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One of our primary goals at Kariba (the team behind Open Source Observer) is to build a network of Impact Data Scientists. However, “Impact Data Scientist” isn’t a career path that currently exists. It’s not even a job description that currently exists.

This post is a first step in changing that. In it, we discuss:

1. Why we think the Impact Data Scientist is an important job of the future
2. The characteristics and job spec of an Impact Data Scientist
3. Ways to get involved if you are an aspiring Impact Data Scientist

One important caveat. This post is focused on building a network of Impact Data Scientists that serve crypto open source software ecosystems. In the long run, we hope to see Impact Data Scientists work in all sorts of domains. We are starting in crypto because there is already a strong culture around supporting open source software and decentralizing grantmaking decisions. We hope this culture of building in public and experimenting crosses over to non-crypto grantmaking ecosystems. When it does, we’d love to help build a network of Impact Data Scientists in those places too!

In baseball, there’s an advanced statistic called WAR, short for Wins Above Replacement. It measures a player’s overall contribution to their team by comparing them to a “replacement-level” player—a hypothetical average player who could easily be brought in from the bench or minor leagues. The higher a player’s WAR, the more valuable they are to their team.

Now, let’s apply this concept to decentralized networks like Ethereum or its Layer 2s, which steward ecosystems of public goods including infrastructure, libraries, and permissionless protocols.

Just as baseball teams aim to build the best roster, ecosystem funds and crypto foundations strive to create the strongest community of developers and users within their networks. They attract these participants through incentive programs like grants and airdrops.

But how can the success of these initiatives be effectively measured? One approach is to evaluate how well these programs retain community members and generate compounding network effects compared to the average across the broader crypto landscape. The best networks are the ones that achieve the highest WAR outright or per unit of capital allocated.

This post explores how an empirically-derived metric similar to WAR might be applied to ecosystem grants programs as a way of measuring ROI. It includes some use case ideas (like a WAR oracle) and strawman WAR formulas for protocols and open source software (OSS) projects. It concludes with some ideas for getting started.

While this is currently a thought experiment, it’s something we at OSO are seriously considering as we develop more advanced metrics for measuring impact.

At Open Source Observer, we have been committed to building everything in the open from the very beginning. Today, we take that openness to the next level by launching the OSO Data Exchange on Google BigQuery. Here, we will publish every data set we have as live, up-to-date, and free to use datasets. In addition to sharing every model in the OSO production data pipeline, we are sharing source data for blocks/transactions/traces across 7 chains in the OP Superchain (including Optimism, Base, Frax, Metal, Mode, PGN, Zora), Gitcoin Data, and OpenRank. This builds on the existing BigQuery public data ecosystem that includes GitHub, Ethereum, Farcaster, and Lens data. To learn more, check out the data portal here:

opensource.observer/data

How Open Source Observer commit to being the most open and reliable source of impact metrics out there.

At Kariba Labs, we believe deeply in the power of open source software. That is why we are building Open Source Observer (aka OSO), an open source tool for measuring the impact of open source projects. In order to achieve our goal of making open source better for everyone, we believe that OSO needs more than just open source code. We are committed to being the most open and reliable source of impact metrics out there. We will achieve this by committing the OSO project to the following practices:

• Open source software: All code is developed using permissive licenses (e.g. MIT/Apache 2.0)

• Open data: All collected and processed data will be openly shared with the community (to the extent allowed by terms of service)

• Open infrastructure: We will open up our infrastructure for anyone to contribute or build upon our existing infrastructure at-cost.