rdap 15: research data integration in the purdue libraries

Research Data Integration in the Purdue Libraries

Lisa Zilinski, Carnegie Mellon University Amy Barton, Purdue University Tao Zhang, Purdue University Line Pouchard, Purdue University Pete Pascuzzi, Purdue University

April 22, 2014 Minneapolis, MN

Data @ Purdue Libraries

The panel Amy Barton Metadata Specialist Tao Zhang, @jimmieego Digital User Experience Specialist Line Pouchard, @linepouchard ComputaKonal Science InformaKon Specialist Pete Pascuzzi Molecular Biosciences InformaKon Specialist Moderator Lisa Zilinski, @l_zilinski Carnegie Mellon University, Data Services Librarian

Other players Liaison Librarians Geographic InformaKon Systems (GIS) Specialist InformaKon Literacy Specialist Scholarly CommunicaKon Specialist Digital Data Repository Specialist Data Specialist

Village People 1979 Photograph: Lynn Goldsmith/Corbis

Today’s discussion •  How does metadata interface with data in the context of a data repository?

•  How do users perceive and experience data management tools and resources?

•  To what extent are issues in Big Data curaKon different from “small” data curaKon?

•  How can a subject-‐specialist incorporate data into liaison responsibiliKes?

METADATA DOCUMENT, INDEX, DISCOVER, ACCESS PURDUE UNIVERSITY LIBRARIES

Amy Barton Assistant Professor of Library Science, Metadata Specialist

OBJECTIVES WHAT I’D LIKE TO SHARE WITH YOU…

My role and responsibilities in the Purdue Libraries Two examples of metadata intersecting with Research Data @ Purdue Libraries Conceptual model representing how libraries and domain expertise interplay in a research project

METADATA SPECIALIST ROLE REPRESENTATION

Services

Research EducaKon

My Background •  B.A. in English & CommunicaKon, MLS Technology Track, Indiana University •  Entrepreneurial posiKon, Metadata Specialist in the context of Research Data

Data

Research: •  Metadata development for a

data repository •  Metadata applicaKon in

research projects Services: •  Metadata consultaKon •  Metadata development EducaKon: •  Graduate student lectures,

educaKonal materials, workshops

METADATA SPECIALIST ENGAGEMENT

Participate in national and international discussions about the emerging and dynamic role of metadata in providing access to information resources. For example: •  DataCite Metadata Working Group •  Research Data Alliance Metadata Directory Working Group •  MetaArchive Metadata Working Group (Co-chair)

THE PURDUE UNIVERSITY RESEARCH REPOSITORY A BRIEF OVERVIEW:

The Purdue University Research Repository (PURR) is a research collaboration and data management solution for Purdue researchers and their collaborators.

Dedicated data repository


The Purdue University Research Repository (PURR) is a research collaboration and data management solution for Purdue researchers and their collaborators.

Data management plan support


•  Funded projects with PIs from Purdue - 100 GB - 10 years or life of grant •  Just trying things out, or don't need much space - 10 GB - 3 years

Project collaboraKon & project management


Publication of and access to datasets with unique Digital Object Identifier (DOI)

DataCite DOI

METADATA DEVELOPMENT METADATA STANDARDS FOR PURR

•  Metadata Encoding and Transmission Standard (METS) •  Wrapper

•  DCMI Metadata Terms (dcterms) •  Descriptive metadata •  User-contributed à Metadata consultation/assignment

•  Metadata Object Description Schema (MODS) •  Dataset producer contact information •  Access condition à embargoed or publically available

•  Preservation Metadata: Implementation Strategies (PREMIS) •  Preservation metadata - MetaArchive

IMPORTANCE OF GOOD METADATA IMPORTANCE OF GOOD DESCRIPTIVE METADATA

DATA DISCOVERABILITY, VALIDATION, REPLICATION & REUSE… PUBLICATION – DATA LINKING, CITATION & IMPACT FACTOR!

PRESERVATION & DISATER RECOVERY

METADATA IN THE CONTEXT OF RESEARCH HOW DISCIPLINARY & LIBRARY EXPERTISE INTERPLAY IN RESEARCH

Amnesty International Urgent Action Bulletin Project •  Digitization & digital collection development •  Research dataset development •  Research database development

“As a record of real-‐Kme informaKon about human rights violaKons and advocacy across the globe, the documents have an unusually strong public interest component.”

Ann Clark

CONTROLLED VOCABULARY COMBINED 3 CONTROLLED VOCABULARIES FOR CODING & DESCRIPTIVE METADATA

hep://www.huridocs.org/resource/micro-‐thesauri/ hep://www2.witness.org/vocab/topics/ hep://uhri.ohchr.org/search/guide

RESEARCH DATA NVIVO & CODING We are developing our data collaboraKon process in both a library

science and a social science context, which both enables qualitaKve use of the documents and also embeds theoreKcally and pracKcally informed coding that can later help the researcher build both numeric and text-‐based data using the digital collecKons.

METADATA-CENTRIC DEVELOPMENT OF MERGED MASTER METADATA TEMPLATE

Controlled Vocabulary

Nvivo ClassificaKons

DigiKzaKon Control Sheet

RESEARCH DATABASE SOLR DATABASE FOR FACETED SEARCHING

Components: •  ConceptualizaKon •  Data collecKon •  Data processing •  CollaboraKon •  Data product •  Data curaKon

CollecKon Funded Project

Scholarly CollaboraKon Cloud

Data Processing

Digital Programs

Data Management &

CuraKon ExperKse

Data Management

Metadata Development

Research Database

Dataset PublicaKon

Scholarly DisseminaKon

Digital CollecKon,

PreservaKon & CuraKon

A Draj Conceptual Model for Libraries ExperKse Conjoining with Domain ExperKse to Apply AcKve Research to Produce Research Data

Color code: Light blue = Research Channel (throughput) Dark blue = Domain experKse & library experKse Lavender = Library services Pink = The collaboraKon of domain experKse, library science

experKse, and library services…

Tao Zhang Digital User Experience Specialist Purdue University Libraries [email protected]

User Experience Design and Research for Data Services

•  M.S. in Human-Computer Interaction, Ph.D. in Human Factors •  User-centered design and research for Purdue Libraries website

MY BACKGROUND

Services

Research EducaKon

Data

Research: •  User research and analyKcs •  InformaKon architecture •  User experience of data tools Services: •  User experience design •  User evaluaKons

UX: All aspects of an end-user’s interaction with a system or service •  Task performance •  Usability •  Satisfaction •  Engagement UX Design: Make things work in ways that people enjoy •  Interface •  Workflow UX Research: •  Understand the user •  Evaluate systems with the user

USER EXPERIENCE = UX

CollecKon Funded Project

Scholarly CollaboraKon Cloud

Data Processing

Digital Programs

Metadata Development

Research Database

Dataset PublicaKon

Scholarly DisseminaKon

Digital CollecKon,

PreservaKon & CuraKon

UX & DATA SERVICES

Data CuraKon Profiles (DCP) Toolkit

Data Management Planning (DMP) Tool

Data Management &

CuraFon ExperFse

Data Management

INTEGRATING UX INTO DATA SERVICES

User study: Researchers’ workflow and data needs Assessment: Usability and user acceptance of data curation/management tools User-Centered Design: Inputs from stakeholders and users, use case planning, and iterative design

PROJECTS

Assessing the Data Curation Profiles (DCP) Toolkit Designing the interface of Data Management Planning (DMP) Tool

The DCP Toolkit Engaging researchers in discussion about data:

•  Interview protocol •  Capture information about dataset across lifecycle •  Explore how data are used and managed •  Identify data curation needs •  DCP profiles generated from interview

PreparaKon Interviews ConstrucKng DCP

ASSESSING DCP TOOLKIT

Source: Davis (1989) Perceived Usability

ASSESSMENT METHOD

Technology Acceptance Model

Survey measured: •  28 external variables •  Perceived usefulness and ease of use •  Intention to use •  Open-ended user feedback

RESULTS

Data analysis •  Likert ratings of external variables -> Exploratory Factor Analysis ->

Regression Analysis •  Qualitative analysis of open-ended responses

Factors

Applicability, Time, Complexity, Experience and Share, Training and Help, Extensibility, Interviewee Requirements

RESULTS

Perceived Usefulness affected by: + Applicability + Experience and Share + Training and Help

Perceived Ease of Use affected by:

+ Applicability - Complexity, Interviewee Requirements

Intention to Use affected by:

+ Applicability, Training and Help, Extensibility - Time, Complexity

RESULTS

Finding the right balance of UX and utility •  Time required for librarians and researchers •  Depth of information in DCP

Applicability

•  Adapting structure and format to contexts •  Making decisions based on results

Extending the DCPT

•  Compact, lite version •  Focus on particular data types and fields •  Community building based on DCPs

DMPTOOL 2

Everything the DMPTool does, plus … •  Plan co-ownership

•  Self-service administrative functions –  Create and edit DMP templates

–  Customize guidance and resource links

–  Maintain individual and institutional profiles •  Refactored UI

•  Optional plan review for lifecycle management

•  And more!

EXPANDED USER ROLES

Dashboard and privileges personalized to role

•  Owner •  Co-‐owner •  InsKtuKonal reviewer •  InsKtuKonal administrator

•  …

PROVIDE DMP DETAILS

Easy access to customized •  InstrucKons •  Suggested

answers •  InsKtuKonal

resources

PREVIEW DMP

Download DMP as •  PDF •  Plain text •  RTF

REVIEW DMPS

•  OpKonal or mandatory

•  Comments can be passed back and forth between

•  Owners •  Co-‐owners •  Reviewers

•  Important for administraKve oversight

UX DESIGN FOR DMPTOOL 2

Research •  Stakeholders: expand growth, streamline operation, achieve

sustainability •  Users: focus groups, feedback from broader user community

Plan •  Use cases for funding agencies and institution policies •  Prototypes evaluated by stakeholders and beta testers

Design •  Wireframes for layout and interaction •  Multiple iterations

Line Pouchard, PhD

Purdue Libraries, Research Data

RESEARCH DATA ACCESS AND PRESERVATION SUMMIT

04/222015

Issues in Big Data CuraFon

BIG DATA @ PURDUE LIBRARIES

Services

Research

EducaKon

Data

DEFINITIONS OF DATA CURATION

•  Data curation is a term used to indicate management activities required to maintain research data long-term such that it is available for reuse and preservation (Wikipedia)

•  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 over time, and this new field includes authentication, archiving, management, preservation, retrieval, and representation (GSLIS)

BIG DATA LIFECYCLE

Assure

ISSUES IN BIG DATA CURATION •  Storage •  Data preparation & clean up •  Quality •  Discoverability •  Selection for preservation •  Privacy and ethics •  Reproducibility

QUESTIONS INFORMING CURATION ACTIVITIES Plan Acquire Prepare

Volume What is an esKmate of volume & growth rate?

What is the most suited storage (databases, NoSQL, cloud)?

How do we prepare datasets for analysis? (remove blanks, duplicates, spliong columns, adding/removing headers)?

Variety Are the data sensiKve? What provisions are made to accommodate sensiKve data?

What are the data formats and steps needed to integrate them?

What transformaKons are needed to aggregate data? Do we need to create a pipeline?

Velocity Is bandwidth sufficient to accommodate input rates?

Will datasets be aggregated into series? Will metadata apply to individual datasets or to series?

What type of naming format is needed to keep track of incoming and derived datasets?

Veracity What are the data sources? What allows us to trust them?

Who collects the data? Do they have the tools and skills to ensure conKnuity?

Are the wrangling steps sufficiently documented to foster trust in the analysis?

QUESTIONS INFORMING CURATION ACTIVITIES Analyse Preserve Discover

Volume Are adequate compute power and analysis methods available?

Should raw data be preserved? What storage space is needed in the long-‐term?

What part of the data (derived, raw, sojware code) will be made accessible to searches?

Variety Are the various analyKcal methods compaKble with the different datasets?

Are there different legal consideraKons for each data source? Are there conflicts with privacy and confidenKality?

What search methods best suit this data – keyword-‐based, geo-‐spaKal searches, metadata-‐based, semanKc searches?

Velocity At what Kme point does the analyKcal feedback need to inform decisions?

When does data become obsolete?

What degree of search latency is tolerable?

Veracity What kind of access to scripts, sojware, and procedures is needed to ensure transparency and reproducibility?

What are the trade-‐offs if only derived products and no raw data are preserved?

Providing well-‐documented data in open access allows scruKny. How is veracity supported with sensiKve and private data?

COLLABORATIONS •  Collaborations on multi-disciplinary proposals and projects

•  Levels of collaboration

•  Developing customized Data Management Plans

•  Organizing your data

•  Describing your data

•  Sharing your data

•  Publishing your datasets

•  Preserving your data

•  Education on best practices

A BIG DATA PROJECT AT PURDUE

Dr. Yung-‐Hsiang Lu, PI

CURATION ISSUES IN CAM2 PROJECT •  Data access and re-use

-  policies of video streams and CCTV -  Sparse legal framework – except UK -  Few policies available

•  Data ownership •  Data storage •  Data organization

-  naming scheme -  metadata

•  Protect metadata storage – where the intellectual property lies

•  Data information literacy skills for Big Data

CONCLUSION •  Big Data looks very different than small data in

maintenance and storage •  Curation primarily focuses on different areas than small

data •  Planning from the beginning is crucial: without planning,

curation will fall short •  Collaborations are more important •  Facilitating access is where efforts need to focus, not

storing the data.

THE END

SOLUTION WHAT DID WE GET?

Approximately 2.25 PB of IBM GPFS Hardware provided by a pair of Data Direct Networks SFA12k arrays, one in each of MATH and FREH datacenters 160 Gb/sec to each datacenter 5x Dell R620 servers in each datacenter

MOLECULAR BIOSCIENCES INFORMATION SPECIALIST

Pete E. Pascuzzi Assistant Professor, Libraries Assistant Professor of Biochemistry (by courtesy)

BACKGROUND

•  B.A. in Biology and Chemistry •  Ph.D. in Biochemistry •  Postdoctoral training in

genomics and bioinformatics

•  Joined Purdue Libraries in 2013 as part of a cluster hire in Systems Biology

Services

Research Education

Data

OVERVIEW EDUCATION

•  Introduction to R and Bioconductor •  Data Management for the Life Sciences •  Embedded lectures •  Workshops •  Graduate student consultations

RESEARCH •  Disciplinary faculty research collaborations •  Libraries’ research

SERVICES

•  Data management planning •  Subject specialist for Purdue University Research Repository

EDUCATION RESEARCH DATA MANAGEMENT

DATA SHARING MANDATES DATA MANAGEMENT PLANS DATA ARCHIVING METADATA STANDARDS

EDUCATION INTRODUCTION TO R AND BIOCONDUCTOR


•  Introductory bioinformatics course •  R is a computer language for statistical computing and visualization •  Bioconductor is the R project that supports bioinformatics •  Eight-week summer course


Course Syllabus •  Basic R programming •  Select Bioconductor packages for bioinformatic analysis •  Manipulation of genome-scale annotation data •  Manipulation and searching genomic sequence data •  Visualization of genome-scale data •  Exploratory data analysis •  RNA-seq analysis


Hidden Agenda •  Data management •  Directory organization •  File naming •  Data discovery and acquisition (using metadata) •  Documentation of analysis (creating metadata) •  Reading and reformatting of genome-scale, tab-delimited text files •  Data and database structures •  Etc.

EDUCATION GRADUATE STUDENT CONSULTATIONS

Pros •  Ongoing collaborations with four research groups through graduate students •  Weekly meetings to assist students with data issues •  Serve on two graduate student thesis advisory committees •  Exposure to range of research questions (human cancer, eye development in fruit fly, RNA processing in yeast, tomato ripening, drug discovery, . . .) •  Informal means to gather information on researchers’ needs •  Avenue to collaborative research publications and grants •  Good PR for Libraries.

Cons •  Time consuming (~ 1 hour/week/student) •  Collaborations can become exploitive, i.e. student wants you to do the work! •  Discussions of data quality are uncomfortable •  Can require domain-specific training •  Graduate student thesis advisory committee meetings can be painful

RESEARCH DISCIPLINARY FACULTY RESEARCH COLLABORATIONS

Acquisition Reformatting Analysis Visualization

Training Proposal Development Consultation

Low High

Commitment

RESEARCH DISCIPLINARY FACULTY RESEARCH COLLABORATIONS

CONCLUSIONS

Contact information Amy Barton, [email protected] Tao Zhang, [email protected] Line Pouchard, [email protected] Pete Pascuzzi, [email protected] Lisa Zilinski, [email protected]

rdap 15: research data integration in the purdue libraries

Education

research data integration

mn data

metadata specialist

dedicated data repository

purdue researchers

purdue university tao

research collaboration

data management solution