Incentives for modern research
Transcript of Incentives for modern research
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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
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The UK position on open accessSteven Hill, Head of Research Policy
01/05/23 Title of presentation (Insert > Header & Footer > Slide > Footer > Apply to all) 3
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The UK position on open access
Steven HillHead of Research Policy
Jisc-CNI conference 06 July 2016
@stevenhill
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Summary
• Policy
• Progress
• Prospects
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Summary
• Policy
• Progress
• Prospects
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UK Government Policy
• Independent reports– Dame Janet Finch – 2012– Professor Adam Tickell – 2016
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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)
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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)
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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
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Summary
• Policy
• Progress
• Prospects
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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/
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Summary
• Policy
• Progress
• Prospects
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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
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Summary
• Policy
• Progress
• Prospects
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Thank you for [email protected]
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Incentives for sharing research dateVeerle Van den Eynden, UKDS
01/05/23
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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
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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
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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
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Case studies
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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’
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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
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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
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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
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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
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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
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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
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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
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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.
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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)
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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
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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
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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
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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
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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
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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
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Incentives to innovateJoe Marshall, NCUB
01/05/23
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Dr Joe Marshall
Incentives to InnovateJisc and CNI Conference 2016
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When in Rome … speak Roman
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National Centre for Universities and Business
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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
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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.
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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.
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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
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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
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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.
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Four conceptsUnderpinning the emerging tool
Aggregating
Interpreting
Matching
Triaging
Inspiring
Guiding
Engage networks
Embedded tool
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Understanding incentives
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Incentives in university collaborationTim Lance, NYSERNet
01/05/23
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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
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NYSERNet’s New York City Optical Network - Manhattan
X
Y
Z
Backbone RingFuture Backbone RingCarrier HotelX
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NYSERNet’s New York City Optical Network - Bronx
Backbone RingFuture Backbone Ring
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NYSERNet’s The Colo@32 at Rudin Management’s The Hub
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Why Manhattan?
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Why Manhattan?
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NYSERNet’s Optical Network
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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
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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
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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
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Giving researchers credit for their dataNeil Jefferies, The Bodleian Digital Library Systems and Services (BDLSS)
01/05/23
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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
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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.
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Jisc Data Spring
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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
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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...
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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...
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#submissionsystems
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#datamanagement
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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)
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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
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Incentives for modern researchChair: Steven Hill, HEFCE
01/05/23
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