Benefits and barriers of open sharing of research data

1. Benefits and barriers of open sharing of research data Research epidemiologists’ views Dr Poppy Lamberton (and Colleagues) Imperial College London

2. Advantages In general more benefits for data users, modellers etc. than data producers: • Increased availability of data • Mathematical modellers • Meta-analyses • Re-analysis, may locate errors =>more accurate analysis • Increased information e.g. Control lines, missing data • But is the data set fully understood • Speed of obtaining data if no permission required • Negative results • Rarely published, could reduce repetition • Animal ethics e.g. rarely publish number of deaths. • Data collection costly financially and temporally

3. Possible disadvantages • Misuse of data • Simply getting it wrong • Confusion on exact methods, controls, margins of error , missing data etc. • Ethics: ethical clearance often very project specific. • Delayed publication until other papers ready to submit. • Possible reduction in funding for data collection as more data already available from previous projects • Fear that in the long run data collectors feel under valued leading to a reduction in that type of research and therefore a loss of empirical data overall • Reduction in collaborations.

4. What’s already out there. e.g schistosomes and other NTDs • SCORE – Want everything available immediately even if before publication • CONTRAST – open access with key partners and others if requested • SCI –Nothing official, but in theory after results published • NHM – Grant application for repository for any schistosome ( snail, adult worm, larvae; epidemiological to molecular) data to be available (associated with SCORE. • APOC – little data widely available

5. General concerns • Will people need any form of permission to use the data? • Data sets often huge – how feasible is this. • ‘Timely’ release of data to repository – proposed as up to one year post publication • Influenza epidemic: required continuous data release • Long term control programmes: • E.g. SCI, CONTRAST, APOC/OCP etc. • historical data (3-4 year up to 25-30 year old data) • Additional longitudinal data, new techniques: when is analysis complete? • Premature publication through fear of being beaten to it • If you didn’t want to publish your data would this have a negative impact on your chance of research publication

6. Ethics and data ownership • Ethical clearance often very project specific not generic • ‘Dryad is not designed to host data that should never be publicly exposed, such as patient records’ • What constitutes such data • Sex and age: too personal for publication in some journals without written consent from every individual for that particular investigation. • ‘Personal data’ often vital in the analysis • Control programmes • Who owns the data • Views on data release varies from country to country • Must protect the interest of developing countries • Multicentre studies • Happy to release data to some institutes but not others: • Or data often owned by authors but permission needed from every author to release. • Will they release at all if going to be published with open access?

7. Responsibilities? • Financial • Proposed to be covered by the journals, but is this assured or will some of the costs be covered by increased publication costs? • Presentation of data – takes time • Tidying of data set • Detailed variable explanations • Missing data • Ethics • Whose responsibility to police use

8. SCI M&E indicators for longitudinal studies • Demographic: age, gender, weight, height • Parasitological exams: egg counts (multiple days) • Ultrasound exams • Clinical exams • Self reported symptoms through personal questionnaires • Blood tests: haematuria, Hb counts • Miracidia stored from multiple stool/urine samples over multiple days 30++

9. Total by Burkina Uganda Niger Mali Tanzania Zambia Year Faso year 2003 0.433 0 0 0 0.100 0 0.533 2004 1.230 1.027 0.672 0 0.442 0 3.371 2005 2.988 2.296 2.010 2.598 2.952 0 12.844 1.511 2.819 1.560 2.175 0.384 0.556 9.005 2006 2007 1.812 0.750 2.066 0.647 2.650 0.245 8. 170 1.497 * 2.697 5.284 * 0 * 1.243 0 10.721 2008 Total by 9.47 9.59 11.59 5.42 7.77 0.80 44.64 country Number of persons treated (millions) for schistosomiasis and STH in SCI-supported countries from 2003-2008. Cumulative Treatments delivered = 44.644 million ** * Treatment incorporated into the new integrated NTD control programme **Burkina Faso was the first country in the WHO African Region to achieve nationwide coverage with anthelminthic drugs against three major so-called neglected tropical diseases (NTDs), namely lymphatic filariasis, schistosomiasis and STH. Fenwick., A., Webster, J.P., Bosque-Oliva, E. et al., (2009) Parasitology

10. Suggestions • Modify the repository to be more of an archive of what kind of data is available, including geographical locations, collection dates, methods and sample sizes • Example / subsample of raw data • Authors contact details (2 or more) • But permission for use needed from how many and who? • Need to counter balance benefits for data users with incentives for data producers, increase in H index? • Must leave some control with the people who have recorded the data, particularly the countries from which it is collected

11. Thank you