how to execute a research paper
DESCRIPTION
Talk on changes in scholarly publishing, University of Lethbridge Dept of e-HumanitiesTRANSCRIPT
How to Execute A Research Paper
Anita de Waard
Disrup8ve Technologies Director
Elsevier Labs
University of Lethbridge, April 3, 2012
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Outline
• Ten people/ideas who/that are changing scholarly publishing: – New forms
– Workflow/data integra8on
– New models of business/aHribu8on
• So what does this mean?
• Some projects to help us move towards these new models
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Theme 1: New forms of publica8on • Main issue: the format of the scien8fic paper comes from a 8me when our communica8on was paper-‐centric
• Solu8on: Rethink the unit and form of the scholarly publica8on from the ground (i.e., the experiment) up
• Three projects doing that:
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Steve PeTfer, U Manchester • Utopia: ‘Everything you always wanted to do with a PDF….’: interac8ve, sharable
• Working on integra8on with DOMEO to add/share annota8ons
• Final goal: don’t ‘reconstruct the cow from a hamburger’: include workflows and models
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Gully Burns, USC ISI • KEfED: model of research as an ac8vity
• Map out dependent/independent variables within an experiment and model them
• Start: appendix to paper; later: precede paper, gra` paper on top of model.
Tim Clark, Harvard/MGH
swande:Claim <hHp://8nyurl.com/4h2am3a>
Intramembranous Aβ behaves as chaperones of other membrane proteins
rdf:type
dct:title
G1
<hHp://example.info/person/1> pav:contributedBy
<hHp://example.info/cita8on/1>
swanrel:referencesAsSupportiveEvidence
G5
G6
• DOMEO: automated en8ty markup + manual mark up of claim/evidence networks
• Working on plagorm for workflow integra8on
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Theme 2: data and workflow integra8on
• Issues: – Format of the research paper hard to integrate within a scien8fic/clinical workflow
– Hard to reproduce/deduce: what methods were used and what data was created for a piece of research, making reproduc8on or even review difficult
• Some solu8ons for sharing workflows and data:
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Results
Logs
Results
Metadata Paper Slides
Feeds into
produces
Included in
produces
Published in
produces
Included in
Included in
Included in
Published in
Workflow 16
Workflow 13
Common pathways
QTL
• Research objects: consist of all academic output, including: - Papers - Workflows - Data - Talks, lectures - Blogs
• Move towards executable work: - Execute periodically to validate - Run automa8cally when data updates – by self or others! - No8fy researchers of new results
Dave DeRoure, Oxford e-‐Research Centre
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Phil Bourne, UCSD
• Big need: keep track of the data in my lab! • Other need: know what I did/what other people did – Yolanda Gil made workflow representa8on, was hard to remember what we did…
• Need: beHer ways to record, share, archive what we did.
• New role for the publisher >
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Deborah McGuinness, RPI • Future Web:
• ‘if everything is everywhere, how do we find it/know what we want?’
• Internet, Web, Grid, Cloud, Seman8c Grid Middleware
• Xinforma8cs: • Where X = geo, eco, econo… • Linked Data to Seman8cs
• Seman8c Founda8ons: • Pushing the boundaries of Seman8c Web standards
• Ontology evolu8on
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Theme 3: New Models for Access/AHribu8on
• Issues: – User-‐created content, crowdsourcing means (scien8fic) impact is measured very differently from the past
– Need new models for copyright/IP – Ci8zen scien8sts par8cipate as well
• Some efforts to address this:
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Paul Groth, VU Amsterdam
Altmetrics: “the crea8on and study of new metrics based on the Social Web for analyzing and informing scholarship.” Including:
- Downloads - Where readers read
- Data cita8on - Social network diffusion - Slide reuse - Peer review contribu8ons - Youtube views
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• ElPub conference series that focus on globally connec8ng informa8on scien8sts
• Bioline Interna8onal system “a not-‐for-‐profit scholarly publishing coopera8ve commiHed to providing open access to quality research journals published in developing countries”:
Leslie Chan, U. Toronto Scarborough
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John Wilbanks, Kauffman/CC • As data becomes more accessible, need:
• raw metadata • standards processes • consensus processes • document submission standards • data archives
• Ways of governing access: • Privacy vs. IP vs. policies • Technology only helps so much… • This is mostly a social/policy issue
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Cameron Neylon, Cambridge
• Main arguments for Open Access: • Ci8zen science is becoming more important • Science changes when it is crowdsourced: Tim Gowers: ‘This is to normal research as driving is to pushing a car’
• Three principles: • Scale and connec8vity • Reduced fric8on to access • Demand-‐side filters
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In summary, scien8sts are working on: • Tools for knowledge…
– Visualisa8on (Steve PeTfer)
– Modeling (Gully Burns)
– Annota8on (Tim Clark)
• Ways to link to
– Workflows (Dave De Roure) – Lab data (Phil Bourne) – Linked research data (Deborah McGuinness)
• And models for
– AHribu8on/credit (Paul Groth) – Allowing new players to par8cipate (Leslie Chan) – Copyright/IP rights (John Wilbanks) – Networked science (Cameron Neylon).
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• Technically, there is no reason to publish in a journal– or even, for that maHer, to publish a paper at all!
• A few good blog posts linked to workflows and data with some valida8on from peers and good download sta8s8cs might serve you just as well – or, in fact, much beHer….
• Is publishing in journals mostly a habit?
So do we s8ll need publishers? Or libraries?
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“Publishers have been thinking we’re going out of business for 20 years, what has suddenly changed?”
The internet! Not the technical web, but the social web….
‘The value of a […] network is propor8onal to the square of the number of users of the system (n²)’
1990’s: Big Player
2000’s: Medium Participant
2015: Irrelevant!
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What do we need?
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1 [3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/network-enabled-research/ ‘
Internet of things: (Bleecker, [1]) Interact with ‘objects that blog’ or ‘Blogjects’, that: track where they are and where they’ve been;have histories of their encounters and experienceshave agency - an assertive voice on the social web [2]
Research Objects: (Bechofer et al, [2]) Create semantically rich aggregations of resources, that can possess some scientific intent or support some research objective
Networked Knowledge: (Neylon, [3]) If we care about taking advantage of the web and internet for research then we must tackle the building of scholarly communication networks. These networks will have two critical characteristics: scale and a lack of friction. [3]
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Some examples of networked science:
• Mathoverflow: virtual network of mathemagicians working collec8vely to answer big, small, clear and fuzzy ques8ons
• Galaxy Zoo: ci8zen science: classify galaxies in the comfort of your own home – like Hanny!
• Tim Gowers, Polymath: “…the real contributors will be the process owners and project leaders that are able to provide horizontal leadership. To support this shi`, organiza8ons will need to reward and recognize horizontal contribu8ons as much, if not more, than hierarchical posi8ons.”
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Some further parts of a solu8on:
• Iden8fying the key claims the authors make and linking them to their suppor8ng evidence both within and across papers
• Develop ‘executable papers’ that contain computable and ‘living’ components
• BeHer integra8ng papers with research workflows and data
• New models for business, aHribu8on and copyright in scholarly publishing
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DOMEO: Annota8ng claims
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Finding ‘Claimed Knowledge Updates’
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Executable Papers
• E.g.: hHp://www.vistrails.org/index.php/User:Tohline/CPM/Levels2and3
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Some other publisher
6. User applications: distributed applications run on this ‘exposed data’ universe.
Wrapping a story around your data:
Concept developed with Ed Hovy, Phil Bourne, Gully Burns and Cartic Ramakrishnan
1. Research: Each item in the system has metadata (including provenance) and relations to other data items added to it.
metadata
metadata
metadata
metadata
metadata
5. Publishing and distribution: When a paper is published, a collection of validated information is exposed to the world. It remains connected to its related data item, and its heritage can be traced.
2. Workflow: All data items created in the lab are added to a (lab-owned) workflow system.
4. Editing and review: Once the co-authors agree, the paper is ‘exposed’ to the editors, who in turn expose it to reviewers. Reports are stored in the authoring/editing system, the paper gets updated, until it is validated.
Review
Edit
Revise
Rats were subjected to two grueling tests (click on fig 2 to see underlying data). These results suggest that the neurological pain pro-
3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document.
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FORCE11 Community of Prac8ce • Workshop in August of 2011: 35 invited aHendees from different
parts of science, industry, funding agencies, data centers
• Goal: map main obstacles preven8ng new models of science publishing and develop ways to overcome them
• Just received funding from Sloan founda8on to:
• Start online community
• Hold next workshop • Look at new efforts
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Summary: • Ten people who are changing scholarly publishing: – New forms
– Workflow/data integra8on
– New models of business/aHribu8on – Networked science!
• We (publishers, editors, libraries, etc)need to revisit if and how we are needed
• Some projects are underway to help us move towards these new models…
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…. but I am sure you can come up with beHer ideas!
hHp://elsatglabs.com/labs/anita