Active research management and sharing

Active research management and sharing
Chair: Professor David De Roure, OERC
14/07/2016
1

Introduction
Chair: Professor David De Roure, OERC
14/07/2016

Open science framework
Jeff Spies, Centre for Open Science
14/07/2016

Open Science Framework
Jeffrey Spies
@jeffspies
Center for Open Science | University of Virginia

Mission
Increase openness, integrity,
and reproducibility of scientific
research.

Problem
The gap between scholarly
values and practices.

Incentives for individual success
are focused on getting it
published, not getting it right.
Nosek, Spies, & Motyl, 2012

Key incentive = Publication
What is published?
Novel results
Positive results
Clean results
What is not?
Replications
Negative/Nulls
Mixed evidence

Norms
Communality
Open sharing
Counternorms
Secrecy
Closed

Norms
Communality
Open sharing
Universalism
Evaluate research on own merit
Counternorms
Secrecy
Closed
Particularlism
Evaluate research by reputation

Norms
Communality
Open sharing
Universalism
Disinterestedness
Motivated by knowledge and
discovery
Counternorms
Secrecy
Closed
Particularlism
Self-interestedness
Treat science as a competition

Norms
Communality
Open sharing
Universalism
Disinterestedness
discovery
Organized skepticism
Consider all new evidence, even
against one’s prior work
Counternorms
Secrecy
Closed
Particularism
Self-interestedness
Organized dogmatism
Invest career promoting one’s own
theories, findings

Norms
Communality
Open sharing
Universalism
Disinterestedness
discovery
Organized skepticism
Consider all new evidence, even
against one’s prior work
Quality
Counternorms
Secrecy
Closed
Particularism
Self-interestedness
Organized dogmatism
Invest career promoting one’s own
theories, findings
Quantity

Anderson, Martinson, & DeVries, 2007

Openness
• Increases process transparency
• Increases accountability
• Facilitates reproducibility
• Facilitates metascience
• Fosters collaboration
• Fosters inclusivity
• Fosters innovation
• Protects against lock-in
• Open Content + APIs + Open Source Services

Openness is a means to increase
research quality and efficiency.

Solution
Technology can operationalize
and incentivize scholarly values.

Openness is the solution;
Workflow makes it practical.

Publish Report
Search/Discovery
Develop Idea
Design Study
Collect Data
Store Data
Analyze Data
Write Report

Scholars should focus on
scholarship.

Publish Report
Search/Discovery
Develop Idea
Design Study
Collect Data
Store Data
Analyze Data
Write Report
OS
F

OSF Application
Framework
• Workflow
• Authentication
• Permissions
• File Storage
• File Rendering
• Meta-database
• Persistence
• Integrations
• Search
• SHARE
osf.io
journals
registries
preprint servers
grants management
university systems

Collaboration
Documentation
Archiving

Other Features
• Granular permissions
• Analytics dashboards
• Forking
• Persistent, citable identifiers
• Persistent content
• Project snapshotting (i.e., registration)

Connects Services
Researchers Use

Publish Report
Search/Discovery
Develop Idea
Design Study
Collect Data
Store Data
Analyze Data
Write Report
Now

OpenSesame
Soon29 grants to develop open tools and services: https://cos.io/pr/2015-09-24/
Publish Report
Search/Discovery
Develop Idea
Design Study
Collect Data
Store Data
Analyze Data
Write Report

Society for the Study of Autism Registry

<menu>
<title>
Society for the Study of...
</title>
<osf-search /> <osf-login />
</menu>
<society-questionnaire>
Author: <osf-user name />
Question 1: <osf-metadata />
Analysis code:
<osf-file upload />
</society-questionnaire>

OSF Application
Framework
• Workflow
• Authentication
• Permissions
• File Storage
• File Rendering
• Meta-database
• Persistence
• Integrations
• Search
• SHARE
registries

OSF Application
Framework
• Workflow
• Authentication
• Permissions
• File Storage
• File Rendering
• Meta-database
• Persistence
• Integrations
• Search
• SHARE
preprint servers

preprint servers
Coming soon:
http://osf.io/preprints

Substantive experts
shouldn’t deal with…
• Workflow integration
• Authentication
• Permissions
• File storage
• File rendering
• Database
– Metadata/Annotations/Co
mmenting
• Persistence
• External service
integrations
• Search
• SHARE Data

…or scaling these:
• Workflow integration
• Authentication
• Permissions
• File storage
• File rendering
• Database
– Metadata/Annotations/Co
mmenting
• Persistence
• External service
integrations
• Search
• SHARE Data

@jeffspies | jeff@cos.io
Find this presentation at
http://osf.io/u4gs7

Active research from lab to
publication
Prof. Simon Coles (s.j.coles@soton.ac.uk)
Director, UK National Crystallography Service

What is “active” here?
Continual management throughout the whole
experimental process
Immediate feedback during the experiment to
inform next steps or direction

Operating a Service
66
Admin
Reporting

Working Across Facilities
67
Conceive Research Propose Experiment Analyse Publish
Approval
Submissi
on
Experim
ent
Analysi
s
Publicati
on
Propos
al
Proposal
Approv
al
Schedul
e
Experim
ent
Archi
ve
Analy
se
Publi
sh
NCS User
NCS
Central
Facility

LIMS: Lifecycle Management
68
Admin
Reporting

Getting Mobile in the Lab
69
Package &
Sample
Tube
Bulk Sample &
Manipulation
Mounted
Sample
Trial
Diffraction
Pattern

Structured Data
71
http://ecrystals.chem.soton.ac.u
k

In the context of ‘traditional’
publishing
• ELN as Supplementary Information for conventional
publication (Chemistry Central Journal 2013, 7:182 )
72

Can we make metadata
do more for us (actively)?
Formal frameworks for real-time capture required…

A semantic framework for chemistry
• Describes and relates different types of process information
74
elnItemManifest
high-level semantic
description of ELN record
Core Scientific
Metadata model
SIMS
Reaction
Procedures
S88
Analytical data
Allotrope
Foundation

elnItemManifest
• Layered metadata model for description, export & packaging
• This is the first (information) layer – leads into knowledge
• Published through Dial-a-Molecule
athttp://wp.me/p2JoQ6-xF & in J. ChemInf 2013, 5:52
75

Core Scientific Metadata model
as a Starting Point
• Doesn’t cover all, but…
• Forms the basis for extensions:
- To derived data
- To laboratory based science
- To secondary analysis data
- To preservation information
- To publication data
76
Investigatio
n
Publication KeywordTopic
Sample
Sample
Parameter
Dataset
Dataset
Parameter
Datafile
Datafile
Parameter
Investigator
Related
Datafile
Parameter
Authorisati
on

SIMS:
Sample Information Management System
• A standard/format for crystallographic sample and
experiment data management and archival
• Supported by CrystalClear and NCS Portal, providing
interaction between facility, instruments and CIF, ImgCIF etc
77

Standards for reactions: S88
78
• Group arising from Dial-a-Molecule consisting of Mettler
Toledo, Pfizer, GlaxoSmithKline, AstraZeneca, Johnson &
Johnson, Southampton University, NextMove, Royal
Society of Chemistry looking to:
– Provide guidance for S88 implementations for synthetic organic
chemistry reaction procedures
– Provide example set
– Agree on controlled vocabularies for elements
– Generate a schema
– IUPAC uptake?

Standards for reactions: S88
79
• Group arising from Dial-a-Molecule consisting of Mettler
Toledo, Pfizer, GlaxoSmithKline, AstraZeneca, Johnson &
Johnson, Southampton University, NextMove, Royal
Society of Chemistry looking to:
– Provide guidance for S88 implementations for synthetic organic
chemistry reaction procedures
– Provide example set
– Agree on controlled vocabularies for elements
– Generate a schema
– IUPAC uptake?
16 (0.101 g, 0.132 mmol) and Cu(OTf)2 (0.006 g, 0.01 mmol) were added to a round-bottom flask under a N2
atmosphere. In a separate vial, 2 (0.155 g, 0.753 mmol) was dissolved in C2H4Cl2 (1.3 mL) and transferred to the reaction
flask. CF3CO2H (0.030 mL, 3 equiv) was added to the reaction mixture, which was refluxed at 100 °C for 1 h. The
reaction mixture was washed with saturated NaHCO3 (15 mL) and extracted with C2H4Cl2 (3 x 5 mL). The organic
fractions were collected, dried (MgSO4), and filtered to give a dark red solution. The solvent was removed, and the
product was purified by column chromatography (SiO2, 30:70 CH2Cl2 : hexane) to yield 17 as a pale yellow powder (0.096
g, 68% yield).

Allotrope Foundation
• Standards for analytical process in (pharma) industry
80

Research Data Alliance
• Chemistry data interest group
• Joint RDA/IUPAC Charter drafted
– Characterise chemical data types
– Leverage to establish standards
– Examine workflows in disciplines interacting with
chemistry
– Cultivate a sharing culture
83

Recording process
• Plan (Prospective
provenance)
85
• Enactment
(Retrospective
provenance)
• Realisation

oreChem Plan for eCrystals
• Machine-readable
representation of methodology
• Describes requirements for
software and data products
86

CREAM:
Collaboration for Research
Enhancement using Active Metadata
• How to collect and use
metadata actively to
capture tacit information
• Active metadata: assemblage of
metadata and annotations used
actively within the process that
generates it (capable of being
reused by another process).
• Central Facilities; Chemistry;
Geosciences; Art; Music…
• Uptake: CODATA; Research
Data Alliance 87
https://blog.soton.ac.uk/cream/

Managing active research in
the university
Robin Rice, University of Edinburgh
14/07/2016

Managing active research in
the University of Edinburgh
Robin Rice
University of Edinburgh
(@sparrowbarley)

Elements of the presentation
• Funded & unfunded research, PGR
students, collaborators
• Managing & support for …
– research grants
– research outputs
– research data
• Simplified research lifecycle
– (before – during – after)
90

New Research Management and Administration
System (grants)
Worktribe - Empowering and supporting research administrators
and investigators from idea through costing, approvals, award,
post-award management and closure.
• A recent requirements and procurement project focused on
delivery of a new 'Worktribe Research Management' system to
support improved pre-award and post-award business processes
across the University.
– 3 pilot schools / institutes November 2015
– Go-live across university April 2016
• The new 'Worktribe Research Management' system is integrated
with the existing Finance, HR and PURE systems.
• https://www.projects.ed.ac.uk/programme/rmas
91

Managing research outputs:
guidance for authors; OJS
1. Make your work Open Access 5. Acknowledge your funder
2. Use your name consistently 6. Statement on research data
3. Use an ORCID identifier 7. Cite the DOI and OA links
4. Institutional affiliation 8. Claim your digital space
‘Ensure your research reaches the widest possible global audience, is
eligible for submission in research assessment exercises, and fulfils funder
requirements.’
92

Type, format volume of data, chosen software
for long-term access, existing data, file naming,
structure, versioning, quality assurance
process.
Information needed for the
data to be read and
interpreted in future,
metadata standards,
methodology, definition of
variables, format & file
type of data.
Restrict access to data, risks
to data security,
appropriate methods to
transfer / share data,
encryption.
Secure & sufficient storage for active
data, regular backups, disaster recovery
Make data publicly
available (where possible)
at the end of a project,
license data, any
restrictions on sharing,
access controls?
Select data to keep,
decide how long data
will be kept, in which
repository, costs
involved in long-term
storage?
Day-to-daymanagementofdata
Managing Research Data
(from researcher training)
93

Who manages active data?
• RDM Policy (2011) sets out roles and responsibilities
for researchers & the institution
– Researchers are responsible for their work
– Enabling role of institution
• Services, including stewardship
• Monitoring compliance
• Principal Investigators
• Research Institutes & Schools
94

From RDM programme to
Research Data Service…
• RDM programme a result of both bottom-up (‘action
group’) activity and top-down policy implementation
• Services had various providers and their purpose and
names were confusing
• New single service has
SO & SOM with Virtual Team
across Information Services
96

After (Simplified data lifecycle)
98

Tools & support for working with data
99

Finding and analysing data
• What is Data Library & Consultancy?
• The Data Library & Consultancy team assists researchers to
discover and use datasets for analysis, learning and teaching.
• Data librarians are available to help you find answers to data-
related questions.
• Tools include a data catalogue and the Survey Documentation
and Analysis online data browser
100

Storing data
• What is DataStore?
• DataStore is file storage for active research data, and is
available to all research staff and postgraduate research
students (PGRs).
• DataStore provides a free individual allocation for each
researcher, as well as shared group spaces. Additional capacity
of virtually any size is available.
101

Transferring data
• What is DataSync?
• DataSync is a tool to synchronise and share research data with
collaborators. It has an app to synchronise data to computers
and mobile devices, and a web interface to allow access to
data from any web browser.
• Data can be shared with anyone who has an email address, via
the web interface.
102

Versioning software
• What is Subversion?
• Subversion is a version control tool which allows users to store code. It is
also available as an extension called SourcEd which provides a web based
collaboration tool integrated with your repository.
• When documents stored in a Subversion repository are updated the old
versions are kept so you can revert if necessary. The service also allows
multiple people to collaborate on documents.
103

Data management support
One-to-one support is available on the following areas of RDM:
• Writing and reviewing DMPs;
• Creating SOPs for metadata collection and publication;
• Creating SOPs for good data management practice;
• Choosing a data repository and preparing data for deposit and
publication
104

Training - Online
• MANTRA: MANTRA is a free, non-credit, self-paced course
designed for postgraduate students and early career
researchers which provides guidelines for good practice in
research data management
• Research Data Management and Sharing MOOC: This free five-
week course - created by the Universities of Edinburgh and
North Carolina - is designed to reach learners across disciplines
and continents.
105

MANTRA sessions by country
2012-2016
107

Sessions by city: January – June 2016
108

www.coursera.org/learn/data-management #RDMSmooc
109

MOOC’s contents
• Understanding Research Data
• Data Management Planning
• Working with Data
• Sharing Data
• Archiving Data
110

Training workshops
• Creating a data management plan for your grant application
• Managing your research data: why is it important and what
should you do?
• Working with personal & sensitive research data
• Good practice in research data management
• Handling data using SPSS
113

RDM training take-up, 2014-2016
114

Making research available - FAIR
principles and Force 11
David De Roure, Oxford e-Research Centre
14/07/2016

David De Roure
@dder
Making research available:
FAIR principles and FORCE11
DIRECTOR, UNIVERSITY OF OXFORD E-RESEARCH CENTRE

A Brief History of Force11
● 2008/2009:
– Elsevier Grand challenge
● 2010:
– Found & connected to Phil
Bourne
– Planned Dagstuhl meeting
● 2011:
– January: Beyond the PDF,
San Diego: 97 Attendees
– August: Force11 at
Dagstuhl: 34 attendees
– November: Manifesto is
published >> Force11!
● 2012: Funding Sloan
● 2013: Beyond the PDF2,
Amsterdam:
– 148 attendees, great discussion
● 2014: Working groups take
off:
– Data Citation Principles Working
group
– Resource Identifier Working
group
● 2015: Force15, Oxford:
– 257 attendees
● 2015: Force 2016,
Portland, Oregon

121https://www.force11.org/fairprinciples

A diverse set of stakeholders -
representing academia, industry,
funding agencies, and scholarly
publishers - have come together to
design and jointly endorse a concise
and measureable set of principles,
for those wishing to enhance the
reusability of their data holdings
Including, but not limited to:
European Open Science Cloud –
High Level Expert Group

These put emphasis on enhancing the ability of
machines to automatically find and use the data, in
addition to supporting its reuse by individual
NOTE: The Principles are high-level; do not suggest
any specific technology, standard, or implementation-

Exemplar implementations,
their (level of) FAIRness and
resulting value-added

Findable
Accessible
Interoperable
Reusable
Thanks to Susanna Sansone

Active research management and sharing14/07/2016
126

Active research management and sharing

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