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Research Data Management

GFII, Paris, February 2014

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Overview

• Key drivers (and barriers) to the increase in volume of

research data, and its sharing

• Research data management today

• What is done with research data, and what is needed

• Elsevier plans with respect to research data management

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Key Driver: Advances in Technology

• Faster, easier, cheaper, more computational methods of doing

science by data reuse

• Coming of age of analytics yield new layers of insight on the same

data

• Easier, better linked-data technologies for building webs of data

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Key Driver: Government Regulations

http://www.nihms.nih.gov/stats/

• Despite their independent nature, scientists need to follow regulations

• Independent grants (as opposed to direct funding) are increasingly driving science

• Example: Open Access submissions to BMC:

Policy is

announced

Non-compliance is

punished

Trends:

• HIPAA (patient privacy act) drove wide-

scale adoption in DMS systems in Health

• Title 21 CFR Part 11 (tracing provenance

of data points) likewise drove usage of

FDA-compliant systems in Pharma

• More detailed data sharing mandates are

in process, in the US and in Europe, e.g.

the OSTP mandate

• Compliance enforcement will be a

main driver for use of data

management systems in academia,

and for sharing of data

“We’re not going to spend any more

money for you to go out and get

more data! We want you first to show

us how you’re going to use all the

data we paid y’all to collect in the

past!”, Barbara Ransom, NSF

Program Director Earth Sciences

2/13

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Key Driver: Research Institutions’ Reactions to Mandates

• Data Management training

• Dedicated Research Data Management staff

• Encouraging and helping researchers with Data Management Plans

• Establishing Research Data Management Infrastructures

• And more …

Encouraging and facilitating good data management

practices, from curation to sharing

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Key Driver: Reward Systems

NSF Grant proposal guidelines Chapter II.C.2.f(i)(c)

Tenopir et al, 2013

Piwowar et al,. 2007

• Growing awareness of citation impact of shared data:

Data not shared Data shared

Citations 2

004

-

2005

• NSF allows data to be a research output relevant for

grant applications

• Increasing number of data journals, enabling

sharing, recognition and citation

• Data citation policies are becoming standardised,

e.g. Datacite, CODATA Data Citation report,

Force11 Data Citation Synthesis group

• Growing interest to gather data-related statistics by

forward-looking research offices

There are various trends driving towards the increased value of data in ‘merit

metrics’:

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Key Driver: Better Science

• Data sharing can (be perceived to) lead to better science. This opinion is

commonly shared by scientists:

Trends:

• Data sharing and reuse are increasingly on the agenda. Along with mandates,

credit, and other aspects this will play a positive role in improved research data

management and sharing

“Data needs to be stored and

organized in a way that will

allow researchers to access,

share, and analyze the

material.”, Tenopir et al, citing

a 2009 PARSE Study

“Integration of disparate data, often at

different biological scales, is a major

characteristic of current and future

biomedical research discoveries …

Data sharing policies are a great step

forward …”, from Phil Bourne’s letter

to apply for the post of NIH’s

Associate Director for Data Science

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Key Barrier: Researcher Mind-set & Effort

• Changing the workflow, managing & sharing data can cost precious time

• Sharing data is not sufficiently part of the “culture”

Trends:

• Tools tailored to the academic environment (e.g. web-based ELNs), likely to

facilitate data management and sharing without disrupting the workflow

• Continued culture shift increasingly encouraging the sharing of data

Constant pressure to

publish! “There’s no way I’m going to spend

more time documenting my

experiments. I wouldn’t have time

left for my research!”,

researcher at King’s College,

London

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Key Barrier: Intellectual Property

• There is also a perception that managing

data for reuse implies data will be shared: storing/curating/sharing are sometimes

conflated – adversely affecting not only sharing, but also use of data management tools

Trends:

• The ‘Open Science’ trend is encouraging data sharing

• Technology will likely be increasingly recognized as facilitating IP protection

• Embargo periods are becoming accepted (sharing data after publication, not before)

“Once I have tenure, I’ll share my data.

Until then, I’ll use it to get tenure. I

don’t want to get scooped.”, PI at

CMU, voicing the view of many … “I really should share my data. I’m not

sure why I don’t. I guess I am afraid that

someone will use it.”, researcher at

University of Michigan

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Research Data Management Today:

Using antibodies

and squishy bits

Grad Students experiment

and enter details into their

lab notebook.

The PI then tries to make

sense of their slides,

and writes a paper.

End of story.

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Where Research Data Currently Goes (and what

needs to be done):

Research Data Output

Majority of data (90%?) is stored on

local/university hard drives

MiRB

PetDB:

TAIR

PDB

SedDB

A small portion of data (1-2%?) stored in small, topic-focused data repositories

Figshare

Dataverse

Institutional Repositories

Some data (8%?) stored in the main

generic data repositories

Zenodo

Dryad 1. INCREASE DATA DIGITISATION

4. DEVELOP SUSTAINABLE MODELS

3. IMPROVE REPOSITORY

INTEROPERABILITY

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Elsevier and Research Data

*Based on the Royal Society’s “Science as an Open Enterprise” Report, 2012

Discoverability Accessibility Intelligibility Assessability Reusability

Linking articles to data in

repositories (ongoing effort)

Publishing data journals / “microarticles”

Collaborative projects with research institutions (ongoing effort)

Journal Data Policies (in 2014)

Data Search

Citation Index

Management

Pure

“Intelligent Openness”*

Longer term efforts

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Elsevier Content Innovation – Adding Value to Content

Elsevier offers a range of possibilities to

interlink articles and data

Linking through in-article data accession

numbers (e.g. GenBank, Protein Data

Bank)

Database banners shown next to the

article on ScienceDirect (e.g. DRYAD)

Data-integration and visualization tools

that integrate relevant data into the article

page view for exploration (e.g. PANGAEA)

Interactive in-article data viewers enable authors to better present their research and

help readers to build unique insights

Chemical

compounds

Interactive plots

3D models

Inline Supplementary Material presents data

within the article, giving the appropriate

context

• Supplementary material inserted at the

place of reference/citation

• Make it easier for readers to find data

• Put data in the right context

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“Urban Legend” Project

Reducing Barriers to Data Sharing

• Pilot project focusing on collection of metadata in an electrophysiology lab in CMU

• How can we increase data digitization and curation, enabling sharing and

collaboration?

Moving from this:

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“Urban Legend” System Overview