Knowledge Access: The Role of Scientific Attribution

Martin Fenner

Department of Hematology, Hemostaseology, Oncology and Stem Cell Transplantation

Proper assigning of credit for scholarly work requires the ability to uniquely identify specific contributors to research.

Proper assigning of credit for scientific work requires the ability to uniquely

identify specific scientific contributions.

304

BMJ | 6 FEBRUARY 2010 | VOLUME 340

RESEARCH METHODS

& REPORTING

1 BioMed Central, 236 Gray’s Inn

Road, London WC1X 8HL

2 Department of Epidemiology

and Biostatistics, Memorial Sloan

Kettering Cancer Center, 1275 York

Avenue, NY, NY 10021, USA

3 Centre for Statistics in Medicine,

University of Oxford, Wolfson

College Annexe, Oxford OX2 6UD

Correspondence to:

I Hrynaszkiewicz

iain.hrynaszkiewicz@

biomedcentral.com

Accepted: 11 December 2009

form Requirements for Manuscripts Submitted to Biomedi-

cal Journals require that patient privacy be protected, and

maintaining confidentiality and privacy is ingrained in

various legal statutes such as the UK Data Protection Act

and the Health Insurance Portability and Accountability

Act (HIPAA) in the US.!

In Europe, the Data Protection Directive (Directive

"#/$%/EC) provides some harmony in data protection leg-

islation, but in the US there is no overarching data protec-

tion law. Therefore, in an increasingly global research and

publishing industry, universally agreed definitions as to

what constitutes anonymised patient information would

benefit clinical researchers. The HIPAA provides a list of

&! items that need to be removed from patient information

in order for it

to be considered anonymous for the pur-

poses of sharing information between the “covered enti-

ties” specified in the act, but the list was not designed with

publication in biomedical journals in mind. A number of

publications from UK bodies provide some form of guid-

ance on identifying information,"-&' but none is as explicit

as the HIPAA.

This article aims to provide practical guidance for those

involved in the publication process by proposing a mini-

mum standard for anonymising (or de-identifying) data for

the purposes of publication in a peer reviewed biomedical

journal or sharing with other researchers, either directly,

where appropriate, or via a third party. Basic advice on

file preparation is also provided, along with procedural

guidance on prospective and retrospective publication of

raw clinical data. Although the focus of this discussion is

on data from randomised trials, the same issues of confi-

dentiality apply to data from any research study involving

human subjects, including cohort, case-control, and case

series designs.

Data preparation guidance

What is the dataset?

For the purposes of th

is guidance, the dataset is the

aggregated collection of patient observations (including

sociodemographic and clinical information) used for

the purposes of producing the summary statistical find-

ings presented in the main report of the research project,

whether previously published or not.

Data are almost always collected at a greater level of

detail than are reported in a journal article. For example,

each participant in a pain study may complete a pain diary

twice a day for () days, with the authors reportin

g “mean

post-treatment pain” for one or m

ore groups of partici-

Iain Hrynaszkiewicz and colleagues

propose a minimum standard for

anonymising datasets to ensure patient

privacy when sharing clinical research data

Many peer-reviewed journals’ instructions for authors

require that authors should be prepared to share their

raw (that is, unprocessed) data with other scientists on

request. Although data sharing is commonplace in some

scientific disciplines and is a requirement of a number of

major research funding agencies’ policies, this culture has

not yet been widely adopted by the clinical research com-

munity. Some journals have appealed to their authors to

increase the availability of medical research data,&

-( rec-

ognising the benefits of such transparency. These benefits

are well documented and include replication of previous

findings, comparisons with independent datasets, testing

of additional hypotheses, teaching, and patient safety.(-%

Moreover, patients themselves are increasingly seeing the

benefits of openly sharing their experiences with others

(www.patientslikeme.com/).

Online journals with unlimited space now provide the

platform for publishing large, raw datasets as supplemen-

tary material,# * but a common concern is confidential-

ity. If there is any doubt over anonymity, publishing data

that have arisen from the doctor-patient or re

searcher-

participant relationship will r

aise issues of privacy

unless explicit and properly informed consent to all of

the intended uses of that data has been obtained. The

International Committee of Medical Journal Editors’ Uni-

Preparing raw clinical data for publication:

guidance for journal editors, authors, and peer reviewers

Iain Hrynaszkiewicz,1 Melissa L Norton,1 Andrew J Vickers,2 Douglas G Altman3

SUMMARY POINTS

Despite journal and funder policies requiring data sharing,

there has been little practical guidance on how data should

be shared

Confidentiality and anonymity are key considerations when

publishing or sharing data relating to individuals, and

this article provides practical advice on data sharing while

minimising risks to patient privacy

Consent for publication of appropriately anonymised raw

data should ideally be sought from partic

ipants in clinical

research

Direct identifie

rs such as patients’ names should be

removed from datasets; datasets that contain three or m

ore

indirect identifie

rs, such as age or sex, should be reviewed

by an independent researcher or ethics committe

e before

being submitted for publication

Cite this as: BMJ 2010;340:c181

doi: 10.113

6/bmj.c181

EDITORIAL by Groves

In order to create the scholarly record, scientific contributions have to be unambiguously assigned to specific contributors.

Text

Systems that measure and evaluate scientific contributions can and should be separate from the databases that hold the scholarly record.

Tools that measure scientific impact should focus on reuse.

Credit systems for scientific contributions should be reevaluated on a regular basis.

In Summary …

?

Other

?

Other

Other ID

Researcher

Other ID

Researcher

Careful balance of costs and benefits. Regular reevaluation

Researcher

ORCID

Evaluation

Open Bibliography

researchers ↔ scientific contributions

Publication

CrossRef DOI

Data

DataCite DOI

010010011010101010111011001010101010101010101010100101010100101010101010101010101010000010111111010101000101010100010010010010010010100100100010010010010010010010010100100111100100

?

Other

Evaluation EvaluationDiscovery

http://www.flickr.com/photos/runesteiness/4983827599/

http://www.flickr.com/photos/mitrika/4976577462/

http://www.flickr.com/photos/inju/4274463847/

http://www.flickr.com/photos/dolor_ipsum/3262262008/

http://www.flickr.com/photos/24622756@N03/3296019009/

Credits

This presentation can be copied and distributed provided that proper

credit is given.