Open Data in a Big Data World: easy to say, but hard to do?

Sarah Callaghan sarah.callaghan@stfc.ac.uk

@sorcha_niORCID: 0000-0002-0517-1031

Geoffrey Boulton, Dominique Babini, Simon Hodson, Jianhui Li, Tshilidzi Marwala, Maria Musoke, Paul Uhlir, Sally Wyatt

3rd LEARN workshop on Research Data Management,

“Make research data management policies work”Helsinki, 28 June 2016

Principles, Policies & Practice

Responsibilities1-2. Scientists3.Research institutions & universities4.Publishers5.Funding agencies6.Scholarly societies and academies7.Libraries & repositories

8. Boundaries of openness

Enabling practices9. Citation and provenance10. Interoperability11. Non-restrictive re-use12. Linkability

http://www.icsu.org/science-international/accord

The Data Deluge

http://www.economist.com/node/21521549

http://www.leadformix.com/blog/2013/02/the-big-data-deluge/

It used to be “easy”…

Suber cells and mimosa leaves. Robert Hooke, Micrographia, 1665

The Scientific Papers of William Parsons, Third Earl of Rosse 1800-1867

…but datasets have gotten so big, it’s not useful to publish them in hard copy anymore

Hard copy of the Human Genome at the Wellcome Collection

Example Big Data: CMIP5

CMIP5: Fifth Coupled Model Intercomparison Project• Global community activity under the World Meteorological Organisation (WMO) via the World Climate Research Programme (WCRP)•Aim:

– to address outstanding scientific questions that arose as part of the 4th Assessment Report process,

– improve understanding of climate, and

– to provide estimates of future climate change that will be useful to those considering its possible consequences.

Many distinct experiments, with very different characteristics, which influence the configuration of the models, (what they can do, and how they should be interpreted).

Simulations:~ 90,000 years~ 60 experiments~ 20 modelling centres (from around the world) using~ 30 major(*) model configurations~ 2 million output “atomic” datasets ~ 10's of petabytes of output~ 2 petabytes of CMIP5 requested output~ 1 petabyte of CMIP5 “replicated” outputWhich are replicated at a number of sites (including ours)

Major international collaboration!Funded by EU FP7 projects (IS-ENES2, Metafor) and US (ESG) and other national sources (e.g. NERC for the UK)

CMIP5 numbers

10

Summary of the CMIP5 example

The Climate problem needs:– Major physical e-infrastructure (networks, supercomputers)– Comprehensive information architectures covering the whole information life

cycle, including annotation (particularly of quality)… and hard work populating these information objects, particularly with

provenance detail.– Sophisticated tools to produce and consume the data and information objects– State of the art access control techniques

Major distributed systems are social challenges as much as technical challenges.

CMIP5 is Big Data, with lots of different participants and lots of different technologies. It also has a community willing to work together to standardise and automate data

and metadata production and curation, and with the willingness to support the effort needed for openness.

Big Data:•Industrialised and standardised data and metadata production•Large groups of people involved•Methods for making the data open, attribution and credit for data creation established

Long Tail Data:•Bespoke data and metadata creation methods•Small groups/lone researchers•No generally accepted methods for attribution and credit for data creation. Often data is closed due to lack of effort to open it

https://flic.kr/p/g1EHPR

Most people have an idea of what a publication is

Some examples of data (just from the Earth Sciences)

1. Time series, some still being updated e.g. meteorological measurements

2. Large 4D synthesised datasets, e.g. Climate, Oceanographic, Hydrological and Numerical Weather Prediction model data generated on a supercomputer

3. 2D scans e.g. satellite data, weather radar data

4. 2D snapshots, e.g. cloud camera5. Traces through a changing medium,

e.g. radiosonde launches, aircraft flights, ocean salinity and temperature

6. Datasets consisting of data from multiple instruments as part of the same measurement campaign

7. Physical samples, e.g. fossils

Open Data is not a new idea

Henry Oldenburg

Data, Reproducibility and Science

Science should be reproducible – other people doing the same experiments in the same way should get the same results.

Observational data is not reproducible (unless you have a time machine)

Therefore we need to have access to the data to confirm the science is valid!

Poor data analysis generates false facts – and false facts & inaccessible data undermine science & its credibility

http://www.flickr.com/photos/31333486@N00/1893012324/sizes/o/in/photostream/

A crisis of reproducibility and credibility?

The data providing the evidence for a published concept MUST be concurrently published, together with the metadata. To do otherwise is scientific MALPRACTICE

Pre-clinical oncology – 89% not reproducibleWhy?•Misconduct/fraud•Invalid reasoning•Absent or inadequate data and/or metadata

We’re only going to get more data

More big data - linked data – machine learning

The internet of things

So, what must we do?•Concurrently publish data and metadata that are the evidence for a published scientific claim – to do otherwise is malpractice•Data science skills for researchers•Re-establish standards of reproducibility for a data-intensive age

• Patterns not hitherto seen• Unsuspected relationships• Integrated analysis of diverse data (e.g. natural & social science)• Complex systems

e.g. complexity: dynamic evolution and system state

But not all research is or needs to be data-intensive

Scientific Opportunities of Big Data

https://www.clickz.com/clickz/column/2389218/create-better-content-via-humor

http://www.tylervigen.com/spurious-correlations

Caveat Emptor!

Data supporting a published claim Other data for re-use & integration

Pillars of the Digital Revolution

Big Data

Volume

Velocity

Variety

Veracity

Linked Data

Many databases

SemanticRelations

Deeper meaning

Foundations : Openness

Machine analysis & learning

The Open Data Edifice

Open Data initiatives in areas of:

Life sciences Earth Science, Environmental Science Food Science Agricultural Science Chemical Crystallography Bioinformatics/Genomics LinguisticsSocial SciencesEvolutionary biologyBiodiversityAstronomy Earth Observation (GEO)ArchaeologyAtmospheric sciences

Elixir programme

It is happening: bottom-up Open Data initiatives

The Open Data Iceberg

The Technical Challenge

The Consent Challenge

The Institutional Challenge

The Funding Challenge

The Support Challenge

The Skills Challenge

The Incentives Challenge

The Mindset Challenge

Processes &Organisation

People

Developed from: Deetjen, U., E. T. Meyer and R. Schroeder (2015). OECD Digital Economy Papers, No. 246, OECD Publishing.

A National Infrastructure

Technology

Scientistsi.Publicly funded scientists have a responsibility to contribute to the public good through the creation and communication of new knowledge, of which associated data are intrinsic parts. They should make such data openly available to others as soon as possible after their production in ways that permit them to be re-used and re-purposed.

ii. The data that provide evidence for published scientific claims should be made concurrently and publicly available in an intelligently open form. This should permit the logic of the link between data and claim to be rigorously scrutinised and the validity of the data to be tested by replication of experiments or observations. To the extent possible, data should be deposited in well-managed and trusted repositories with low access barriers.

From the Accord: Responsibilities

Creating a dataset is hard work!

"Piled Higher and Deeper" by Jorge Chamwww.phdcomics.com

Documenting a dataset so that it is usable and understandable by others is extra work!

“I’m all for the free sharing of information, provided it’s them sharing their information with us.”

http://discworld.wikia.com/wiki/Mustrum_Ridcully

Mustrum Ridcully, D.Thau., D.M., D.S., D.Mn., D.G., D.D., D.C.L., D.M. Phil., D.M.S., D.C.M., D.W., B.El.L, Archancellor, Unseen University, Anhk-Morpork, Discworld

- As quoted in “Unseen Academicals”, by Terry Pratchett

Open is not enough!

“When required to make the data available by my program manager, my collaborators, and ultimately by law, I will grudgingly do so by placing the raw data on an FTP site, named with UUIDs like 4e283d36-61c4-11df-9a26-edddf420622d. I will under no circumstances make any attempt to provide analysis source code, documentation for formats, or any metadata with the raw data. When requested (and ONLY when requested), I will provide an Excel spreadsheet linking the names to data sets with published results. This spreadsheet will likely be wrong -- but since no one will be able to analyze the data, that won't matter.”

- http://ivory.idyll.org/blog/data-management.html https://flic.kr/p/awnCQu

Incentives for Open Data

• Need reward structures and incentives for researchers to encourage them to make their data open

• Data citation and publication

• (again, issues with treating data as a special case of publications…)

The Understandability Challenge: Article

What the data set looks like on disk

What the raw data files look like.

I could make these files open easily, but no one would have

a clue how to use them!

The Understandability

Challenge: Data

It’s ok, I’ll just put it out there and if it’s important other people will figure it out

These documents have been preserved for thousands of years!But they’ve both been translated many times, with different meanings each time.

We need Metadata to preserve Information We can’t rely on Data Archaeology

Phaistos Disk, 1700BC

http://theupturnedmicroscope.com/comic/negative-data/

It’s not just data!

• Experimental protocols• Workflows• Software code• Metadata• Things that went wrong!• …

Usability, trust, metadata

http://trollcats.com/2009/11/im-your-friend-and-i-only-want-whats-best-for-you-trollcat/

When you read a journal paper, it’s easy to read and get a quick understanding of the quality of the paper.

You don’t want to be downloading many GB of dataset to open it and see if it’s any use to you.

Need to use proxies for quality:•Do you know the data source/repository? Can you trust it?•Is there enough metadata so that you can understand and/or use the data?

In the same way that not all journal publishers are created equal, not all data repositories are created equal

Example metadata from a published dataset:“rain.csv contains rainfall in mm for each month at Marysville, Victoria from January 1995 to February 2009”

Lindenmayer, David B.; Wood, Jeff; McBurney, Lachlan; Michael, Damian; Crane, Mason; MacGregor, Christopher; Montague-Drake, Rebecca; Gibbons, Philip; Banks, Sam C.; (2011): rain; Dryad Digital Repository. http://doi.org/10.5061/DRYAD.QP1F6H0S/3

Should ALL data be open?

Most data produced through publically funded research should be open.

But!• Confidentiality issues (e.g.

named persons’ health records)• Conservation issues (e.g. maps

of locations of rare animals at risk from poachers)

• Security issues (e.g. data and methodologies for building biological weapons) There should be a very good

reason for publically funded data to not be open.

Getting scoopedhttp://www.phdcomics.com/comics/archive.php?comicid=795

It happened to me!I shared my data with another research group. They published

the first results using that data. I wasn’t a co-author. I didn’t get an acknowledgement.

Citeable does not equal Open!

Just like you can cite a paper that is behind a paywall, you can cite a dataset that isn’t open.

Making something citeable means that:

• You know it exists• You know who’s responsible for it• You know where to find it• You know a little bit about it (title,

abstract,…)

Even if you can’t download/read the thing yourself.

Citation gives benefits that encourage data producers to

make their data open

Be careful of your citations!

Inputs Outputs

Open access

Administrative data (held by

public authorities e.g.

prescription data)

Public Sector Research data

(e.g. Met Office weather

data)

Research Data (e.g.

CERN, generated in universities)

Research publications (i.e. papers in journals)

Open data

Open science A direction of travel?

Collecting the dataDoing

research

Doing science openly

Researchers - Govt & Public sector - Businesses - Citizens - Citizen scientists

(communication/dialogue – joint production of knowledge)

Stakeholders

• Communication/dialogue must be audience-sensitive• Is it – with all stakeholder groups?

Summary and maybe conclusions?

• We need to open the products of research• to encourage innovation and collaboration• to give credit to the people who’ve created them• to be transparent and trustworthy

• Openness does come at a cost!• It’s not enough for data to be open

• it needs to be usable and understandable too

• Data citation and publication are ways of encouraging researchers to make their data open

• or at least tell the world that their data exists!• We need a culture change – but it’s already happening!

http://www.keepcalm-o-matic.co.uk/default.aspx#createposter

Thanks!Any questions?

sarah.callaghan@stfc.ac.uk @sorcha_ni

http://citingbytes.blogspot.co.uk/

“Publishing research without data is simply advertising, not science” - Graham Steel

http://blog.okfn.org/2013/09/03/publishing-research-without-data-is-simply-advertising-not-science/

http://heywhipple.com/dont-show-me-a-something-about-show-me-something/