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Global Challenges, Enabling Cultures: Enriching collaborations for the digital age? Wendy White, University of Southampton ALPSP Standing on the Digits of Giants, 08 March 2016

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Page 1: Global Challenges, Enabling Cultures: Enriching … · Global Challenges, Enabling Cultures: Enriching collaborations for the digital age? Wendy White, University of Southampton ALPSP

Global Challenges, Enabling Cultures: Enriching collaborations for the digital age?

Wendy White, University of SouthamptonALPSP Standing on the Digits of Giants, 08 March 2016

Page 2: Global Challenges, Enabling Cultures: Enriching … · Global Challenges, Enabling Cultures: Enriching collaborations for the digital age? Wendy White, University of Southampton ALPSP

5D 360 TB/disc Data Storage

Nanostructured glass and femtosecond laser writing

Thermal stability up to 1,000 degrees Celsius, life of 13.8 billion years at room temperature

Documents recorded using ultrafast laser with 3 layers of nanostructured dots separated by 5 micrometres

Read by optical microscope

http://www.southampton.ac.uk/news/2016/02/5d-data-storage-update.page

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Presenting UDHR to UNESCO

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Can we really think very long term?

For preservation as well as

storage?

Not just technical issues

How do we record information so

in 1000 years they know

Organisation – what is the UN?

Concepts – what is a “right”?

Cultural context significant

challenge

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Cultural Collaborations: Metadata

Metadata for the longer term will only be collected if it is valued when created which can then enrich future value

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Collaboration for Research Enhancement by Active Metadata (CREAM)

Goal - “Enable new research to be built on a platform provided by previous, already existing, outputs, by reusing and repurposing, to generate new technology and outputs by “standing on the shoulders of others”

Encourage the use of active metadata across other disciplines by demonstrating the potential for curation, reuse, reproducibility, reinterpretation, and validation.

https://blog.soton.ac.uk/cream/

5Southampton, Edinburgh, University of the Arts, STFC, Nine by Nine

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Metadata - how to make it forward thinking?

Produce exemplars

Promote interdisciplinary reuse

Make use of existing standards

Active metadata needs to be part of active researcher’s activity 6

Procedural Blending Model (Garrelfs,

2015) – facilitate recording of decision

points and motivations as well as

provenance, objects etc.

Can help bridge link between mapping

concepts and process

iterative, agile, creative

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Allotrope Foundation

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• Subject Matter Experts• Project Funding

Member Companies

• Project Management• Legal & Logistical Support

Secretariat

• Framework Development• Technical Leadership

ProfessionalSoftware Firm

• Requirements & Specifications• Contributions, PoC Applications

Partner Network

AbbVieAmgenBaxterBayer

Biogen

BoehringerIngelheim

Bristol-Myers Squibb

Eli Lilly

Genentech/Roche

GlaxoSmithKline

Merck & Co.Pfizer

ACD/Labs

Agilent

Biovia

BSSN

IDBS

Mestrelab Research

Mettler Toledo

Persistent

Riffyn

Sartorius

Shimadzu

Thermo Scientific

Waters

Erasmus Univ. Med

CenterUniversity of Southampton

@2015 Allotrope Foundation

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PIs as change agents

• Academic lead, library co-ordination and/or input

e.g. ORCID take up

Academic buy-in

Tech development behaviour led

PIs are editors

Library & publishers working actively & closely with PIs in scholarly communications environment

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Principles for collaboration

• Work within framework of existing standards and identifiers e.g. ISO, NISO, WC3, OAI, OASIS, ORCID

• Enabling platform neutral take up of standards and data exchange

• Collaboration must have active research input plus specialist input (libraries, software developers, project management etc.)

• Stakeholders with different expertise valuable – core principles help ensure benefits not stasis due to differing views

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Research Facilities and Equipment Sharing

DATA SHARING LANDSCAPE IN ACADEMIA

Academia

data.ac.uk data sharing and development community

equipment.data.ac.uk National equipment sharing web

portal

UNIQUIP

Project

(Network,

standards and

outcomes)

N8

GW4

Project deliverables feeding

into data.ac.uk

Accessing “data”

know how

Contributing to or

searching equipment data

M5

SES5

RCUK

Strategy & policy

drivers

Sector technology and

standards e.g. ePrints,

Pure, Agresso, Kit

Catalogue, CERIF,

CASRAI and ORCID

Data and

technology

standards sharing

(creating added

value datasets)

RCUK

“Gateway to

Research”

Project

Sharing knowledge and

outputs with industry

providers (e.g. user groups

and knowledge networks)

Regional consortia

projects contribution

to project

JISC

Support for sector

technology and standards

development

Input from development

projects into Gateway to

Research portal (e.g. G4HE,

CERIF in Action, CASRAI UK

Dictionary)

Knowledge exchange

through UNIQUIP

network

Creating visibility

of research

outputs

Diagram - Adrian Cox

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Cultural Collaborations: Publishing

Published data is only useful when it is fully reusable and innovative data visualisation drives engagement

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Lab Notebook granular data visualisation

12Simon Coles, Andy Milstead

Visualise live changing data, DOIs for snapshots

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Quick wins for all

• Embedding a DOI for the data in the publication (could be in a book not just articles) adds value for everyone wherever the data is hosted and stored

promotes data creators, authors, publishers & institutions

• Data host to provide CCO metadata and easy assignation of CC, Government or other reasonable reuse licence

• Helps to recognise:

• More than one provider may have a good reason for needing to incorporate the metadata and possibly dataset in a repository

• Different environment to articles as institutions usually hold the rights to the data not the data creators

• Institutions as well as individual researchers are both consumers and contributors to publishing

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Learned Societies and Publishers as Innovators

• Zika initiative positive, but should not be exception

http://www.wellcome.ac.uk/News/Media-office/Press-releases/2016/WTP060169.htm

• Can engage with new publishing models and initiatives

Example: Greynet www.greynet.org

Link grey literature and data

Working with national Data Archiving Services and Open Preservation Foundation

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Preparing and depositing data – journal

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Networks of repositories

• National and international disciplinary centres

• Institutional repositories - increasingly developing to improve preservation

metadata quality, file formats JHOVE, integration with services like Arkvium, archivmatica

• Data journals

• New UK National initiatives – Jisc Research Data Discovery Service, Research Data Management Shared Service

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All of these types of repository can contribute at a useful

stage in a researcher’s workflow depending on context

and policy environment

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Unlocking theses data

• Survey of institutions and case studies looking at current practice and ideas for future practice

• Aim for workflows which capture data to promote reuse as well as thesis text

• Relationships between objects and identifiers - DOIs and ORCID

• Aligned with British Library for integrated national and institutional services

http://unlockingthesisdata.wordpress.com

Other great Jisc dataspring projects http://researchatrisk.ideascale.com/

18with wide community

input in UK

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Shared Service Engagement

EnthusiastCommunity

Engagement

Organisational Commitment

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Shared Services

Further Innovation Communities of Practice

Informal groups

Research Data Alliance

RLUK, ALPSP

Standards/Profiles

CASRAI, OPDs

Aggregated Services

UK Research Data

Discovery

Archival Services

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Cultural Collaborations:Learning

Successful data preservation is dependent on developing a culture of continuous reflective learning and practice where researchers, librarians, technicians, publishers & data scientists co-develop networks of knowledge transfer & support 20

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Library triaged research data services

Layered approach to networks

• Doctoral College workshops

• Academic input through disciplinary /interdisciplinary modules and bespoke courses

• 1-2-1 deskside consultancy service

• one stop shop advisory service

• collaborations with publishers and editors

• Specialist partnership advice e.g. Software Sustainability Institute 21

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PICK & MIX

INTENSE &

SEASONAL

EMERGENCY BOOST

INTEGRATED PATHWAYS

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Reviewing curricula

Content People

Mode Time

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Module & course profiles

Level

Themes

PGR Directors

Programme & Module Leaders

Supervisors

PGR & ECR researchers

Workshops

Labs

Online

Point of need

Progression

pathway

Modular

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Common data focussed PGT & PGR modules

ETHICS

DATA ANALYSIS

RESEARCH METHODS

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Working collaboratively with PhD and Early Career Researchers: agents for change

DataPoolBuilding Capacity, Developing Skills, Supporting Researchers

http://datapool.soton.ac.uk/

Project key aims and activity: • Working to build good research data management practices across all disciplines

throughout the data lifecycle, focusing on cultural change and embedding activity.

• Implementation of a formal Research Data Management Policy with associated one-stop-shop guidance and desk-side support for Data Management Planning

• Developing formal and informal support for research data management

• Evolve technical improvement improvements to institutional systems to enhance management, storage and accessibility

Imaging Case StudiesSupporting PhD and Early Career Researchers to investigate 2D and 3D imaging requirements across disciplines“The Datapool 3D and 2D Raster case studies represent a chance to find out how specific kinds of data are created, used and managed across a large institution such as the University of Southampton. The Datapool project recognises that effective data management policy stems not only from ensuring the efficient use of facilities but also from developing deep understandings of how we work and the role which data plays in our research. This aspect of the project aims to survey facilities and resources dedicated to working of these 3D and 2D Raster data and to find out how these resources are used. Through making contact and interviewing individuals and groups we are developing better understandings of how different communities work and collaborate and manage their use of digital media.” Gareth Beale and Hembo Pagi

MultidisciplinaryWorking with events through the existing network of University Strategic Research Groups which focus on multidisciplinary approaches to tackling leading edge societal issues, including the Southampton Multidisciplinary Research Forum for Early Career ResearchersFor example, Twitter Harvesting using ePrints workshop with Web Science DTC reported on Digital Economy USRG blog

Training modelDeveloping training for PhD students with a Service/PhD student co-delivery model for Faculties and an organic model of exploring issues with the Web Science Doctoral Training Centre through their seminar series and spin-out activities

Training Materials

Case study based training guide"We looked at five researchers' work from medicine, materials engineering, aerodynamics, chemistry and archaeology, and produced case studies showing the similarities and differences between the data types they produce. A guide for first year postgraduate students was created containing the case studies and an introduction to research data management. The concepts in the guide has been presented as training lectures, ensuring students start considering the problems associated with research data management early in their careers. The feedback from students suggest that being made to think about these issues is necessary and useful, and engaging them at this stage helps cultivate good practices.“ Mark Scott

Future: Build on structure by adding case studies in other disciplines, for example, English

Technical testingA group of PhD researchers from Music, Physics, Medicine, Geography and Archaeology engaged with user testing for data deposit and management. First test was the SharePoint development with a mixture of structured feedback through a template and informal iteration and mapping to their research questions. Lots of discussion and lunch

Embedding and working with other data management developments to aid support for all disciplines

For example,

“The Heterogeneous Data Centre (HDC) project (JISC funded, Materials Data Centre) is delivering a system to encourage sharing of data sets between materials engineering and medical communities. It was built for managing materials engineering data ranging from small files (kilobytes) to very large (gigabytes, for example, microfocus computer tomography data), using a file system for file storage and a monitoring service to update a metadata database when data sets change. An interface for managing additional data set features has been written in Microsoft SharePoint. The system supports data sets, data set metadata, relationships and collections of data sets, security (to grant others access to a data set), plugins at the data set and file levels, search, data set recommendations, and compatibility with Eprints - the main data catalogue being developed through the DataPool project. The generic approach we have adopted is enabling us to use it in a wider range of application areas such as Medicine.” Mark Scott

Acknowledgements:Authors: Byatt, D., Beale, G., Earle, G., Pagi, H., Scott, M., Coles, S., White, W.

References: Scott, M., Boardman, R., Reed, P., & Cox, S. (2012) Introducing Research Data Faculty of Engineering and the Environment, University of Southampton

(http://eprints.soton.ac.uk/338816/ )

Future – “Across all disciplines”Further expand support across all disciplines - ? Embed digital data experts in disciplines as nodes for multidisciplinary knowledge transferAgents for change - Informing and piloting new developments, developing new case studies

Scott, M et al (2012)Medical Data Fig. 5 & 6 Introducing Research Data. University of

Southampton p.9

Scott, M. et al (2012) Data lifecycle Fig.1 Introducing Research Data. University of Southampton p.4

http://digitaleconomy.soton.ac.uk/

http://tweets.soton.ac.uk/

Reflectance Transformation Imaging capture of brickstamps in Italy. Portus

Project Photo: Hembo Pagi, 2011

Rock Art Libya Photo: Hembo Pagi, 2009

Portus Project 3D laser scanning: Researchers are producing more and more 3D data within increasingly diverse research

contexts. http://www.portusproject.org/Photo: Gareth Beale, 2012

Portus Project 3D laser scanning: Researchers are

producing more and more 3D data within increasingly diverse

research contexts. http://www.portusproject.org/

Photo: Gareth Beale, 2012

http://www.southampton.ac.uk/library/research/researchdata/

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Skin health sensing Data management

Literature (636 documents)

Clinical (36)

circulation (39)

ulceration (14)

Infection diagnosis (1)glycemic control ( 15)

Gait analysis

Sweat (5)

neuropathy (19)

costs (7)

Measurableperameters

Mechanicalproperties of skin (41)

humidity (2)

temperature (6)

Bioimpedance (75)

oximetery/perfusion (21)

Activety (3)

AccelerationForce/Force (35)

PH (3)

Fatigue (3)Shear (22)

Ultasonic (10)

background (48)

Technology (88)

Data monitoiring (21)

Orthotics/prosthetics (20)

Testing (48 formal)

Ethics approval (10)

Test protocols (5 and increasing)

Test data

Calibration protocols

Calibration data

Outputs

Thesis

9 month report (1)

Transfer report (1)

Final Thesis (1)

Papers (some)

Poster (4)

Presentaions (5)

Sensor design

Sensor selection (9)

Sensorexcitaioncircuit (9)

Sensor calibration (xx)

Sensor validation (xx)

Sensor software (19)

Protoytypesoftware (>30)

Dataaquisition

software (3)

Prototype software (10)

Sensor prototypes (15)

Sensor layout (5)

Design drawings (9)

Meeting notes (53)

Notebooks (40)

Onenote (13)

Training/lecture courses (7)

Notes

Confermation ofattendance

Results

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Shared vision and capacity building

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The effective use of Data Science technologies requires new skills and demands for new professions, usually referred as the Data Scientist: an expert who is capable both to extract meaningful value from the data collected and also manage the whole lifecycle of Data, including supporting Scientific Data e-Infrastructures.

The future Data Scientists must posses knowledge (and obtain competencies and skills) in data mining and analytics, information visualisation and communication, as well as in statistics, engineering and computer science, and acquire experiences in the specific research or industry domain of their future work and specialisation. We call this profession the Data Science Professional (DSP).

http://edison-project.eu/news

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Embedded

Librarianshttp://www.itutility.ac.uk/

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Research Data - more than management

• Need to integrate support with areas of active research interest

• RDM often implicit in ethics/analysis/methods modules - could do more to foreground this and integrate

• Support for more “full data journey” stories from ethics to sharing – need to make more links between ethics processes, data management planning, data capture & data analysis, data publication, data preservation

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