The archive has 4 projects:

    Project Owner Description Summary
    CDC Real-time Forecasts
    khoa Guidelines and forecasts for a collaborative U.S. influenza forecasting project.
    43 models, 2164 forecasts, 13.6 million rows as of 2020-03-30
    CDC Retrospective Forecasts
    katie Guidelines and forecasts for a collaborative U.S. influenza forecasting project.
    27 models, 5647 forecasts, 31.7 million rows as of 2020-03-30
    Docs Example Project
    nick A full description of my project is here. You could include narrative details about what seasons are included, what group has provided data, whether the project focuses on real-time or retrospective forecasts.
    One model, one forecast, 52 rows as of 2020-03-30
    Impetus Province Forecasts
    nick Impetus Project forecasts for real-time dengue hemorrhagic fever (DHF) in Thailand. Beginning in May 2017, this project contains forecasts for biweekly DHF incidence at the province level in Thailand. Specifically, each timezero date is associated with a biweek in which data were delivered from the Thai Ministry of Public Health to servers in the US. We use standard biweek definitions described in the supplemental materials of Reich et al. (2016). Each timezero also has a data-version-date that represents the day the forecast model was run. This can be the same as the timezero, but cannot be earlier.\n\nFiles follow the naming conventions of `[timezero]-[modelname]-[data-version-date].cdc.csv`, where dates are in YYYYMMDD format. For example, `20170917-gam_lag1_tops3-20170919.cdc.csv`.\n\nFor each timezero, a forecast contains predictive distributions for case counts at [-1, 0, 1, 2, 3] biweek ahead, relative to the timezero. Predictive distributions must be defined according to this binned-interval structure:{[0,1), [1, 10), [10, 20), [20, 30), ..., [1990, 2000), [2000, Inf)}.
    One model, 37 forecasts, 73,405 rows as of 2020-03-30