research metadata mechanics - simon porter

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The New Research Data Mechanics…��Simon Porter��VP Research Engagement & Knowledge Architecture��Digital Science��@sjcporter #CASRAI15 ��also presented at #VIVO15��http://dx.doi.org/10.6084/m9.figshare.1509911


Before we begin…

This work extends on work and concepts that I began whilst at The University of Melbourne. I am grateful for the permission to build upon it at Digital Science.


Expectations around Research Information Systems are undergoing a period of rapid transformation

Images Modified from Louis K, C-‐0T Autobot Transforma;on And h=ps://www.flickr.com/photos/ppapadimitriou/ Blocks source Flikr

Paper based Administration -mid late 90’s

Current Research Information Systems-mid 2000’s Late 2000’s onwards: VIVO/ORCID’s/

Research Data Management /OA compliance/ Altmetrics/Open Science/Team Building/Interdisciplinary Collaborations


How do we describe the discipline that provides the foundations to make these aspirations happen? ? ?

? ? ?


Why is it safe to raise these expectations now?

We know that Universities can be good at managing information about their research

•  htcacheclean -‐d5 -‐n -‐i -‐p/servers/apache_mod_proxy -‐l150M

AOer 14 years of publica;ons repor;ng, there are over 150,000 data points on this visualiza;on

(presented at VIVO14) Porter, S

Examples From the University of Melbourne

The Funding Pipeline

Funds awarded in: q  2006 q  2007 q  2008 q  2009 q  2010 q  2011 q  2012 q  2013 q  2014 q  2015

In 2017, almost all Research will be funded by awards yet to be won

$ Total Funding by Alloca;on Year for Department X

2014

9 years of sustained quality informa;on on agreements went into construc;ng this pipeline

(presented at VIVO14, Porter, S)

Examples From the University of Melbourne

Australian Research Ins;tu;ons are in command of their Research Informa;on

Feeding the Machine….

h=ps://www.flickr.com/photos/hikosaemon/


The Evolution from Data Entry to Data Glue•  Data Entry -> 2009•  Harvesting a single source (like WOS or )->

2010•  Harvesting multiple sources (WOS, Scopus,

Repec, Arxiv, pubmed, …) 2012 (Symplectic)•  Over this time, researcher interaction has

moved from data entry (or email) to: “we think this is yours, please confirm”

An example from the University of Melbourne

(Source: TEST DATA from UAT)

Now an ins;tu;ons ‘glued data’ can be used as ‘tentacles’ to reach out and gather new perspec;ves

h=ps://www.flickr.com/photos/tomsaint/

A Tradi;onal Use Case: Gluing University data to InCites

Bibliometric data

Gluing University Publica;ons data with WOS

Example From the University of Melbourne: (Porter ARMS 2014)


Altmetric For Institutions…

Examples From the University of Melbourne Presented at Digital Science Showcase 2015

Melbourne Ar;cles with the highest Altmetric scores…


At least a year too late…


Using Altmetrics to their fullest poten;al demands a different way of engaging with

informa;on…


From Data Glue to Data Mechanics…

h=ps://www.flickr.com/photos/ronwls/13987847602/in/photolist-‐nj4nLf-‐F329z

The Goal of Research Data Mechanics

1)  In all cases, we seek to replace manual

interven;on with cogs turning between an understood system of research

2)  To build and increase the trust network of researchers, ins;tu;ons, funding bodies, publishers, and internal and external service providers


Another perspective on Research Data Mechanics:

In the case of QM or Classical Mechanics these laws of mo;on are determined by the forces felt by the par;cle ...in the case of Research Data Mechanics, our par;cles are items of data and the underlying laws of mo;on are university, government, publisher and funder policies and prac;ces.

ORCID as the ‘people cogs’

What Becomes Possible…

How does it change what ins;tu;ons can measure/value?

Journal ar;cles +

Conversa;on Ar;cles

If we can measure it, we can reward it

✔

✔

✔

✔

✔

✔

✔

And from publica;ons to Research Data…

How does This.. Become… This?

A Possible Future…

Research data can be enhanced as it travels through systems…

Enriched data publica;on links Research grants…

Research Data as it is shared

What become possible…..

And another thing…

Both are examples of reducing barriers between the act of research collabora;on, and the knowing of it

Formalizing Research Data Mechanics…

A Generic System Component

Component

Policy

(Informa;on Transformed by People & processes)

Component configura;on and behavior is Influenced by the upstream and downstream components

System components in the context of one possible VIVO configura;on

HR

Policy

Finance

Policy

Policy

Grant Management

Policy

{{

J J J J F F F F

Inves;ga;ve Power with reference to the system

•  Examples – University Level Benchmarking – Compara;ve Inter -‐ Department Data Analysis

JJJJ

JJJJ

Inves;ga;ve Power with reference to the system

–  University Level Benchmarking (Grants Awarded)

–  University Level Funding Pipeline Analysis

–  University Level Funding Pipeline Analysis (difficult)

FFFF

FFF Grant Management

F F F Grant Management




F F F Grant Management ?

A Deeper view of Research Data Mechanics STAR METRICS (2009)

FFF Grant Management

Finance System

DUNS database

Payroll System

h=p://www.nsf.gov/sbe/sosp/workforce/lane.pdf

(an extended version of research data mechanics)


Some challenges for Research Data Mechanics

•  Extending the system of components and the trust network

•  Crea;ng common ‘core’ capacity across all research ins;tu;ons, Funding bodies, Publishers

•  Crea;ng a research data ‘machine’ equally capable of preserving the history of research, as well facilita;ng the needs of the ‘now’


Challenge 1) Identifying and removing system boundaries –  System boundaries cause

•  informa;on that is already know to be recreated •  Informa;on Loss

–  Reasons for systems boundaries include •  Too much data fric;on created from a lack of standards/apis for communica;ng informa;on

•  Insufficiently structured informa;on at the source of crea;on

•  Misconfigured policy •  Insufficiently developed trust networks •  A lack of awareness of possibility


Practical Ways that VIVO is extending boundaries

HR

Policy

Finance

Policy

Policy

Grant Management

Policy

Department Websites Department

Websites Department Websites Department

Websites


2) Creating common ‘core’ capacity across all research institutions��

•  If your ins;tu;on can produce ‘sustainable’ VIVO data capable of represen;ng your en;re research ins;tu;on, then, as of now, you have reached core capacity…

•  What is the core capacity for a funding body? •  For a publisher?


3) Creating a machine capable of writing history

C RIS


Or…

CHRIS


h=ps://en.wikipedia.org/wiki/Aqueduct_(water_supply)#/media/File:Pont_du_Gard_Oct_2007.jpg

In Research Data Mechanics we are not just building pipes…


Thanks.

Anyquestions?

research metadata mechanics - simon porter

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