From 12756b22520b1433c29825e7aa69b3eaff60031c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Javier=20R=C3=ADos?= Date: Tue, 8 Oct 2024 19:54:27 +0200 Subject: [PATCH] feat: public open collective dataset (#2319) * add: `open-collective` dataset overview * add: `javi` as an author * add: open collective transactions dataset `blog` * add: analytics hub link --- ...ation_amount_vs_contribution_frequency.png | Bin 0 -> 64175 bytes .../index.md | 106 ++++++++++++++++++ .../total_amount_donated_babel.png | Bin 0 -> 107887 bytes .../total_amount_donated_oc.png | Bin 0 -> 85794 bytes .../total_amount_donated_pandas.png | Bin 0 -> 91546 bytes apps/docs/blog/authors.yml | 6 + apps/docs/docs/integrate/overview/index.mdx | 36 ++++++ .../integrate/overview/open-collective.png | Bin 0 -> 19500 bytes 8 files changed, 148 insertions(+) create mode 100644 apps/docs/blog/2024-10-08-open-collective-transactions-datasets/donation_amount_vs_contribution_frequency.png create mode 100644 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Collective](https://opencollective.com) is a platform that enables groups +to collect and disburse funds transparently. It is used by many open-source +projects, communities, and other groups to fund their activities. Notable +projects include [`Open Web Docs`](https://openwebdocs.org) (maintainers of +[MDN Web Docs](https://developer.mozilla.org/en-US/)), +[`Babel`](https://babel.dev), and [`Webpack`](https://webpack.js.org). + +At Open Source Observer, we have been working on collecting and processing Open +Collective data to make it available for analysis. This includes **all +transactions** made on the platform, such as donations, expenses, and transfers. +Datasets are updated weekly. + + + +## Datasets + +Transactions are the core of Open Collective data. They represent the flow of +funds between entities on the platform. We currently provide two datasets for +Open Collective transactions: `expenses` and `deposits`. These datasets contain +all transactions of the respective type. + +Both datasets follow the same schema. This schema contains important fields such +the transaction's id, group, type and amount. For the full reference, see the +BigQuery schema. + +## Use cases + +The Open Collective transactions datasets can be used for a variety of +applications. Here are some examples: + +### Donor contribution patterns + +One use case is to analyze overall donor contribution patterns. This involves +examining the frequency of donations, the amounts donated, and the growth of +donations over time. + +For example, the plot below illustrates the frequency of different donation +amounts. We can see that the most common donation is around **$1,500**, with +approximately **600,000** donations at this level. This indicates that people +tend to donate around $1,500 more frequently than other amounts, suggesting that +establishing a donation tier at this level would be a good idea. + +![Frequency of donation quantities](./donation_amount_vs_contribution_frequency.png) + +### Analyzing Donation Trends and Retention + +Another use case is to analyze donation trends and retention. This involves +examining how donations have changed over time and how many donors have +continued to donate over time. + +This plot illustrates the total donations made to the +[`Pandas`](https://pandas.pydata.org) project over time. Notably, donations +reached an all-time high in April 2022 but have since declined. As of September +2024, there has been a slight rebound in donations; however, they remain at less +than one-eighth of their peak average. + +![Pandas donations](./total_amount_donated_pandas.png) + +On the other hand, the plot below shows the total amount donated to the +[`Babel`](https://babeljs.io) project over time. One interesting observation is +that donations spiked in the beginning of 2021. This spike was likely due to the +project's widespread usage during the global pandemic, as many developers turned +to open-source tools to build applications. + +![Babel donations](./total_amount_donated_babel.png) + +## Getting started + +To get started with the Open Collective transactions datasets, you can access +them directly in BigQuery. The datasets are available in the +`opensource-observer` project. For instance, getting the total amount of +donations per month in USD can be done with the following query: + +```sql +SELECT + DATE_TRUNC(DATE(created_at), MONTH) AS donation_month, + SUM(CAST(JSON_VALUE(amount, "$.value") AS FLOAT64)) AS total_amount +FROM + `opensource-observer.open_collective.deposits` +WHERE + JSON_VALUE(amount, "$.currency") = "USD" + AND DATE(created_at) >= DATE("2020-01-01") +GROUP BY + donation_month +ORDER BY + donation_month; +``` + +This data can then be visualized to gain insights into donation patterns and +trends. We can see that donations in Open Collective have massively increased in +the last year, with almost **8M USD** donated in the last months. + +![Total donations per month](./total_amount_donated_oc.png) + +These are just some examples of the insights that can be gained from the Open +Collective transactions datasets. We hope that these datasets will be useful for +researchers, data scientists, and anyone interested in understanding the flow of +funds in the open-source ecosystem. diff --git a/apps/docs/blog/2024-10-08-open-collective-transactions-datasets/total_amount_donated_babel.png b/apps/docs/blog/2024-10-08-open-collective-transactions-datasets/total_amount_donated_babel.png new file mode 100644 index 0000000000000000000000000000000000000000..cf7085749c39f7e220c26d89b0b5424bd6576bea GIT binary patch literal 107887 zcma&O2Rzp8`#yfRHw~#|R8&etWmRYx4JsAcMA^5IkrCQNNK;5fQEq$OvZ7%$jN-OO znb{$G{g12hJfC>Jzt`XE^Lcryd%Ul8p2v9{$9Y{la!78$T(-Fs3T46mebPrMlo?bC zWoqo~nfRMXm62@x61SF7u|8(5Z+-UUnbQ=7lhzid=GLZ0x~ptYpRqDBH`^*8F0f_O zDnn~)3#;v$H&g$4fq?lLgUzcCO)JBP%(2*~YDJ+ipCtdE@=Pk!h%$vj*)P5OxLrV7 z)mgh^r^jc0-_cxsn~C>`;pt<_Whv*~X4mdHuEjDXG3ZPF=+In&x4m-(SXdON+~2q{ z$ZgAq8)>Il)o&c-i^u#k&o+fyP4)Xb@@;y9#Kpx2*QHspy>#l{$tI)7 zvT)I#A0N4bFipyzKP_4`%Ir^<{^y7DvV!`We}0%>hw%UNN}pLL3-7x9`N_EyZFKOT 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+15,9 @@ ravenac95: title: Engineer url: https://github.com/ravenac95 image_url: https://github.com/ravenac95.png + +jabolol: + name: Javier RĂ­os + title: Engineer + url: https://github.com/jabolol + image_url: https://github.com/jabolol.png diff --git a/apps/docs/docs/integrate/overview/index.mdx b/apps/docs/docs/integrate/overview/index.mdx index 15bdae94b..8da30f7d1 100644 --- a/apps/docs/docs/integrate/overview/index.mdx +++ b/apps/docs/docs/integrate/overview/index.mdx @@ -15,6 +15,7 @@ import GitcoinLogo from "./gitcoin.png"; import OpenrankLogo from "./openrank.png"; import ArbitrumLogo from "./arbitrum.png"; import EasLogo from "./eas.png"; +import OcLogo from "./open-collective.png"; First, you need to set up your BigQuery account. You can do this by going to the [Get Started](../../get-started/index.mdx) @@ -570,6 +571,41 @@ where **Remember to replace 'YOUR_PROJECT_NAME' with the name of your project in the query.** +### Open Collective Data + + + +