1. An Open Science Framework for Managing and Sharing Research Workflows
David Mellor
Journal and Funder Initiatives
| | @OSFramework

2. Norms Counternorms Communality Universalism Disinterestedness
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Organized skepticism
Consider all new evidence, even against one’s prior work
Quality
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Organized dogmatism
Invest career promoting one’s own theories, findings
Quantity
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume
Merton, 1942

3. Norms Counternorms Communality Universalism Disinterestedness
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Organized skepticism
Consider all new evidence, even against one’s prior work
Quality
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Organized dogmatism
Invest career promoting one’s own theories, findings
Quantity
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume
Merton, 1942

4. Norms Counternorms Communality Universalism Disinterestedness
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Organized skepticism
Consider all new evidence, even against one’s prior work
Quality
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Organized dogmatism
Invest career promoting one’s own theories, findings
Quantity
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume
Merton, 1942

5. Norms Counternorms Communality Universalism Disinterestedness
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Organized skepticism
Consider all new evidence, even against one’s prior work
Quality
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Organized dogmatism
Invest career promoting one’s own theories, findings
Quantity
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume
Merton, 1942

6. Norms Counternorms Communality Universalism Disinterestedness
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Organized skepticism
Consider all new evidence, even against one’s prior work
Quality
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Organized dogmatism
Invest career promoting one’s own theories, findings
Quantity
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume
Merton, 1942

7. Norms Counternorms Communality Universalism Disinterestedness
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Organized skepticism
Consider all new evidence, even against one’s prior work
Quality
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Organized dogmatism
Invest career promoting one’s own theories, findings
Quantity
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume
Merton, 1942

8. Anderson, Martinson, & DeVries, 2007

9. Anderson, Martinson, & DeVries, 2007

10. Anderson, Martinson, & DeVries, 2007

11. Incentives for individual success are focused on getting it published, not getting it right
Nosek, Spies, & Motyl, 2012

12. Flexibility in analysis
Problems
Flexibility in analysis
Selective reporting
Ignoring nulls
Lack of replication
Examples from:
Button et al – Neuroscience
Ioannidis – why most results are false (Medicine)
GWAS
Biology
Two possibilities are that the percentage of positive results is inflated because negative results are much less likely to be published, and that we are pursuing our analysis freedoms to produce positive results that are not really there. These would lead to an inflation of false-positive results in the published literature.
Some evidence from bio-medical research suggests that this is occurring. Two different industrial laboratories attempted to replicate 40 or 50 basic science studies that showed positive evidence for markers for new cancer treatments or other issues in medicine. They did not select at random. Instead, they picked studies considered landmark findings. The success rates for replication were about 25% in one study and about 10% in the other. Further, some of the findings they could not replicate had spurred large literatures of hundreds of articles following up on the finding and its implications, but never having tested whether the evidence for the original finding was solid. This is a massive waste of resources.
Across the sciences, evidence like this has spurred lots of discussion and proposed actions to improve research efficiency and avoid the massive waste of resources linked to erroneous results getting in and staying in the literature, and about the culture of scientific practices that is rewarding publishing, perhaps at the expense of knowledge building. There have been a variety of suggestions for what to do. For example, the Nature article on the right suggests that publishing standards should be increased for basic science research.
[It is not in my interest to replicate – myself or others – to evaluate validity and improve precision in effect estimates (redundant). Replication is worth next to zero (Makel data on published replications; motivated to not call it replication; novelty is supreme – zero “error checking”; not in my interest to check my work, and not in your interest to check my work (let’s just each do our own thing and get rewarded for that)
Irreproducible results will get in and stay in the literature (examples from bio-med). Prinz and Begley articles (make sure to summarize accurately)
The Nature article by folks in bio-medicine is great. The solution they offer is a popular one in commentators from the other sciences -- raise publishing standards.
Sterling, 1959; Cohen, 1962; Lykken, 1968; Tukey, 1969; Greenwald, 1975; Meehl, 1978; Rosenthal, 1979

13. Figure by FiveThirtyEight.com Silberzahn et al., 2015

14. A Garden of Forking Paths
“Does X affect Y?”
Exclude outliers?
Control for year?
Median or mean?
A series of perfectly reasonable decisions coupled with motivated reasoning can quickly lead us to a subset of statistically significant results. In effect, our hypothesis changed without us even realizing it. Our confirmatory, hypothesis testing became exploratory, hypothesis generating without our permission!
Jorge Luis Borges; Gelman and Loken

15. Likelihood of Null Effects of Large NHLBI Clinical Trials Has Increased over Time
Kaplan and Irvin, 2015

16. Likelihood of Null Effects of Large NHLBI Clinical Trials Has Increased over Time
Positive result rate dropped from 57% to 8% after preregistration became required for clinical trials.
Kaplan and Irvin, 2015

17. Metascience: Estimates Reproducibility

21. Reproducibility in other fields
Developmental Psychology
Michael Frank
Ecology
Emilio Bruna
Health Sciences
Computer Science
Leslie McIntosh
Cynthia Hudson-Vitale
Christian Collberg Todd Proebsting

22. Solutions? Appeal to intentions, values, and goals.
“Hey You! Behave by your values! Be objective!”

23. Incentives for individual success are focused on getting it published, not getting it right
Nosek, Spies, & Motyl, 2012

24. Rewards What is published? What is not? Novel results Positive results
Clean results
Replications
Negative results
Mixed evidence

25. Flexibility in analysis
Problems
Flexibility in analysis
Selective reporting
Ignoring nulls
Lack of replication
Examples from:
Button et al – Neuroscience
Ioannidis – why most results are false (Medicine)
GWAS
Biology
Two possibilities are that the percentage of positive results is inflated because negative results are much less likely to be published, and that we are pursuing our analysis freedoms to produce positive results that are not really there. These would lead to an inflation of false-positive results in the published literature.
Some evidence from bio-medical research suggests that this is occurring. Two different industrial laboratories attempted to replicate 40 or 50 basic science studies that showed positive evidence for markers for new cancer treatments or other issues in medicine. They did not select at random. Instead, they picked studies considered landmark findings. The success rates for replication were about 25% in one study and about 10% in the other. Further, some of the findings they could not replicate had spurred large literatures of hundreds of articles following up on the finding and its implications, but never having tested whether the evidence for the original finding was solid. This is a massive waste of resources.
Across the sciences, evidence like this has spurred lots of discussion and proposed actions to improve research efficiency and avoid the massive waste of resources linked to erroneous results getting in and staying in the literature, and about the culture of scientific practices that is rewarding publishing, perhaps at the expense of knowledge building. There have been a variety of suggestions for what to do. For example, the Nature article on the right suggests that publishing standards should be increased for basic science research.
[It is not in my interest to replicate – myself or others – to evaluate validity and improve precision in effect estimates (redundant). Replication is worth next to zero (Makel data on published replications; motivated to not call it replication; novelty is supreme – zero “error checking”; not in my interest to check my work, and not in your interest to check my work (let’s just each do our own thing and get rewarded for that)
Irreproducible results will get in and stay in the literature (examples from bio-med). Prinz and Begley articles (make sure to summarize accurately)
The Nature article by folks in bio-medicine is great. The solution they offer is a popular one in commentators from the other sciences -- raise publishing standards.
Sterling, 1959; Cohen, 1962; Lykken, 1968; Tukey, 1969; Greenwald, 1975; Meehl, 1978; Rosenthal, 1979

27. Mission: Increase the openness, integrity, and reproducibility of scientific research.

28. Our Strategy: Evidence to encourage change.
Incentives to embrace change.
Technology to enable change.
Improving scientific ecosystem

29. Incentives to embrace change

30. Agnostic to discipline
TOP Guidelines
Transparency and Openness Promotion
Agnostic to discipline
Low barrier to entry
Modular

31. Transparency & Openness Promotion Guidelines
Eight Standards
Data citation
Design transparency
Research materials transparency
Data transparency
Analytic methods (code) transparency
Preregistration of studies
Preregistration of analysis plans
Replication
Three Tiers
Disclose
Require
Verify
Signatories
539 Journals
59 Organization
Learn more at

32. Transparency & Openness Promotion Guidelines
Eight Standards
Data citation
Design transparency
Research materials transparency
Data transparency
Analytic methods (code) transparency
Preregistration of studies
Preregistration of analysis plans
Replication
Three Tiers
Disclose
Require
Verify
Signatories
539 Journals
59 Organization
Learn more at

33. Transparency & Openness Promotion Guidelines
Eight Standards
Data citation
Design transparency
Research materials transparency
Data transparency
Analytic methods (code) transparency
Preregistration of studies
Preregistration of analysis plans
Replication
Three Tiers
Disclose
Require
Verify
Signatories
757Journals
64 Organization
Learn more at

34. Signals: Making Behaviors Visible Promotes Adoption
Badges
Open Data
Open Materials
Preregistration
Psychological Science (Jan 2014)

36. 40%
30%
% Articles reporting that data was available
20%
10% 0%

37. 100% 75% 50% 25% 0% % of Articles reporting that data was available
On the y axis, we have % of articles reporting data available in an independent, open access location
On the x axis, we have five categories: reportedly available, accessible, correct, usable, and complete data
25% 0%
Reportedly Available Accessible Correct Data Usable Data Complete Data

38. 100% 75% 50% 25% 0% % of Articles reporting that data was available
In an ideal world, we’d see straight lines for articles published in all journals, and in psychological science before and after badges
This would mean that all articles that reported available data had data that was accessible, correct, usable, and complete
25% 0%
Reportedly Available Accessible Correct Data Usable Data Complete Data

39. 100% 75% 50% 25% 0% % of Articles reporting that data was available
In Psychological Science prior to badges and in other comparison conditions, only 39% or less (20%, 16%) of articles with reportedly available data in the comparison conditions had data that was accessible, correct, usable, and complete
25% 0%
Reportedly Available Accessible Correct Data Usable Data Complete Data

40. 100% 75% 50% 25% 0% % of Articles reporting that data was available
Of articles in Psychological Science that earned badges, over 75% of articles with data reportedly available had data that was accessible, correct, usable, and complete
While not perfect, reportedly available data were more likely to be persistent when badges were earned -- perhaps due to the accountability of receiving a badge on the publication.
25% 0%
Reportedly Available Accessible Correct Data Usable Data Complete Data

41. The $1,000,000 Preregistration Challenge
Another incentive for researchers to try out preregistration.

42. Exploratory research:
Finds unexpected trends
Pushes knowledge into new areas
Results in a testable hypothesis

43. Confirmatory research:
Puts a hypothesis to the test
Does not allow data to influence the hypothesis
Results are held to the highest standard of rigor

44. https://cos.io/prereg

46. Data collection methods
Research questions
Data collection methods
Variables
Statistical tests
Outliers

47. Technology to enable change

49. http://osf.io/ free, open source
Share data, share materials, show the research process – confirmatory result make it clear, exploratory discovery make it clear; demonstrate the ingenuity, perspiration, and learning across false starts, errant procedures, and early hints – doesn’t have to be written in painstaking detail in the final report, just make it available.
free, open source

50. Collaboration Documentation Archiving
And it does things that many content management systems do. But it always balances things researchers care about while either automatically or making it very easy to align with values. Again, scholars should focus on scholarship--and we respect that they have a workflow—this has to be frictionless.

51. So here, it’s very easy to curate a project, add contributors, share, but we also keep an activity log. To the users that’s either meaningless or an easy way to keep up-to-date, but to us, this is provenance—this is important information about how science happens—science is a process, this is great time series metadata for metascience

52. Put data, materials, and code on the OSF

53. Manage access and permissions

54. Version Control

56. Registration
Registrations: Freezing the current state of the project giving it a unique identifier.

57. We also collect a bit of metadata as well.
People traditionally use these like clinical trials: state hypotheses, plan, and then see if you confirm those results, conduct that plan

58. Merges Public-Private Workflows
We don’t mandate openness—you can use the site publically or privately.

59. But being open is only 2 clicks away.
You’ll also note that components can have independent authorship lists as well as permissions and privacy settings.

60. Incentives to embrace change

61. Persistent
Citable
Identifiers

63. Incentives for Openness File downloads
We can do small things like offer rich analytics to incentivize more open practices. Public projects gain immediate access to analytics showing visits over time, sources of traffic, geographic location of traffic, time of traffic, and download counts for files. This is a much faster reward for one’s effort than waiting months or longer until something is published and then even longer until it is cited.

64. Visits File downloads Forks
We can do small things like offer rich analytics to incentivize more open practices. Public projects gain immediate access to analytics showing visits over time, sources of traffic, geographic location of traffic, time of traffic, and download counts for files. This is a much faster reward for one’s effort than waiting months or longer until something is published and then even longer until it is cited.
Forks

65. Connecting the workflow is critical to enabling change

66. OpenSesame

67. OpenSesame

68. https://api.osf.io/v2/docs/
All of this is now possible with the new OSF API.
API Docs

71. Substantive experts shouldn’t have to deal with…
Workflow integration
Authentication
Permissions
File storage
File rendering
Database
Metadata/Annotations/Commenting
Persistence
External service integrations
Search
SHARE Data

72. Substantive experts should focus on…
Defining community standards
Changing community values and incentives
Determining specific community requirements
Doing scholarship
Just let experts be experts!

73. Society-branded Preprint Servers

74. OSF for Institutions

75. Thank you!
David Mellor, Find me online: Find this presentation: