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This is an exciting era for exo-planetary exploration. The recently launchedJWST, and other upcoming space missions such as Ariel, Twinkle and ELTs are setto bring fresh insights to the convoluted processes of planetary formation andevolution and its connections to atmospheric compositions. However, with newopportunities come new challenges. The field of exoplanet atmospheres isalready struggling with the incoming volume and quality of data, and machinelearning (ML) techniques lands itself as a promising alternative. Developingtechniques of this kind is an inter-disciplinary task, one that requires domainknowledge of the field, access to relevant tools and expert insights on thecapability and limitations of current ML models. These stringent requirementshave so far limited the developments of ML in the field to a few isolatedinitiatives. In this paper, We present the Atmospheric Big Challenge Database(ABC Database), a carefully designed, organised and publicly available databasededicated to the study of the inverse problem in the context of exoplanetarystudies. We have generated 105,887 forward models and 26,109 complementaryposterior distributions generated with Nested Sampling algorithm. Alongsidewith the database, this paper provides a jargon-free introduction to non-fieldexperts interested to dive into the intricacy of atmospheric studies. Thisdatabase forms the basis for a multitude of research directions, including, butnot limited to, developing rapid inference techniques, benchmarking modelperformance and mitigating data drifts. A successful application of thisdatabase is demonstrated in the NeurIPS Ariel ML Data Challenge 2022.

ESA-Ariel Data Challenge NeurIPS 2022: Introduction to exo-atmospheric studies and presentation of the Atmospheric Big Challenge (ABC) Database