1. David Mellor osf.io/qthsf @EvoMellor
Building infrastructure to connect, preserve, speed up, and improve scholarship
David Mellor
osf.io/qthsf
@EvoMellor
Center for Open Science
cos.io
@OSFramework

2. Evidence to encourage change Incentives to embrace change
Mission: To increase the openness, integrity, and reproducibility of scholarship.
Strategy:
Evidence to encourage change
Incentives to embrace change
Infrastructure to enable change
Improving scientific ecosystem

3. Infrastructure
Metascience
Community

4. The combination of a strong bias toward statistically significant findings and flexibility in data analysis results in irreproducible research

5. The combination of a strong bias toward statistically significant findings and flexibility in data analysis results in irreproducible research
Fanelli D (2010) “Positive” Results Increase Down the Hierarchy of the Sciences. PLoS ONE 5(4): e doi: /journal.pone

6. The Garden of Forking Paths
The combination of a strong bias toward statistically significant findings and flexibility in data analysis results in irreproducible research
The Garden of Forking Paths
Control for time?
Exclude outliers?
Median or mean?
“Does X affect Y?”
Gelman and Loken, 2013

7. The combination of a strong bias toward statistically significant findings and flexibility in data analysis results in irreproducible research
p-values
Original Studies Replications
97% “significant”
37% “significant”

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

10. Incentives to embrace change
Improving scientific ecosystem

12. cos.io/prereg

13. Preregistration increases credibility by specifying in advance how data will be analyzed, thus preventing biased reasoning from affecting data analysis.
cos.io/prereg

14. What is preregistration?
A time-stamped, read-only version of your research plan created before the study.
Preregistration
Study plan:
Hypothesis
Data collection procedures
Manipulated and measured variables
Preregistration
Study plan:
Hypothesis
Data collection procedures
Manipulated and measured variables
Analysis plan:
Statistical model
Inference criteria

15. What is preregistration?
A time-stamped, read-only version of your research plan created before the study.
Preregistration
Study plan:
Hypothesis
Data collection procedures
Manipulated and measured variables
Preregistration
Study plan:
Hypothesis
Data collection procedures
Manipulated and measured variables
Analysis plan:
Statistical model
Inference criteria

16. What is preregistration?
A time-stamped, read-only version of your research plan created before the study.
Preregistration
Study plan:
Hypothesis
Data collection procedures
Manipulated and measured variables
Preregistration
Study plan:
Hypothesis
Data collection procedures
Manipulated and measured variables
Analysis plan:
Statistical model
Inference criteria
When the research plan undergoes peer review before results are known, the preregistration becomes part of a Registered Report

17. What problems does preregistration fix?
The file drawer

18. What problems does preregistration fix?
The file drawer
P-Hacking: Unreported flexibility in data analysis

19. What problems does preregistration fix?
The file drawer
P-Hacking: Unreported flexibility in data analysis
HARKing: Hypothesizing After Results are Known
Dataset
Hypothesis
Kerr, 1998

20. What problems does preregistration fix?
The file drawer
P-Hacking: Unreported flexibility in data analysis
HARKing: Hypothesizing After Results are Known
Dataset
Hypothesis
Kerr, 1998

21. What problems does preregistration fix?
Preregistration makes the distinction between confirmatory (hypothesis testing) and
exploratory (hypothesis generating)
research more clear.
The file drawer
P-Hacking: Unreported flexibility in data analysis
HARKing: Hypothesizing After Results are Known
Dataset
Hypothesis
Kerr, 1998

22. Confirmatory versus exploratory analysis
Context of confirmation
Traditional hypothesis testing
Results held to the highest standards of rigor
Goal is to minimize false positives
P-values interpretable
Context of discovery
Pushes knowledge into new areas
Finds unexpected relationships
Goal is to minimize false negatives
P-values meaningless!
“In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true. This is because circular reasoning (double dipping) would be involved: something seems true in the limited data set, therefore we hypothesize that it is true in general, therefore we (wrongly) test it on the same limited data set, which seems to confirm that it is true. Generating hypotheses based on data already observed, in the absence of testing them on new data, is referred to as post hoc theorizing (from Latin post hoc, "after this").
The correct procedure is to test any hypothesis on a data set that was not used to generate the hypothesis.”
Presenting exploratory results as confirmatory increases the publishability of results at the expense of credibility of results.

23. Confirmatory versus exploratory analysis
Context of confirmation
Traditional hypothesis testing
Results held to the highest standards of rigor
Goal is to minimize false positives
P-values interpretable
Context of discovery
Pushes knowledge into new areas
Finds unexpected relationships
Goal is to minimize false negatives
P-values meaningless!
“In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true. This is because circular reasoning (double dipping) would be involved: something seems true in the limited data set, therefore we hypothesize that it is true in general, therefore we (wrongly) test it on the same limited data set, which seems to confirm that it is true. Generating hypotheses based on data already observed, in the absence of testing them on new data, is referred to as post hoc theorizing (from Latin post hoc, "after this").
The correct procedure is to test any hypothesis on a data set that was not used to generate the hypothesis.”
Presenting exploratory results as confirmatory increases the publishability of results at the expense of credibility of results.

24. Confirmatory versus exploratory analysis
Context of confirmation
Traditional hypothesis testing
Results held to the highest standards of rigor
Goal is to minimize false positives
P-values interpretable
Context of discovery
Pushes knowledge into new areas/ data-led discovery
Finds unexpected relationships
Goal is to minimize false negatives
P-values meaningless
“In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true. This is because circular reasoning (double dipping) would be involved: something seems true in the limited data set, therefore we hypothesize that it is true in general, therefore we (wrongly) test it on the same limited data set, which seems to confirm that it is true. Generating hypotheses based on data already observed, in the absence of testing them on new data, is referred to as post hoc theorizing (from Latin post hoc, "after this").
The correct procedure is to test any hypothesis on a data set that was not used to generate the hypothesis.”
Presenting exploratory results as confirmatory increases the publishability of results at the expense of credibility of results.

25. Confirmatory versus exploratory analysis
Context of confirmation
Traditional hypothesis testing
Results held to the highest standards of rigor
Goal is to minimize false positives
P-values interpretable
Context of discovery
Pushes knowledge into new areas/ data-led discovery
Finds unexpected relationships
Goal is to minimize false negatives
P-values meaningless
“In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true. This is because circular reasoning (double dipping) would be involved: something seems true in the limited data set, therefore we hypothesize that it is true in general, therefore we (wrongly) test it on the same limited data set, which seems to confirm that it is true. Generating hypotheses based on data already observed, in the absence of testing them on new data, is referred to as post hoc theorizing (from Latin post hoc, "after this").
The correct procedure is to test any hypothesis on a data set that was not used to generate the hypothesis.”
Presenting exploratory results as confirmatory increases the publishability of results at the expense of credibility of results.

26. Collect New Data Example Workflow 1
Theory driven, a-priori expectations
Collect New Data

27. Collect New Data Confirmation Phase Hypothesis testing
Example Workflow 1
Theory driven, a-priori expectations
Collect New Data
Confirmation Phase
Hypothesis testing

28. Hypothesis generating
Example Workflow 1
Theory driven, a-priori expectations
Collect New Data
Confirmation Phase
Hypothesis testing
Discovery Phase
Exploratory research
Hypothesis generating

29. Example Workflow 2
Few a-priori expectations
Collect Data

30. Hypothesis generating
Example Workflow 2
Few a-priori expectations
Collect Data
Keep these data secret!
Split Data
Discovery Phase
Exploratory research
Hypothesis generating

31. Hypothesis generating
Example Workflow 2
Few a-priori expectations
Collect Data
Keep these data secret!
Split Data
Discovery Phase
Exploratory research
Hypothesis generating
Confirmation Phase
Hypothesis testing

32. How do you preregister?

33. https://osf.io/prereg

37. Tips for writing up preregistered work
Include a link to your preregistration
(e.g.
Report the results of ALL preregistered analyses
ANY unregistered analyses must be transparent

38. Registered Reports

39. Registered Reports Are the hypotheses well founded?
Are the methods and proposed analyses feasible and sufficiently detailed?
Is the study well powered? (≥90%)
Have the authors included sufficient positive controls to confirm that the study will provide a fair test?

40. Registered Reports Did the authors follow the approved protocol?
Did positive controls succeed?
Are the conclusions justified by the data?

41. None of these things matter
Chambers, 2017

42. 49 Journals currently accept RRs. Learn more at: cos.io/rr

44. Technology to enable change
Improving scientific ecosystem

45. 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

46. Collaboration Documentation Archiving
Content management and collaboration system
Free service
Connect, curate, search all aspects of the research project
We don’t want to repeat ourselves
Dataverse S3
Figshare
Dropbox
Service vs Application interface
the service build new applicatoins
this is right in line with SHARE-NS - openning, unlcoking this data
allows for innovation
From day one, we’ve been very excited about the SHARE partnerhship:
community, expertise bringing to the table
as well as
shared interests
mission
Technical perspective, this project fit very much in line with what are building and more importantly how we are building it

47. Content management and collaboration system
Free service
Connect, curate, search all aspects of the research project
We don’t want to repeat ourselves
Dataverse S3
Figshare
Dropbox
Service vs Application interface
the service build new applicatoins
this is right in line with SHARE-NS - openning, unlcoking this data
allows for innovation
From day one, we’ve been very excited about the SHARE partnerhship:
community, expertise bringing to the table
as well as
shared interests
mission
Technical perspective, this project fit very much in line with what are building and more importantly how we are building it

48. Put data, materials, and code on the OSF
quite simply, it is a file repository that allows any sort of file to be stored and many types to be rendered in the browser without any special software. This is very important for increasing accessibility to research.

49. Manage access and permissions

50. Automate versioning
With hashes

51. Connects Services
Researchers Use

53. OpenSesame

54. OpenSesame

55. Thank you! Find this presentation at: https://osf.io/
Contact me at
Our mission is to provide expertise, tools, and training to help researchers create and promote open science within their teams and institutions. Promoting these practices within the research funding and publishing communities accelerates scientific progress.

56. Literature cited
Chambers, C. D., Feredoes, E., Muthukumaraswamy, S. D., & Etchells, P. (2014). Instead of “playing the game” it is time to change the rules: Registered Reports at AIMS Neuroscience and beyond. AIMS Neuroscience, 1(1), 4–17.
Franco, A., Malhotra, N., & Simonovits, G. (2014). Publication bias in the social sciences: Unlocking the file drawer. Science, 345(6203), 1502–
Franco, A., Malhotra, N., & Simonovits, G. (2015). Underreporting in Political Science Survey Experiments: Comparing Questionnaires to Published Results. Political Analysis, 23(2), 306–312.
Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Department of Statistics, Columbia University. Retrieved from
Kaplan, R. M., & Irvin, V. L. (2015). Likelihood of Null Effects of Large NHLBI Clinical Trials Has Increased over Time. PLoS ONE, 10(8), e
Kerr, N. L. (1998). HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology Review, 2(3), 196–
Nosek, B. A., Spies, J. R., & Motyl, M. (2012). Scientific Utopia: II. Restructuring Incentives and Practices to Promote Truth Over Publishability. Perspectives on Psychological Science, 7(6), 615–631.
Ratner, K., Burrow, A. L., & Thoemmes, F. (2016). The effects of exposure to objective coherence on perceived meaning in life: a preregistered direct replication of Heintzelman, Trent & King (2013). Royal Society Open Science, 3(11),