baker - evolution of data products and designated audiences

A Mul&-‐Decade Case: The Evolu&on of Data Products and Designated Audiences

NISO 2016

Karen S. Baker Graduate School of Informa<on Sciences University of Illinois Urbana-‐Champaign

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The story traces the evolu<on of a set of data products, asking

•  How is knowledge mobilized? •  What are the data products? •  Who are the designated communi<es?

We present a three decade data story •  Karen Baker, Ruth Duerr, and Mark Parsons, •  Scien<fic Knowledge Mobiliza<on: Co-‐evolu<on of Data

Products and Designated Communi<es •  Interna<onal Journal of Digital Cura<on 10(2), 2015

A Story About Data Product Development

Note on coauthors: Ruth Duerr now at Ronin Ins<tute for Independent Scholarship Mark Parsons now Secretary General of the Research Data Alliance (RDA)

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Where the Story Takes Place: Na<onal Snow and Ice Data Center (NSIDC):

From Baker & Duerr, in press, Data & the Diversity of Repositories. In Cura<ng Research Data: A Handbook of Current Prac<ce

NSIDC

NSIDC

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A data product is data at a par<cular stage of processing that can be iden<fied uniquely and described.

Digital Data Products

Kinds of data products •  Ini<al recorded data •  Calibrated data •  Cleaned data •  Gridded/Interpolated data •  Interpreted data

•  Derived data •  Transformed data •  Synthesized data

Note: Data product development is influenced by the intended use of the product.

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Discussion Points

•  Data Product Descrip<on §  Collec<on of data products §  Data product teams

•  Data Product Development §  Mul<-‐level collec<on §  Mul<-‐cycle trajectory

•  Data Product Delivery §  Diverse audiences §  Mul<-‐mode communica<on

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Collec<on of Sea Ice Data Products

Redrawn circa 2010 from original work by Donna Scoa, who manages the NSIDC Passive Microwave Product Team.

Preliminary – gold box

Source – brown box Final – green hexagon

Near real-‐<me – blue oval Value added – red octagon

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NSIDC-‐0081 Near-‐Real-‐Time DMSP SSM/I Daily Polar Gridded Sea Ice

Concentra<ons

Remote Sensing Systems F17 Tbs (Wentz)

NSIDC-‐001 SSM/I Polar Gridded Tbs

NSIDC-‐0051 Preliminary Sea Ice Concentra<ons from

Nimbus-‐7 SSMR and DMSP SSM/I

NSIDC-‐0051 Sea Ice Concentra<ons from Nimbus-‐7 SSMR and DMSP

SSM/I

G02135 Sea Ice index

Arc<c Sea Ice News and Analysis

From the Sea Ice Data Products Collec<on

Preliminary – gold box

Source – brown box Final – green hexagon

Near real-‐<me – blue oval Value added – red octagon

Data Product Teams

Roles -‐ Skill Sets •  Data managers •  Programmers •  Technical writers •  Scien<sts •  Instrument engineers •  Science communicators •  Systems/Database managers •  User support specialists

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Data Product Team Intermediaries

Roles -‐ Skill Sets •  Data managers •  Programmers •  Technical writers •  Scien<sts •  Instrument engineers •  Science communicators •  Systems/Database managers •  User support specialists

“This ac<ve human element of data management is not always recognized by funding agencies, nor is it explicit in the OAIS Reference Model …” – Parsons and Duerr, 2005

Parsons, M. A., & Duerr, R. (2005). Designa<ng user communi<es for scien<fic data: challenges and solu<ons. Data Science Journal, 4, 31-‐38.

Intermediaries

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OAIS Reference Model

A Narra<ve Framework: Open Archive Informa<on System

OAIS Archive

Ingest Access

Archive

Data Mgmt

Administration

Producer

Preservation Planning

Consumer

MANAGEMENT

SIP

AIP AIP

DIP

Descriptive Information

Descriptive Information

Func4onal model

CCSDS. (2012). Consulta<ve Commiaee for Space Data Systems, Reference Model for an Open Archival Informa<on System (OAIS). Washington DC: CCSDS 650.0-‐M-‐2, Magenta Book. Issue 2. June 2012.

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Informa4on Package Concepts


Submission Informa<on Package

Preserva<on Informa<on Package

Dissemina<on Informa<on Package

SIP

PIP

DIP

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OAIS Archive Responsibili4es


• Nego<ate for and accept informa<on • Obtain sufficient control to ensure long-‐term preserva<on • Designate one or more communi<es as designated audience

who should be able to understand what is • Ensure that the informa<on is independently understandable to them • Follow documented procedures and policies for data preserva<on and access • Make the informa<on available with evidence suppor<ng its authen<city

haps://public.ccsds.org

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The Data Landscape: In Development

Data System

Informa<on System Data Repository Data Archive

Dataset Data set

Data Package Metadata

repositories web of

Data Data Element & Interconnec<ons

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Discussion Points

•  Data Product Descrip<on ü  Collec<on of data products ü  Data product teams

•  Data Product Development §  Mul<-‐level collec<on §  Mul<-‐cycle trajectory


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Sea Ice Data Products: Dependencies & Levels

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Levels of Data Products

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Con<nuing Development of Data Products

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Figure 2. A simplified view of the con<nuing development of scien<fic data products. Each cycle is ini<ated by one or more events that create a new audience that leads to genera<on of a new data product in response to the needs of a recently iden<fied designated user community.

Data Products: Mul<-‐cycle Trajectory

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Discussion Points

•  Data Product Descrip<on ü  Collec<on of data products ü  Data product teams

•  Data Product Development ü  Mul<-‐level collec<on ü  Mul<-‐cycle trajectory


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To a remote sensing community, the world is: •  Large-‐scale earth coverage using well-‐defined plaoorms •  A series of images with gridded pixels that can be manipulated

computa<onally

To ecologists, the world is: •  A set of observa<ons/measurements captured as parameters such as

temperature and popula<on counts •  A system of interac<ng systems with dependencies among the

parameters that vary con<nuously

To the public, the world is: •  The place within which their neighborhood resides •  A place where decision-‐making is increasing in complexity due to the

interdependencies of natural systems and human systems

* following Mark Parsons, Ben Domenico, and Stefano Na<vi

Who is the audience? What is their worldview?

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Greenland Ice Sheet Melt Data Products

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Knowledge Mobilized via Data Product Genera<on

1. Data workforce and data work are changing •  Data product descrip<on

ü  Collec<on of data products ü  Data product teams

2. Data products gain value curated as a con<nuing collec<on •  Data product development

ü  Mul<-‐level collec<on ü  Mul<-‐cycle trajectory

3. Data product delivery takes many forms •  Data product delivery

ü  Diverse audiences ü  Mul<-‐mode communica<on

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Developing the Workforce for Data

NRC (2015). Preparing the Workforce for Digital Cura<on: Commiaee on Future Career Opportuni<es and Educa<onal Requirements for Digital Cura<on; Board on Research Data and Informa<on; Policy and Global Affairs.

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Developing Workforce for Data Work

Making the time to tell the story

… to multiple audiences

… in multiple formats

… with multiple intermediaries

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Karen Baker [email protected]

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Karen Baker [email protected]

Acknowledgement: Data Cura<on Educa<on in Research Centers (DCERC) project, funded by the Ins<tute of Museum and Library Services (RE-‐02-‐10-‐0004-‐10), co-‐led by Carole Palmer. Par<cipants at the Na<onal Snow and Ice Data Center including Donna Scoa who manages the NSIDC Passive Microwave Product Team.

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baker - evolution of data products and designated audiences

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