RESEARCH DATA SHARING:A BASIC FRAMEWORK

Paul Groth @pgroth

pgroth.com

Elsevier Labs @elsevierlabs

LERU Summer School 2016

Data Stewardship for Scientific Discovery and Innovation

WHAT IS DATA?

WHAT IS DATA?

“Data refers to entities used as evidence of phenomena for the purposes of research or scholarship”

[Borgman Big Data, Little Data, No Data 2015 p.29]

WHY COLLECT DATA?

WHY COLLECT DATA?

Borgman, C. L. (2012). The conundrum of sharing research data. Journal of the American Society for Information Science and Technology.

HOW IS DATA OBTAINED

HOW IS DATA OBTAINED

Borgman, C. L. (2012). The conundrum of sharing research data. Journal of the American Society for Information Science and Technology.

WHY SHARE DATA?

WHY SHARE DATA?• R1: reproduce or verify research,

• R2: make results of publicly funded research available to the public

• R3: enable others to ask new questions of extant data

• R4: advance the state of research and innovation.

Borgman, C. L. (2012). The conundrum of sharing research data. Journal of the American Society for Information Science and Technology.

• All empirical papers must archive their data upon acceptance in order to be published unless the authors provide a compelling reason why they cannot (e.g., expense, confidentiality). The action editor will be the final arbiter of whether the reason is sufficiently compelling.

• “Data” refers to an electronic file containing nonidentified responses that are potentially already coded. Normally, the data would

represent an early stage of electronic processing, before individual responses have been aggregated. The data must be in a form that allows all reported statistical analyses to be reproduced while retaining the confidentiality of individual participants. This entails that the data are formatted and documented in a way that makes the structure of the data set readily apparent.

• Archiving consists either of submitting the data to the journal (to be displayed as supplementary material at the end of the article), sending it to some other archive that is accessible to established researchers and maintained by a substantial established institution, or authors making the data available on their own website, assuming that they can assure us the site will be maintained by a recognized institution for a reasonable period of time. Again, action editors will be the final arbiters of the appropriateness of an archive.

• Any publication that reports analyses of or refers to archived data will be expected to cite the original publication in which the data were reported.

• This policy is new and therefore open to modification. Our aim is to implement a policy that maximizes transparency while minimizing the burden on authors.

THE IMPORTANCE OF CITING DATA

Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014 [https://www.force11.org/group/joint-declaration-data-citation-principles-final].

1. Importance2. Credit and Attribution3. Evidence4. Unique Identification5. Access6. Persistence7. Specificity and Verifiability8. Interoperability and Flexibility

10 ASPECTS OF HIGHLY EFFECTIVE RESEARCH DATA

https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data

https://storify.com/chenghlee/dataformathellhttp://isps.yale.edu/sites/default/files/files/IDCC14_DQR_PeerGreenStephenson.pdf

ALL DATA ISN’T SUCCESSFUL

BARRIERS TO REACHING SUCCESSFUL DATA?

Common practice: data is very fragmented

Using antibodiesand squishy bits Grad Students experimentand enter details into theirlab notebook. The PI then tries to make sense of their slides,and writes a paper. End of story.

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ALL DATA ISN’T CURATED

Cost of documentation

http://www.indoition.com/en/services/costs-prices-software-documentation.htm

20Yolanda GilUSC Information Sciences Institute gil@isi.edu

Measuring Time Savings with “Reproducibility Maps” [Garijo et al PLOS CB12]

2 months of effort in reproducing published method (in PLoS’10)

Authors expertise was requiredComparison of ligand binding sites

Comparison of dissimilar protein structures

Graph network generation

Molecular Docking

Work with D. Garijo of UPM and P. Bourne of UCSD

CURRENT STRATEGIES FOR DATA SHARING

SUBJECT SPECIFIC REPOSITORIES

SUBJECT SPECIFIC REPOSITORIES

COMMUNITY SPECIFIC REPOSITORIES

GENERIC REPOSITORIES

http://data.mendeley.com/

Each dataset receives a versioned DOI, so it can be cited

The citation for the associated article is

displayed

DATA PUBLICATION

BENEFITS OF MACHINE READBILITY

HOW DO WE MOVE UP THE PYRAMID

https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data

60 % OF TIME IS SPENT ON DATA PREPARATION

CURATED DATA SETS

http://ivory.idyll.org/blog/replication-i.html

MORE SEMANTICS

A FRAMEWORK FOR HELPING RESEARCHERS SHARE DATA • What data?

• Determine the context

• Why is data being collected?• How is data obtained?

• What is the researchers’ reason for sharing?

• Document

• Understand Cost/benefit tradeoffs• Target audience• Automation

FURTHER READING• Syllabus for Data Management and Practice, Part I, Winter 2016. Data

Management and Practice, Part I (2016)Christine L Borgmam. https://works.bepress.com/borgman/381/

• Christine L. Borgman. “Big Data, Little Data, No Data”

• Reference list ://www.zotero.org/groups/borgman_big_data_little_data_no_data

• Borgman, C. L. (2012). The conundrum of sharing research data. Journal of the American Society for Information Science and Technology.

• Goodman A, Pepe A, Blocker AW, Borgman CL, Cranmer K, et al. (2014) Ten Simple Rules for the Care and Feeding of Scientific Data. PLoS Comput Biol 10(4): e1003542. doi: 10.1371/journal.pcbi.1003542