BoF
Education and Skills Developmentin Data Intensive Science
RDA meeting 18-20 March 2013, Gothenburg(Ljusvagen room)(Yuri Demchenko, University of Amsterdam)

Outline• Agenda bashing• Round of introduction and interests expression• Introduction to discussion (Yuri Demchenko, UvA)
• Any ad hoc discussions (TBD)• Discussion on further steps• BoF summary as reported to RDA Plenary meeting

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BoF summary as reported to RDA Plenary

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BoF on Education and Skills Development in
Data Intensive Science• Attended by 16 representatives from universities,
libraries, e-Science, data centers, researchcoordination bodies• Ad hoc agenda included– Round of introduction and interests expression– Introduction to discussion• H2020 priority area, existing approaches and experience indevelopment of instructional materials for new technologies– Discussion on further steps• Priority topics to address and potential WG organisation

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Topics discussed• What experience do we have on component
technologies to suport Scientific Data?
• Existing instructional and educational concepts andtechnologies• How to benefit from collective knowledge andexperience of RDA community?
• Scientific Data and Big Data in industry– Multidisciplinary domain and needs cooperation of
specialists from multiple knowledge, scientific andtechnology domains

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BoF Outcome and Decisions• People agreed to work together both on defining joint activity andpreparation of the next meeting• Proceed with formal establishment of WG• Involve LERU, LIBER and more US and overseas partners• Hold next BoF with wider non-technical scope– Reach wider and targeted community and potential stakeholders andinterested parties– Involve universities working on the DIS education programs• Define output of potential WG addressing education and training
on all aspects of (Scientific & Big) Data
– Data management– Technology and infrastructure– Domain specific knowledge and user support• Define skills/competencies vocabulary and Common Body ofKnowledge for Data Intensive Science– Review existing/known education and training programs and frameworks
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BoF Meeting slides and discussion

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7Possible discussion topics• How to share experience between universities
started programs development on Data Science?
– What experience do we have on component
technologies?
– Existing instructional and educational concepts andtechnologies• How to benefit from collective knowledge andexperience of RDA community?
• TBD

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8Introduction to discussion on Education andTraining for Data Intensive Science• Big Data divide and need for Professional Education andTraining• Horizon2020 Priority area 7: Skills and new professions forresearch data
• Data Intensive Science and foundation technologies– Scientific Data Infrastructure (SDI) and Big Data Infrastructure (BDI)• Example Cloud Computing Curriculum development– Cloud Computing as enabling technology for SDI and BDI– Cloud Computing Common Body of Knowledge– Course instructional approach: Bloom’s Taxonomy and Andragogy– Course structure Cloud Computing technologies and services design
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Research Data e-Infrastructures:
Framework for Action in Horizon2020
http://ec.europa.eu/digital-agenda/en/content/consultation-research-datainfrastructures-framework-action (comments deadline 27 March 2013)

e-Infrastructure fiches 01 – 07
01 - Community support data services
02 - Infrastructure for Open Access
03 - Storing, managing and preserving research data
04 - Discovery and provenance of research data
05 - Towards global data e-infrastructures
06 - Authentication and Authorisation e-infrastructures
07 - Skills and new professions for research data
https://ec.europa.eu/digital-agenda/sites/digital-agenda/files/
data_einfra_h2020_fiches_on-line_consult.pdf

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10e-Infrastructure fiche 07: Skills and new

professions for research data
Scenario:
In a fast developing data-intensive world of scientific and scholarly research, what kind ofskills are needed for creating, handling, manipulating, analysing and storing for re-useof large amounts of data by others? Some researchers in data-intensive research areas
have acquired considerable skills in handling and managing data themselves or have acolleague who has these skills, but in many cases researchers turn to the institutional IT
services or library for assistance and advice. Current data scientists usually end up in theirroles accidentally as formal education hardly exists.
Actions address:
Defining or updating university curricula and sharing best practices across Europe;developing training programmes for data scientists working as part of a team of researchers
or in close collaboration with them as responsible for computing facilities, storage and access;developing training programmes for data librarians from the library community who are
specialised in the curation, preservation and archiving of data.
Stakeholders:e-Infrastructure providers; software designers, education tools designers, associations such as
LIBER, LERU, EAU, ScienceEurope, Universities, Libraries, etc.

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11Knowledge and/vs Skills in Data Intensive
Science (DIS)
Target skills ( knowledge? )• Create, handle, manipulate, analyse and store large amounts of data for re-use of by others• Develop, design and operate related data infrastructure shaped for specific projects, tasks,
workflows
Currently expertise available with/from• Practicing e-Scientists, institutional IT service specialists, or librarians dealing with data
Suggested Actions• Defining or updating university curricula + defining training programs• Sharing best practices across Europe
Developing training programmes for target specialist groups• Data scientists who will work as part of a team of researchers or in close collaboration withthem as responsible for computing facilities, storage and access• Data librarians from the library community who will be specialised in the curation,
preservation and archiving of data

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12Skills Vocabulary vs Common Body Knowledge• Skills vocabulary describe what skills are identified• CBK define what knowledge constitute professional
knowledge in the area
• IEEE WG12: Learning Object Metadata (LOM) standards(2002-2004)– 1484.12.1: IEEE Standard For Learning Object Metadata– 1484.12.2 - 4: Binding For Learning Object Metadata Data for XML
and RDF

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Scientific (Big) Data Infrastructure definition• Map/relate Big Data attributes/features to DFT/RDA
terminology and Data Architecture/Model (by RDA?)• Relation between Scientific (Big) Data and Big Data inindustry

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Big Data Definition• Termed as the Fourth Paradigm *)“The techniques and technologies for such data-intensive science are so
different that it is worth distinguishing data-intensive science fromcomputational science as a new, fourth paradigm for scientificexploration.” (Jim Gray, computer scientist *)*) The Fourth Paradigm: Data-Intensive Scientific Discovery.
Edited by Tony Hey, Stewart Tansley, and Kristin Tolle.
Microsoft Corporation, October 2009. ISBN 978-0-9825442-0-4• IDC definition (conservative and strict approach) of Big Data
"A new generation of technologies and architectures designed toeconomically extract value from very large volumes of a wide variety ofdata by enabling high-velocity capture, discovery, and/or analysis"

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5 V’s of Big Data
Volume••••Terabytes
Records/ArchTransactions
Tables, Files

Variety••••

Velocity
•
•
•
•

Batch
Real/near-time
Processes
Streams

5 Vs ofBig Data

Structured
Unstructured
Multi-factor
Probabilistic
•
•
•
•
•

Value
•
•
•
•

Statistical
EventsCorrelations
Hypothetical

Trustworthiness
Authenticity
Origin, Reputation
Availability
Accountability

Commonly accepted
3V’s of Big Data

Veracity

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Big Data Infrastructure Components•

Cloud base infrastructure services for data
centric applications (storage, compute,infrastructure/VM management)
–
–

•
•

•
•
•
•
•

•

Software Defined InfrastructureHigh performance switched network

Hadoop/cluster related services and tools
Specialised data analytics tools (logs,
events, data mining, etc.)
MPP (Massively Parallel Processing)applications (comoute&storage)
Databases/Servers SQL, NoSQLBig Data Management
Registries, Indexing/search, semantics,
namespaces
Security infrastructure (access control,
policy, confidentiality, trust, privacy)
Collaborative environment (federation,groups management)

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Big Data Infrastructure Components• Cloud base infrastructure services for data centric applications(storage, compute, infrastructure/VM management)– Software Defined Infrastructure– High performance switched network• Hadoop/cluster related services and tools
•
•
•
•
•
•

MPP (Massively Parallel Processing) applications (comoute&storage)
Specialised data analytics tools (logs, events, data mining, etc.)
Databases/Servers SQL, NoSQLBig Data Management
Registries, Indexing/search, semantics, namespaces
Security infrastructure (access control, policy, confidentiality, trust,
privacy)• Collaborative environment (federation, groups management)

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Scientific Data Infrastructure Definition andRequirements

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E-Science Features
•

•

•
•
•

•

Automation of all e-Science processes including data collection, storing,classification, indexing and other components of the general data curation andprovenanceTransformation of all processes, events and products into digital form bymeans of multi-dimensional multi-faceted measurements, monitoring andcontrol; digitising existing artifacts and other content
Possibility to re-use the initial and published research data with possible data
re-purposing for secondary research
Global data availability and access over the network for cooperative group of
researchers, including wide public access to scientific data
Existence of necessary infrastructure components and management tools thatallows fast infrastructures and services composition, adaptation andprovisioning on demand for specific research projects and tasks
Advanced security and access control technologies that ensure secure
operation of the complex research infrastructures and scientific instruments andallow creating trusted secure environment for cooperating groups andindividual researchers.

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Scientific Data Types
EC Open Access Initiative
Requires data linking at alllevels and stages

Publicationsand Linked
Data

Published Data
Structured Data
Raw Data

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• Raw data collected from observationand from experiment (according to an
initial research model)• Structured data and datasets that went
through data filtering and processing(supporting some particular formal
model)• Published data that supports one or
another scientific hypothesis, research
result or statement• Data linked to publications to support
the wide research consolidation,
integration, and openness.

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Scientific Data Lifecycle Management (SDLM)
Model
Data Lifecycle Model in e-Science

User
Researcher

Data discovery

Data Curation(including retirement and clean up)
Data
recycling

Raw Data
Experimental
Data

Project/
Experiment
Planning

Data collection
andfiltering

Structured
Scientific
Data

Data analysis

DB

Data archiving

Data
Re-purpose

Data linkage
to papers

Data sharing/
Data publishing

Data Re-purpose

Data Linkage Issues•
Persistent Identifiers (PID)•
ORCID (Open Researcher andContributor ID)•
Lined Data

Data Clean up and Retirement•
Ownership and authority•
Data Detainment

Data Intensive Science Education and Skills

End of project

Open
PublicUse

Data Links

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Data
archiving

Metadata &
Mngnt

22

General requirements to SDI for emerging Big
Data Science
•
•
•
•
•
•
•
•

Support for long running experiments and large data volumes generated
at high speed
Multi-tier inter-linked data distribution and replication
On-demand infrastructure provisioning to support data sets andscientific workflows, mobility of data-centric scientific applications
Support of virtual scientists communities, addressing dynamic usergroups creation and management, federated identity management
Support for the whole data lifecycle including metadata and data source
linkage
Trusted environment for data storage and processing
Support for data integrity, confidentiality, accountability
Policy binding to data to protect privacy, confidentiality and IPR

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SDI Architecture Model

ScientificDatasetApplicScientific
ApplicScientific
ApplicScientificTechnologies andsolutions
Scientific specialist
applications
Library resources

Policy and distributed Collaborative
Groups Support

Layer B5
Access andFederation Layer

Network Infrastructure
eduGAIN, REFEDS,
VOMS, InCommon

Shared Scientific Platform and Instruments(specific for scientific area, also Grid based)

Layer B4
Scientific Platform
and Instruments

PRACE/DEISA

Cloud/Grid Infrastructure
Virtualisation and Management
Middleware
ComputeResources

Sensors andDevices

Network infrastructure

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Layer B6
Scientific
Applications

User/Scientific Applications Layer

User
portals

Metadata and Lifecycle Management

Security and AAI

Operation Support and Management Service (OSMS)

LayersMiddleware
security

StorageResources

Layer B3
Infrastructure
Virtualisation

Layer B2
Datacenter andComputing Facility
Layer B1
Network Infrastructure

Data Intensive Science Education and Skills

Grid/Cloud

CloudsAutobahn, eduroam

24

SDI Architecture Layers• Layer D1: Network infrastructure layer represented by the general
purpose Internet infrastructure and dedicated network infrastructure• Layer D2: Datacenters and computing resources/facilities, including
sensor network• Layer D3: Infrastructure virtualisation layer that is represented by the
Cloud/Grid infrastructure services and middleware supporting
specialised scientific platforms deployment and operation• Layer D4: (Shared) Scientific platforms and instruments specific fordifferent research areas• Layer D5: Access Infrastructure Layer: Federation infrastructurecomponents, including policy and collaborative user groups support
functionality• Layer D6: Scientific applications and user portals/clients

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DIST Program development principles andexample• Reuse experience from the existing related program– Cloud Computing Technologies and Tools– Theoretical Informatics, Data Analytics and Artificial
Intelligence• General interactive education principles– Common Body of Knowledge– Bloom’s taxonomy– Pedagogy vs Andragogy– Discussion forum for online education, and Research andReading assignments for on-campus eductaion

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Example:

Cloud Computing Curriculum development• Cloud Computing as enabling technology for Scientific Data
Infrastructure (SDI)• Cloud Computing Common Body of Knowledge• Course instructional approach: Bloom’s Taxonomy andAndragogy• Course structure Cloud Computing technologies andservices design

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Slide_27

Example: Common Body of Knowledge (CBK) in
Cloud Computing
CBK refers to several domains or operational categories into which Cloud
Computing theory and practices breaks down•

Still in development but already piloted by some companies, including industry
certification program (e.g. IBM, AWS?)CBK Cloud Computing elements
1. Cloud Computing Architectures, service and deployment models
2. Cloud Computing platforms, software/middleware and API’s
3. Cloud Services Engineering, Cloud aware Services Design
4. Virtualisation technologies (Compute, Storage, Network)
5. Computer Networks, Software Defined Networks (SDN)
6. Service Computing, Web Services and Service Oriented Architecture (SOA)
7. Computing models: Grid, Distributed, Cluster Computing
8. Security Architecture and Models, Operational Security
9. IT Service Management, Business Continuity Planning (BCP)
10. Business and Operational Models, Compliance, Assurance, Certification
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Example:
CKB-Cloud Components LandscapeCloud Platforms, API

Cloud Architectures, Service
ModelsBusiness/Operational Models, Compliance, Assurance

Cloud Computing Fundamentals
Cloud Services Engineering&Design
DesignEngineering

Virtualisation

Networking

Web Services, SOA

Security, ID Management

Computing Models: Grid, Distributed, Cluster

IT Systems Management

Cloud Computing Common Body of Knowledge (Full)

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Example: Multilayer Cloud Services Model (CSM) –Taxonomy of Existing Cloud Architecture Models

Security InfrastructureManagement

Operations Support System

User/Client Services* Identity services (IDP)* Visualisation

User/Customer Side Functions and Resources

Administration andManagement Functions(Client)

Content/Data Services* Data * Content * Sensor * Device

1

Endpoint Functions* Service Gateway* Portal/Desktop

Access/Delivery
InfrastructureInter-cloud Functions* Registry and Discovery* Federation Infrastructure

SaaS

PaaS

PaaS-IaaS IF
PaaS-IaaS Interface

IaaS – Virtualisation Platform Interface

Cloud Management
Software(Generic Functions)

Cloud Management Platforms
OpenNebula

OpenStack

KVM

Virtualisation Platform

VM

VM

VPN

Other
CMS

XEN

VMwareNetwork
Virtualis

Proxy (adaptors/containers) - Component Services and Resources

StorageResources

ComputeResources

Contrl&Mngnt Links

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Layer C5
Services
Access/Delivery

Layer C4Cloud Services(Infrastructure,
Platforms,
Applications,
Software)

Cloud Services (Infrastructure, Platform, Application, Software)

IaaS

Layer C6
User/Customer
side Functions

Hardware/PhysicalResources

Network
Infrastructure

Layer C3
Virtual Resources
Composition andControl(Orchestration)

CSM layers(C6) User/Customer side
Functions(C5) Services
Access/Delivery(C4) Cloud Services(Infrastructure, Platform,
Applications)(C3) Virtual Resources
Composition andOrchestration(C2) Virtualisation Layer(C1) Hardware platform anddedicated network
infrastructure

Layer C2
Virtualisation

Layer C1
PhysicalHardwarePlatform andNetwork

Control/
Mngnt Links

Data Links

Data Links

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Example:
Relations Course Components and CSM
Layer C6
User/Customer
side Functions

User Applications

Layer C5
Services
Access/Delivery

Cloud Services API and Tools

Hybrid Clouds,
Integrated Infrastructure

User Applications

1

Cloud Service Design, Operational
proceduresManagement

Security

Operations Support System

Cross-layer
Service s (Planes)

Cloud/Intercloud Services Integration, Enterprise
IT infrastructure migration

1Customer Enterprise/Campus Infrastructure/Facility

Layer C4Cloud Services(Infrastructure,
Platforms,
Applications,
Software)Layer C3
Virtual Resources
Composition andControl(Orchestration)

Layer C2
Virtualisation

Cloud Services Platform

Cloud Service Provider
Datacenters and Infrastructure
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Layer C1
PhysicalHardwarePlatform andNetwork

31

Example:
Professional Knowledge in Cloud Computing• (General) Professional level of knowledge includes but not limited to– Knowing basic concepts and major application areas– Knowing similar concepts (and concepts inter-relation) and alternatives, as well as
application specific areas– Knowing basic technologies and their relation to basic concepts– Knowing authoritative (and not authoritative) sources of information and how toevaluate quality of information• Ability to work with standards (what is not an easy source of information)• Ability to critically evaluate and filter some inconsistent information, e.g. popular sites like
wikipedia and similar, blogs, etc.
• Critically evaluate vendors’ information which is sometimes biased and/or doesn’t provide
enough background information• Cloud computing is a new technology but it is becoming a common
preferred base/platform for all current and future developments• Becoming an expert in Cloud Computing– General professional knowledge and understanding of the main developmentareas– Practical development and experience with few projects, writing reports, technicaldocuments, following and contributing to standardisation– Cloud aware and cloud powered analysis and thinking
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Example: Professional Education in Cloud Computing Principles• Provide knowledge both in Cloud Computing as a newtechnology and background technologies• Empower the future professionals with ability to develop
new knowledge and build stronger expertise, prepare basis
for new emerging technologies such as Big Data
• Bloom’s Taxonomy as a basis for defining learning targets
and modules outcome• Provides a basis for knowledge testing and certification• Andragogy vs Pedagogy as instructional methodology for
professional education and training• Course format: On-campus education and training,
online courses, self-study

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Example:Bloom’s Taxonomy – Cognitive Activities
Knowledge
Exhibit memory of previously learned materials by recalling facts, terms, basic concepts and answers••••

Knowledge of specifics - terminology, specific facts
Knowledge of ways and means of dealing with specifics - conventions, trends and sequences, classifications and categories, criteria, methodology
Knowledge of the universals and abstractions in a field - principles and generalizations, theories and structures
Questions like: What are the main benefits of outsourcing company’s IT services to cloud?

Comprehension
Demonstrate understanding of facts and ideas by organizing, comparing, translating, interpreting, describing, and stating the main ideas••

Translation, Interpretation, Extrapolation
Questions like: Compare the business and operational models of private clouds and hybrid clouds.

Application
Using new knowledge. Solve problems in new situations by applying acquired knowledge, facts, techniques and rules in a different way•

Questions like: Which cloud service model is best suited for medium size software development company, and why?

AnalysisExamine and break information into parts by identifying motives or causes. Make inferences and find evidence to support generalizations••

Analysis of elements, relationships, organizational principles
Questions like: What cloud services are needed to support typical business processes of a web trading company, give suggestions how these
services can be implemented with PaaS or IaaS clouds. Provide references to support your statements.

Synthesis
Compile information together in a different way by combining elements in a new pattern or proposing alternative solutions••

Production of a unique communication, a plan, or proposed set of operations, derivation of a set of abstract relations
Questions like: Describe the main steps and tasks for migrating IT services of an example company to clouds, what services and data can be
moved to clouds and which will remain at the enterprise premises.

Evaluation
Present and defend opinions by making judgments about information, validity of ideas or quality of work based on a set of criteria••

Judgments in terms of internal evidence or external criteria
Questions like: Do you think that cloudification of the enterprise infrastructure creates benefits for enterprises, short term and long term?

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Example: Mapping Bloom’s Taxonomy from
Cognitive Domain to Professional Activity Domain
•
•

•

•
•

Perform standard tasks,
use API and Guidelines
Create own complex
applications using
standard API (simple
engineering)
Integrate different
systems/components,
e.g. Cloud provider andenterprise (complex
engineering)
Extend existing services,
design new services
Develop newarchitecture and models,
platforms andinfrastructures

RDA, 18-20 March 2013

Remember

Understand

Apply

Analyse

Evaluate

Data Intensive Science Education and Skills

Create35

Example: Mapping Course Components, Cloud
Professional Activity and Bloom’s Taxonomy
Customer Enterprise/Campus Infrastructure/Facility

Comprehension

Application
Analysis

User Applications
Cloud Services
API and Tools

1

Hybrid Clouds, Integrated
Infrastructure

User Applications

Cloud Service Design, Operational procedures

Knowledge

Cloud/Intercloud Services Integration, Enterprise IT
infrastructure migration

Taxonomy
Cognitive
Domain [3]RDA, 18-20 March 2013

Perform standard tasks,
use standard API and
Guidelines

Create own complex
applications using
standard API (simple
engineering)
Integrate different
systems/components,
e.g. provider andenterprise infrastructure

Cloud Services Platform

Synthesis
Evaluation

Taxonomy
Professional
Activity Domain

Cloud Service Provider
Datacenters and Infrastructure

Data Intensive Science Education and Skills

Extend existing services,
design new services
Develop new architecture
and models, platforms
and infrastructures
36

Example: Pedagogy vs Andragogy
Pedagogy (child-leading) and Andragogy (man-leading)• On-campus and on-line education• Developed by American educator Malcolm Knowles, stated with
six assumptions related to motivation of adult learning:– Adults need to know the reason for learning something(Need to Know)– Experience (including error) provides the basis for learning activities(Foundation)– Adults need to be responsible for their decisions on education;
involvement in the planning and evaluation of their instruction(Self-concept)– Adults are most interested in learning subjects having immediate
relevance to their work and/or personal lives(Readiness)– Adult learning is problem-centered rather than content-oriented(Orientation)– Adults respond better to internal versus external motivators(Motivation)
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Example: Applying Andragogy to Self-Education
and Online Training - Problems• Andragogy concept is widely used in on-line education.
However– Based on active discussion activities guided/moderated byinstructor/moderator– Combined with the Bloom’s taxonomy• Self-education (guided) and online training specifics– Course consistency in sense of style, presentation/graphics,etc.
– Requires the course workflow to be maximum automated• Especially if coupled with certification or pre-certification– Less time to be devoted by trainee• Estimated 1 hour per lesson, maximum 3 lessons per topic– Knowledge control questionnaires at the end of lessons or
topics
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Example Cloud Computing Course StructurePart 1.1. Cloud Computing definition and general usecases
Part 1.2. Cloud Computing and enabling technologies
Part 2.1. Cloud Architecture models and industry standardisation: Architectures
Part 2.2. Cloud Architecture models and industry standardisation: Standard interfaces
Part 3.1. Major cloud provider platforms
Part 3.2. Major cloud provider platforms: Research and Community Clouds
Part 4. Cloud middleware platforms (architecture, API, usage examples)
Part 5.1. Cloud Infrastructure as a Service (IaaS): Architecture, platform and providers
Part 5.2. Cloud Infrastructure as a Service (IaaS): IaaS services design andmanagement
Part 6.1. Cloud Platform as a Service (PaaS): Architecture, platform and providers
Part 6.2. Cloud Platform as a Service (PaaS): PaaS services design and management
Part 7.1. Security issues and practices in clouds
Part 7.2. Security services design in clouds; security models and Identity management
Part 8 (Advanced). InterCloud Architecture Framework (ICAF) for Interoperability andIntegration: Architecture definition and design patterns
Basic parts & Advanced parts
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Example: Defining Target Audience• Basic profile ("essentials" or “fundamentals”) is for IT
decision makers, informed users:– all concepts are explained, clouds opportunities are demonstrated,
general use cases are analysed, examples of use are provided– general security issues in clouds are explained– intended the course will allow this group of listeners to be able tounderstand what they need to learn more.

• "Advanced" part is for engineers/practitioners developing
cloud services, and doing integration/consulting work:– different cloud architectures are explained, details on the different
cloud related open interfaces (like CDMI, OVF, OCCI) and proprietary
API (like Amazon AWS API) are provided, detailed overview of
popular cloud platform/middleware (like OpenStack, OpenNebula,
Eucalyptus) is provided– security models (including main cloud providers) and technologies
explained, federated cloud identity and access control is explained– provide advice/suggestions where to look for further information
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