Scientific endeavor at stake Society at large Scientists Highest priority groups to engage & benefit: Innovators

1. Scientific endeavor at stake Society at large Scientists Highest priority groups to engage & benefit: Innovators (Domain & computer sci) Early career Universities Professional societies Aligned institutions & initiatives Stakeholders

2. basic: how to cite software directories and files units and dimensional analysis hypothesis generation how to archive data computational literacy visual literacy units and dimension analysis data wrangling inter-disciplinary thinking data-enabled science skills collaboration skills fundamental computing architecture conceptual modeling pseudocode data management best practices On a continuum: annotation, metadata, documentation version control intellectual property knowledge (software licenses etc) basic command line programming data enabled science skills Knowledge and skills

3. On a continuum: annotation, metadata, documentation version control intellectual property knowledge (software licenses etc) basic command line programming data enabled science skills intermediate: data archiving basic command line programming data structures diversity of algorithms uncertainty analysis/model assumptions / error spatial analysis exploratory data analysis bug tracking open science standards and tools knowledge of public repositories workflows semantics, ontologies, and taxonomies, vocabularies software life cycle Knowledge and skills

4. On a continuum: annotation, metadata, documentation version control intellectual property knowledge (software licenses etc) basic command line programming data enabled science skills advanced: model interoperability numerical analysis parallelization - code, hardware numerical stability verification (code) cloud computing object-oriented design code that interacts with the web algorithms - code for big data (scalability) interoperability (package API) unit testing advanced metadata high performance computing software licensing for coders hardware knowledge scalability of computation Knowledge and skills

5. Basic skills: web resources – e.g. pages, course materials, videos, interactive hands-on, moocs traditional courses conferences boot camps NSF REU grants – ISEES is partner summer courses for K12 teachers and other “train the trainers” opportunities encouraging computer literacy or computational courses at universities Phased tutorials Modes of fostering knowledge and skills

6. Intermediate skills: self-guided learning certificate programs collaboration working groups speed funding for learners workshops undergrad or high school summer courses (similar to NCEAS summer course) Khan academy-like videos – popular with teachers distributed courses short courses at conferences Modes of fostering knowledge and skills

7. Advanced skills: Working groups hack-a-thons internships on sight (REU etc) visiting scholars mentorships remote internships mentoring and support groups for young programmers short courses at ISEES Modes of fostering knowledge and skills

8. Attention to partnerships Establishing metrics, evaluation & oversight Guidance from pedagogical literature (e.g. theory vs tools) Sociologists & historians: how did “scientific programmers” today get where they are, what were these paths? Modes of fostering knowledge and skills

9. Who does ISEES want to focus on skill development? Move the basic level of skill up across the board Strategically move forward groups & individuals at intermediate levels into advanced Modes of fostering knowledge and skills

10. Fostering multiple rotes to success Success stories Role models New models Career trajectories

11. Problem: Software suffers -> Science suffers Career trajectories

12. Jobs available tenure track soft money academic federal and research labs postdocs industrial research contractor small “business” owners (create software and make $ off of it) state and local agencies NGOs and NPs Career trajectories

13. Obstacles low pay stability status lack of opportunities to stay in science lack of knowledge or preparation for other trajectories sociocultural attitudes rare profile misaligned reward systems lack of specific training lack of confidence Career trajectories

14. Opportunities institutional reorganizations funding incentives new institutions (e.g., NEON) increased remote collaboration untapped opportunities in other fields (e.g., health, agriculture) scientific progress and discovery; societal importance more trainees available due to tech pervasiveness exciting new computing challenges and data availability industrial partnerships early in the science progress pipeline improved work conditions (e.g., pay) Career trajectories

15. Opportunities digital libraries connections & library transformation socially meaningful work synergistic and serendipitous opportunities increase awareness of the issue encourage institutions, committees to value these activities create hard money positions at institutions w/ appropriate evaluation developing out role models for these careers (esp underrepresented) Career trajectories