We are very grateful for volunteers that take time out of their days to transcribe label data in the portals! It is instrumental in moving the project forward and helping to achieve more complete specimen records. To ensure that labels are transcribed as fully and accurately as possible, we are providing a few tips below that will guide volunteers in the process of capturing data in the online forms.
First, if you are going to edit a specimen record, please edit it fully! Once a specimen is processed, it is taken out of the crowdsourcing queue and can no longer be edited by other volunteers. This means that fields left blank will remain blank. If a label is too complicated or looks like more work than you want to put in at the moment, you can always skip it and move onto the next record.
We have noticed many cases where a label’s coordinate data has not been entered, but doing so is actually quite simple. See an example record below:
- Latitude/longitude or UTM data can either be entered into the “Verbatim Coordinates” field or by using the “Tools” button. Clicking “Tools” will open a drop-down menu to fill in this data (#1). Coordinates can then be transcribed from the label (highlighted in yellow) into the tool’s fields. By clicking “Insert Lat/Long Values” (#2) or “Insert UTM Values”, the data will be moved into the correct field and format. Note that the small globe icon next to these fields will open Google Maps; this can be used as a way to check that coordinates were correctly entered.
- UTM (“Universal Transverse Mercator”) is an alternate means of denoting location, and some more recent labels contain UTM data instead of latitude and longitude. UTM data consists of three values: East, North, and a numerical “zone”. Unfortunately, we have found that the zone is not always listed on labels. An internet search can likely indicate the correct zone for the label in question. Below, see an example of a label with UTM data (highlighted in yellow), along with a map of UTM zones. On this particular label, zone was not specified, but the description of the locality (Massachusetts: Plymouth County) would fall within UTM Zone 19.
Image from Wikipedia.org
Next, don’t be afraid to look things up. I’ve found Google and Wikipedia to be very useful tools when capturing data. If you can’t quite make out the handwriting on a label, try searching for what you think it says. The internet could easily point you in the right direction and you could also learn something in the process.
- For example, I came across this label (above) with substrate data I couldn’t decipher and the name of a tree I didn’t know. A quick Google search for what I thought it read, “palm of gilead”, led to an article about the Balm of Gilead tree, which has apparently been used for healing purposes since biblical times and is ascribed great cultural significance. I have since come across many more labels with the same substrate data.
Lastly, it can always be helpful to review what types of data are meant to go in particular fields. We’ve noticed there is often a mix up between specific fields, such as “Locality”, “Habitat”, and “Substrate”. Below, see a label with all three types of data present:
- Locality consists of named places where the specimen was collected, below the levels of State/Province and County. On this label, locality is “Circular Marsh near Barrow”. (Note that “Alaska” would already be captured in in the “State/Province” field.)
- Habitat is the ecological area where the specimen was found. This is a description of the physical environment and can also include factors such as temperature, moisture, and sun exposure. In this example, habitat would be “old beach ridge”.
- Substrate is the surface a specimen lives on, and from which it was collected. Here it would be “reindeer bones”.
- Not present on the above label, but worth mentioning: scientific terms are often used to denote substrate. For example, corticolous means growing on bark; saxicolous means growing on rock; and lignicolous means growing on wood. This may be another situation that warrants a quick Google-ing to help us better complete our data.
This is surely not an exhaustive list of things to watch out for when transcribing label data; there will always be interesting quirks and curiosities found in scientific collections of any type. But we hope that these tips can help careful, attentive volunteers capture accurate data.