Funding Software in Academia

Daniel S. Katzdkatz@nsf.gov & d.katz@ieee.org

@danielskatz

Program Director, Division of

Advanced Cyberinfrastructure

(http://www.slideshare.net/danielskatz/

funding-softwareinadademia

Academia Town Hall at UW, Seattle, WA, 2 Feb 2015

SoftwareScience

Software

Computing Infrastructure

• Software (including services)

essential for the bulk of science- About half the papers in recent issues

of Science were software-intensive

- Research becoming dependent upon

advances in software

- Wide range of software types: system,

applications, modeling, gateways, analysis,

algorithms, middleware, libraries

- Significant software-intensive project across NSF: e.g.

NEON, OOI, NEES, NCN, iPlant, etc

• Software is not a one-time effort, it must be

sustained• Development, production, and maintenance are people

intensive

• Software life-times are long vs hardware

• Software has under-appreciated value

Research Software vs

Infrastructure Software

• Some software is intended for

research

– Funded by many parts of NSF,

sometimes explicitly, often implicitly

– Intended for use by developer

• Other software is intended as

infrastructure

– Funded by many parts of NSF, often

ACI, almost always explicitly

– Intended for use by community

Research Software Challenges

• Things are mostly good

• Challenges

– “Software Engineering” skills for

developers

– Reproducibility

– Inheritance

• All of these are also important for

infrastructure software, plus...

Software as Infrastructure

• Cyberinfrastructure Framework for 21st Century Science and

Engineering (CIF21)

– Cross-NSF portfolio of activities to provide integrated cyber

resources that will enable new multidisciplinary research

opportunities in all science and engineering fields by

leveraging ongoing investments and using common

approaches and components (http://www.nsf.gov/cif21)

• ACCI task force reports (http://www.nsf.gov/od/oci/taskforces/index.jsp)

– Campus Bridging, Cyberlearning & Workforce

Development, Data & Visualization, Grand Challenges,

HPC, Software for Science & Engineering

• Software Vision and Strategy Report

– http://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf12113

• Implementation of Software Vision

– http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504817

See http://bit.ly/sw-ci for current projects

SI2: 5 rounds of

funding, 65 SSEs

SI2: 4 rounds of

funding, 35 SSIs

SI2: 2 rounds of

funding, 14 S2I2

conceptualizations

NSF Software Infrastructure Projects &Software Infrastructure for Sustained Innovation (SI2)

SSE & SSI – NSF 14-520: Cross-NSF, all Directorates participating

Next SSEs due today; Next SSIs due June 2015

NSF Software as Infrastructure Challenges

• In these programs, ACI works with other NSF

units to support projects that lead to software

as an element of infrastructure

• Issue: amount of software that is

infrastructure grows over time, and grows

faster than NSF funding

Q: How can NSF ensure that software as

infrastructure continues to appear, without

funding all of it?

A: Incentives

To judge software, need to

understand/forecast impact

Other Software Challenges

• Working Towards Sustainable Software for

Science: Practice and Experience

(WSSSPE)

– http://wssspe.researchcomputing.org.uk

– 3 workshops held

• Lessons:Many of the issues in developing

sustainable software are social, not

technicalSoftware work is inadequately visible in

ways that “count” within the reputation

system underlying science

Challenges & Hypothesis

• To judge software, need to understand/forecast impact

• Q: How can NSF ensure that software as infrastructure

continues to appear, without funding all of it?

• A: Incentives

• Many of the issues in developing sustainable software are

social, not technical

• Software work is inadequately visible in ways that “count”

within the reputation system underlying science

Hypothesis: better measurement of

contributions can lead to rewards

(incentives), leading to career paths,

willingness to join communities, leading to

more sustainable software

Consequences & Discussion

• Metrics – How to measure software contributions,

particularly in academic system?

– Not just authors by order, but for all contributors

– Need institutional buy-in, e.g., researcher profiles

– Publisher involvement is essential

• Career paths – Is there a role for non-tenure-track

researchers who produce software, data, etc. in

universities?

– Assuming yes, do universities recognize and support this? If

not, how to get them to?

• Reproducibility and related requirements

• Tools, including provenance systems

• Lots of players, e.g. NSF, NIH, DOE, JISC, RCUK, Sloan &

Moore, Wellcome, universities, Mozilla, Apache, Zenodo,

GitHub, publishers, DataCite, CrossRef, VIVO, ...