1. What is the National Data Service?
National consortium to bring together infrastructure supporting the publication, discovery, and reuse of data
Coordinating as a broad community of
data stakeholders
Large-scale Data Service Interoperability
Distributed cloud and compute
Innovation in the gaps: services, software, integration
Incubator of Data Projects & Pilots
Quick start sandbox
Coordination resources

2. Who We Are: NDS Consortium
Communities
Astronomy, Biology, Engineering, Geoscience, Information Science, Material Science, Medicine, Social Science
Universities, Libraries, Archives, and Publishers
CU Boulder, Harvard, Indiana, Johns Hopkins, Notre Dame, Purdue, UC San Diego, UIC, UIUC, U Michigan, ICPSR …
Nature, Science, APS, IEEE, PLOS, Elsevier, …
Computing and Data Centers/Cyberinfrastructure
ANL, NCSA, PSC, SDSC, TACC
Brown Dog, Data Excacell, DataONE, DFC, CyVerse, GABBs, IN-CORE, iRODS, Globus, SciServer, SEAD, Terra Populus, TERRA REF, Whole Tale, LSST, LIGO, ...

3. From the Internet to the DataNet
An analogy from the Research Data Alliance compares the current situation faced by scientists with data management tools to that of early Internet users, before standard protocols such as TCP/IP and HTTP or markup languages such as HTML were widely adopted. While Internet technologies vary widely, with many choices available for each component, this tends to not affect the user in terms of overall capabilities. A user can choose one of any number of browsers and still interoperate with other web technologies as they communicate via standard protocols and interfaces such as HTTP and HTML. This works so well, we take it for granted. Data management software is similar with archive software filling a role analogous to that of a web server and portals filling the role of browsers with a key distinction being the lack of standard protocols and interfaces.
The NDSC aims to foster the establishment of a broadly usable and sustainable data management pipeline and a National Data Service by directly addressing interoperability between developed data management components and pursuing the implementation of standardized interfaces between them.

4. Shifting to an infrastructure perspective
What are the greatest challenges we are facing in implementing solutions for research data management?
Shifting to an infrastructure perspective
from project/prototype emphasis
to sustained operations, integrated services, evolution of software, coordination of providers
Urban legends
Costs
Researchers won’t publish
More formal metadata is required
Reuse won’t happen
Good enough is already here…

5. How has your work in data management enabled scientific progress?
Each consortium member has their success stories, e.g.
National Data Service
NDS Labs
Hosting data apps and services for demo, testing, and classes
Dozens of publications, 4+ TB of data, research published in top journals
Data rescue, ongoing collection development, thesis research
Hydrology, biology, modeling, cyberinfrastructure, digital humanities,…
‘Viral’ use
Computation Over Data
TERRA-REF
DataDNS astronomy demonstrations of general mechanism

6. Coordination across stakeholders is critical
What key best practices has your organization discovered or enabled as you implement data management solutions?
Coordination across stakeholders is critical
Researchers, developers, libraries, resource providers, …
More value as an ecosystem
Universally Accessible Data Publications
Labs, DataDNS
Socio-technical solutions
PIDs critical to citation and credit
Services/hosting important in supporting ownership
Integrating viz/analysis/metadata extraction, instruments, curation, and publication adds value and drives adoption

7. More info: http://nationdataservice.net
Christine Kirpatrick NDS Executive Director
Jim Myers Technical Advisory Board Member
Join the NDS mail list, slack channel, …
Thanks to Christine and Kenton McHenry for discussion/slides for this presentation