Developing Data Services to Support eScience/eResearch

School of Information Studies Syracuse University

Developing Data Services to Support eScience/eResearch

2012 Priscilla M. Mayden Lecture eScience and the Evolution of Library Services

Jian Qin


http://eslib.ischool.syr.edu/

February 22, 2012


The morning ahead

Priscilla M. Mayden Lecture 2012, Utah 2

An environmental scan •  E-‐Science, cyberinfrastructure, and data •  What do all these have to do with me?

Case study: The gravitational wave research data management

Group work: Role play in developing data management initiatives


Overview of E-‐Science and Data

An environmental scan •  E-‐Science, cyberinfrastructure, and data •  What do all these have to do with me?

Characteristics of e-‐science Data sets, data collections, and data

repositories Why does it matter to libraries?


E-‐Science

“In the future, e-‐Science will refer to the large scale science that will increasingly be carried out through distributed global collaborations enabled by the Internet. ”

Priscilla M. Mayden Lecture 2012, Utah

National e-Science Center. (2008). Defining e-Science. http://www.nesc.ac.uk/nesc/define.html

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Characteris>cs of e-‐science •  Digital data driven •  Distributed •  Collaborative •  Trans-‐disciplinary •  Fuses pillars of science

–  Experiment –  Theory –  Model/simulation –  Observation/correlation


Greer, Chris. (2008). E-‐Science: Trends, Transformations & Responses. In: Reinventing Science Librarianship: Models for the Future, October 2008. http://www.arl.org/bm~doc/ff08greer.pps

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Shi? in Science Paradigms Thousand years ago

A few hundred years ago

A few decades ago

Today

Science was empirical

describing natural phenomena

Theoretical branch

using models, generalizations

A computational approach simulating complex

phenomena

Data exploration (eScience) unify theory, experiment, and

simulation -- Data captured by instruments or generated by simulator -- Processed by software -- Information/Knowledge stored in computer -- Scientist analyzes database/files using data management and statistics

Gray, J. & Szalay, A. (2007). eScience – A transformed scienti_ic method. http://research.microsoft.com/en-‐us/um/people/gray/talks/NRC-‐CSTB_eScience.ppt



X-‐Informa>cs •  The evolution of X-‐Informatics and Computational-‐X for each discipline X

•  How to codify and represent our knowledge

•  Data ingest •  Managing a petabyte •  Common schema •  How to organize it •  How to reorganize it •  How to share with others

•  Query and Visualization tools •  Building and executing models •  Integrating data and Literature •  Documenting experiments •  Curation and long-‐term preservation

The Generic Problems

Experiments & Instruments

Simulations

answers questions

Literature Other Archives facts

facts ?

Gray, J. & Szalay, A. (2007). eScience – A transformed scienti_ic method. http://research.microsoft.com/en-‐us/um/people/gray/talks/NRC-‐CSTB_eScience.ppt



Useful resources


http://research.microsoft.com/en-us/collaboration/fourthparadigm/

Part 2: Health and Wellbeing

•  The healthcare singularity and the age of semantic medicine

•  Healthcare delivery in developing countries: challenges and potential solutions

•  Discovering the wiring diagram of the brain

•  Toward a computational microscope for neurobiology

•  A unified modeling approach to data-intensive healthcare

•  Visualization in process algebra models of biological systems


FUNDAMENTALS OF DATA

What are data? What are some of the major data formats? Why data formats?




What are data? (1)

An artist’s conception (above) depicts fundamental NEON observatory instrumentation and systems as well as potential spatial organization of the environmental measurements made by these instruments and systems. http://www.nsf.gov/pubs/2007/nsf0728/nsf0728_4.pdf

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What are data? (2)



Medical and health data


http://www.weforum.org/issues/charter-health-data

Standardization Compliance Security


The mul>-‐dimensions of data


Data form

ats

Data types

Res

earc

h or

ient

atio

n

Levels of processing


Scien>fic data formats


Common data format Image formats Matrix formats

Microarray _ile formats Communication protocols


Scien>fic & medical data formats


•  Medical and Physiological Data Formats –  BDF — BioSemi data format (.bdf)

–  EDF — European data format (.edf)

•  Molecular Biology data Formats –  PDB — Protein Data Bank format (.pdb)

–  MMCIF — MMCIF 3D molecular model format (.cif)

•  Medical Imaging –  DICOM — DICOM annotated medical images (.dcm, .dic)

•  Chemical Formats –  XYZ — XYZ molecule geometry _ile (.xyz)

–  MOL — MDL MOL format (.mol) –  MOL2 — Tripos MOL2 format (.mol2) –  SDF — MDL SDF format (.sdf) –  SMILES — SMILES chemical format (.smi)

•  Bioinformatics Formats –  GenBank — NCBI GenBank sequence format (.gb, .gbk)

–  FASTA — bioinformatics sequence format (.fasta, .fa, .fsa, .mpfa)

–  NEXUS — NEXUS phylogenetic data format (.nex, .ndk)


Why data formats?


• Archiving – Preservation for posterity

•  Storage – Availability for “arbitrary” access

• Transmission – delivery across • hardware • software • administrative

– system boundaries • Analysis – availability for processing


Summary •  Scienti_ic data formats are closely tied to scienti_ic computing –  Data structure, model, and attributes –  Self-‐descriptive with header/metadata –  API for manipulating the data –  Interoperability: conversion between different formats

•  No one-‐format-‐_its-‐all standard •  Each standard has one or more tools for creating, editing, and annotating dataset



DATASETS, METADATA, AND DATA MANAGEMENT

What is a dataset? What are some of the metadata standards for describing datasets? What is data management?



Dataset classifica>on


Volume Large-‐volume Small-‐volume



Ecological data example: Instantaneous streamflow by watershed http://www.hubbardbrook.org/data/dataset.php?id=1



Diabetes data and trends—Country level estimates: http://apps.nccd.cdc.gov/DDT_STRS2/NationalDiabetesPrevalenceEstimates.aspx?mode=PHY ; Diabetes Data & Trends home page: http://apps.nccd.cdc.gov/ddtstrs/default.aspx



Clinical trials data management: http://www.clinicaltrials.gov/ct2/show/NCT00006286?term=TADS+NIMH&rank=1


Common in the examples

•  Attributes of a dataset tell users/managers: – What the dataset is about – How data was collected – To which project the data is related – Who were responsible for data collection – Who you may contact to obtain the data – What publications the data have generated –  ??




Metadata standards in medical & health sciences

Structure

Healthcare Medical images

Bioinfomatics

Semantics

HL7 DICOM

NCBI Taxonomy NCBO Bioportal

UMLS MeSH (Medical Subject

Headings) SNOMED CT (Systematized Nomenclature of Medicine--

Clinical Terms)

GenBank GenBank GenBank


Research data collec>ons


Size Metadata Management Standards

Larger, discipline-‐based

Smaller, team-‐based

None or random

Multiple, comprehensive

Heroic individual inside the

team

Organized Institutionalized,


Research collec>ons •  Limited processing or long-‐term management

•  Not conformed to any data standards

•  Varying sizes and formats of data _iles

•  Low level of processing, lack of plan for data products

•  Low awareness of metadata standards and data management issues



Resource collec>ons •  Authored by a

community of investigators, within a domain or science or engineering

•  Developed with community level standards

•  Life time is between mid-‐ and long-‐term



Reference collec>on •  Example: Global Biodiversity Information Facility

–  Created by large segments of science community –  Conform to robust, well-‐established and comprehensive standards, e.g. •  ABCD (Access to Biological Collection Data) •  Darwin Core •  DiGIR (Distributed Generic Information Retrieval) •  Dublin Core Metadata standard •  GGF (Global Grid Forum) •  Invasive Alien Species Pro_ile •  LSID (Life Sciences Identi_ier) •  OGC (Open Geospatial Consortium)



Datasets, data collec>ons, and data repositories

•  Data collections are built for larger segments of science and engineering

•  Datasets –  typically centered around an event or a study

–  contain a single _ile or multiple _iles in various formats

–  coupled with documentation about the background of data collection and processing

Data repository

System for storing, managing, preserving, and providing access to datasets

A repository may contain one or more data collections A data collection may contain one or more datasets A dataset may contain one or more data files 30 Priscilla M. Mayden Lecture 2012, Utah


Data management for science research •  De_inition from Wikipedia: http://en.wikipedia.org/wiki/Data_management

•  Key concepts in data management: –  Data ownership –  Data collection –  Data storage –  Data protection –  Data retention –  Data analysis –  Data sharing –  Data reporting


How do they relate to responsible conduct of research? http://ori.hhs.gov/images/ddblock/data.pdf


An aPempt to define DM •  In the context of libraries:

–  Data management is a process in which librarians plan, design, and implement data services to support eScience/eResearch.

–  Data services that libraries may provide: •  Institutional or community data repositories •  Data management plan for pre-‐ and post-‐award of grants •  Metadata creation, linking, and discovery •  Data archiving, preservation, and curation •  Consultation for research group’s data management projects •  Data management and data literacy training for graduate students and faculty



Ini>a>ves in research libraries

•  Pressure points: –  Lack of resources –  Dif_iculty acquiring the appropriate staff and expertise to provide eScience and data management or curation services

–  Lack of a unifying direction on campus


Data support and services in institutions:

45%

Libraries involved in supporting eScience:

73%

Source: Soehner, C., Steeves, C. & Ward, J. (2010). E-Science and data support services: A study of ARL member institution. http://www.arl.org/bm~doc/escience_report2010.pdf


Data preserva>on challenges •  Data formats

–  Vary in data types, e.g. vector and raster data types –  Format conversions, e.g. from an old version to a newer one

•  Data relations –  e.g. there are data models, annotations, classi_ication schemes, and symbolization _iles for a digital map

•  Semantic issues –  Naming datasets and attributes



Data access challenges •  Reliability •  Authenticity •  Leverage technology to make data access easier and more effective – Cross-‐database search –  Integration applications –  “Science-‐ready” datasets



Suppor>ng digital research data •  Lifecycle of research data

–  Create: data creation/capture/gathering from laboratory experiments, _ield work, surveys, devices, media, simulation output…

–  Edit: organize, annotate, clean, _ilter… –  Use/reuse: analyze, mine, model, derive additional data, visualize, input to instruments /computers

–  Publish: disseminate, create, portals /data. Databases, associate with literature

–  Preserve/destroy: store / preserve, store /replicate /preserve, store / ignore, destroy…



Suppor>ng data management


The data deluge Numerical, image, video Models, simulations, bit streams XML, CVS, DB, HTML

Specialized search engines to discover the data they need Powerful data mining tools to use and analyze the data

Researchers need:


Research data management


Institution

Financial and policy support

Community

User requirements

Science domain

Data content idiosyncrasies

Institutional repository

Community repository

National repository

International repository

Evolving and interconnecting –

eScience librarian

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Implica>ons to scholarly communica>on process


Publishing Curation Archiving

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Maintaining, preserving and adding value to digital research data throughout its lifecycle.

The long-‐term storage, retrieval, and use of scienti_ic data

and methods.

Data publishing; New scholarly publishing models—open access, institutional and

community repositories, self-‐publishing, library

publishing, ....


Summary •  E-‐Science development has raised expectations to research libraries – Working knowledge and skills in e-‐Science –  Focus on process (data and team science) rather than product (reference services)

– Proactive, collaborative, integrative, and interdisciplinary



Case Study: Learning Data Management

Needs from Scien>sts


Gravita>onal Wave (GW) Research



What is the problem? •  Tracking data output and work_lows is dif_icult due to lack of provenance data

•  Search of datasets is limited due to lack of speci_ic options

• Within the LIGO community, data sharing and reuse is dif_icult without provenance metadata

Data provenance case study 43


Understand the research workflow

•  Interview the scientist –  Listening (good listening skills) – Asking questions (don’t be afraid of asking questions)

– Use your librarian brain to ingest the conversation: • How does the research _low from one point to next? • What consists of the research input and output at each stage of research in terms of data?




Mapping out the knowledge v0.1

School of Information Studies Syracuse University Mapping out the knowledge v0.2




Mapping out the knowledge v1.0


Lessons learned •  Science is learnable even if you don’t have a subject background –  Learn enough to understand the research process and work_low

•  Scientists are eager to get help •  Librarians need to be technical-‐minded

–  Data, metadata, database –  Structures, models, work_lows

•  Librarians need to be good listeners while staying good conversation leaders –  Know when and how to lead the conversation to get what you need for data management planning and implementation

–  Do your homework on the subject so that you can be an intelligent listener



Case Discussions


Case Study #1: To build or not to build a data repository?


A university library has developed an institutional repository for preserving and providing access to the scholarly output by the researchers in this institution. Now the new challenge arises from e-science research demanding data management plan by the funding agency and the linking between publications and data by the authors and users. You already know that some faculty use their disciplinary data repository for submitting their datasets (e.g., GenBank for microbiology research data). The problem you face now is whether an institutional data repository should be built for those who do “small science” and don’t have funding nor expertise to manage their data. Questions to be addressed: •  What are the strategies you will use to approach the problem? •  What are the possible solutions for the problem? •  What are some of the tradeoffs for the solutions you will adopt?


Case study #2: Developing a data taxonomy


The concept of research data management is a stranger to many faculty as well as your library staff. What is data? What is a data set? These seemingly simple terms can be very confusing and have different interpretations in different context and disciplines. As part of the data management strategies, you decide to develop an authoritative data taxonomy for the campus research community. This data taxonomy will benefit the creation and use of institutional data policies, data repository or repositories, and data management plans required of funding agencies. Questions to be addressed: •  What should the data taxonomy include? •  What form should it take, a database-driven website or a static HTML page? •  Who should be the constituencies in this process? •  Who will be the maintainer once the taxonomy is released?


Case study #3: Developing a data policy


Data policies play an important role in governing how the data will be managed, shared, and accessed. It is also an instrument that will fend off potential legal problems. Data policies have several types: data access and use, data publishing, and data management. Your university’s Office of Sponsored Research has some existing policy on data, but it is neither systematic nor complete. Many of the terms were defined years ago and did not cover the new areas such as the embargo period of data. As the university has decided to build a data repository for managing and preserving datasets, a data policy has become one of the top priorities for both the institution and the data repository. Questions to be addressed: •  What should the data policy include? •  Who should be the constituencies in this process? •  Who will be the interpretation authority for the data policy?


Case study #4: Cataloging datasets


Describing datasets is the process of creating metadata for datasets. In scientific disciplines, several metadata standards have been developed, e.g., the Content Standard for Digital Geospatial Metadata (CSDGM), Darwin Core, and Ecological Metadata Language (EML). Each of these metadata standards contains hundreds of elements and requires both metadata and subject knowledge training in order to use them. Besides, creating one record using any of these standards will require a tremendous time investment. But you library does not have such specialized personnel nor have the fund to hire new persons for the job. The existing staff has some general metadata skills such as Dublin Core. In deciding the metadata schema for your data repository, you need to address these questions: •  Should I adopt a scientific metadata standard or develop one tailored to our need? •  How can I learn what metadata elements are critical to dataset submitters and searchers? •  What are some of the benefits and disadvantages for adopting a standard or developing a local schema?


Case study #5: Evalua>ng data repository tools


Research data as a driving force for e-science is inherently a tool-intensive field. Tools related to data management can be divided into two broad categories: those for creating metadata records and those for data repository management. An academic institution decided to build their own data repository as part of the supporting service for researchers to meet the data management plan requirement of funding agencies. This data repository development task was handed down to the library. You the library director have to decide whether to develop an in-house system or use an off-the-shelf software system. As usual, you put together a taskforce to find a solution to this challenge. The questions to be addressed by the taskforce include: •  What are the options available to us? •  What evaluation criteria are the most important to our goal? •  What are the limitations for us to adopt one option or the other? •  How will this option be interoperate with existing institutional repository system? Or, can the existing repository system used for data repository purposes?

Developing Data Services to Support eScience/eResearch

Education

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