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The Global View

The Research Data Alliance

Professor John WoodSecretary-General ACU

Co-chair Global Research Data AllianceEC Spokesperson on Science 2.0

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Thousand years ago – Experimental ScienceDescription of natural phenomena

Last few hundred years – Theoretical ScienceNewton’s Laws, Maxwell’s Equations…

Last few decades – Computational Science - Simulation of complex phenomena

Today – Data-Intensive ScienceScientists overwhelmed with data sets

from many different sources • Data captured by instruments• Data generated by simulations• Data generated by sensor networks

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e-Science is the set of tools and technologiesto support data federation and collaboration

• For analysis and data mining• For data visualization and exploration• For scholarly communication and dissemination (With thanks to Jim Gray)

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Traffic increase drives upgrade to UbuntuNet and GÉANT interconnection

Following an agreement between the GÉANT and UbuntuNet Alliance communities, Sub-Saharan Africa is set to become the first world region outside North America to gain dedicated circuit capacity with Europe. An increase in IP connectivity to 10 Gbps as well as a 10 Gbps link for dedicated circuits will greatly benefit campuses in connected countries, and the Hartesbeesthoek Radio Astronomy Observatory in South Africa which is part of the JIVE project in the Netherlands. It is expected that this release of pent-up demand will cause dramatic growth in Research and Education traffic between Europe and Sub-Saharan Africa.

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The Centrality of Research Infrastructures for Innovation

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• First edition 2006 and updated in 2008 with 44 projects

• Includes projects in Humanities, Social Sciences in addition to Science and Engineering

The ESFRI Roadmap

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Survey of Health, Ageing and Retirement in Europe- SHARESurvey of Health, Ageing and Retirement in Europe- SHARE

•multidisciplinary and crossnational panel database of

micro data•more than 30,000 individuals aged 50 or over•harmonised with the U.S. Health and Retirement Study and the English Longitudinal Study of Ageing•the third wave is conducted in 19 nations

www.share-project.org

Brussels, 25 September 2008

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RAMIRI Hamburg Sept 2009 - Steven Krauwer 9

Common Language Resources and technology Common Language Resources and technology Initiative - CLARINInitiative - CLARIN

• Common Language Resources and Technology Infrastructure (http://www.clarin.eu)

• Basic idea: – federation of digital archives with language data and tools

(text, speech, multimodal, gesture …)– target audience humanities and social sciences scholars – with uniform single sign-on access to the archives– with access to language and speech technology tools to

retrieve, manipulate, enhance, explore and exploit data– all languages are equally important– to cover all EU and associated countries

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Large-scale e-Infrastructures for Biodiversity Research

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SKA

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One quarter of Commonwealth Countries involved

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Bernd Panzer-Steindel, CERN

Located on top of the French-Swiss border in Geneva (Switzerland)

2600 Staff members and Fellows plus 6800 visitors on-site

1100 MCHF (730 million Euro) Annual Budget

“Conseil Européen pour la Recherche Nucléaire” “European Organisation for Particle Physics”

Basic Research Laboratory

World’s largest particle physics centre

Founded in 1954

CERNCERNCERNCERN

www.cern.ch

162/17/2010

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Bernd Panzer-Steindel, CERN

Creating conditions similar

to the Big Bang

The most powerful microscope in the world

‘snapshot of nature’‘snapshot of nature’

Particle AcceleratorParticle Accelerator

one snapshot == one event == 1.5 MByte 172/17/2010

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RAW data copies

Enhanced copies

RAW data

Sub-samples

Enhanced copies

Data and Bookkeeping Data and Bookkeeping

10 PB RAW data per year+ derived data, extracted physicsdata sets, filtered data sets, artificialdata sets (Monte-Carlo), multiple versions,Multiple copies 20 PB of data at CERN per year

50 PB of data in addition transferred and stored world-wide per year

Data copies safety and access/performance 18Bernd Panzer-Steindel, CERN2/17/2010

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Data ingest Managing petabytes+ Common schema(s) How to organize? How to re-organize?

How to coexist & cooperate with other scientists and researchers?

Data query and visualization tools Support/training Performance

Execute queries in a minute Batch (big) query scheduling

Experiments &Instruments

Simulationsfacts

facts

answers

questions

?Literature

Other Archives facts

facts

>

Global collaboratories

• They can engage in whole new forms of scientific inquiry and treat information at a scale we are only beginning to see.

• … and help us solving today’s Grand Challenges such as climate change and energy supply.

Vision 2030 high-level experts group on

Scientific Data“Our vision is a scientific e-Infrastructure that supports seamless access, use, re-use and trust of data. In a sense, the physical and technical infrastructure becomes invisible and the data themselves become the infrastructure – a valuable asset, on which science, technology, the economy and society can advance.”

High-Level Group on Scientific Data“Riding the Wave: how Europe can gain from the raising tide of scientific data”

Vision 2030

(6) The public has access and can make creative use of the huge amount of data available; it can also contribute to the data store and enrich it. All can be adequately educated and prepared to benefit from this abundance of information. Embed data science in all training and academic qualifications. Integrate into gaming and social networksIMPACT IF ACHIEVED Citizens get a better awareness of and confidence in sciences, and

can play an active role in evidence based decision making and can question statements made in the media.

Big issues facing Universities and Public at Large

• Open Access to both publications and data “all publicly funded research must be publicly available

• Who is providing the data, can it be trusted?

• How do I know the provenance – has it been corrupted?

• Is it accessible, what type of metadata is there?

• Personal and persistent identifiers, a loss of personal freedom or a passport to participate?

• IPR and legal position

• Personal health data – how can this be handled?

• How to teach and research in this global environment?

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“Knowledge is the engine of our economy. And data is its fuel.”

(Neelie Kroes, Vice-President of the European Commission)

Why?

Data will become the key resource to tackle the big challenges in the world

Building the Research Data Alliance

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How?

How to tackle big challenges if data is highly fragmented by disciplines, domains and countries?

How to tackle big challenges if data is highly fragmented by disciplines, domains and countries?

RDA is building bridges to overcome the hurdles

for easy data access, data sharing

and interoperability by facilitating collaboration between experts from all over

the world belonging to different disciplines and organisations

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RDA – How does it work?

Experts and Data practitioners come together in a bottom-up manner in RDA Working and

Interest Groups to overcome concrete hurdles

They discuss matters at the Plenaries and on the RDA online platform

They discuss matters at the Plenaries and on the RDA online platform

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Global community-driven organization launched in March 2013 to accelerate data-driven innovation

Focus is on building the social, organizational and technical infrastructure to

reduce barriers to data sharing and exchange

accelerate the development of coordinated global data infrastructure

CREATE ADOPT USE: RDA Working Groups operate for 12-18 months to build and use targeted pieces of data infrastructure

Over 30 groups formed since the start. Growing rapidly

The Research Data Alliance

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RDA focuses on Impact and Implementation

RDA Interest Groups are specific communities mapping out infrastructure that will enable data sharing and exchange, with concrete pieces of infrastructure then proposed as Working Groups

RDA Working Groups are “Tiger Teams” focusing on short-term deliverables:

•Focused pieces of adopted code, policy, infrastructure, standards, or best practices that enable data to be shared and exchanged

•“Harvestable” efforts for which 12-18 months of work can eliminate a roadblock for a substantial community

•Efforts that have substantive applicability to “chunks” of the data community, but may not apply to everyone

•Efforts for which working scientists and researchers can start today while more long-term or far-reaching solutions are appropriately discussed in other venues

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RDA Interest Groups

* in review

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What is the likely impact of a What is the likely impact of a large-scale earthquake?large-scale earthquake?

How does disease How does disease

spread in large urban spread in large urban

populations? populations?

Data Driving Solutions to Complex Science and Societal Challenges

How can we increase How can we increase wheat yields? wheat yields?

Disease spread models couple

Medical data +Population data

+Envt. data

Earthquake simulations integrateFault geometry data+Subsurface data+Structure data

+Population data+Physical infrastructure data

Productivity analysis leverages interoperability ofGermplasm data

+Genetic and phenotypic data+Statistical data

+Bibliographic data

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Agriculture Data Interest Group

The Agricultural Data Interest Group is a domain oriented interest group to work on all issues related to data important for the development of global agriculture. The interest group aims to represent all stakeholders producing, managing, aggregating, sharing and consuming data for agricultural research and innovation. Efforts will be made to get an active representation of the major international institutions, which work on agricultural research and innovation. It will take stock of existing problems and experiences and will pave the way for a number of domain specific working groups to make precise proposals for solution in specific area. This interest group will help to promote good practices in our research domain : data sharing policies, data management plan, data interoperability.

The Agricultural Data Interest Group has a specific interest in data interoperability. This refers not only to exchange of data of the same type, but also to data of a different types, which refers to the same object. In this context the interest group is planning a first working group on "wheat data interoperability" in close collaboration with the "Global Wheat Initiative".

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ELIXIR Bridging Force IG Case Statement Life sciences are becoming increasingly more data intensive, especially resulting from the huge improvements in large-scale gene sequencing and other “omics” techniques. There is need for large-scale sustainable data storage methods allowing secure and easy access to these highly complex data.

Simultaneously, since life science research projects increasingly depend on more than one type of measurement, there is a wide–felt need for integrating data of different types. Very similar issues and similar data sets come up in the different sectors of life sciences, such as health, agriculture, bio-industry and marine life, calling also for interoperability between these sectors.

Distribution of 2,353 Individual RDA Members in 96 Countries12 September 2014

Map courtesy traveltip.org

Austral-pacific5%

Africa3%

SouthAmerica1%

Asia5%

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Organizational Structure

Community Impact: Working Groups

Community Planning: Interest Groups

TABSecretaria

tOAB

Council

RDA Colloquium

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Working Group Deliverables Users

Data Foundations and Terminology

Data Model, Defined terminology in a registry

OIF, EUDAT, DASISH

Data Type Registries Federation between data type registries

CNRI, EUDAT, IDF

Persistent Identifier Information Types

Core informational types, Prototype protocol/api that implements these.

DKRZ, CNRI

Practical Policy Example policy sets for tasks (including exemplar implementations)

Participants

First Deliverables

Deliverables not always technical DSA-WDS Certification

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PRACTICAL POLICY WG

Reaping the RDA Fruits, April 21, 2023 4th Plenary Update

THE

PLUMComputer actionable policies are used to enforce management, automate administrative tasks, validate assessment criteria, and automate scientific analyses. The Practical Policy Working Group assembles a collection of production and research policies, analyze the submitted policies to identify best practices, and promote the formation of policy-based data management systems.

Computer actionable policies are used to enforce management, automate administrative tasks, validate assessment criteria, and automate scientific analyses. The Practical Policy Working Group assembles a collection of production and research policies, analyze the submitted policies to identify best practices, and promote the formation of policy-based data management systems.

GOALS: To bring together practitioners in policy making and policy implementation To identify typical application scenarios for policies such as replication, preservation etc. To collect and to register practical policies To enable sharing, revising, adapting, and re-using of computer actionable policies

OUTCOMES a survey identified eleven generic policy areas of interest for the majority of the institutions and in common to almost all data management systems templates for the production policies to be reported in the “Outcomes Policy Templates: Practical Policy Working Group, September 2014” document a collection of example policies, their description (in English) to be included in the document “Implementations: Practical Policy Working Group, September 2014”

Who?Adopters and reviewers of the policy sets:• practitioners in policy making and policy implementation• data centers• communities building up data collections

Who?Adopters and reviewers of the policy sets:• practitioners in policy making and policy implementation• data centers• communities building up data collections

39http://www.independent.co.uk/life-style/gadgets-and-tech/g8-open-data-charter-will-increase-transparency-and-fuel-innovation-8665696.html

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Slide courtesy of Jian Qin

New Skills Needed

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What is a Data Scientist?

Data Engineer

People who are expert at •Operating at low levels close to the data, write code that manipulates•They may have some machine learning background. •Large companies may have teams of them in-house or they may look to third party specialists to do the work.

Data Analyst

People who explore data through statistical and analytical methods•They may know programming; May be an spreadsheet wizard.•Either way, they can build models based on low-level data.•They eat and drink numbers; They know which questions to ask of the data. Every company will have lots of these.

Data Steward

People who think to managing, curating, and preserving data.•They are information specialists, archivists, librarians and compliance officers.•This is an important role: if data has value, you want someone to manage it, make it discoverable, look after it and make sure it remains usable.

What is a data scientist? Microsoft UK Enterprise Insights Blog, Kenji Takedahttp://blogs.msdn.com/b/microsoftenterpriseinsight/archive/2013/01/31/what-is-a-data-scientist.aspx