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The following project assessment is from year 2 of 2i2c’s operation (08/2021 through 09/2022). It is copied from our previous report to CZI, as a historical record of the project’s progress.

Brief summary

In year 2 of 2i2c’s operations, we gradually scaled the communities that we served in order to test and refine our new service model. This helped us understand the bottlenecks that existed in our previous model, and the ways in which it did not match the communities that we work with. As a result we have come to a better understanding of the roles and collaborative model to use around this service, which we will define, implement, and scale in the final year of this grant.

Key outcomes

Strategic planning and capacity building for Jupyter in research and education

Below are several major accomplishments over the last year that grew our capacity to partner with research and education communities:

Facilitating communications and connections within the Jupyter developer community and its stakeholders in research/education

In addition to our strategic and capacity building efforts above, 2i2c’s Executive Director also led a number of key efforts at growing connections and capacity within the Jupyter ecosystem.

Enabling the execution of projects and collaborations for Jupyter in research and education

While most effort on this grant was spent on major strategic and capacity building efforts within 2i2c, we wish to highlight some of our operational accomplishments that required extra management effort.

Areas for growth

We identified a number of ways we must improve our service moving forward. Much of these were described in our annual strategic update blog post. Below is a brief summary:

  • Engineering roles are not enough to achieve the impact we wanted via our Managed JupyterHub Service. Communities also need support in using the infrastructure. This led to the creation of the Product and Community Lead role described above.
  • Our pricing and service offerings must be further-refined to reflect the diversity of communities we wish to serve. We must explore models that are inclusive to a global population, particularly those with fewer resources (we describe some of these challenges in our recent CZI proposal for communities in Latin America and Africa).
  • Billing and invoicing quickly became a bottleneck that limited the number of communities we can partner with at our current capacity (and cost recovery model). We are exploring ways to more sustainably invoice the communities that we work with - both for 2i2c and for their administrative teams.

Artifacts, publications, and software code

Below are links to public reports and blog posts that 2i2c produced over the past year. Many of these posts are linked above with more context as well.

In addition, 2i2c Executive Director co-authored several pre-prints and published articles that mentioned 2i2c’s model as a part of domain-specific visions for cloud-enabled collaborative interactive computing. Below are links to notable publications, blog posts, pre-prints, and presentations given by personnel funded on this grant.

Finally, these presentations were given on behalf of the Jupyter community at various workshops and events.

This is a brief update of major developments with 2i2c over the first nine months of its existence (and of this grant). Much of this information also exists in various places in the 2i2c Team Compass, which is an open resource about the 2i2c team, our major projects, and our organizational structure. For a high level view of our strategy and goals, see the Team Compass strategy page.

References
  1. 2i2c, Carpentries, T., Center For Scientific Collaboration And Community Engagement, Invest In Open Infrastructure, MetaDocencia, & Open Life Science. (2022). A Collaborative Interactive Computing Service Model for Global Communities. Zenodo. 10.5281/ZENODO.7025288
  2. DuPre, E., Holdgraf, C., Karakuzu, A., Tetrel, L., Bellec, P., Stikov, N., & Poline, J.-B. (2022). Beyond advertising: New infrastructures for publishing integrated research objects. PLOS Computational Biology, 18(1), e1009651. 10.1371/journal.pcbi.1009651
  3. Uchida, T., Le Sommer, J., Stern, C., Abernathey, R. P., Holdgraf, C., Albert, A., Brodeau, L., Chassignet, E. P., Xu, X., Gula, J., Roullet, G., Koldunov, N., Danilov, S., Wang, Q., Menemenlis, D., Bricaud, C., Arbic, B. K., Shriver, J. F., Qiao, F., … Wallcraft, A. (2022). Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models. Geoscientific Model Development, 15(14), 5829–5856. 10.5194/gmd-15-5829-2022
  4. Rokem, A., Dichter, B., Holdgraf, C., & Ghosh, S. S. (2021). Pan-neuro: interactive computing at scale with BRAIN datasets. 10.31219/osf.io/mwh2b
  5. Holdgraf, C. (2022). Open Infrastructure for Open Science. 10.5281/ZENODO.7233586