• Why are we here
• Research computing in developmental research
• Where are we now
• Where do we want to go
• Preview of strategic plan elements

• Open Data & Developmental Science (ODDS) initiative
  • R Bootcamps, 2017, 2018, 2019 (with M. Hallquist)
  • Grad course(s) on reproducible research practices
  • Policies and best practices (e.g., Gilmore, Cole, et. al., in press, Child Development Perspectives; SRCD Policy)
  • Databrary.org; Play & Learning Across a Year (PLAY) project
  • Local hosting of ABCD database (M. Hallquist)

• Data science initiatives
  • Institute for Computational & Data Science (ICDS)
  • Center for Social Data Analytics (SoDA)
  • Data Science Community meetings

• New faces at Old Main
  • New Senior VP for Reseach (Lora Weiss)
  • New Associate CIO for Research Computing (Greg Madden)
  • National search for new CIO/VP IT
• Faculty governance of research computing
  • Research Computing & Cyberinfrastructure (RCCI) groups
• Research computing services web site
• University-wide strategic plan update this spring

• How we gather data
  • Instruments (scanners, EEG, physio, eye tracking, smartphones/tablets)
  • Digital video/audio
  • Computer-based tasks, surveys
• Where/how we store data
• Where/how we analyze data
• How we protect data
• Where/how/when/with whom we share data & materials

“How can our faculty and students be more productive?”

“What resources (people, technologies, and expertise) will enable Penn State’s developmental community to expand its impact and reach?”

“What barriers (policies, technologies, and expertise) limit or slow progress?”

"How do emerging trends around sharing, transparency, and openness affect our work?

• What are we confident we know? (known-knowns)
• What are we confident we don’t know? (known-unknowns)
• What might we have overlooked, neglected, or forgotten? (unknown-knowns)

…for you with research computing…

• How we gather data
• Where/how we store data
• Where/how we analyze data

• Where/how/when/with whom we share data (and materials)

• technologies, expertise, policies

• What aren’t we doing but could be?
• What must we continue? What might be streamlined or phased-out?
• What are the highest priority/highest impact areas where we need to improve or build capacity?

• We will ensure that researchers have access to national-caliber advanced computational services for traditional high performance computing and high throughput computing workloads.

• We will ensure that researchers working with emerging technologies (e.g. artificial intelligence, machine learning, immersive technologies, etc.) have access to the tools and expertise they need to accomplish their research.

• We will ensure that researchers have access to resources and expertise to accomplish their goals whether they are working on local-scale computational clusters, university-scale computational clusters, national-scale computational clusters, or cloud-native computational environments.

• We will ensure that researchers have access to modern software for administering their research.

• We will ensure that researchers have access to appropriate resources for providing public access to their research, whether through website tools or university-scale analytical tools.

• We will ensure that researchers have the tools to collaborate effectively across the university, the nation, and the world.

• We will partner with the research-data-related offices across the university to put in place a holistic approach to managing research data from data acquisition through data archiving or disposal.

• Through a combination of network technologies and data transfer applications, we will ensure that large datasets can be efficiently moved around the university, and to partners elsewhere, as needed.

• We will partner with an appropriate subset of the research-data-related offices across the university to ensure that the university can properly secure any research data subject to access restrictions.

• We will partner with an appropriate subset of the research-data-related offices across the university to ensure that the university can properly comply with all public access to data requirements.

This talk was produced on 2020-02-25 in RStudio using R Markdown. The code and materials used to generate the slides may be found at https://github.com/psu-psychology/open-data-and-developmental-science-ODDS/. Information about the R Session that produced the code is as follows:

## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
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## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## attached base packages:
## [1] stats     graphics  grDevices utils     datasets 
## [6] methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] compiler_3.6.2  magrittr_1.5    htmltools_0.4.0
##  [4] tools_3.6.2     yaml_2.2.1      Rcpp_1.0.3     
##  [7] stringi_1.4.6   rmarkdown_2.1   knitr_1.28     
## [10] stringr_1.4.0   digest_0.6.25   xfun_0.12      
## [13] packrat_0.5.0   rlang_0.4.4     evaluate_0.14