and policy makers. But is this so, and was this successful, even credible (Brembs, 2019)? I think not, but evidently (too) many established scientists have (over)esteemed this role (and reputation) of such journals. Of course, the decisive factor fostering the ensuing impact factor hunting of many scientists was the importance of so-called ‘high-impact publications’ (published in those journals) for academic job search committees. Judging from my own experience, committees thereby all too often happily delegated their precious influence on the future of university teaching and research by trusting the evaluation of (mainly) Oxbridge or Ivy League trained science journalists with a Bachelors (and sometimes a PhD) degree, thus ultimately nurturing commercial interests as well as numerous incentives and human behaviours detrimental for science (Brembs, 2019). This reliance on the judgement of publishers must clearly stop as this particular function of general journals is becoming unnecessary because citations, presumably reflecting quality, relevance and/or ingenuity of scientific papers now can, and should, be scored directly (by alternative so-called ‘Altmetrics’) without regard to where they are published (see https://sfdora.org). Supra-topical journals should consequently concentrate on writing about science to fulfil their primary outreach function, not publish science. By contrast, the status and function of topical journals such as JEB derives from the scientific authority of their editorial board. I, and many colleagues, would therefore prefer the scientific community making up academic societies not to adhere to an outdated publication system (via journals) solely for this reason, even if the described mechanism historically lifted many of us into our prestigious jobs.

The aforementioned is of course not the only legitimate motivation of scientists for clinging on to the traditional publication system with journals. Besides genuine interest in (sneak) previewing currently ongoing work, running a journal, as editors, confers tremendous influence on the course of the scientific discourse. In their function as editors of major journals they decide what is good and what is bad science (in any particular field), what is right and what wrong, what may be ultimately published or not, be it for genuine or ulterior motives. Because science, including the major scientific journals, is (still) severely dominated by the Anglo-American and European ‘first world’, this motivation has been criticized to introduce a fair amount of cultural chauvinism into the scientific enterprise, which of course is not devoid of fashions and commercial interests (see Gordin, M.D. 2015. Scientific Babel: How science was done before and after global English). In modern parlance, adhering to the old publication system hampers augmenting diversity in science in terms of cultural background, gender, geography, language, etc. (e.g. Nuñez, et al. 2019; or Lund, B.D. 2021).

What can, should replace journals, then? Well, web sites of some sort, such as e.g. https://www.biorxiv.org/. Authors nowadays perform most of the text and figure processing themselves and can produce a decent presentable layout, such that classic publishing services largely also have become superfluous. The key question is: who should run these web sites? And who pays how? In my experience, it would be most efficient if the major science funders and employers of scientists (the universities, or their libraries) would assume indirect, if not direct responsibility for such web sites to host all work they funded: a modern ‘University Press’. This is already happening, as most major state, but also private funding organisations and universities now have print servers hosting the publications of their awardees and employees (see e.g. European OpenAire website, the websites of the Swiss National ScienceFoundation or the University of Zürich (see Ensenrink, 2017). After all, the employers and funders ultimately must also investigate and bear the consequences of any misconduct of their scientists, as they are paying them. While such a system may lead to multiple publication of the same paper in various versions on various such web sites, this does not matter and can be organised sensibly; for instance, similarly to the publication of type specimens in museums, many of which host specimens of the same species, but only one holotype exists defining the species historically (e.g. https://www.iczn.org/). Such a system of course ultimately delegates the quality control and hence the responsibility to the scientific community as a whole, as represented by the major disciplinary academic societies.

Therefore, even if scientific journals as we know them may fade in the future, peer review, the true hallmark of scientific publishing and crucial quality control by experts to distinguish true from fake science, will not become superfluous, and therefore must be maintained and reorganized. The fact that peer review has been tightly linked to scientific publishing and journals in the past does not mean that this must remain so in the future. This leaves intact a large part of the day-to-day work of scientists as reviewers and editors. Currently this work already is indirectly supported by our employers (the universities), inasmuch as a significant part (ca. 10-15%) of e.g. my time goes into editing and reviewing, which historically is poorly (if at all) remunerated by the publishing companies, the journal (here JEB), or the societies running society journals (here ESEB). In fact, such reviewing work is poorly esteemed in general. New initiatives such as PCI (https://peercommunityin.org/) demonstrate that a peer review system which is detached from publication can be viable. I personally regard the reorganisation of peer review, independent from publishing and journals, as the major duty of scientific societies in the future (see next section).  

Economically, all these transformations together mandate a change of a business model that many Anglo-American and European science publishers have embraced over the past century and are therefore protecting. Much has been written about this aspect too, culminating in the launching of the (primarily European) PlanS, so I do not need to dwell on this here (see e.g. Ellers et al., 2017; Alizon, 2018; McNutt, 2019; https://www.coalition-s.org/). The goal should be to bring the commercialized system closer to home again, back to those who actually pay and orchestrate the majority of research conducted in the world: the (regional, national, supra-national, or private) funding agencies, the universities as the main employers of scientists conducting basic research, and the network of academic societies providing the organized, communal expertise. An added value of a so restructured system would be that the money flows more directly, clearly and transparently. All this also does imply a fundamental change in the role of academic societies.

A shifted role for Academic Societies

Scientific societies originally formed for multiple purposes. According to their web site, ESEBs main current activities (alike many other societies) are: 1) dissemination, discussion, orchestration, and quality control (via reviewing) of scientific knowledge by way of scientific journals (or now also blogs); 2) organisation of scientific congresses (the classic form of scientific discourse); 3) financial (via funding opportunities and prizes) and ideal support of young researchers in various ways; 4) outreach activities for the public (e.g. via blogs); and 5) lobbying of policy makers. Some, though not all of these activities require substantial financial means. Income typically derives from subsidies by various organisations or the state, donations, membership fees (which are naturally limited), but for many societies often from profits of their society journal (e.g. JEB) being contractually shared with the publisher. This system intimately ties many academic societies to a publisher, even though their interests do not necessarily coincide. If only because the ongoing reorganisation of scientific publishing strongly affects their income from journals, the (business model of) professional societies must likely change as well. Nonetheless, in principle the major goals and activities of academic societies listed above will largely remain the same, however likely with major shifts in focus and how money flows.

Except for few true employees (e.g. ESEBs managing secretary and JEBs editorial manager), society officers are elected and do not get paid (much); that is, their work for the society gets indirectly paid, i.e. subsidized by their employers (typically universities). As membership fees of many societies tend to be low (in the teens) to attract members and membership is limited, limited resources are therefore available for the above-listed activities, were it not for sometimes substantial profit shares (in the 100000s) derived from the journal via the publisher. These profits traditionally derive from library subscriptions with copyright relegated away from the authors to the publishers. These however are now being replaced by so-called Article Processing Charges (APCs) paid by the funders via the authors to warrant open access (no copyright). The necessary transition is currently bridged by so-called transitionary deals between publishers and (university) libraries (or entire political entities running universities), following political lobbying by various parties including universities, publishers and academic societies. Besides society journals (objective 1 above), academic society income is generally being spent to subsidize rather expensive large scientific congresses (objective 2 above), in addition to various funding schemes and prizes for research (3 above; see e.g. https://eseb.org/prizes-funding/). Lobbying (5) and outreach activities (4) of societies can be maintained with few financial means by society officers supported by their employers, although universities typically have their own agendas in all this, for instance by trying to save money on library contracts and at the same time expanding their control of the publication process. The anticipated loss of society journal income if journals indeed fade will mandate a reduction of subsidies for the expensive expenditures of academic societies, i.e. congresses (2) and funding schemes for young scientists (3). In my opinion, congresses can easily be organized without subsidies (as done e.g. by the American evolutionary societies SSE & ASN), which instead demands more direct payment by delegates, their employers and/or funders. Exerting influence via alternative support opportunities through academic societies for young scientists will indeed be missed. Augmenting income via donations and subsidies from local universities and/or (supra)national funding organisations would certainly help academic societies achieve their goals.

In summary, with the anticipated change towards open access publication of science, many traditional society, but also old and new commercial journals will likely fade if not disappear altogether, implying reduced profits for publishers to be shared with academic societies. The entire publication business model is likely to change, ultimately curtailing the influence of the large publishing companies (similar to what happened in the music business). As a consequence, academic societies such as ESEB will be less or no longer at all involved in publishing science, but still need to maintain a leading role in the quality control of science, as they constitute the experts. (I note in passing that online open journals can be maintained at minimal cost with a much smaller than present budget, such as e.g. Alpine Entomology, the journal of the Swiss Entomological Society (https://alpineentomology.pensoft.net/), which operates with moderate membership fees plus some donations/subsidies (in the 1000s). However, this might not be a fruitful approach given the ongoing market reorganisation.) Crucially, scientific discourse and reviewing can and must be organized separately from publishing, as shown by e.g. PCI (cf. above), and professional academic societies should maintain a leading role in this quality control system, not least also to keep academic misconduct in check. In line with the Open Science movement, however, reviewing must not be anonymous in order to assume a form of open and transparent scholarly discussion (see Stern & O’Shea, 2019). Papers should be ‘published’ in a presentable layout by making them available first on (e.g. university) (pre)print servers (as already happens), to subsequently accrue expert reviews linked to the papers for everybody to openly follow the ensuing scientific discussion – the more, the better. Scientific discussions can be orchestrated in this way, such that scientific ‘knowledge’ is ‘curated’ by the entire community of experts to become improved, or its flaws laid open, ultimately rendering reviewing much more efficient overall and less cleaquey (Stern & O’Shea, 2019).

For such a system to work, the format of reviews needs to be (somewhat) more standardized, with a general description of what the paper is about followed by a general critique and/or praise, both of which should remain published. By contrast, detailed edits and suggestions for revisions must still be supplied initially, but these will be eliminated eventually as the paper reaches its final form. Such a system is already in use, e.g. by eLife (https://elifesciences.org) and also PCI. Scientists who publish have a moral obligation (and interest) to engage in such open reviewing as experts, on average providing three times as many reviews as they publish papers (because we currently expect 2-3 reviews per published paper as a minimum). Such open reviews (or comments) are citable and hence add to the reputation of scientists as reviewers, not merely as authors. Personally, I have signed my reviews since becoming active as reviewer around 1990, and this helped build my reputation, eventually making me EiC of a major evolutionary society journal (and subject editor of several others). Moreover, the underlying data nowadays can, should be, and are published as a supplement to a paper, or independently of papers at various web sites (e.g. DRYAD; https://datadryad.org/stash), again with the aim to be openly accessible. This boosts the value of data per se, relative to the past (in evolutionary or behavioural ecology) when data were hoarded by scientists and typically not shared, such that the reputation of scientists entirely depended on how well they can craft and ‘sell’ their papers. As authors, we must keep in mind that primacy of ‘ideas’ is difficult, if not impossible to ascertain because they tend to linger in the Zeitgeist, whereas data may indeed be ‘owned’ more easily. In the end, however, the data generate, support and transport the scientific ideas and presentation, and thus rightfully should be credited by themselves for others to be creatively used as well. Molecular (e.g. GenBank), phylogenetic (e.g. TreeBASE), weather and (para)medical data bases and repositories are leading the way.

Overall therefore, academic societies should redirect their efforts away from publishing towards openly developing a quality control system as experts via reviews, scientific disputes, meetings, public outreach and lobbying. This strengthens their credibility and thus hopefully curtails scientific misconduct within the community. All scientific communities of experts must stand together and collaborate as much as possible according to subfields, requiring (re)organisation of an entire worldwide network in some sort of federalist (cascading) set-up, thereby augmenting their own cultural diversity and political effectiveness. Cleaquiness and competition among societies with similar goals is counterproductive in such a system.

Within my lifetime as a scientist I’ve seen the era of Behavioural / Evolutionary Ecology wax and wane, in which I grew up academically ever since visiting the first Behavioral Ecology Congress in Albany, NY, in 1986; and with it, I believe, the era of profuse scientific journals. Some enlightened scholars have seen all this long coming (e.g. Rosenzweig, 1999: Editorial – And now for something not completely different. Evolutionary Ecology Research 1999, 1: 1). The subfield will linger, of course, as one of many in evolutionary biology. What I argued herein may happen sooner, later, or perhaps not all. But the system will change: whether for better or worse depends on the scientific community at large. The final verdict on this transformation towards open science is not out yet, and I wish all, especially my peers at ESEB and JEB, all the best in handling the situation.