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Fall 2021 In Review: A Budding Grants Commons Community

A rundown of the work that was completed in Fall 2021 from September 2021 to December 2021

Published onJan 10, 2022
Fall 2021 In Review: A Budding Grants Commons Community

Introduction: Getting a Grants Commons and its components started

With the new year quickly approaching, we would like to take the time to reflect on all of the work we have done this fall, specifically. We have come a long way in building a common dataset, working on informative visualizations, and spreading the word about our efforts.

The fall was spent in three main areas of activity:

1) Outreach - connecting with community partners and organizations that have similar goals to an open grant commons

2) Data curation - building trust networks with those organizations and finding new ways to curate the data on a knowledge graph that is informative to our community

3) Visualizations - leveraging this curated data into useful visualizations that our partners can use for their recruiting and retention efforts and building internal tools that help make the grants and proposals space more efficient.

Through these avenues, we hope to build out our concept of the data commons, complete with its two component modules: 1) the data that supports independent work as well as a medium to do important predictive machine learning work in the future and 2) a visualizations atlas that captures important and informative visuals for non-profit, philanthropic organizations and people working there.

Outreach: Welcoming MIT Solve and Lever for Change, supporting work at the University of Michigan School of Medicine

Our main partners, who have been so graciously sharing their data to initialize our community, are MIT Solve and MacArthur Foundation’s Lever for Change.

Each partner has expressed interest in what our open grants commons modules are doing, and the tasks that we are working on with these organizations can be seen in the Biographies section (to get an idea of our teammates) and the current tasks kanban. We expect more Pubs to be written as more research is done around the more advanced topics that are being implemented.

In addition, we have been working as a support system for post-doctoral students at the University of Michigan School of Medicine who are interested in leveraging machine learning techniques in projects focused on diversity, equity, and inclusion in biomedical research. This group meets regularly every month and is affiliated with both Map of Science and Caleb Smith.

Data Curation: Creating Philanthrobotics and all the Python Plumbing

We will call our project Philanthrobotics and Philanthrolytics (introduced in the following pubs)

Philanthrobotics is mainly seen as the predictive machine learning side of our operation — is a collection of coding tools for the community but also a knowledge graph that is constructed with the data provided by MIT Solve and Lever for change.

We hope that it will be a shared repository of tools like data pipelines, helper scripts, and codebases that can be used and reused to help community members derive their own exciting insights from our data. In addition, we would like to enable our partners to release independent projects that are open source that help others out as well.

An update to enabling independent projects (Winter 2021): our collaborators at Lever for Change (LFC) have released an open source UMAP repository to map some of Philanthrobotics data.

An update to curation of the data (Spring 2022): A Neo4j instance of the data is available via the Philanthrobotics page. More on the views of this data in this publication. To access the graph, please enter the credentials of username: neo4j and password: 8W5_P719rG0GbVjEYw0JNIJM2HviyLGM6pFQEcmHNYI

A universe of grants and proposals in Neo4j can be seen below (over 3300 nodes spanning 5+ years of data!), and a publication on the views can be accessed here:

Visualizations: Philanthrolytics and Key Areas of Grants Data

The other main pillar of Philanthrobotics is Philanthrolytics, our data analytics platform that allows curious users to look at interesting coverage of grants and contests across the globe in many key areas, but we have formulated some of the most important that we hope to focus on:

  • Climate Change

  • Mental Health

  • Refugee Status/Economic Development

Working with Lever for Change and MIT Solve, we hope to curate interesting catalogs of views for our community.

Update (Spring 2022): A short catalog of views has been published on the Philanthrolytics page. It shows an MIT Solve dashboard of climate change-related data (some data redacted via NDA), and a collection of views that combine LFC and Solve data, as well as data from the United Nations and other economic data.

Update (Summer 2022): A sample visualization made to-date via DataWrapper

We have also inherited the project Scaling Science, and we plan to incorporate these metrics into our visualizations in the near future.

Future Directions (for Spring 2022)

As we look to the future of our project, we hope to drill down on specific questions that are important to philanthropy and the people who want to make philanthropy more effective.

As such, we have formulated some of our goals for Spring 2022:

  • Recruiting demographics by geographic location - MIT Solve - Philanthrolytics

    • Expanding insights into existing geographic recruiting that happens at each of these organizations, and visualizing them in unique ways.

  • Data alignment - Lever for Change and MIT Solve - Philanthrobotics

    • Building shared schemas for proposal data and metadata so that each organization can be visualized together effectively

    • Common application questions for shared subsets of applications

    • Common review questions for peer reviewers (mainly: first-pass review)

For an updated list of our goals in Spring 2022, please see the Spring 2022 update newsletter.

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