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Perspectives on Academic Data Sharing Benedikt Fecher, German Institute for Economic Research Berlin, 19th April 2016 8,65 Data Sharing in Academia Agenda 1. The potential of data sharing 2. An ideal professed but not practiced * 1. Researchers

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Perspectives on Academic Data Sharing

Benedikt Fecher, German Institute for Economic Research Berlin, 19th April 2016

Data Sharing in Academia

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Agenda

1. The potential of data sharing
2. An ideal professed but not practiced*
1. Researchers
2. Funders
3. Journals
3. An integrated approach

Data Sharing in Academia

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Data Sharing in Academia

The great potential of data sharing Erroneous analyses can inform political decisions.

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Data Sharing in Academia

The great potential of data sharing

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»Economists treat replication the way teenagers treat chastity— as an ideal to be professed but not to be practiced«

Hamermesh, 2007

Data Sharing in Academia

The great potential of data sharing

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»60% to 90% of respondents in all disciplines agree with the statement that they would use other researchers' datasets if their datasets were easily accessible.«

Tenopir et al., 2011

Data Sharing in Academia

The great potential of data sharing

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The great potential of data sharing »The current situation can [...] be characterized as too few cases for too many

factors. The size of sample

sets even large institutions can collect is too small for evidence-based and highly stratified medicine.«

Floca, 2014

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Open Data in Research: A Catalyst for Scientific Progress? § Increases replicability § Creates synergies § Allows (new) methods

Data Sharing in Academia

The great potential of data sharing

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»The 'dirty little secret' behind the promotion

f data sharing is that not much sharing may

be taking place.«

Borgman 2012: 1059

Data Sharing in Academia

An ideal professed but not practiced

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§ Researchers § Funding policies § Journal policies

Data Sharing in Academia

An ideal professed but not practiced

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Researchers § 83% say that open research data is a major contribution to scientific progress; 13% have shared publicly in the past § „If others can publish before me“ is by far the biggest impediment (4.25) § „Criticism or Falsification“ the least important impediment (1.99) § 61 % of the natural scientists say they know how and where to archive data; 42 % of social scientists and economics

Data Sharing in Academia

An ideal professed but not practiced

Source: Own survey among 1564 German researchers

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Have shared data publicly in the past Humanities Natural Science Agricultural Science Engineering Social Sciences & Economics Medicine 19% 17% 14% 12% 9% 8%

Data Sharing in Academia

Data Sharing in Academia Study

Source: Own survey among 1564 German researchers

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Funding Guidelines § from 36, 23 (66%) mention data management § Documentation: 18 (51%) mention standards § 12 (34%) mandatory disclosure, 11 (31%) recommend, 12 (34%) no policy § 14 (40%) mention embargo periods

Data Sharing in Academia

An ideal professed but not practiced

Source: Own analysis of 36 funding guidelines

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Journals § 346 journals in economics and business studies § 49 (14%) have a data availability policy § 37 (11%) require data and supporting material § From the 126,500 studies published in the top 50 economics journals (1974-2014), 131 were replication studies § more than half of the top-50-journals have never published a replication study

Data Sharing in Academia

An ideal professed but not practiced

Source: Vlaeminck & Herrmann, 2015 Mueller-Langer et al, 2015

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§ Create Incentives § Reduce uncertainties § Reduce effort § Facilitate Reuse

An integrated approach

Data Sharing in Academia

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Data Sharing in Academia Data Donor

122 (24/98)

Data

Research org. Data provider Research community Data infrastructure Data recipients Norms

Non-academic recipient Academic recipient Data flow ¡

Source: Own systematic review

An integrated approach

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Data Sharing in Academia Data Donor

122 (24/98)

Data

Research org. Data provider Research community Data infrastructure Data recipients Norms

Non-academic recipient Academic recipient Data flow ¡

Create Incentives

An integrated approach

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Data Sharing in Academia Data Donor

122 (24/98)

Data

Research org. Data provider Research community Data infrastructure Data recipients Norms

Non-academic recipient Academic recipient Data flow ¡

Reduce uncertainties

An integrated approach

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Data Sharing in Academia Data Donor

122 (24/98)

Data

Research org. Data provider Research community Data infrastructure Data recipients Norms

Non-academic recipient Academic recipient Data flow ¡

Reduce effort

An integrated approach

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Data Sharing in Academia Data Donor

122 (24/98)

Data

Research org. Data provider Research community Data infrastructure Data recipients Norms

Non-academic recipient Academic recipient Data flow ¡

Facilitate reuse

An integrated approach

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Fecher B, Friesike S, Hebing M (2015) What Drives Academic Data Sharing? PLoS ONE 10(2): e0118053. Fecher B, Friesike S, Hebing M, Linek S, Sauermann A (2015) A Reputation Economy: Results from an Empirical Survey on Academic Data

Sharing. DIW Berlin

Discussion

Data Sharing in Academia

Fecher B, Fräßdorf M, Wagner GG. Perceptions and Practices of Replicatiion by Social and Behavioral Scientists: Making Replication a Mandatory Element of Curricula Would be Useful. DIW Discussion Papers. 2016; ¡

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References

§ Cassidy, John (2013) The Reinhart and Rogoff Controversy: A Summing Up. http://www.newyorker.com/news/john-cassidy/the- reinhart-and-rogoff-controversy-a-summing-up. § Fecher, Benedikt, Sascha Friesike, and Marcel Hebing (2015) What Drives Academic Data Sharing? Robert S. Phillips, ed. PLOS ONE 10(2): e0118053. § Fecher, Benedikt, Sascha Friesike, Marcel Hebing, Stephanie Linek, and Armin Sauermann 2015 A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing. DIW Berlin Discussion Paper 1454. http://dx.doi.org/10.2139/ssrn.2568693. § Hamermesh, Daniel (2012) Six Decades of Top Economics Publishing: Who and How? w18635. Cambridge, MA: National Bureau

f Economic Research. http://www.nber.org/papers/w18635.pdf, accessed April 12, 2015.

§ Ian, Sample (2015) Study Delivers Bleak Verdict on Validity of Psychology Experiment Results. http://www.theguardian.com/ science/2015/aug/27/study-delivers-bleak-verdict-on-validity-of-psychology-experiment-results. § Tenopir, Carol, Suzie Allard, Kimberly Douglass, et al. (2011) Data Sharing by Scientists: Practices and Perceptions. Cameron Neylon, ed. PLoS ONE 6(6): e21101. § The Economist (2013) Problems with Scientific Research: How Science Goes Wrong. http://www.economist.com/news/leaders/ 21588069-scientific-research-has-changed-world-now-it-needs-change-itself-how-science-goes-wrong. Data Sharing in Academia