Incentives for modern researchChair: Steven Hill, HEFCE

01/05/23

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IntroductionChair: Steven Hill, HEFCE

01/05/23 Title of presentation (Insert > Header & Footer > Slide > Footer > Apply to all) 2

The UK position on open accessSteven Hill, Head of Research Policy

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The UK position on open access

Steven HillHead of Research Policy

Jisc-CNI conference 06 July 2016

@stevenhill

Summary

• Policy

• Progress

• Prospects

Summary

• Policy

• Progress

• Prospects

UK Government Policy

• Independent reports– Dame Janet Finch – 2012– Professor Adam Tickell – 2016

UK Government Policy

“I am confident that, by 2020, the UK will be publishing almost all of our scientific output through open access. The advantages of immediate ‘gold’ access are well recognised, and I want the UK to continue its preference for gold routes where this is realistic and affordable. I also accept the validity of green routes, which will continue to play an important part in delivering our open access commitments.”

Jo Johnson, Minister for Universities and Science

Image: Public Domain (https://commons.wikimedia.org/wiki/File:Jo_Johnson_Photo_Speaking_at_the_British_Museum.jpg)

UK Government Policy

“I am confident that, by 2020, the UK will be publishing almost all of our scientific output through open access. The advantages of immediate ‘gold’ access are well recognised, and I want the UK to continue its preference for gold routes where this is realistic and affordable. I also accept the validity of green routes, which will continue to play an important part in delivering our open access commitments.”

Jo Johnson, Minister for Universities and Science

Image: Public Domain (https://commons.wikimedia.org/wiki/File:Jo_Johnson_Photo_Speaking_at_the_British_Museum.jpg)

UK Policy Landscape• Research Councils UK

– Journal articles and conference proceedings– Preference for immediate, CC-BY access– Accept access after 6 months (STEM) or 12 months (AHSS) with CC-BY-NC– Block grant to HEIs for APCs (pure OA and hybrid)

• Charity Open Access Fund– 7 major medical research funders (including Wellcome Trust)– Journal articles, conference proceedings and monographs– Deposit in PubMedCentral or EuropePMC– Require immediate, CC-BY access

• Research Excellence Framework– Journal articles and conference proceedings– Deposit in institutional or subject repository– Accessible for read and download at least 12 months (STEM) or 24 months (AHSS)– Encourage: immediate access, liberal licencing, monographs

Summary

• Policy

• Progress

• Prospects

Wellcome Trust compliance analysis

• 2014/15: 30% of articles for which APC paid not compliant with policy

• E.g. 392 articles not deposited in PMC/EuPMC - £765,000 APC value

• Hybrid journals main source of non-compliance:

Source: https://blog.wellcome.ac.uk/2016/03/23/wellcome-trust-and-coaf-open-access-spend-2014-15/

Summary

• Policy

• Progress

• Prospects

Prospects

• REF policy – significant increase in open content• Possible action by funders on hybrid journals (see DFG, Norwegian Research

Councils)• Offsetting deals• The effect of Sci-Hub?• Further developments on policy/implementation; 4 working groups of

Universities UK OA group:– Efficiency– Service standards– Repositories– Monographs

Summary

• Policy

• Progress

• Prospects

Thank you for listenings.hill@hefce.ac.uk

@stevenhillopenaccess@hefce.ac.uk

Incentives for sharing research dateVeerle Van den Eynden, UKDS

01/05/23

Incentives and motivations for sharing research data, a researcher’s perspective

Jisc / CNI conference: International advances in digital scholarshipOxford, 6 July 2016

Veerle Van den EyndenUK Data ServiceUniversity of Essex

Why study incentives for data sharing ?

• Barriers to data sharing well know• Wide variation in data sharing policies across Europe

• where policies are weak or not present, must rely on norms and incentives

• While overall benefits of data sharing are clear, benefits for individual researcher can be weak or mixed

• Incentives a better basis for data / research collaboration

Qualitative study of incentives, 2014

• 5 case studies – active data sharing research groups• 5 European countries: FI, DK, GE, UK, NL• 5 disciplines: ethnography, media studies, biology,

biosemantics, chemistry• 22 researchers interviewed• Q: research, data, sharing practices, motivations,

optimal times, barriers, future incentives,….

http://www.data-archive.ac.uk/about/projects/incentive

Case studies

Different modes of data sharing

• Private management sharing• Collaborative sharing• Peer exchange• Sharing for transparent governance• Community sharing• Public sharing (repository)

• Mutual benefits vs data ‘donation’

Data sharing practices in case studies

• Data sharing = part of scientific process• Collaborative research• Peer exchange• Supplementary data to publications

• Sharing early in research (raw)• Sharing at time of publication (processed)• Well established data sharing practices in some

disciplines: crystallography, genetics• Development of community / topical databases:

BrassiBase, LARM archive• Some sharing via public repositories: chemistry,

ethnography, biology

Incentives – direct benefits

• For research itself: • collaborative analysis of complex data• methods learning• research depends on data /information, data mining• suppl. data as evidence for publications• research = creating data resources

• For research career: • visibility, also of research group • reciprocity• reassurance, e.g. invited to share

• For discipline & for better science

Incentives – norms

• Sharing = default in research domain, research group, institution• Hierarchical sharing throughout research career• Challenge conservative non-sharing culture• Openness benefits research, but individual researchers reluctant to take lead

Incentives – external drivers

• Funders directly fund data sharing projects• Journals expects suppl. Data • Learned societies develop infrastructure & resources • Data support services• Publisher and funder policies and expectations

• may not push data sharing as much as could do, e.g. supplementary data in journal poor quality; mandated repository deposits minimal, exclude valuable data

• slowly change general attitudes, practices, norms

Future incentives for researchers

• Policies and agreements – create level playing field• Training – sharing to become standard research practice• Direct funding for RDM support• Infrastructure and standards• Micro-publishing/micro-citation• Broaden norms

Recommendations

• Changing norms• Encourage direct benefits: science, careers• Leadership from funders, institutions, learned societies, publishers• “Mixed economy” of incentives that consider:

• phase in research data life cycle• career stage of researcher• context of discipline / research environment

• European level:• invest in ‘rich’ data resources: data + context

Recommendations for funders

• All research funders data sharing policy - expectations for data accessibility; budget share for RDM

• Funding support services, cf. funding publication costs• Invest in data infrastructure with rich context• Fund data sharing training for students and doctoral

researchers• Target funding at reuse of existing data resources• DMP evaluation guidance for peer reviewers of bids

Recommendations for learned societies

• Research recognition for data sharing and data publishing

• Data sharing expectations for the disciplines, e.g. code of conduct.

• Data sharing agreements for discipline• Promote developing data sharing resources and

standards for the research discipline.

Recommendations for research institutions

• Data ‘publishing’ recognition in research assessment / career progression

• Data impact in PhD career assessment, e.g. impact portfolio, data CV

• Set data sharing expectations for institution• Data sharing training part of standard student research

training• Integrated RDM support services (one-stop-shop)

Recommendations for publishers / editorial boards

• Boost direct career benefits of data sharing:• data citation • data sharing metrics• micro-citation• tools: DOIs, ORCID, digital watermarking

• Publication of negative findings, failed experiments• Full datasets as supplementary material• All supplementary data openly available• Correct data citation• (Open) standards for file formats and supplemental

documentation

Recommendations for data centres / repo’s

• Pull factors for data sharing, e.g. invitations for data• Specialist data sharing training• Flexible access systems for data for data owners• Rich data resources, with context of publications, etc

What other research found

Youngseek, K and Adler, M (2015) Social scientists’ data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories. International Journal of Information Management 35(4): 408–418. •online survey of 361 social scientists in USA academia•predict data sharing behaviour through theory of planned behaviour (individual motivation is based on own motivations and availability of resources) and institutional theory (institutional environment produces structured field of social expectations and norms, using (dis)incentives to shape behaviour and practices)•main drivers for data sharing:

• personal motivations: perceived  career benefit and risk, perceived effort, attitude towards data sharing

• perceived normative pressure•funders, journals and repositories are not significant motivators

What other research found

Sayogo, D.S. and Pardo, T.A. (2013) Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data. Government Information Quarterly, 30(1): 19-31. •Online survey with 555 researchers, cross-disciplinary, 75% USA•Ordered logistic regression to assess the determinants of data sharing, analysing willingness to publish datasets as open data against 7 variables: organisational support, DM skills, data reuse acknowledgement, legal and policy conditions owner sets for data reuse, concern for data misinterpretation, economic motive, funder requirement•Main determinants are:

• DM skills and institutional support• data reuse acknowledgement, legal and policy conditions owner sets for data reuse

What other research foundExpert Advisory Group on Data Access (2014) Establishing incentives and changing cultures to support data access. •interviews with key stakeholders: funders, senior academic managers, postdoctoral researchers, chair REF panel, senior data manager•web survey with researchers and data managers (Nr responses unknown)•recommended incentives:

• research funders: • strengthen and finance data management and sharing planning• Fund and develop infrastructure and support services• recognise high quality datasets as valued research outputs in REF• career paths and progression for data managers in research teams

• research institutions:• clear policies on data sharing and preservation• training and support for researchers to manage data effectively

• journals• clear policies on data sharing and processes• datasets underlying published papers readily accessible• appropriate data citation and acknowledgement

Thanks

• Knowledge Exchange• Interview partners:

• Anders Conrad (DK)• Damien Lecarpentier & Irina Kupiainen (FL)• Jens Nieschulze & Juliane Steckel (GE)• Joeri Nortier (NL)

• Interviewees

• Van den Eynden, V. and Bishop, L. (2014). Sowing the seed: Incentives and Motivations for Sharing Research Data, a researcher's perspective. Knowledge Exchange. http://repository.jisc.ac.uk/5662/1/KE_report-incentives-for-sharing-researchdata.pdf

Questions

Contact details

sharing@ukdataservice.ac.ukhttp://ukdataservice.ac.uk/manage-data.aspx

Incentives to innovateJoe Marshall, NCUB

01/05/23

Dr Joe Marshall

Incentives to InnovateJisc and CNI Conference 2016

When in Rome … speak Roman

National Centre for Universities and Business

The Productivity PuzzleUK strength•UK world leading on measure research measures•Strength of UK-research base attractive to global business R&D activities

UK weakness•Yet, UK lags behind international competitors in terms of productivity rates•Encouraging more business-led innovation critical

Mayfield: Improving Productivity “Innovation is the lifeblood of long-term productivity growth – new products, new markets, and new ways of doing things create new opportunities”

•Innovation is a collaborative activity – innovators need other innovators to work with.•Need to create the space to bring ideas from the UK’s research base into thriving, disruptive new businesses.

Academic operability:impacting impactful impact

Understanding the operating environments in which our academics and universities operate today.•Recent report by National Centre canvassed all UK academics about how and why they engage with business and others.

Discoverability: an era of open “openness”

“Democratisation” of opening up information•Openness agenda complex, multifaceted, profound•Opening up new opportunities (and challenges)

Information in and of itself is significant•Information and data generated at exponential rates•Cloud not limited boundaries of geography or power•Information is a click away

Discoverability:An ORCiD by any other name

Businesses interested to know WHO is doing WHAT research WHERE:•ORCiD (and other tools) make it easier to discover researchers and research•Tools like equipment.data and _connect give insights into other opportunities for others

•Discoverability provides an important incentive to academics to promote their activities

Connectivity• Dowling Review (and others) have highlighted

issues SMEs (and corporates) can have: – Awareness of breadth of opportunities available;– Knowing what to ask, how to ask and what it means;– Addressing issues of proximity and time.

Four conceptsUnderpinning the emerging tool

Aggregating

Interpreting

Matching

Triaging

Inspiring

Guiding

Engage networks

Embedded tool

Understanding incentives

Incentives in university collaborationTim Lance, NYSERNet

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Engaging the Researchers: Incentives in University Collaboration

Tim Lance, NYSERNet July 6, 2016

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New York State Education & Research NetworkA private 501(c)(3) not-for-profit established in 1985.One of the original NSFNET regional networks.Creator of two for-profit companies: AppliedTheory & PSInet.A member organization; activities supported by member fees.Board of Directors composed primarily of non-profit CIOs.Offices in Syracuse and Troy.Staff of sixteen, majority in Syracuse.... not a state agency.Working with members to solve technology-related problems of mutual concern.

About NYSERNet

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About NYSERNet

Since inception 31 years ago, NYSERNet has been committed to sustaining advanced research networks for the most demanding, data intensive applications. In the beginning, this included seminal contributions to the concept of the network:

“In response to the Connections solicitation, NSF received innovative responses from what would become two of the major regional networks: SURANET and NYSERNET. They proposed a regional, distributed network design rather than one with all universities independently connected to the regional supercomputing center (a “star” design).

“The NYSERNET and SURANET examples caused a major paradigm shift at NSF. Instead of funding institutional connections to supercomputer centers, the NSF shifted to funding connections of ‘cohesive’ regional networks. ... NSFNET is not a network. It is an internetwork - i.e., a network of networks, which are organizationally and technically autonomous but which interoperate with one another.”

Steven Wolff, NSF

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Tim Lance, NYSERNetGary Roberts, Alfred UniversityJuan Montes, American Museum of Natural HistorySharon Pitt, Binghamton UniversityTom Schlagel, Brookhaven National LaboratoryBrian Cohen, CUNYGaspare LoDuca Columbia UniversityDave Lifka, Dave Vernon, Cornell UniversityBob Juckiewicz, Hofstra University Patricia Kovatch, Icahn School of Medicine at Mount Sinai Bill Thirsk, Marist College

Daniel Barchi, NY Pres. HospitalMarilyn McMillan, New York UniversityJohn Kolb, Rensselaer Polytechnic Inst.Jeanne Casares, Rochester Institute of Tech.Armand Gazes, Rockefeller UniversityJustin Sipher, St. Lawrence UniversityMelissa Woo, Stony Brook UniversityChris Sedore, NYSERNetChris Haile, University at AlbanyBrice Bible, Tom Furlani University at Buffalo Dave Lewis, University of Rochester

NYSERNet Board

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David Ackerman, NYUToby Bloom, New York Genome CenterDuncan Brown, Syracuse UniversityChris Carothers, Rensselaer Polytechnic InstituteJim Dias, University at AlbanyJon Dordick, Rensselaer Polytechnic InstituteJim DuMond, Marist CollegeStratos Efstathiadis, New York UniversityTom Furlani, University at BuffaloRobert Harrison, Stony Brook, BrookhavenHalayn Hescock, Columbia University

Patricia Kovatch, Icahn School of Medicine at Mt. SinaiMichael Kress, College of Staten IslandTim Lance, NYSERNetDave Lifka, Cornell UniversityBrendan Mort, University at RochesterBill Owens, NYSERNetVijay Agarwala, New York Genome CenterRyne Raffaelle, Rochester Institute of TechnologyTom Schlagel, Brookhaven National LaboratoryJill Taylor, Wadsworth CenterAndrew White, Stony Brook University

Research Advisory Council

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Background: September 11, 2001

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ICONOS image, summer 2000

60 Hudson Street

140 West Street

World Trade Center

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ICONOS image after 9/11 attack

140 West Street

60 Hudson Street

World Trade Center

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Impact of 9/11 on NYSERNet Initiatives

NYSERNet’s New York City Optical Network - Manhattan

X

Y

Z

Backbone RingFuture Backbone RingCarrier HotelX

NYSERNet’s New York City Optical Network - Bronx

Backbone RingFuture Backbone Ring

NYSERNet’s The Colo@32 at Rudin Management’s The Hub

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Why Manhattan?

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Why Manhattan?

NYSERNet’s Optical Network

The Atrium - Home of NYSERNet’s Business Continuity Center

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Driving to work in Syracuse

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Reaching Across

Boundaries

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Government and Industry

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Broader Community

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LHC

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Genomics

UB to co-lead efforts to make NYS national leader in genomic research

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LIGO

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LIGO

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The Brain and Computing

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Climate

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Research after Sandy

Transfer from CERN to Brookhaven during and after Sandy

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Overarching problemsthat bring us together

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Very Big Data

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General Assertion

The size of data sets and complexity of necessary computations are growing faster than the technologies to move, store, manipulate, and calculate.

Question: Is everything growing exponentially?

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The exponential function

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More than exponential growth???

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Big Data Examples

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Big Data Examples

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Big Data Examples

Raw data

Featureextraction metadata

Domain linkages

Fullcontextual analytics

Location risk

Occupational risk

Dietary risk

Family history

Actuarial data

Government statisticsEpidemic data

Chemical exposure

Personal financial situation

Social relationships

Travel history

Weather history

. . .

. . .

Patient records

Data Multiplier EffectFactorial explosion in context

From IBM

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The Black Box

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Black Box Aspects

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First, assess resources

Moore’s Law comes with many slopes

Processing

Memory

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Baby steps (for giants)

Part of the Cooley-Tukey FFT Algorithm

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Find closed forms

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And that leaves the

Really Hard Stuff

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Data Decision TreePreservation and CurationLegal and EthicalSustaining PartnershipsEducationBringing Government and the Public Along

Really Hard Stuff

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So what do we do?

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Continue the conversation!

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David Ackerman, NYUToby Bloom, New York Genome CenterDuncan Brown, Syracuse UniversityChris Carothers, Rensselaer Polytechnic InstituteJim Dias, University at AlbanyJon Dordick, Rensselaer Polytechnic InstituteJim DuMond, Marist CollegeStratos Efstathiadis, New York UniversityTom Furlani, University at BuffaloRobert Harrison, Stony Brook, BrookhavenHalayn Hescock, Columbia University

Patricia Kovatch, Icahn School of Medicine at Mt. SinaiMichael Kress, College of Staten IslandTim Lance, NYSERNetDave Lifka, Cornell UniversityBrendan Mort, University at RochesterBill Owens, NYSERNetVijay Agarwala, New York Genome CenterRyne Raffaelle, Rochester Institute of TechnologyTom Schlagel, Brookhaven National LaboratoryJill Taylor, Wadsworth CenterAndrew White, Stony Brook University

Research Advisory Council

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Tim Lance, NYSERNetGary Roberts, Alfred UniversityJuan Montes, American Museum of Natural HistorySharon Pitt, Binghamton UniversityTom Schlagel, Brookhaven National LaboratoryBrian Cohen, CUNYGaspare LoDuca Columbia UniversityDave Lifka, Dave Vernon, Cornell UniversityBob Juckiewicz, Hofstra University Patricia Kovatch, Icahn School of Medicine at Mount Sinai Bill Thirsk, Marist College

Daniel Barchi, NY Pres. HospitalMarilyn McMillan, New York UniversityJohn Kolb, Rensselaer Polytechnic Inst.Jeanne Casares, Rochester Institute of Tech.Armand Gazes, Rockefeller UniversityJustin Sipher, St. Lawrence UniversityMelissa Woo, Stony Brook UniversityChris Sedore, NYSERNetChris Haile, University at AlbanyBrice Bible, Tom Furlani University at Buffalo Dave Lewis, University of Rochester

NYSERNet Board

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NYSERNet, Inc.www.nysernet.org

100 S. Salina St., Suite 300Syracuse, NY 13202

315-413-0345

385 Jordan Rd.Troy, New York 12180

518-283-3584

Thank You

Giving researchers credit for their dataNeil Jefferies, The Bodleian Digital Library Systems and Services (BDLSS)

01/05/23

Concept: “Carrot” for Data Deposit“Submit data paper” button on data repository item Researcher gets…

Another publication/citation opportunityPreservation of dataAvoid publisher submission system

Publisher getsMore/faster data paper submissionsBetter metadata qualityLink referrals from data repositories

Repositories getMore data depositsBetter metadata qualityLink referrals from publishers

Funders getMore re-use, more impactReproducability

Schematic

Helper App

DataRepo

Publisher

DataPaper

DataCiteORCID

EnhancedMetadata+ Text etc.

CrossRef

1. Press button – SWORD2 package sent to helper app with DataCite DOI and submitters ORCID

Text(Gdocs)

CoAuthors(ORCID)2. In Helper App – Select journal,

write paper using template, add coauthors from ORCID and agree to publisher T&C’s..

3. Enhanced SWORD2 package sent to the publisher. Ingested automatically into publisher submission system.

4. Publication updates ORCID Profile for Repo to harvest.

Jisc Data Spring

Phase 1 – Feasibility StudyRDA Publisher Workflow Analysis

Strawman spec for Helper app/API Most data papers and related data is open

Questionnaire for Repositories and Publishers

Confirm requirements Gauge interest in proposal

Overwhelmingly positive feedback Offers of collaboration

Phase 2 – Proof of Concept Detailed API Spec (SWORD2/DataCite) Protoype helper app “Data Paper Companion”

Fedora Repository/Hydra Sword Client/Server Ruby Gems Repository -> Publisher in <10 minutes (if you

have the text written) Community building

F1000 Research, Elsevier (Data in Brief and Mendeley Data), ORCID, RDA/THOR

Many more collaboration offers than we could handle

Figshare, OJS, Dryad, Nature...

Phase 3 – The Business Case

We started to look for indications of the time this app would save scholars to quantify the possible benefits...

We were expecting to measure efficiency gains of maybe tens of minutes per submission or a bit more...

#submissionsystems

#datamanagement

Phase 3 - ConsolidationDemonstrate real paper(s) published using the

workflowJoin forces with Streamlining Deposit project

team UX expertise Align metadata requirements Expect repo-led and publication-led workflows to co-exist

Sustainability Steering Committee to initiate governance structures API Spec as a formal publication

Code as a reference implementation/test harness THOR project – identifier ecosystem for research entities Jisc shared services ORCID Cloud hosting: Azure (Microsoft Research Grant)

Phase 3 - ExpansionExpanding reach/integration

More outreach activities Updated SWORD modules for EPrints, Dspace, OSP Work with structured repositories such as EBI, NCBI etc.

(domain/data specific) Take up other publisher offers: Nature, OUP Datasets in ORCID Journal Policy Registry by the back door?

Roadmap (not development) for additional use cases Multiple datasets (other people's data) Non-open data (DataShield?) Not just data papers REF/Impact metric* friendliness

Incentives for modern researchChair: Steven Hill, HEFCE

01/05/23

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