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We present a Beta version of the Timeseries Integrated Knowledge Engine (TIKE) Science Platform, a JupyterHub service for accessing and analyzing cloud-hosted data products at the Mikulski Archive for Space Telescopes (MAST). To support high-throughput access to TESS mission data, MAST hosts a copy of its collection in Amazon Web Services (AWS). As of early 2021, these products are freely and anonymously accessible without an AWS account. TIKE provides a user-friendly, Python-focused computing environment for engaging with these datasets from within AWS, for both beginners and experts. As astronomical survey data volumes grow, they become more accessible if we can bring scientists to data in addition to providing tools to transfer data to scientists. TIKE benefits include high data throughput between the TESS data in AWS and TIKE computing, introductory documentation and tutorials, as well as a Python environment pre-installed with 20+ community software packages. These include both core software such as Astropy, Tensorflow, and Matplotlib, plus timeseries-focused packages such as Everest, Lightkurve, and others.
Data Analysis Techniques
Data Analysis Techniques
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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