An Open Framework to
Manage Time Series Data
Track vintages, automate data ingestion and leverage public sources for research. Data stored on GitHub, accessed directly from R.
Scientific-grade public time series data, revision-tracked and ready to use.
Built for the Research Community
Low maintenance footprint - homecourt advantage for researchers.
Track Data Revisions
Every revision of your time series data is preserved with full history. Travel back to any point in time to see exactly what your data looked like.
Community Hosted Data
Find datasets from different public sources turned into scientific-grade time series data. Realtime information in one homogeneous format across origins.
Free Hosting
Leverage GitHub for free data hosting and version control. No infrastructure costs, no server maintenance, just reliable data access.
R Integration
Stay at home while consuming data. Work with existing core data science workflows and tools without thinking about Advanced Version Control.
Run Your Own Archives
Use our boilerplate package to set up your own archive including versions and meta information. Host it privately or contribute your archive to github/opentsi.
Web Previews for Visibility
Benefit from built-in static web content generators to provide clean, informative dataset information sites without additional effort.
Open Time Series Initiative Toolbox
Two R packages. One for consuming data, one for managing it.
Read, query, and explore time series data from any OpenTSI archive.
# Read the latest version of a time series
library(opentimeseries)
ts_data <- read_open_ts(
"leading",
remote_archive = "opentsi/ch.kof.globalbaro"
)remotes::install_github("opentsi/opentimeseries")Set up and manage archives with full revision history. Time travel included.
# Initialize a new archive
library(deloRean)
archive_init(
archive_name = "ch.provider.dataset",
parent_dir = "~/opentsi/"
)remotes::install_github("opentsi/deloRean")