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Data Science and What It Means to Library and Information Science - - PowerPoint PPT Presentation

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Data Science and What It Means to Library and Information Science Jian Qin School of Information Studies Syracuse University iSpeaker Series at Sungkyunkwan University Seoul, Korea, December 8, 2015 2 12/8/2015 iSpeaker Series at

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Data Science and What It Means to Library and Information Science

Jian Qin School of Information Studies Syracuse University

iSpeaker Series at Sungkyunkwan University Seoul, Korea, December 8, 2015

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Agenda

  • What is data science?
  • What is a data scientist?
  • What areas of library work can benefit from data

science?

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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  • What is data science?

“An emerging area of work concerned with the collection, presentation, analysis, visualization, management, and preservation of large collections

  • f information.”

Stanton, J. (2012). Introduction to Data Science. http://ischool.syr.edu/media/documents/2012/3/DataScienc eBook1_1.pdf

The whole lifecycle of data from collection to analysis to preservation

LCAS DM workshop, Beijing, 2015 12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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“We’re increasingly finding data in the wild, and data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to

  • thers.”

Loukides, M. (2011). What is data science? Sebastopol, CA: O’Reilly.

What is data science?

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Gathering and massaging data to tell its story

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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A systematic enterprise that builds and

  • rganizes knowledge in the form of

testable explanations and predictions. The study of the generalizable extraction of knowledge from data, which involves data and statistics or the systematic study of the organization, properties, and analysis of data and its role in inference, including our confidence in the inference.

Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12): 64-73.

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Why is data science different from statistics and other existing disciplines?

  • Raw material, the “data” part of data science, is

increasingly heterogeneous and unstructured and often emanating from networks with complex relationships between the entities.

  • Analysis of data requires integration, interpretation, and

sense making that is increasingly derived through tools from computer science, linguistics, econometrics, sociology, and other disciplines.

  • Data are increasingly generated by computer and for

computer consumption, that is, computers increasingly do background work for each other and make decisions automatically

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12): 64-73, p. 64.

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Main fields in data science

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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What is a data scientist?

  • Math skills: Statistics and linear algebra
  • Computing skills: programming and infrastructure design
  • Able to communicate: ability to create narratives around

their work

  • Ask the right questions: involves domain knowledge and

expertise, coupled with a keen ability to see the problem, see the available data, and match up the two.

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Analysis of data problems: Story 1

  • Domain: Global migration studies
  • What’s involved: migrants, refuges, detention centers, refuge

camps, Asylums, …

  • Data types: interview audio recordings, photos, articles, clippings,

written notes, …

  • Analysis software: Atlas.ti, SPSS
  • Bottleneck problem:
  • difficulty in finding the data by person, interview, and related artifacts and in

transforming the data into analysis software

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

We’ve got a problem

Researcher: How to use Atlas.ti?

Data scientist: What data do you have? Data scientist: How do you collect them? Data scientist: What do you do with the data?

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Analysis of data problems: story 2

  • Domain: Thermochronology and tectonics
  • Data types: Excel data files (lots of them), spectrum and microscopic images,

annotations

  • Analysis: modeling by combining data from multiple data files with specialized

software

  • Bottleneck problem:
  • manually matching/merging/filtering data is extremely cumbersome and the problem is

compounded by the difficulty finding the right data files

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

What is involved: workflows in a research lifecycle

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Analysis of data problem: story 3

  • Domain: collaboration networks in a data repository
  • What’s involved: metadata describing DNA sequences
  • Data types: semi-structured data in plain text format
  • Analysis: identify entities and relationships, build the

data into a database for querying and extraction

  • Bottleneck problems:
  • Extremely large data sets with multiple entities, which makes

manual processing impossible

  • Disambiguation of author names and correctly linking between

entities

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Analysis of data problems

Analysis of domain data

Requirement analysis Workflow analysis Data modeling Data transformation needs analysis Data provenance needs analysis

Analysis of data problems is an analysis of domain data, requirements, and workflows that will lead to the development of solutions.

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Skills required to perform analysis of domain data problems

Requirement analysis Workflow analysis Data modeling Data transformation needs analysis Data provenance needs analysis

Interview skills, analysis and generalization skills Ability to capture components and sequences in workflows Ability to translate domain analysis into data models Ability to envision the data model within the larger system architecture

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Example 1: modeling research data for gravitational wave research

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  • 1. Understand research lifecycle
  • 2. Workflows: steps and relationships
  • 3. Data flows: what goes in and out at

which step

  • 4. Entities and attributes, relationships
  • 5. Researcher’s practice and habits in

documenting and managing data

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Example 2: asking the right question in mining metadata

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Metadata describing datasets is big data that can used to study:

  • Collaboration networks
  • Scholarly

communication patterns

  • Research frontiers and

trends

  • Knowledge transfer
  • Research impact

assessment

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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What areas of library work can benefit from data science?

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Data services and data-driven services

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Library Data services that support research, learning, and policy making (external) Data-driven services that support library planning, management, and evaluation (internal) Data literacy training Data discovery Data consulting Data mining Data collection Data integration

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Data-drive organization

  • Consumer internet companies
  • Google, Amazon, Facebook, LinkedIn
  • Brick-mortar companies:
  • Walmart, UPS, FedEx, GE
  • “A data-driven organization

acquires, processes, and leverage data in a timely fashion to create efficiencies, iterate on and develop new products, and navigate the competitive landscape...”

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Is your library (company, research center, etc.) a data- driven organization?

Patil, D.J. & Mason, H. (2015). Data Driven: Creating a Data

  • Culture. Sebastopol, CA: O’Reilly Media, p. 6.

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Data curation

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“the active and ongoing management of data through its life cycle of interest and usefulness to scholarship, science, and

  • education. Data curation activities enable

data discovery and retrieval, maintain its quality, add value, and provide for reuse

  • ver time, and this new field includes

authentication, archiving, management, preservation, retrieval, and representation.” –UIUC GSLIS

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Data collection

  • Build data collections through
  • Institutional repositories
  • Community repositories
  • Developing tools for researchers to submit,

manage, preserve, and discover data

  • Develop data collections
  • Specialized
  • Analysis-ready
  • Reusable
  • Actionable

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  • For library service planning, decision

making, and evaluation

  • To support policy making, research, and

learning

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Data discovery

  • Complex data landscape:
  • International, national, regional
  • Disciplinary, community
  • Open access vs. closed access
  • Data sources for various purposes:
  • Utility data sources: open, reusable
  • Census data: open, but need additional

processing/meshing to reach the analysis- ready state

  • Government data: open, reusable, but require

additional processing

  • Disciplinary research data: access varies,

require special knowledge to access and use

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Data involving human subjects are under strict control by law and often follow additional compliance

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Data consulting

  • Search, locate, and verify data for

particular research purposes

  • Plan, design, and implement data

curation and/or data analysis projects

  • Provide training and consulting for

statistical methods and tools

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Data mining

  • Using internal data:
  • Users, uses, expenses, collections, staff
  • Goal: improve efficiencies and service

quality

  • Using external data:
  • Trends and indicators in scholarly

communication, technology, economy, and culture

  • Goal: adjust current services and plan for

new services

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12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Data integration

Data integration is the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information.

  • -IBM, http://www.ibm.com/analytics/us/en/technology/data-

integration/

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A process of understanding, cleansing, monitoring, transforming, and delivering data, which offers opportunities to develop data products as an infrastructure for research, learning, policymaking, and decision making.

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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A home buyer’s information integration

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What houses for sale under $250K have at least 2 bathrooms, 2 bedrooms, a nearby school ranking in the upper third, in a neighborhood with below-average crime rate and diverse population? Information integration Realtor School rankings Crime rate Demographics

12/8/2015 iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Research data integration

Diabetes data and trends—Country level estimates:

http://apps.nccd.cdc.gov/D DT_STRS2/NationalDiabet esPrevalenceEstimates.aspx ?mode=PHY ;

Diabetes Data & Trends home page:

http://apps.nccd.cdc.gov/dd tstrs/default.aspx

12/8/2015

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iSpeaker Series at Sungkyunkwan University, Seoul, Korea

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Summary

  • Data science is not a new discipline, but rather, a new way of

utilizing data, methods, and tools to ask the right questions in solving problems.

  • Practicing data science requires strong skills in math,

computing, interpersonal communication, and asking the right questions

  • Libraries are at a strategic position in practicing data science.

How to leverage this position relies on the

  • vision
  • courage of risk taking
  • knowledge of data science and related topics
  • careful planning
  • collaboration

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Thank you! Questions?