T3/Oat Sandbox

Quick Links

What's New

Data Submission

Seven new data submission tutorials have been added to T3/Oat and are available from the data submission page.

The tutorials provide additional information to aid in the submission of germplasm information, phenotype data and genotype data using the data submission templates.

Genotype Data

Data from the 2017 Public Oat Genotyping Initiative (POGI) is now available.

Detect outliers for selected traits and trials

Outlier detection in trial mean data is performed using a Bonferroni-Holm test to judge residuals standardized by the re-scaled Median Absolute Deviation (MAD). The “Outlier Threshold” refers to the threshold level used by the Bonferroni-Holm test in the detection of outliers. A lower threshold level report fewer outliers. As a default a commonly used threshold level of 0.05 is used. Outliers can be saved, then you will have the option of excluding these measurements while performing Analysis and Download functions.

Bernal-Vasquez AM, Utz HF, Piepho HP (2016). Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML. Theor Appl Genet, 129:787-804. doi: 10.1007/s00122-016- 2666-6

Select a set of lines, trials, and traits.

In 2009 the Toronto International Data Release Workshop agreed on a policy statement about prepublication data sharing. Accordingly, the data producers are making many of the datasets in T3 available prior to publication of a global analysis. Guidelines for appropriate sharing of these data are given in the excerpt from the Toronto Statement

I agree to the Data Usage Policy as specified in Toronto Statement.