Scott Edmunds talk at AIST: Overcoming the Reproducibility Crisis: and why I stopped worrying a learned to love open data (& methods)
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This document discusses the growing reproducibility crisis in scientific research and proposes open data and transparent methods as solutions. It notes several studies finding a lack of reproducibility in published research due to inaccessible data and methods. Consequences of this include a large and growing number of retractions as well as perceptions that some regions have higher rates of fraudulent research due to lack of transparency. The document argues that open data, software and peer review can help address these issues by enabling credit for sharing and reusing research objects. Examples of initiatives that aim to reward open practices and improve reproducibility through open data publishing and peer review are also provided.
![“Faked
research is
endemic in
China”
Global perceptions of Asian Research
Million RMB rewards for high IF publications = ?
475, 267 (2011)
New Scientist, 17th
Nov 2012: http://www.newscientist.com/article/mg21628910.300-fraud-fighter-faked-research-is-endemic-in-china.html
Nature, 29th
September 2010: http://www.nature.com/news/2010/100929/full/467511a.html
Science, 29th
November 2013: http://www.sciencemag.org/content/342/6162/1035.full
Nature 20th
July 2011: http://www.nature.com/news/2011/110720/full/475267a.html
“Wide distribution of information is key to scientific progress, yet
traditionally, Chinese scientists have not systematically released
data or research findings, even after publication.“
“There have been widespread complaints from scientists inside
and outside China about this lack of transparency. ”
“Usually incomplete and unsystematic, [what little supporting
data released] are of little value to researchers and there is
evidence that this drives down a paper's citation numbers.”](https://image.slidesharecdn.com/japantalk-140701041707-phpapp01/85/Scott-Edmunds-talk-at-AIST-Overcoming-the-Reproducibility-Crisis-and-why-I-stopped-worrying-a-learned-to-love-open-data-methods-9-320.jpg)
Scott Edmunds talk at AIST: Overcoming the Reproducibility Crisis: and why I stopped worrying a learned to love open data (& methods)
- 1. Overcoming the Reproducibility Crisis: and why I stopped worrying a learned to love open data (& methods) Scott Edmunds 1st July 2013
- 2. Harnessing Data-Driven Intelligence Using networking power of the internet to tackle problems Can ask new questions & find hidden patterns & connections Build on each others efforts quicker & more efficiently More collaborations across more disciplines Harness wisdom of the crowds: crowdsourcing, citizen science, crowdfunding Enables: Enabled by: Removing silos, open licenses, transparency, immediacy
- 3. Dead trees not fit for purpose 18121665 1869
- 4. The problems with publishing • Scholarly articles are merely advertisement of scholarship . The actual scholarly artefacts, i.e. the data and computational methods, which support the scholarship, remain largely inaccessible --- Jon B. Buckheit and David L. Donoho, WaveLab and reproducible research, 1995 • Lack of transparency, lack of credit for anything other than “regular” dead tree publication. • If there is interest in data, only to monetise & re-silo • Traditional publishing policies and practices a hindrance
- 5. The consequences: growing replication gap 1. Ioannidis et al., (2009). Repeatability of published microarray gene expression analyses. Nature Genetics 41: 14 2. Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8) Out of 18 microarray papers, results from 10 could not be reproduced Out of 18 microarray papers, results from 10 could not be reproduced
- 6. Consequences: increasing number of retractions >15X increase in last decade Strong correlation of “retraction index” with higher impact factor 1. Science publishing: The trouble with retractions http://www.nature.com/news/2011/111005/full/478026a.html 2. Retracted Science and the Retraction Index ▿ http://iai.asm.org/content/79/10/3855.abstract?
- 7. Consequences: growing replication gap 1. Ioannidis et al., 2009. Repeatability of published microarray gene expression analyses. Nature Genetics 41: 14 2. Science publishing: The trouble with retractions http://www.nature.com/news/2011/111005/full/478026a.html 3. Bjorn Brembs: Open Access and the looming crisis in science https://theconversation.com/open-access-and-the-looming-crisis-in-science-14950 More retractions: >15X increase in last decade At current % > by 2045 as many papers published as retracted Insufficient methods
- 8. “Faked research is endemic in China” Global perceptions of Asian Research Million RMB rewards for high IF publications = ? 475, 267 (2011) New Scientist, 17th Nov 2012: http://www.newscientist.com/article/mg21628910.300-fraud-fighter-faked-research-is-endemic-in-china.html Nature, 29th September 2010: http://www.nature.com/news/2010/100929/full/467511a.html Science, 29th November 2013: http://www.sciencemag.org/content/342/6162/1035.full Nature 20th July 2011: http://www.nature.com/news/2011/110720/full/475267a.html
- 9. “Faked research is endemic in China” Global perceptions of Asian Research Million RMB rewards for high IF publications = ? 475, 267 (2011) New Scientist, 17th Nov 2012: http://www.newscientist.com/article/mg21628910.300-fraud-fighter-faked-research-is-endemic-in-china.html Nature, 29th September 2010: http://www.nature.com/news/2010/100929/full/467511a.html Science, 29th November 2013: http://www.sciencemag.org/content/342/6162/1035.full Nature 20th July 2011: http://www.nature.com/news/2011/110720/full/475267a.html “Wide distribution of information is key to scientific progress, yet traditionally, Chinese scientists have not systematically released data or research findings, even after publication.“ “There have been widespread complaints from scientists inside and outside China about this lack of transparency. ” “Usually incomplete and unsystematic, [what little supporting data released] are of little value to researchers and there is evidence that this drives down a paper's citation numbers.”
- 10. STAP paper demonstrates problems: …to publish protocols BEFORE analysis …better access to supporting data …more transparent & accountable review …to publish replication studies Need:
- 11. • Data • Software • Review • Re-use… = Credit } Credit where credit is overdue: “One option would be to provide researchers who release data to public repositories with a means of accreditation.” “An ability to search the literature for all online papers that used a particular data set would enable appropriate attribution for those who share. “ Nature Biotechnology 27, 579 (2009) New incentives/credit
- 12. GigaSolution: deconstructing the paper www.gigadb.org www.gigasciencejournal.com Utilizes big-data infrastructure and expertise from: Combines and integrates: Open-access journal Data Publishing Platform Data Analysis Platform
- 14. Validationchecks Fail – submitter is provided error report Pass – dataset is uploaded to GigaDB. Submission Workflow Curator makes dataset public (can be set as future date if required) DataCite XML file Excel submission file Submitter logs in to GigaDB website and uploads Excel submission GigaDB DOI assigned Files Submitter provides files by ftp or Aspera XML is generated and registered with DataCite Curator Review Curator contacts submitter with DOI citation and to arrange file transfer (and resolve any other questions/issues). DOI 10.5524/100003 Genomic data from the crab-eating macaque/cynomolgus monkey (Macaca fascicularis) (2011) Public GigaDB dataset See: http://database.oxfordjournals.org/content/2014/bau018.abstract
- 15. • Aspera = 10-100x faster up/download than FTP • Multi-omics/large scale biomedical data focus • Provide (ISA) curation & integration with other DBs (e.g. INSDC, MetaboLights, PRIDE, etc.) For more see: http://database.oxfordjournals.org/content/2014/bau018.abstract
- 16. IRRI GALAXY Beneficiaries/users of our work
- 17. IRRI GALAXY Rice 3K project: 3,000 rice genomes, 13.4TB public data Beneficiaries/users of our work
- 18. NO Diverse Data Types Cyber-centipedes & virtual worms
- 19. More transparency: open peer review BMC Series Medical Journals
- 20. More transparency (and speed): pre-prints
- 21. Real-time open-review = paper in arXiv + blogged reviews Reward open & transparent review http://tmblr.co/ZzXdssfOMJfywww.gigasciencejournal.com/content/2/1/10
- 22. Real-time open-review = paper in arXiv + blogged reviews Reward open & transparent review
- 23. GigaScience + Publons = further credit for reviewers efforts Reward open & transparent review
- 24. Readers are interested in open review Next step to link to ORCID
- 25. Cloud solutions? Reward better handling of metadata… Novel tools/formats for data interoperability/handling.
- 26. Implement workflows in a community-accepted format http://galaxyproject.org Over 36,000 main Galaxy server users Over 1,000 papers citing Galaxy use Over 55 Galaxy servers deployed Open source Rewarding and aiding reproducibility
- 28. Visualizations & DOIs for workflows
- 30. How are we supporting data reproducibility? Data sets Analyses Linked to Linked to DOI DOI Open-Paper Open-Review DOI:10.1186/2047-217X-1-18 >26,000 accesses Open-Code 7 reviewers tested data in ftp server & named reports published DOI:10.5524/100044 Open-Pipelines Open-Workflows DOI:10.5524/100038 Open-Data 78GB CC0 data Code in sourceforge under GPLv3: http://soapdenovo2.sourceforge.net/>20,000 downloads Enabled code to being picked apart by bloggers in wiki http://homolog.us/wiki/index.php?title=SOAPdenovo2
- 31. 7 referees downloaded & tested data, then signed reports Reward open & transparent review
- 32. Post publication: bloggers pull apart code/reviews in blogs + wiki: SOAPdenov2 wiki: http://homolog.us/wiki1/index.php?title=SOAPdenovo2 Homologus blogs: http://www.homolog.us/blogs/category/soapdenovo/ Reward open & transparent review
- 33. SOAPdenovo2 workflows implemented in galaxy.cbiit.cuhk.edu.hk
- 34. SOAPdenovo2 workflows implemented in galaxy.cbiit.cuhk.edu.hk Implemented entire workflow in our Galaxy server, inc.: • 3 pre-processing steps • 4 SOAPdenovo modules • 1 post processing steps • Evaluation and visualization tools Also will be available to download by >36K Galaxy users in
- 35. Can we reproduce results? SOAPdenovo2 S. aureus pipeline
- 36. Taking a microscope to peer review
- 37. The SOAPdenovo2 Case study Subject to and test with 3 models: DataData Method/Experi mental protocol Method/Experi mental protocol FindingsFindings Types of resources in an RO Wfdesc/ISA- TAB/ISA2OWL Wfdesc/ISA- TAB/ISA2OWL Models to describe each resource type
- 39. 1. While there are huge improvements to the quality of the resulting assemblies, other than the tables it was not stressed in the text that the speed of SOAPdenovo2 can be slightly slower than SOAPdenovo v1. 2. In the testing an assessment section (page 3), based on the correct results in table 2, where we say the scaffold N50 metric is an order of magnitude longer from SOAPdenovo2 versus SOAPdenovo1, this was actually 45 times longer 3. Also in the testing an assessment section, based on the correct results in table 2, where we say SOAPdenovo2 produced a contig N50 1.53 times longer than ALL-PATHS, this should be 2.18 times longer. 4. Finally in this section, where we say the correct assembly length produced by SOAPdenovo2 was 3-80 fold longer than SOAPdenovo1, this should be 3-64 fold longer.
- 40. Case Study: Lessons Learned • Most published research findings are false. Or at least have errors. • Is possible to push button(s) & recreate a result from a paper • On a semantic level (via nanopublications) can still have minor errors in text (interpretation not data) • Reproducibility is COSTLY. How much are you willing to spend? • Much easier to do this before rather than after publication
- 41. Aiding reproducibility of imaging studies OMERO: providing access to imaging data View, filter, measure raw images with direct links from journal article. See all image data, not just cherry picked examples. Download and reprocess.
- 42. OMERO: Aiding reproducibility, adding value
- 43. The alternative... ...look but don't touch
- 44. Step 1: get your code out there • Put everything in a code repository. Even if it is ugly (see CRAPL) • Version control. Make sure you document exact version in the paper (big problem with lots of our papers). • If system environments are important, consider VMs http://matt.might.net/articles/crapl/
- 45. Beyond Commenting Code: Step 2: Open lab books, dynamic documents • Facilitate reuse and sharing with tools like: Knitr, Sweave, iPython Notebook Sweave • Working towards executable papers…
- 46. E.g.
- 47. E.g.
- 48. Some testimonials for Knitr Authors (Wolfgang Huber) “I do all my projects in Knitr. Having the textual explanation, the associated code and the results all in one place really increases productivity, and helps explaining my analyses to colleagues, or even just to my future self.” Reviewers (Christophe Pouzat) “It took me a couple of hours to get the data, the few custom developed routines, the “vignette” and to REPRODUCE EXACTLY the analysis presented in the manuscript. With few more hours, I was able to modify the authors’ code to change their Fig. 4. In addition to making the presented research trustworthy, the reproducible research paradigm definitely makes the reviewer’s job much more fun!
- 49. Full reproducibility Levels of reproducibility Dynamic results Usability (e.g. Galaxy Toolshed) Rich metadata, documentation Basic code/data Availability
- 50. Give us data, papers & pipelines* What else can you do? scott@gigasciencejournal.com editorial@gigasciencejournal.com database@gigasciencejournal.com Contact us: * APC’s currently generously covered by BGI until 2015 www.gigasciencejournal.com
- 51. Ruibang Luo (BGI/HKU) Shaoguang Liang (BGI-SZ) Tin-Lap Lee (CUHK) Qiong Luo (HKUST) Senghong Wang (HKUST) Yan Zhou (HKUST) Thanks to: @gigascience facebook.com/GigaScience blogs.biomedcentral.com/gigablog/ Peter Li Huayan Gao Chris Hunter Jesse Si Zhe Nicole Nogoy Laurie Goodman Amye Kenall (BMC) Marco Roos (LUMC) Mark Thompson (LUMC) Jun Zhao (Lancaster) Susanna Sansone (Oxford) Philippe Rocca-Serra (Oxford) Alejandra Gonzalez-Beltran (Oxford) www.gigadb.org galaxy.cbiit.cuhk.edu.hk www.gigasciencejournal.com CBIITFunding from: Our collaborators:team: Case study:
Editor's Notes
- #27: Over 20,000 users on the main server Over 500 papers citing the use of Galaxy Over 55 servers deployed on the Web
- #52: That just leaves me to thank the GigaScience team: Laurie, Scott, Alexandra, Peter and Jesse, BGI for their support - specifically Shaoguang for IT and bioinformatics support – our collaborators on the database, website and tools: Tin-Lap, Qiong, Senhong, Yan, the Cogini web design team, Datacite for providing the DOI service and the isacommons team for their support and advocacy for best practice use of metadata reporting and sharing. Thank you for listening.