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License: Apache License 2.0
Individual contributor analysis (see shuffleboard)
License: Apache License 2.0
Identify what people are posting about using the usual means, hashtags etc. Not sure how useful this is tbh but worth exploring.
TL;DR: How do others currently define individual participation metrics?
This is part of a larger effort to understand how individuals are contributing to open source projects. Ultimately we need to quantify this activity. Open source projects are often run by non-profit open source foundations that need to justify their existence in order to procure funding. One thing many open source foundations care about is tracking contributor activity. This might include looking at the rate of people joining the community (new contributors) and the rate of people leaving the community, as well as how long someone has been involved in the community. They might also provide some way of ranking or rating the contributors. The goal of this issue is to discover how open source communities are currently quantifying contributor activity.
For each article you look at
Some suggestions:
When getting commit histories from an individual's commit log, improve the sort order of returned repositories to see if it results in a better identification.
per Issue #6
Contribute ideas for metrics here? Or possibly use things from here for our own purposes unrelated to their efforts?
Given the metrics identified in Issue #1 determine what data we need to gather from the Github Event data to compute them.
Of particular interest are R and Python
Rather than just considering "top" or "most active" develop categories of contributors based on their activity profiles.
One idea: take random samples of contributors to the project in a small enough size for manual analysis (n=10), manually identify which ones appear to be the most "valuable" to a given project and then come up with a list of parameters that defines their "value". Then we can do further analysis using the parameters that are most easily available given the data we have to see if we can find any obvious correlations that could help us build event data queries.
What types of event activity are most strongly correlated with higher valued contributors (if any)? We could iterate on this to incorporate the event payload field (which has to be parsed). The goal here would be to maximize the probability of identifying key contributors while attempting to minimize the amount of "crud" we have to analyze to find them.
Initially use a known set, contributors that have already been identified and see where they have profiles and what identifying information is available. Once that's done, take a small sample of unknowns and try to identify them as well. Ideally this should be automated even if just in one-off scripts to document the paths taken.
More interesting than Issue #18 would be a way to determine if Twitter accounts are bots and to perform demographic analysis on the Twitter bots themselves to see how they are evolving.
This issue is to collect information about existing innovation trackers. What metrics are they tracking and how are they collecting data?
In addition to considering what companies are represented, look for areas of expertise (contributors can have more than one). How does this correlate with other "innovation" factors?
Questions:
Explore options for automatically building profiles.
Once areas of interest are identified, automate web searches for different topics. If a particularly valuable data source is found then register an additional alert for it.
Additional automation tools for tracking content updates:
Given the metrics identified in Issue #1 determine what data we need to gather from the Github API to compute them.
Develop a better method for identifying who a commit author is. Keep track of their email addresses for given time frames and determine a way to "rank" them. If there is a choice between a hosted email address and a company one for the same time period, the company one gets priority.
author -> min_date,max_date -> authoritative email_address
Given an email address + date, find the author
email_address + date -> author
GBQ has a Github repos dataset that has commit sha's. For repos of interest, try cross-referencing commits to identify authors so even activity can also be identified.
Given a domain extracted from an email address, identify the company.
Data should be stored in GBQ dataset
How many contributors with minimal github info are able to be identified this way? Do the email addresses improve other identification results?
This came from Issue #9 which was originally going to just use DuckDuckGo. On further consideration, it makes more sense to write a script that takes a list of inputs and a query argument (or an argument indicating some pre-determined query pattern).
This would just provide some insight on a) what combinations of search terms yield the best results and b) what our match rate is.
These results would need be manually analyzed until a better method was determined. Right now other than manually searching one at a time, it's hard to get a sense of what proportion of the contributors are able to be matched.
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