Data Lab — An open and public science platform

Data Lab (https://datalab.noao.edu) is NSF's OIR Lab's open science exploration platform. Launched in 2017 as the NOAO Data Lab, its original goal was to enable access and compute capabilities close to large public data products generated by NOAO survey programs, most prominently from the Dark Energy Camera. The scope and magnitude of Data Lab have evolved significantly since then. In addition to large-scale survey data such as DES and Legacy Survey catalogs, Data Lab also provides access to images and spectra, and allows users to share and publish their own data with collaborators or the wider community. Data Lab furnishes users with compute resources, virtual storage (disk and database) and interfaces to assist in data analysis, filtering, cross-matching, processing and visualization. Additionally, access to local copies of high-value reference data (e.g. Gaia, AllWISE, SDSS, etc.) as well as external data resources provides an integrated environment that is a great resource for anyone interested in large scale survey science, and especially for researchers seeking readiness for LSST, whose workflows and access modes closely match those of the Data Lab science platform. Today, Data Lab hosts 17 major surveys, totaling ~70 TB of catalog data with ~150 billion rows, and provides access to ~1 PB of images and other data products. The poster introduces the Data Lab ecosystem, its functionalities, and database holdings including a uniformly-processed, all-sky NOAO Source Catalog (NSC) of public data obtained with NOAO instruments, which holds almost 3 billion objects reaching to 23rd magnitude over many epochs. We point out the many functionalities of Data Lab, and invite readers to try it out by registering for a free account which grants immediate access to all data holdings, provides 1 TB of virtual storage, 250 GB of user database storage, and a set of example Jupyter notebooks curated by the Data Lab team and community, which range from extracting light curves of variable objects, to the detection of Milky Way dwarf satellites, to exploring M31, and many more. This will hopefully inspire readers to bring their own big-data science questions to an integrated science platform such as Data Lab.