Section 6.3: Interpreting Data
Understand how scientists interpret the data they collect from their respective research.
Interpret how scientists connect dendrochronology data and XRF data.
Hypothesis / Relative Elemental Concentration / Research Question
See content or Module Glossary for definition
How is Dendrochronology Data Interpreted?
There are two different sets of data attained from MAD Lab, each with their own interpretations.
The total number of tree rings.
This will be used to calculate the age of the tree, assuming the date the tree was cored is known, by counting backwards in years by the total number of tree rings present in the tree core sample.
The thickness of the tree rings.
Once these are standardized, the widths are labelled as growth above or below the standardized average.
Growth above the standardized average indicates the trees local environment was very suitable for growth and the tree was not harmed during the growth period.
Growth below the standardized average indicates the trees local environment was not suitable for growth or the tree was harmed during the growth period.
How is XRF Data Interpreted?
Since each element has a unique fluorescence pattern, elements can be identified by matching the patterns in the spectral graph (recall Figure 9) to those found in literature. The process of identifying elements is as follows:
Identify the energy of the largest peak on the XRF graph.
Find which element(s) fluoresce at that energy.
Look up the energy the expected second peak should be at for the element in question and check to see if there is a peak at that energy in the data.
Look for these other peaks on the spectral graph to confirm which element produced the first peak.
Repeat the process until all peaks have been identified on the spectral graph.
Some elements may fluoresce at energies that produce peaks where another element's peak is and this is why multiple peaks are used to determine if an element is present in the sample.
An image of the XRF Summary Sheet (for IDEAS) will be provided when we return your data to you but it only contains some of the most common elements detected on IDEAS in XRF.
XRF Data Booklet
To find out other elements that are present in your sample and are not provided on the XRF cheat sheet that we send to you, check out this link for the complete XRF booklet: http://xdb.lbl.gov/xdb-new.pdf
Figure 11 shows a periodic table that we refer to while using the IDEAS beamline. Due to the capabilities of the beamline, the elements that are shown in red and blue are the ones that are able to be detected by IDEAS.
How do Dendrochronological and XRF Data Relate?
Remember that the MAD Lab obtains information on the tree ring widths. After standardizing these widths to account for the natural decrease in ring size as the tree grows, this data creates what is called a ring width index. The ring width index shows whether or not the tree growth is above or below average for the trembling aspen tree sampled.
The data gained from the XRF done at the CLS shows what elements are present and absent and the relative elemental concentration that IDEAS can detect (note not absolute concentrations). Relative elemental concentration means that the peak height within one sample can be compared to each other (for example, in one XRF scan, a higher peak of one element indicates more of that element). With relative elemental concentration, however, scientists are NOT able to compare between different samples without more intensive techniques.
By putting the elemental abundance and ring width index onto a three-axis graph, a graph that displays two different data sets along a similar axis (Figure 12), we can see how dendrochronology and XRF data may be interpreted. The right y-axis will display the ring width index and the left y-axis the elemental abundance overlapping each other. The x-axis will be the year corresponding to the rings and their associated elemental abundances. Note that this graph is just one way to show a relationship between dendrochronology and XRF data. The data from your samples will be in a slightly different from than the graph in Figure 12 (see Section 6.4).
Connecting to a Research Question
BEFORE students really dig into their data, what will help with their learning is coming up with a research question (RQ). A RQ is simply the question a researcher comes up with and will try to answer through sampling and experiments, which will hopefully lead to some type of answer. For TREE, the MAD Lab researchers are interested in understanding how trembling aspen respond to different environments across Canada. Creating a research question and subsequently, a hypothesis (your proposed answer to your research question) are some of the first steps in the scientific method. These two points help guide the design and action tasks for any research project and for classes/groups, can help students engage more with their TREE data.
We encourage you to check out our Creating a Research Question and Hypothesis Resource as well as our TREE Teacher Inquiry Guide. These two resources help foster student inquiry and research in your class, within the context of TREE.
Creating a RQ & Hypothesis Resource
Check out this resource which helps students to create a meaningful Research Question and hypothesis to guide a research project with TREE: https://bit.ly/3UGbN5T