  Scientists aim to extract simplicity from observations of the complex world.
An important component of this process is the exploration of data in search of
trends. In practice, however, this tends to be more of an art than a science.
Among all trends existing in the natural world, one-dimensional trends, often
called sequences, are of particular interest as they provide insights into
simple phenomena. However, some are challenging to detect as they may be
expressed in complex manners. We present the Sequencer, an algorithm designed
to generically identify the main trend in a dataset. It does so by constructing
graphs describing the similarities between pairs of observations, computed with
a set of metrics and scales. Using the fact that continuous trends lead to more
elongated graphs, the algorithm can identify which aspects of the data are
relevant in establishing a global sequence. Such an approach can be used beyond
the proposed algorithm and can optimize the parameters of any dimensionality
reduction technique. We demonstrate the power of the Sequencer using real-world
data from astronomy, geology as well as images from the natural world. We show
that, in a number of cases, it outperforms the popular t-SNE and UMAP
dimensionality reduction techniques. This approach to exploratory data
analysis, which does not rely on training nor tuning of any parameter, has the
potential to enable discoveries in a wide range of scientific domains. The
source code is available on github and we provide an online interface at
\url{http://sequencer.org}.
