When I lived in Cannon Mills, I would go to the Royal Botanic Garden Edinburgh every Sunday and sketch. Someone joked that this was my version of church, and from that point I decided to draw whatever plant appeared to be most god-touched, the most personally moving and magical on that given day. Below is an interactive map made with over a year's worth of sketches drawn on these trips.
The above map seeks to give a sense of when and where someone might find a plant looking particularly godly. Raw data is displayed scattered across a basic trail map, which shows exactly where each plant was, what it was, and the ensuing sketch. Data can also be filtered in this view to a given month or stretch of consecutive months, such as a season. The heat map view gives a more general idea of where and when, tallying the number of different months each region of the gardens had at least one sighting. Finally, in keeping with the theme of finding mythology in a random collection of points in space, the scatter map is reimagined as a constellation map, simplifying clusters of points and giving a bolder view of where to go on a seasonal basis. Constellations shown here could act as a starting point for those planning a walking route.
One major area of plant science research is the study of how plants react to different growing conditions. For example, how a crop plant might react to drought, shading, or changes in soil nutrient levels. Plants can react to changing conditions by altering the expression levels of different genes, strengthening or weakening different developmental signals. Determining which genes are expressed differently between conditions can be used to determine which developmental processes are affected, and to what extent. Additionally, changes in gene expression need not be uniform across all plant tissues. Determining where a gene is differentially expressed can therefore also be useful in understanding developmental changes. For example, if a gene is expressed greatly under drought conditions specifically in leaves, it is presumed that some aspect of leaf development is altered in response to drought. Things are not usually this simple, as genes often act together, and act differently based on their interactions, but this type of pattern identification is still useful for more exploratory studies, or in conjunction with other data.
Below is an example of how dummy differential gene expression data might be displayed, using stonecrop grown under normal and drought condition, with flowers, stem, young leaves, and mature leaves, sampled.
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The above graphic first allows users to familiarise themselves with the anatomy of a stonecrop. For a real analysis, this natural view could also show a plant with altered development or with tissue close ups, to give the background needed to contextualise the specific gene expression data. Within the gene expression view, the heat map offers an easy way to compare different genes, while the anatomical map offers an intuitive means of filtering data and for viewing it in the context of tissue locations. For analyses with more tissues, this latter feature becomes particularly helpful, as it makes it easier to spot patterns on across different yet locally similar tissues, which would be difficult to see with a heat map alone.
Of the four fake genes modelled here, its clear to see that gene one is unaffected by drought, that gene two is only altered around the apex of shoots in new leaves and flowers, gene three is only altered in leaves, and gene four is altered in a nonspecific manner. Of the tissues shown, young leaves are affected most by drought.