Standard metadata fields

Column 1: strain

This needs to match the name of a sequence in the FASTA file exactly and must not contain characters such as spaces, or ()[]{}|#><. In our example we have a strain called NewZealand/01/2020 so there should be a sequence in the FASTA file for >NewZealand/01/2020 (sequence names in FASTA files always start with the > character, but this is not part of the name).


“Strain” here carries no biological or functional significance and should be thought of as synonymous with sample.

Column 2: virus

Name of the pathogen.

Column 3: gisaid_epi_isl

If this genome is shared via GISAID then please include the EPI ISL here. In our example this is EPI_ISL_413490.

Column 4: genbank_accession

If this genome is shared via GenBank then please include the accession number here. In our example this is ? indicating that it hasn’t (yet) been deposited in GenBank. (See above for more information on how to encode missing data.)

Column 5: date (really important!)

This describes the sample collection date (not sequencing date!) and must be formatted according as YYYY-MM-DD. Our example was collected on Feb 27, 2020 and is therefore represented as 2020-02-27.

You can specify unknown dates or month by replacing the respected values by XX (ex: 2013-01-XX or 2011-XX-XX) and completely unknown dates can be shown with 20XX-XX-XX (which does not restrict the sequence to being in the 21st century - they could be earlier). Please be aware that our current pipeline will filter out any genomes with an unknown date, however you can change this for your pipeline!

See this guide to formatting dates in Excel.

Column 6: region

The region the sample was collected in - for our example this is Oceania. Please use either Africa, Asia, Europe, North America, Oceania or South America. If you sequence a genome from Antarctica, please get in touch!

Column 7: country

The country the sample was collected in. Our example, NewZealand/01/2020, was collected in ……. New Zealand. You can run tail +2 data/metadata.tsv | cut -f 7 | sort | uniq to see all the countries currently present in the metadata. As of April 10, 2020, there were 64! 🌎

Column 8: division

Division currently doesn’t have a precise definition and we use it differently for different regions. For instance for samples in the USA, division is the state in which the sample was collected here. For other countries, it might be a county, region, or other administrative sub-division. To see the divisions which are currently set for your country, you can run the following command (replace New Zealand with your country):

tail +2 data/metadata.tsv | cut -f 7,8 | grep "^New Zealand" | cut -f 2 | sort | uniq

Column 9: location

Similarly to division, but for a smaller geographic resolution. This data is often unavailable, and missing data here is typically represented by an empty field or the same value as division is used. In our example the division is “Auckland”, which conveniently (or confusingly) is both a province of New Zealand and a city.


Columns 10-12 (*_exposure) are no longer used in the analysis pipeline, and may no longer be kept up to date in our curated metadata. They remain here as they may be useful for certain questions.

Column 10: region_exposure

If the sample has a known travel history and infection is thought to have occurred in this location, then represent this here. In our example, which represents New Zealand’s first known case, the patient had recently arrived from Iran, thus the value here is “Asia”. Specifying these travel histories helps inform the model we use to reconstruct the geographical movements of the virus.

If there is no travel history then set this to be the same value as region.

Column 11: country_exposure

Analogous to region_exposure but for country. In our example, given the patient’s travel history, this is set to “Iran”.

Column 12: division_exposure

Analogous to region_exposure but for division. If we don’t know the exposure division, we may specify the value for country_exposure here as well.

Column 13: segment

Unused. Typically the value “genome” is set here.

Column 14: length

Genome length (numeric value).

Column 15: host

Host from which the sample was collected. Currently we have multiple values in the dataset, including “Human”, “Canine”, “Manis javanica” and “Rhinolophus affinis”.

Column 16: age

Numeric age of the patient from whom the sample was collected. We round to an integer value. This will show up in auspice when clicking on the tip in the tree which brings up an info box.

Column 17: sex

Sex of the patient from whom the sample was collected. This will show up in auspice when clicking on the tip in the tree which brings up an info box.

Column 18: originating_lab

Please see GISAID for more information.

Column 19: submitting_lab

Please see GISAID for more information.

Column 20: authors

Author of the genome sequence, or the paper which announced this genome. Typically written as “LastName et al”. In our example, this is “Storey et al”. This will show up in auspice when clicking on the tip in the tree which brings up an info box.

Column 21: url

The URL, if available, pointing to the genome data. For most SARS-CoV-2 data this is

Column 22: title

The URL, if available, of the publication announcing these genomes.

Column 23: date_submitted

Date the genome was submitted to a public database (most often GISAID). In YYYY-MM-DD format. See date for more information on this formatting.