Tag: Open Science

Since F1000Research uses a somewhat different publication scheme than most journals, I still haven’t understood if this paper is formally published after peer review, but I start to assume it is. There have been very little changes since the last version, so hence I will be lazy and basically repost what I wrote in April when the first version (the “preprint”) was posted online. The paper (1) is the result of a workshop arranged by the JRC in Italy in 2017. It describes various challenges arising from the process of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance genes in next generation sequencing data.

The paper discusses issues about the benchmarking datasets used, testing samples, evaluation criteria for the performance of different tools, and how the benchmarking dataset should be created and distributed. Specially, we address the following questions:

• How should a benchmark strategy handle the current and expanding universe of NGS platforms?
• What should be the quality profile (in terms of read length, error rate, etc.) of in silico reference materials?
• Should different sets of reference materials be produced for each platform? In that case, how to ensure no bias is introduced in the process?
• Should in silico reference material be composed of the output of real experiments, or simulated read sets? If a combination is used, what is the optimal ratio?
• How is it possible to ensure that the simulated output has been simulated “correctly”?
• For real experiment datasets, how to avoid the presence of sensitive information?
• Regarding the quality metrics in the benchmark datasets (e.g. error rate, read quality), should these values be fixed for all datasets, or fall within specific ranges? How wide can/should these ranges be?
• How should the benchmark manage the different mechanisms by which bacteria acquire resistance?
• What is the set of resistance genes/mechanisms that need to be included in the benchmark? How should this set be agreed upon?
• Should datasets representing different sample types (e.g. isolated clones, environmental samples) be included in the same benchmark?
• Is a correct representation of different bacterial species (host genomes) important?
• How can the “true” value of the samples, against which the pipelines will be evaluated, be guaranteed?
• What is needed to demonstrate that the original sample has been correctly characterised, in case real experiments are used?
• How should the target performance thresholds (e.g. specificity, sensitivity, accuracy) for the benchmark suite be set?
• What is the impact of these performance thresholds on the required size of the sample set?
• How can the benchmark stay relevant when new resistance mechanisms are regularly characterized?
• How is the continued quality of the benchmark dataset ensured?
• Who should generate the benchmark resource?
• How can the benchmark resource be efficiently shared?

Of course, we have not answered all these questions, but I think we have come down to a decent description of the problems, which we see as an important foundation for solving these issues and implementing the benchmarking standard. Some of these issues were tackled in our review paper from last year on using metagenomics to study resistance genes in microbial communities (2). The paper also somewhat connects to the database curation paper we published in 2016 (3), although this time the strategies deal with the testing datasets rather than the actual databases. The paper is the first outcome of the workshop arranged by the JRC on “Next-generation sequencing technologies and antimicrobial resistance” held October 4-5 2017 in Ispra, Italy. You can find the paper here (it’s open access).

On another note, the new paper describing the UNITE database (4) has now got a formal issue assigned to it, as has the paper on tandem repeat barcoding in fungi published in Molecular Ecology Resources last year (5).

References and notes

1. Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, Coque TM, Hammer P, Heß S, Kagkli DM, Krumbiegel C, Lanza VF, Madec J-Y, Naas T, O’Grady J, Paracchini V, Rossen JWA, Ruppé E, Vamathevan J, Venturi V, Van den Eede G: The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies. F1000Research, 7, 459 (2018). doi: 10.12688/f1000research.14509.1
2. Bengtsson-Palme J, Larsson DGJ, Kristiansson E: Using metagenomics to investigate human and environmental resistomes. Journal of Antimicrobial Chemotherapy, 72, 2690–2703 (2017). doi: 10.1093/jac/dkx199
3. Bengtsson-Palme J, Boulund F, Edström R, Feizi A, Johnning A, Jonsson VA, Karlsson FH, Pal C, Pereira MB, Rehammar A, Sánchez J, Sanli K, Thorell K: Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics, 16, 18, 2454–2460 (2016). doi: 10.1002/pmic.201600034
4. Nilsson RH, Larsson K-H, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, Kennedy P, Picard K, Glöckner FO, Tedersoo L, Saar I, Kõljalg U, Abarenkov K: The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research, 47, D1, D259–D264 (2019). doi: 10.1093/nar/gky1022
5. Wurzbacher C, Larsson E, Bengtsson-Palme J, Van den Wyngaert S, Svantesson S, Kristiansson E, Kagami M, Nilsson RH: Introducing ribosomal tandem repeat barcoding for fungi. Molecular Ecology Resources, 19, 1, 118–127 (2019). doi: 10.1111/1755-0998.12944

This weekend, F1000Research put online the non-peer-reviewed version of the paper resulting from a workshop arranged by the JRC in Italy last year (1). (I will refer to this as a preprint, but at F1000Research the line is quite blurry between preprint and published paper.) The paper describes various challenges arising from the process of designing a benchmark strategy for bioinformatics pipelines (2) in the identification of antimicrobial resistance genes in next generation sequencing data.

The paper discusses issues about the benchmarking datasets used, testing samples, evaluation criteria for the performance of different tools, and how the benchmarking dataset should be created and distributed. Specially, we address the following questions:

• How should a benchmark strategy handle the current and expanding universe of NGS platforms?
• What should be the quality profile (in terms of read length, error rate, etc.) of in silico reference materials?
• Should different sets of reference materials be produced for each platform? In that case, how to ensure no bias is introduced in the process?
• Should in silico reference material be composed of the output of real experiments, or simulated read sets? If a combination is used, what is the optimal ratio?
• How is it possible to ensure that the simulated output has been simulated “correctly”?
• For real experiment datasets, how to avoid the presence of sensitive information?
• Regarding the quality metrics in the benchmark datasets (e.g. error rate, read quality), should these values be fixed for all datasets, or fall within specific ranges? How wide can/should these ranges be?
• How should the benchmark manage the different mechanisms by which bacteria acquire resistance?
• What is the set of resistance genes/mechanisms that need to be included in the benchmark? How should this set be agreed upon?
• Should datasets representing different sample types (e.g. isolated clones, environmental samples) be included in the same benchmark?
• Is a correct representation of different bacterial species (host genomes) important?
• How can the “true” value of the samples, against which the pipelines will be evaluated, be guaranteed?
• What is needed to demonstrate that the original sample has been correctly characterised, in case real experiments are used?
• How should the target performance thresholds (e.g. specificity, sensitivity, accuracy) for the benchmark suite be set?
• What is the impact of these performance thresholds on the required size of the sample set?
• How can the benchmark stay relevant when new resistance mechanisms are regularly characterized?
• How is the continued quality of the benchmark dataset ensured?
• Who should generate the benchmark resource?
• How can the benchmark resource be efficiently shared?

Of course, we have not answered all these questions, but I think we have come down to a decent description of the problems, which we see as an important foundation for solving these issues and implementing the benchmarking standard. Some of these issues were tackled in our review paper from last year on using metagenomics to study resistance genes in microbial communities (3). The paper also somewhat connects to the database curation paper we published in 2016 (4), although this time the strategies deal with the testing datasets rather than the actual databases. The paper is the first outcome of the workshop arranged by the JRC on “Next-generation sequencing technologies and antimicrobial resistance” held October 4-5 last year in Ispra, Italy. You can find the paper here (it’s open access).

References and notes

1. Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, Coque TM, Hammer P, Heß S, Kagkli DM, Krumbiegel C, Lanza VF, Madec J-Y, Naas T, O’Grady J, Paracchini V, Rossen JWA, Ruppé E, Vamathevan J, Venturi V, Van den Eede G: The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies. F1000Research, 7, 459 (2018). doi: 10.12688/f1000research.14509.1
2. You may remember that I hate the term “pipeline” for bioinformatics protocols. I would have preferred if it was called workflows or similar, but the term “pipeline” has taken hold and I guess this is a battle where I have essentially lost. The bioinformatics workflows will be known as pipelines, for better and worse.
3. Bengtsson-Palme J, Larsson DGJ, Kristiansson E: Using metagenomics to investigate human and environmental resistomes. Journal of Antimicrobial Chemotherapy, 72, 2690–2703 (2017). doi: 10.1093/jac/dkx199
4. Bengtsson-Palme J, Boulund F, Edström R, Feizi A, Johnning A, Jonsson VA, Karlsson FH, Pal C, Pereira MB, Rehammar A, Sánchez J, Sanli K, Thorell K: Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics, 16, 18, 2454–2460 (2016). doi: 10.1002/pmic.201600034

First of all, I am happy to announce that the webinar I participated in on the (un)recognised pathways of AMR: Air pollution and food, organised by Healthcare Without Harm is now put online so that you can view it, in case you missed out on this event. To be honest it is probably not one of my best public appearances, but the topic is highly interesting.

Second, next week I am taking part in Vetenskapsfestivalen – the Science Festival in Gothenburg. Specifically, I will be on of the researchers participating in the Science Roulette, taking place in the big ferris wheel at Liseberg. This will take place between 17.00 and 18.00 on May 11th. The idea is that people will be paired with researchers in diverse subjects, of which I am one, and then have a 20 minute chat while the wheel is spinning. Sounds like potential for lot of fun, and I hope to see you there! I will discuss antibiotic resistance, and for how much longer we can trust that our antibiotics will work.

I am happy to announce that our Viewpoint article on strategies for improving sequence databases has now been published in the journal Proteomics. The paper (1) defines some central problems hampering genomic, proteomic and metagenomic analyses and suggests five strategies to improve the situation:

1. Clearly separate experimentally verified and unverified sequence entries
2. Enable a system for tracing the origins of annotations
3. Separate entries with high-quality, informative annotation from less useful ones
4. Integrate automated quality-control software whenever such tools exist
5. Facilitate post-submission editing of annotations and metadata associated with sequences

The paper is not long, so I encourage you to read it in its entirety. We believe that spreading this knowledge and pushing solutions to problems related to poor annotation metadata is vastly important in this era of big data. Although we specifically address protein-coding genes in this paper, the same logic also applies to other types of biological sequences. In this way the paper is related to my previous work with Henrik Nilsson on improving annotation data for taxonomic barcoding genes (2-4). This paper was one of the main end-results of the GoBiG network, and the backstory on the paper follows below the references…

References

1. Bengtsson-Palme J, Boulund F, Edström R, Feizi A, Johnning A, Jonsson VA, Karlsson FH, Pal C, Pereira MB, Rehammar A, Sánchez J, Sanli K, Thorell K: Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics, Early view (2016). doi: 10.1002/pmic.201600034
2. Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, Bates ST, Bruns TT, Bengtsson-Palme J, Callaghan TM, Douglas B, Drenkhan T, Eberhardt U, Dueñas M, Grebenc T, Griffith GW, Hartmann M, Kirk PM, Kohout P, Larsson E, Lindahl BD, Lücking R, Martín MP, Matheny PB, Nguyen NH, Niskanen T, Oja J, Peay KG, Peintner U, Peterson M, Põldmaa K, Saag L, Saar I, Schüßler A, Senés C, Smith ME, Suija A, Taylor DE, Telleria MT, Weiß M, Larsson KH: Towards a unified paradigm for sequence-based identification of Fungi. Molecular Ecology, 22, 21, 5271–5277 (2013). doi: 10.1111/mec.12481
3. Nilsson RH, Hyde KD, Pawlowska J, Ryberg M, Tedersoo L, Aas AB, Alias SA, Alves A, Anderson CL, Antonelli A, Arnold AE, Bahnmann B, Bahram M, Bengtsson-Palme J, Berlin A, Branco S, Chomnunti P, Dissanayake A, Drenkhan R, Friberg H, Frøslev TG, Halwachs B, Hartmann M, Henricot B, Jayawardena R, Jumpponen A, Kauserud H, Koskela S, Kulik T, Liimatainen K, Lindahl B, Lindner D, Liu J-K, Maharachchikumbura S, Manamgoda D, Martinsson S, Neves MA, Niskanen T, Nylinder S, Pereira OL, Pinho DB, Porter TM, Queloz V, Riit T, Sanchez-García M, de Sousa F, Stefaczyk E, Tadych M, Takamatsu S, Tian Q, Udayanga D, Unterseher M, Wang Z, Wikee S, Yan J, Larsson E, Larsson K-H, Kõljalg U, Abarenkov K: Improving ITS sequence data for identification of plant pathogenic fungi. Fungal Diversity, 67, 1, 11–19 (2014). doi: 10.1007/s13225-014-0291-8
4. Nilsson RH, Tedersoo L, Ryberg M, Kristiansson E, Hartmann M, Unterseher M, Porter TM, Bengtsson-Palme J, Walker D, de Sousa F, Gamper HA, Larsson E, Larsson K-H, Kõljalg U, Edgar R, Abarenkov K: A comprehensive, automatically updated fungal ITS sequence dataset for reference-based chimera control in environmental sequencing efforts. Microbes and Environments, 30, 2, 145–150 (2015). doi: 10.1264/jsme2.ME14121

Backstory
In June 2013, the Gothenburg Bioinformatics Group for junior scientists (GoBiG) arranged a workshop with two themes: “Parallelized quantification of genes in large metagenomic datasets” and “Assigning functional predictions to NGS data”. The following discussion on how to database quality influenced results and what could be done to improve the situation was rather intense, and several good ideas were thrown around. I took notes from the meeting, and in the evening I put them down during a warm summer night at the balcony. In fact, the notes were good enough to be an early embryo for a manuscript. So I sent it to some of the most active GoBiG members (Kaisa Thorell and Fredrik Boulund), who were positive regarding the idea to turn it into a manuscript. I wrote it together more properly and we decided that everyone who contributed with ideas at the meeting would be invited to become co-authors. We submitted the manuscript in early 2014, only to see it (rather brutally) rejected. At that point most of us were sucked up in their own projects, so nothing happened to this manuscript for over a year. Then we decided to give it another go, updated the manuscript heavily and changed a few parts to better reflect the current database situation (at this point, e.g., UniProt had already started implementing some of our suggested ideas). Still, some of the proposed strategies were more radical in 2013 than they would be now, more than three years later. We asked the Proteomics editors if they would be interested in the manuscript, and they turned out to be very positive. Indeed, the entire experience with the editors at Proteomics has been very pleasant. I am very thankful to the GoBiG team for this time, and to the editors at Proteomics who saw the value of this manuscript.

Some of you who think ITSx is running slowly despite being assigned multiple CPUs, particularly on datasets with only one kind of sequences (e.g. fungal) using the -t F option might be interested in trying out Andrew Krohn’s parallel ITSx implementation. The solution essentially employs a bash script spawning multiple ITSx instances running on different portions of the input file. Although there are some limitations to the script (e.g. you cannot select a custom name for the output and you will only get the ITS1 and ITS2 + full sequences FASTA files, as far as I understand the script), it may prove useful for many of you until we write up a proper solution to the poor multi-thread performance of ITSx (planned for version 1.1). In the coming months, I recommend that you check this solution out! See also the wiki documentation.

My speed tests shows the following (on a quite small test set of fungal ITS sequences):
ITSx parallel on 16 CPUs, all ITS types (option “-t all“):
3 min, 16 sec
ITSx parallel on 16 CPUs, only fungal ITS types (option “-t f“):
54 sec
ITSx native on 16 CPUs, all ITS types (options “-t all --cpu 16“):
4 min, 59 sec
ITSx native on 16 CPUs, only fungal types (options “-t f --cpu 16“):
5 min, 50 sec

Why fungal only took longer time in the native implementation is a mystery to me, but probably shows why there is a need to rewrite the multithreading code, as we did with Metaxa a couple of years ago. Stay tuned for ITSx updates!

In an interesting development, Nature Publishing Group has launched a new initiative: Scientific Data – a online-only open access journal that publishes data sets without the demand of testing scientific hypotheses in connection to the data. That is, the data itself is seen as the valuable product, not any findings that might result from it. There is an immediate upside of this; large scientific data sets might be accessible to the research community in a way that enables proper credit for the sample collection effort. Since there is no demand for a full analysis of the data, the data itself might quicker be of use to others, without worrying that someone else might steal the bang of the data per se. I also see a possible downside, though. It would be easy to hold on to the data until you have analyzed it yourself, and then release it separately just about when you submit the paper on the analysis, generating extra papers and citation counts. I don’t know if this is necessarily bad, but it seems it could contribute to “publishing unit dilution”. Nevertheless, I believe that this is overall a good initiative, although how well it actually works will be up to us – the scientific community. Some info copied from the journal website:

Scientific Data’s main article-type is the Data Descriptor: peer-reviewed, scientific publications that provide an in-depth look at research datasets. Data Descriptors are a combination of traditional scientific publication content and structured information curated in-house, and are designed to maximize reuse and enable searching, linking and data mining. (…) Scientific Data aims to address the increasing need to make research data more available, citable, discoverable, interpretable, reusable and reproducible. We understand that wider data-sharing requires credit mechanisms that reward scientists for releasing their data, and peer evaluation mechanisms that account for data quality and ensure alignment with community standards.

It seems like our paper on the recently launched database on resistance genes against antibacterial biocides and metals (BacMet) has gone online as an advance access paper in Nucleic Acids Research today. Chandan Pal – the first author of the paper, and one of my close colleagues as well as my roommate at work – has made a tremendous job taking the database from a list of genes and references, to a full-fledged browsable and searchable database with a really nice interface. I have contributed along the process, and wrote the lion’s share of the code for the BacMet-Scan tool that can be downloaded along with the database files.

BacMet is a curated source of bacterial resistance genes against antibacterial biocides and metals. All gene entries included have at least one experimentally confirmed resistance gene with references in scientific literature. However, we have also made a homology-based prediction of genes that are likely to share the same resistance function (the BacMet predicted dataset). We believe that the BacMet database will make it possible to better understand co- and cross-resistance of biocides and metals to antibiotics within bacterial genomes and in complex microbial communities from different environments.

The database can be easily accessed here: http://bacmet.biomedicine.gu.se, and use of the database in scientific work can cite the following paper, which recently appeared in Nucleic Acids Research:

Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DGJ: BacMet: Antibacterial Biocide and Metal Resistance Genes Database. Nucleic Acids Research. Database issue, advance access. doi: 10.1093/nar/gkt1252 [Paper link]

For a couple of years, I have been working with microbial ecology and diversity, and how such features can be assessed using molecular barcodes, such as the SSU (16S/18S) rRNA sequence (the Metaxa and Megraft packages). However, I have also been aiming at the ITS region, and how that can be used in barcoding (see e.g. the guidelines we published last year). It is therefore a great pleasure to introduce my next gem for community analysis; a software tool for detection and extraction of the ITS1 and ITS2 regions of ITS sequences from environmental communities. The tool is dubbed ITSx, and supersedes the more specific fungal ITS extractor written by Henrik Nilsson and colleagues. Henrik is once more the mastermind behind this completely rewritten version, in which I have done the lion’s share of the programming. Among the new features in ITSx are:

• Robust support for the Cantharellus, Craterellus, and Tulasnella genera of fungi
• Support for nineteen additional eukaryotic groups on top of the already present support for fungi (specifically these groups: Tracheophyta (vascular plants), Bryophyta (bryophytes), Marchantiophyta (liverworts), Chlorophyta (green algae), Rhodophyta (red algae), Phaeophyceae (brown algae), Metazoa (metazoans), Oomycota (oomycetes), Alveolata (alveolates), Amoebozoa (amoebozoans), Euglenozoa, Rhizaria, Bacillariophyta (diatoms), Eustigmatophyceae (eustigmatophytes), Raphidophyceae (raphidophytes), Synurophyceae (synurids), Haptophyceae (haptophytes) , Apusozoa, and Parabasalia (parabasalids))
• Multi-processor support
• Extensive output options
• Virtually zero false-positive extractions

ITSx is today moved from a private pre-release state to a public beta state. No code changes has been made since February, indicative of that the last pre-release candidate is now ready to fly on its own. As far as our testing has revealed, this version seems to be bug free. In reality though, researchers tend to find the most unexpected usage scenarios. So please, if you find any unexpected behavior in this version of ITSx, send me an e-mail and make us aware of the potential shortcomings of our software.

We expect this open-source software to boost research in microbial ecology based on barcoding of the ITS region, and hope that the research community will evaluate its performance also among the eukaryote groups that we have less experience with.

Browsing the Pfam web site today, I discovered that the database finally has launched its Wikipedia co-ordination efforts.

This has happened along with the 25th release of the Pfam database (released 1st of April), and basically means that Wikipedia articles will be linked to Pfam families. Gradually, this will (hopefully) improve the annotation of Pfam families, which has in many cases been rather poor. The Xfam blog post related to Pfam release 25 says the change will be happening gradually, which might actually be good thing, given the quirks that might pop up.

(…) a major change is that Pfam annotation is now beginning to be co-ordinated via Wikipedia. Unlike Rfam, where every entry has a Wikipedia entry, we expect this to be a more gradual transition for Pfam, so not all entries currently have a corresponding Wikipedia article. For a more detailed discussion, check the help page.  We actively encourage the addition of new/updated annotations via Wikipedia as they will appear far quicker than waiting for a Pfam release.  If there are articles in Wikipedia that you think correspond to a family, then please mail us!

I have awaited this change for a long time, and is very happy that Pfam has finally taken this step. Congratulations and my sincerest thanks to the Pfam team! Now, let’s go editing!

In December, Alex Bateman, whose opinions on open science I support and have touched upon earlier, wrote a short correspondence letter to Nature [1] in which he again repeated the points of his talk at FEBS last summer. He concludes by the paragraph:

Many in the scientific community will admit to using Wikipedia occasionally, yet few have contributed content. For society’s sake, scientists must overcome their reluctance to embrace this resource.

I agree with this statement. However, as I also touched upon earlier, but like to repeat again – bold statements doesn’t make dreams come true – action does. Rfam, and the collaboration with RNA Biology and Wikipedia is a great example of such actions. So what other actions may be necessary to get researchers to contribute to the Wikipedian wisdom?

First of all, I do not think that the main obstacle to get researchers to edit Wikipedia articles is reluctance to doing so because Wikipedia is “inconsistent with traditional academic scholarship”, though that might be a partial explanation. What I think is the major problem is the time-reward tradeoff. Given the focus on publishing peer-reviewed articles, the race for higher impact factor, and the general tendency of measuring science by statistical measures, it should be no surprise that Wikipedia editing is far down on most scientists to-do lists, so also on mine. The reward of editing a Wikipedia article is a good feeling in your stomach that you have benefitted society. Good stomach feelings will, however, feed my children just as little as freedom of speech. Still, both Wikipedia editing and freedom of speech are extremely important, especially as a scientist.

Thus, there is a great need of a system that:

• Provides a reward or acknowledgement for Wikipedia editing.
• Makes Wikipedia editing economically sustainable.
• Encourages publishing of Wikipedia articles, or contributions to existing ones as part of the scientific publishing process.

Such a system could include a “contribution factor” similar to the impact factor, in which contribution of Wikipedia and other open access forums was weighted, with or without a usefulness measure. Such a usefulness measure could easily be determined by links from other Wikipedia articles, or similar. I realise that there would be severe drawbacks of such a system, similar to those of the impact factor system. I am not a huge fan of impact factors (read e.g. Per Seglen’s 1997 BMJ article [2] for  some reasons why), but I do not see that system changing any time soon, and thus some kind of contribution factor could provide an additional statistical measure for evaluators to consider when examining scientists’ work.

While a contribution factor would be an incitement for  researchers to contribute to the common knowledge, it will still not provide an economic value to do so. This could easily be changed by allowing, and maybe even requiring, scientists to contribute to Wikipedia and other public fora of scientific information as part of their science outreach duties. In fact, this public outreach duty (“tredje uppgiften” in Swedish) is governed in Swedish law. In 2009, the universities in Sweden have been assigned to “collaborate with the society and inform about their operations, and act such that scientific results produced at the university benefits society” (my translation). It seems rational that Wikipedia editing would be part of that duty, as that is the place were many (most?) people find information online today. Consequently, it is only up to the universities to demand 30 minutes of Wikipedia editing per week/month from their employees. Note here that I am referring to paid editing.

Another way of increasing the economic appeal of writing Wikipedia articles would be to encourage funding agencies and foundations to demand Wikipedia articles or similar as part of project reports. This would require researchers to make their findings public in order to get further funding, a move that would greatly increase the importance of increasing the common wisdom treasure. However, I suspect that many funding agencies, as well as researchers would be reluctant to such a solution.

Lastly, as shown by the Rfam/RNA Biology/Wikipedia relationship, scientific publishing itself could be tied to Wikipedia editing. This process could be started by e.g. open access journals such as PLoS ONE, either by demanding short Wikipedia notes to get an article published, or by simply provide prioritised publishing of articles which also have an accompanying Wiki-article. As mentioned previously, these short Wikipedia notes would also go through a peer-review process along with the full article. By tying this to the contribution factor, further incitements could be provided to get scientific progress in the hands of the general public.

Now, all these ideas put a huge burden on already hard-working scientists. I realise that they cannot all be introduced simultaneously. Opening up publishing requires time and thought, and should be done in small steps. But doing so is in the interest of scientists, the general public and the funders, as well as politicians. Because in the long run it will be hard to argue that society should pay for science when scientists are reluctant to even provide the public with an understandable version of the results. Instead of digging such a hole for ourselves, we should adapt the reward, evaluation, funding and publishing systems in a way that they benefit both researchers and the society we often say we serve.

1. Bateman and Logan. Time to underpin Wikipedia wisdom. Nature (2010) vol. 468 (7325) pp. 765
2. Seglen. Why the impact factor of journals should not be used for evaluating research. BMJ (1997) vol. 314 (7079) pp. 498-502