# Special Issue on Open Source Machinery and Laboratory Instrument using the Arduino Software and Hardware Ecosystem

**URL:** <https://discuss.tinyml.seas.harvard.edu/t/special-issue-on-open-source-machinery-and-laboratory-instrument-using-the-arduino-software-and-hardware-ecosystem/1056>\
**Category:** Education\
**Created:** [March 29, 2022, 12:34am UTC](https://discuss.tinyml.seas.harvard.edu/t/special-issue-on-open-source-machinery-and-laboratory-instrument-using-the-arduino-software-and-hardware-ecosystem/1056 "2022-03-29T00:34:39Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![ajosephau](https://yyz1.discourse-cdn.com/flex027/user_avatar/discuss.tinyml.seas.harvard.edu/ajosephau/32/301_2.png) [@ajosephau](https://discuss.tinyml.seas.harvard.edu/u/ajosephau)\
**Post date:** [March 29, 2022, 12:34am UTC](https://discuss.tinyml.seas.harvard.edu/t/special-issue-on-open-source-machinery-and-laboratory-instrument-using-the-arduino-software-and-hardware-ecosystem/1056/1 "2022-03-29T00:34:39Z")

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I can’t find the original tweet, but I saw that the HardwareX journal is seeking submissions for their [Special Issue on Open Source Machinery and Laboratory Instrument using the Arduino Software and Hardware Ecosystem](https://journals.elsevier.com/hardwarex/call-for-papers/special-issue-on-open-source-machinery-and-laboratory-instrument-using-the-arduino-software-and-hardware-ecosystem)

They explicitly call out TinyML as an area of interest:

> Finally we would like to see machine learning applications in the embedded realm using any of the existing frameworks (tinyML, AIeFS…). In such case we expect to see open training datasets, as well as NN topologies.