Datasets

Red Hen Lab has an interest in making datasets available to its members. Red Hen Lab is organized as a cooperative, not a service. Members share their work and developments with other members. But in some cases, there might be funding or at least energy that can be spent to make a dataset more widely available. This page lists such projects. Red Hen Lab (redhenlab@gmail.com) is always interested to hear from full-stack developers or those who want to become full-stack developers who would like to help Red Hen develop such datasets.

1. DS-TalkShow-2020-05-01. A dataset for the detection of gestures.

Analogous data science fields

Materials science. A list of datasets and a review of data science issues is available in Himanen, Lauri, Amber Geurts, Adam Stuart Foster, Patrick Rinke. 2019. Data‐Driven Materials Science: Status, Challenges, and Perspectives. Advanced Science 6:21. https://doi.org/10.1002/advs.201900808

Other articles include:

1. Claudia Draxl and Matthias Scheffler. Nomad:The Fair Concept For Big Data-driven materials science. MRS Bulletin, 43(9):676–682,2018.
2. James E Saal, Scott Kirklin, Muratahan Aykol, Bryce Meredig, and Christopher Wolverton. 2013. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd). Jom, 65(11):1501–1509.
3. Giovanni Pizzi, Andrea Bellotti, Riccardo Sabatini,Nicola Marzari, and Boris Kozinsky. 2016. Aiida: automated interactive infrastructure and database for computational science. Computational Materials Science, 111:218–230.
4. Stefano Curtarolo, Wahyu Setyawan, Gus LW Hart, Michal Jahnatek, Roman V Chepulskii, Richard H Taylor, Shidong Wang, Junkai Xue, Kesong Yang, Ohad Levy, and MJ Mehl. 2012. A flow: an automatic framework for high-throughput materials discovery. Computational Materials Science, 58:218–226,