New Perspectives for Business Intelligence: Library and Research Technologies and Research Collaboration for New Data Models

November 7, 2012

New Perspectives for Business Intelligence: Library and Research Technologies and Research Collaboration for New Data Models

William Barnett-Indiana UniversityRobert H. McDonald (@mcdonald)-Indiana UniversityMike Winkler (@winkler4)-University of PennsylvaniaJoe Zucca-University of Pennsylvania

November 8, 2012

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Outline

General Strategies for Research Business Intelligence in the Academy

Data Openness/Transparency for Research Business Intelligence

Research Business Intelligence Use Cases Research Support/Team Science Libraries

Discussion on comprehensive strategies and needs for Research Business Intelligance in the academy


Team Science

Team science (TS) initiatives are designed to promote collaborative, and often cross-disciplinary (which includes multi-, inter-, and transdisciplinary) approaches to answering research questions about particular phenomena.

Science of Team Science (SciTS) encompasses both conceptual and methodological strategies aimed at understanding and enhancing the processes and outcomes of collaborative, team-based research.


Consortia Science

Encode Project Consortium Laying Groundwork for

Consortia Science in Genomics

Cross-Institutional Experiments using CI

Computational or CI Experimental Workflow

Machine Learning Enhancements

Birney, Nature 489


BI Data Supporting Consortia Science


IU Faculty Profile Mapping Data

Warehouse People

Management Publications Research

Profile Systems

Central Open Data Profile


Typical VIVO Data Ingest/Cleaning Workflow

https://github.com/dgcliff/RIS2N3

RIS2N3 Components

LOCAL CLIENT

RIS FILES

RIS2N3

N3

Jena

MySQL

VIVO

Jena

SERVER CONTEXT


Varying Views of Research Intelligence Data

Administrator View Researcher View

Development View Team Science View

Research Intelligence


Types of Systems in the RI Path

Faculty Profile Systems Faculty Annual Review Systems Research Profile Systems

Research Profile System Comparative Analytics (Peer to Peer)

Resource Profile Systems Research Management Systems


Open Source vs Vended Systems

Faculty Profile/Networking Systems Open Source

VIVO Digital Vita Loki Harvard Profiles CAP/Stanford Profiles

Vended Symplectics (MacMillan part of Digital Science) SciVal Experts (Elsevier) Pivot (Proquest) Research In View (Thomson Reuters)


Data Openness and Transparency


Research Data, Instruments and Resources

Research Data Publication Association with Faculty

and Resource Profiles Instruments

Sequencers Telescopes Sensor Networks

Resources Labs Facilities


Linked Open Data in the Enterprise

Not at Enterprise Level Graph Databases NoSQL Stacks Semantic Triple Store

Systems Data Policy/ Governance

Public Profiles Faculty Resources Instruments


Thank you!

Robert H. [email protected]

Business Intelligence in Translational Research: Research Networking as a Test Case

William Barnett, Indiana University



How Traditional Research Works…

Write Proposal

Get Funding

Publish Results

Do Research


How Traditional Medical Research Works…

Basic Pre-clinical Clinical

Pharma


How CTSAs want Translational Research to Work


Why Research Networking?

Translational research is a team sport

1. Investigators don’t know of potential collaborators in their institutions to improve research

2. Investigators don’t know of complementary investigators or opportunities to make their projects more competitive.

3. Investigators don’t know of partners to cross translational boundaries.

4. Investigators don’t know of non-research partners (industry, public sector, public) needed for trials recruitment, implementation, or commercialization


What is Research Networking?

1. An approach that strives to help overcome barriers by connecting people to undertake translational research

2. Institutional repositories to manage rich faculty profiles of grants, publications, classes, etc. and expose them publicly.

3. An information model based on individuals and cohorts.

4. A national federated architecture of Linked Open Data that can connect these repositories.

5. Applications that consume these profile data to accomplish translational goals


NIH Investments in Research Networking

VIVO – a project to develop an ontology and architectural standards to create, manage, and share rich faculty profile information.

Eagle-I – a project to develop an ontology and architectural standards to create, manage, and share rich resource profile information

CTSAConnect – a project to create an integrated ontology to connect faculty, resource, and other data

It’s all about Linked Open Data…


What are Big Challenge Use Cases in Translational Research?

1. Finding Funding

2. Recruiting Volunteers for Clinical Trials

3. Creating Translational Teams/Processes

4. Education and Training


How Does NIH measure Translational Success?

Logic Model from each CTSA, documented as XML files, exported to NIH annually.

Logic Model is: Activities – things that happen Outcomes – science that results from the things

that happen Impacts – what good comes of the science that

comes from the things that happen

This is what we’ll use today…


How can Research Networking HelpFind Funding?By matching investigators with funding opportunities

Activities – Community of Science and SciVal Funding commercial applications potentially provide better funding matches

Outcomes – unknown if they are any better than traditional means

Impacts – unclear if there is any differentiation


How can Research Networking HelpRecruiting Volunteers?

By matching researchers with community groups and volunteers

Activities – A few initial attempts to start developing VIVO-like profiles of community groups

Outcomes - None

Impacts - None


How can Research Networking HelpCreating Translational Teams?

With Applications that are used to discover complimentary and next step collaborators

Activities – Many faculty profile systems developed and implemented and one national pilot, direct2experts.org, has been launched

Outcomes – some CTSAs show increased activity among groups that have not collaborated before

Impacts – some new teams and multi-team systems have begun to form. Unclear of link to profile systems.


Direct2Experts

• 44 Institutions (at present).

• Returns summary numbers by institution.

• Finding individuals is a manual institution-by-institution basis.


CTSAConnect (ctsaconnect.org)

A semantic framework that will facilitate the production and consumption of Linked Open Data about investigators, physicians, biomedical research resources, services, and clinical activities. Use cases:

Team Formation Cross-Institutional Collaboration Evaluation and Reporting

But… Where are the applications?


How can Research Networking HelpEducation and Training?

Mentor matching and MD – PhD awareness

Activities - mentoring has been happening and translational education programs have sprung up without these systems

Outcomes – no use of Research Networking

Impacts – none.


Other BI Roles for Research Networking?

Investigators Automated CV generation, particularly for

center grants Research topic trend analysis

Administrators Competitive landscape review Productivity assessments for tenure, etc.

Research Network Science and Science of Team Science


Rich profiles could help find collaborators, subjects, resources, mentors, and funding and pursue other, undetermined, great things.

There are business models that can help sustain an institutional strategy

Challenges: applications that deliver value, policy, data quality, and momentum.

It is early yet, and this has so far been a technology looking for a problem to solve.


Thank you!

Bill [email protected]

Thanks to Dave Eichmann for reviewing an early version of this presentation!

mailto:[email protected]

Libraries and Business Intelligence:Learning That Transforms How We Support Scholarship

Mike Winkler | Joe ZuccaUniversity of Pennsylvania Libraries


Prologue


Near-constant innovation is the only way to respond

success-

fully to near-constant disruption. …the key

requirement for institutional success is to move from

scalable efficiency to scalable learning. Said

differently, the rate of learning, innovation, and

performance improvement within the institution must

match (or exceed) that of the surrounding

environment if the institution is to survive (or thrive).

Now we have a new infrastructure, a digital infrastructure creating near-constant disruption. By freeing people to interact and collaborate with others outside of traditional hierarchical organizations, by reducing information asymmetries between producers of goods and services and those who buy them, by democratizing control over communications and media--in these and other ways our digital infrastructure is granting new autonomy and freedom to individuals, both as consumers and as employees.

HBR Blog Network / John Hagel III and John Seely Brown The New Organization Model: Learning at Scale


The Task: Learning


Deepen our understanding of the community and… [how users work, what they want, need, expect, and what’s expected of them]

…Deepen our understanding of the library’s role within it.

Heighten the observational power of our organizations and with that, the capacity of those organizations to learn.


The Challenge: Our Service Complexity


What products and services does your library offer or directly support for the university?


Acquisition and licensing services Access to massive e-content Discovery service Supply chain services [local circulation & third party - ILL] Inventory control Research | Instructional services Courseware service (e.g., Blackboard) Learning environments (e.g. MOOCs) Author service Copyright compliance service Technology support services (e.g., desktops, digital media,

scanning) Digitization services (image production, description, curation) Metadata service Data services Repository services (internal and public-facing digital

archives) Research intelligence services (e.g., VIVO)


SFX

SOLRlog

ILLIAD

VIVO

Blackboard

BePressMicroServices Repo

SIPX (©)

Relais

Banner IDM

COUNTERGoogle Analytics

ARES

HR db

STAFF

SpringShare

AEON

ILSAPACHE

log

EZPROXYlog

AnalyticsRepository


The B.I. Framework


AnalyticsRepository

Extract Trnsfrm Load

Data Mart AnalysisEnterprise Products &Services

Resolvers


It Starts With the Event


xxx.xx.xxx.xxx|-|zucca|[26/Jul/2007:15:41:01 -0500]| GET https://proxy.library.upenn.edu:443/login?proxySessionID=10335905&url=http://www.csa.com/htbin/dbrng.cgi?username=upenn3&access=upenn34&cat=psycinfo&adv=1 HTTP/1.1| 302|0|http://www.library.upenn.edu/cgibin/res/sr.cgi?community=59| Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en) AppleWebKit/418.9.1 (KHTML, like Gecko) Safari/419.3| NGpmb6dT6JXswQH __utmc=94565761;ezproxy=NGpmb6dT6JXswQH; hp=/;proxySessionID=10335514; __utmc=247612227; __utmz=247612227.1184251774.1.1.utmccn=(direct)|utmcsr=(direct)|utmcmd=(none);UPennLibrary=AAAAAUaWP5oAACa4AwOOAg==; sfx_session_id=s6A37A3E0-3B8E-11DC-80E985076F88F67F


User & Program

Parameters

College | Dept

Rank

Course

Host College

Host Dept

Instructor

Grant Spnsr

Library Parameters

Srvice Genre

Cognzt Staff

Orgn’l Unit

Budget cntr

Environmental Parameters

Bibliographic Parameters

Title

URI

Format

Cost| Supplr

Date | Time

Location

IP Address

URL

EVENT

An Event Abstracted


a framework for:

Extracting event data from systems

Transforming those data into readable, normalized formats

Loading transformed/normalized payload into a repository

Supporting analysis through local and collaborative dissemination channels.


Strategic Priorities: Infrastructure | Collaboration


It’s NOT about

Software, IT Systems, ProjectsProducts: Commercial, Open Source, Home-grown, Assessment Plans, or Culture…

…it IS about strategic infrastructure

AnalyticsRepository

Services Analytics Organizational Knowledge


“Colleges and universities have long competed

against one another, measuring themselves in

comparison to each other and holding tightly to

their idiosyncrasies as defining elements of their

status. But today, the distribution and reuse of

information digitally via the Internet is rapidly

changing the game, rewarding those who instead

aggregate and scale toward a common

infrastructure. It is becoming increasingly clear

that neither the challenges that confront colleges

and universities nor the solutions to those

challenges are unique to each institution.”Chuck Henry and Brad Wheeler“The game has changed”, Educause Review, March 2012

Cited in Lorcan Dempsey's "The Research Library: Scalable Efficiency and Scalable Learning”, Oct.5, 2012


EpilogueOpportunity? | Transformation?


BI as a strategic priority can transform how libraries do business.Take that a stage higher: Can it transform our role in the university?Do libraries have a role in supporting B.I. infrastructure at the enterprise level? Think, data integration for research and learning analytics? Developing ontologies? Providing software support?


Joe Zucca, Director for Planning and Organizational [email protected]

Michael Winkler, Director for Information Technology and Digital [email protected]


Questions?

Online – Tweet them to us: #EDU12 #busintel

Or write them into our engage page http://bit.ly/RHg0C8 (open googledoc)




Abstracts 4 key functions, exposes interfaces for interoperability

Target Source, e.g. Relais, Illiad, ILS

Ingest Log

Parse

Format

Refined output

1. Extract

Resolution Sources e.g. IdM, WorldCat

Refined output

Resolve Codes & IDs

Normalize

2. Transform

Query Srvc

Data Repo

3. Load

MetriDoc Tiered Architecture

User Interface

LocalData Stores

Results Document

Query Document

4. Query