  Quantifying performance of methods for tracking and mapping tissue in
endoscopic environments is essential for enabling image guidance and automation
of medical interventions and surgery. Datasets developed so far either use
rigid environments, visible markers, or require annotators to label salient
points in videos after collection. These are respectively: not general, visible
to algorithms, or costly and error-prone. We introduce a novel labeling
methodology along with a dataset that uses said methodology, Surgical Tattoos
in Infrared (STIR). STIR has labels that are persistent but invisible to
visible spectrum algorithms. This is done by labelling tissue points with
IR-fluorescent dye, indocyanine green (ICG), and then collecting visible light
video clips. STIR comprises hundreds of stereo video clips in both in-vivo and
ex-vivo scenes with start and end points labelled in the IR spectrum. With over
3,000 labelled points, STIR will help to quantify and enable better analysis of
tracking and mapping methods. After introducing STIR, we analyze multiple
different frame-based tracking methods on STIR using both 3D and 2D endpoint
error and accuracy metrics. STIR is available at
https://dx.doi.org/10.21227/w8g4-g548
