As your quest for relevant data continues, we’ll discuss how creating and maintaining a data inventory can be an effort that saves you heartache and greatly accelerates many of the technical facets of your project. Similarly, we’ll provide you some tools and best practices for making sure that your meetings are impactful, necessary, and accessible to all participants so that meeting with the group doesn’t cost you any momentum!
Good meeting design starts with understanding your purpose and objectives, as well as your participants. Once you understand why you need to meet (your overarching goal) and what you want to accomplish (the specific outcomes you are driving toward), you can turn to how you will accomplish your purpose (i.e. the agenda of activities, timings, and tech) and who will play what roles. You want participants to know their role and how to be at their best.
A good rule of thumb is to allow 2-3x as much time to plan a meeting as its duration.
It’s very difficult to both facilitate a conversation and engage fully in it as a participant. If you add taking notes on top of that, it’s sure to become overwhelming. So recruit some help. The number of roles you need to fill will depend on the size of the group and the complexity of the process. Online meetings particularly benefit from a team approach to facilitation. Share and rotate duties over time:
As you get to know your team members, you can start to match people to these different roles based on their skills and recruit them to help. Also, be aware of who has been performing a given role so that you don’t risk repeatedly trapping one group member in an administrative role across many meetings where they can’t contribute fully on an intellectual level.
Online meetings benefit from all the same considerations as in person meetings, plus a little extra care and planning. Keeping your team engaged is doubly challenging in a virtual setting: our computers are full of distractions (email! notifications! internet rabbit holes!) and as the facilitator, it’s harder to tell whether participants are engaged when all you have to go on is a small video window. Managing people’s energy and attention and creating opportunities for real human connection are real challenges. On the flip side, online meetings allow distributed teams to stay connected and can provide a dynamic and rich platform for shared work.
In addition to the general tips above, in online settings:
Differences in thinking and learning styles, disciplinary background, power, and other dimensions of diversity mean that there’s no such thing as a one-size-fits-all approach for participatory processes. Nonetheless, we tend to default to a small set of traditional ways of sharing information and engaging people when we meet. These conventional structures are often either too limiting (presentations, status reports, and managed discussions) or too free-form and disorganized (open discussions and brainstorms) to effectively tap the wisdom of the group (Lipmanowicz and McCandless, 2014). To support the engagement of all participants, we need to break out of those traditional ways of meeting.
Books and websites like Liberating Structures, Gamestorming, and the Facilitator’s Guide to Participatory Decision Making offer dozens of alternative group processes (see Resources). Known as microstructures or knowledge games, these simple, fun activities are designed to include everyone, distribute control, and unleash creativity. One or more activities can be matched to your intended outcomes and arranged in a sequence to advance the team toward your overall goal. Liberating Structures offers a matching matrix to help you identify microstructures that could fit your needs and an app you can use to browse and assemble strings of activities. Gamestorming organizes their activities into categories (e.g. games for opening, games for decision-making) for exploration.
Here are a few microstructures that work well for small group virtual meetings. They also work for larger groups and in person settings:
| Microstructure | Thinking Preference | Purpose | How It Works |
|---|---|---|---|
| Icebreaker / check in | Relational | Connect as a team, start on a positive, human note | Many versions exist, e.g., one word to describe how you are arriving; one thing you are feeling grateful for today; coolest thing you’ve learned lately; describe where you grew up without using any place names, etc. |
| Round robin / go around | Analytical, Relational | Hear from everyone | Everyone answers the same prompt. Alternatives to going in order: each speaker calls on the next person after they have shared - keeping track of who has / hasn’t spoken keeps people paying attention; popcorn-style - people share in the order that they feel moved to speak |
| 1,2,4,all | Analytical, Practical, Experimental, Relational | Engage everyone in generating questions, ideas, and suggestions | Individual reflection; Pair share; Two pairs combine and share as a group of 4; Small groups share highlights with whole group |
| Min specs | Experimental | Specify simple rules the group must follow to achieve your purpose | 1,2,4,all format; Individuals brainstorm things the group must do or must not do to achieve its purpose; Share in pairs or small groups; Pare the list down to the minimum set of rules you could follow and still achieve the purpose |
| Affinity Map | Analytical, Relational | Surface ideas, detect patterns, and analyze | Brainstorm ideas using sticky notes on a wall or virtual whiteboard; Cluster into categories; If useful, prioritize within categories |
| Brainwriting | Analytical, Practical, Experimental, Relational | Surface and elaborate ideas | (1) Brainstorm ideas in a google doc or virtual whiteboard (or on index cards in person); (2) Read and add to each other’s ideas; (3) Discuss |
| What, So What, Now What | Analytical, Practical, Experimental | Make sense of past progress or experiences and decide on future actions | What - As a group, compile the facts and observations relevant to the context; So What - Reflect on the facts and their implications, identify patterns, generate hypotheses; Now What - Draw conclusions - What actions make sense? |
| Fist to Five / Gradient of Agreement | Practical, Relational | Assess degree of consensus; seek closure | Use when ready to close a discussion or make a decision; Invite participants to rate their level of agreement with a proposal on a scale of 0-5; Five fingers means “absolute, total agreement or support” and a fist means “complete opposition” |
| Polling | Analytical, Practical | Rank alternatives | Before you start - clarify how you will use the results - are you gathering information or taking a vote to make a decision?; Decide how many votes per person; In person - use sticky dots; Virtually - use +1s in a google doc or a digital polling tool (e.g., Zoom, Mural, slido) |
| Feasibility-Impact Matrix (see figure below) | Analytical, Practical, Experimental | Compare alternatives | Discuss and agree on definitions for two criteria for evaluating ideas: feasibility of implementation and impact potential; Rate each idea against these two axes and map onto 2x2 grid |
As you go, and definitely before your meeting is over, engage your team in synthesizing and capturing the information that has been discussed. This helps you to deepen understanding, document your workflow and decisions, and pick up easily next time. Use a consistent system - like a running notes document linked in the calendar item. Graphics or drawings can be a valuable complement to oral and written content in making thinking visible.
Consider using:
When capturing notes, try to use people’s own words; if necessary ask them to distill long or complex points into a headline you can capture. Invite them to offer corrections if the notetaker didn’t capture what they meant.
As shown in the graphic below, visualization can be valuable throughout the lifecycle of a synthesis project, albeit in different ways at different phases of a project.
Exploratory data visualization is an important part of any scientific project. Before launching into analysis it is valuable to make some simple plots to scan the contents. These plots may reveal any number of issues, such as typos, sensor calibration problems or differences in the protocol over time.
These “fitness for use” visualizations are even more critical for synthesis projects. In synthesis, we are often re-purposing publicly available datasets to answer questions that differ from the original motivations for data collection. As a result, the metadata included with a published dataset may be insufficient to assess whether the data are useful for your group’s question. Datasets may not have been carefully quality-controlled prior to publication and could include any number of ‘warts’ that can complicate analyses or bias results. Some of these idiosyncrasies you may be able to anticipate in advance (e.g. spelling errors in taxonomy) and we encourage you to explicitly test for those and rectify them during the data harmonization process. Others may come as a surprise.
During the early stages of a synthesis project, you will want to gain skill to quickly scan through large volumes of data. The figures you make will typically be for internal use only, and therefore have low emphasis on aesthetics.
Specific applications of exploratory data visualization include identifying:
This may include temporal, spatial, or taxonomic coverage, to name a few.
For example, the metadata might indicate a dataset covers the period 2010-2020. That could mean one data point in 2010 and one in 2020! This may not be useful for a time-series analysis.
Do the units “make sense” with the figure? Typos in metadata do occur, so if you find yourself with elephants weighing only a few grams, it may be necessary to reach out to the dataset contact.
Consider the following:
In addition to those other uses of exploratory visualization, you may also find:
Identifying all of these pieces of information is an important precursor to the data harmonization stage, where you will process the datasets you have selected into an analysis-ready format. Visualization is a–relatively–easy way of doing these checks!
It may sound facile, but one of the best things you can do to make your life easier when you ‘actually’ start making graphs is to draw your hypothetical graph. You will likely find that creating a small sketch of your ideal figure will help you make some critical decisions. This can save you time so you don’t laboriously code a graph that–once created–doesn’t meet your expectations. You may still change your mind once you’ve seen the graph your code produces (and that is okay!) but you’ll likely make some of the necessary larger decisions with pen and paper and streamline your process in generating a visualization that perfectly meets your needs.
ggplot2You may already be familiar with the ggplot2 package in R but if you are not, it is a popular graphing library based on The Grammar of Graphics. Every ggplot is composed of four elements:
ggplot function callNote that the theme component may be implicit in some graphs because there is a suite of default theme elements that applies unless otherwise specified.
This module will use example data to demonstrate these tools but as we work through these topics you should feel free to substitute a dataset of your choosing! If you don’t have one in mind, you can use the example dataset shown in the code chunks throughout this module. This dataset comes from the lterdatasampler R package and the data are about fiddler crabs (Minuca pugnax) at the Plum Island Ecosystems (PIE) LTER site.
# Load needed libraries
library(tidyverse); library(lterdatasampler)
# Load the fiddler crab dataset
data(pie_crab)
# Check its structure
str(pie_crab)tibble [392 × 9] (S3: tbl_df/tbl/data.frame)
$ date : Date[1:392], format: "2016-07-24" "2016-07-24" ...
$ latitude : num [1:392] 30 30 30 30 30 30 30 30 30 30 ...
$ site : chr [1:392] "GTM" "GTM" "GTM" "GTM" ...
$ size : num [1:392] 12.4 14.2 14.5 12.9 12.4 ...
$ air_temp : num [1:392] 21.8 21.8 21.8 21.8 21.8 ...
$ air_temp_sd : num [1:392] 6.39 6.39 6.39 6.39 6.39 ...
$ water_temp : num [1:392] 24.5 24.5 24.5 24.5 24.5 ...
$ water_temp_sd: num [1:392] 6.12 6.12 6.12 6.12 6.12 ...
$ name : chr [1:392] "Guana Tolomoto Matanzas NERR" "Guana Tolomoto Matanzas NERR" "Guana Tolomoto Matanzas NERR" "Guana Tolomoto Matanzas NERR" ...With this dataset in hand, let’s make a series of increasingly customized graphs to demonstrate some of the tools in ggplot2.
Let’s begin with a scatterplot of crab size on the Y-axis with latitude on the X. We’ll forgo doing anything to the theme elements at this point to focus on the other three elements.
ggplot(data = pie_crab, mapping = aes(x = latitude, y = size, fill = site)) +
geom_point(pch = 21, size = 2, alpha = 0.5)ggplot() function call above, this geometry can assume those mappings without re-specifying
We can improve on this graph by tweaking theme elements to make it use fewer of the default settings.
ggplot(data = pie_crab, mapping = aes(x = latitude, y = size, fill = site)) +
geom_point(pch = 21, size = 2, alpha = 0.5) +
theme(legend.title = element_blank(),
panel.background = element_blank(),
axis.line = element_line(color = "black"))element_... helper functions. element_blank removes theme elements but otherwise you’ll need to use the helper function that corresponds to the type of theme element (e.g., element_text for theme elements affecting graph text)
We can further modify ggplot2 graphs by adding multiple geometries if you find it valuable to do so. Note however that geometry order matters! Geometries added later will be “in front of” those added earlier. Also, adding too much data to a plot will begin to make it difficult for others to understand the central take-away of the graph so you may want to be careful about the level of information density in each graph. Let’s add boxplots behind the points to characterize the distribution of points more quantitatively.
ggplot(data = pie_crab, mapping = aes(x = latitude, y = size, fill = site)) +
geom_boxplot(pch = 21) +
geom_point(pch = 21, size = 2, alpha = 0.5) +
theme(legend.title = element_blank(),
panel.background = element_blank(),
axis.line = element_line(color = "black"))
ggplot2 also supports adding more than one data object to the same graph! While this module doesn’t cover map creation, maps are a common example of a graph with more than one data object. Another common use would be to include both the full dataset and some summarized facet of it in the same plot.
Let’s calculate some summary statistics of crab size to include that in our plot.
# Load the supportR library
library(supportR)
# Summarize crab size within latitude groups
crab_summary <- supportR::summary_table(data = pie_crab, groups = c("site", "latitude"),
response = "size", drop_na = TRUE)
# Check the structure
str(crab_summary)'data.frame': 13 obs. of 6 variables:
$ site : chr "BC" "CC" "CT" "DB" ...
$ latitude : num 42.2 41.9 41.3 39.1 30 39.6 41.6 33.3 42.7 34.7 ...
$ mean : num 16.2 16.8 14.7 15.6 12.4 ...
$ std_dev : num 4.81 2.05 2.36 2.12 1.8 2.72 2.29 2.42 2.3 2.34 ...
$ sample_size: int 37 27 33 30 28 30 29 30 28 25 ...
$ std_error : num 0.79 0.39 0.41 0.39 0.34 0.5 0.43 0.44 0.43 0.47 ...With this data object in-hand, we can make a graph that includes both this and the original, un-summarized crab data. To better focus on the ‘multiple data objects’ bit of this example we’ll pare down on the actual graph code.
ggplot() +
geom_point(pie_crab, mapping = aes(x = latitude, y = size, fill = site),
pch = 21, size = 2, alpha = 0.2) +
geom_errorbar(crab_summary, mapping = aes(x = latitude,
ymax = mean + std_error,
ymin = mean - std_error),
width = 0.2) +
geom_point(crab_summary, mapping = aes(x = latitude, y = mean, fill = site),
pch = 23, size = 3) +
theme(legend.title = element_blank(),
panel.background = element_blank(),
axis.line = element_line(color = "black"))ggplot2 graph you need to leave this top level ggplot() call empty! Otherwise you’ll get weird errors with aesthetics later in the graph
It is sometimes the case that you want to make a single graph file that has multiple panels. For many of us, we might default to creating the separate graphs that we want, exporting them, and then using software like Microsoft PowerPoint to stitch those panels into the single image we had in mind from the start. However, as all of us who have used this method know, this is hugely cumbersome when your advisor/committee/reviewers ask for edits and you now have to redo all of the manual work behind your multi-panel graph.
Fortunately, there are two nice–entirely scripted–alternatives that you might consider: Faceted graphs and Plot grids. See below for more information on both.
In a faceted graph, every panel of the graph has the same aesthetics. These are often used when you want to show the relationship between two (or more) variables but separated by some other variable. In synthesis work, you might show the relationship between your core response and explanatory variables but facet by the original study. This would leave you with one panel per study where each would show the relationship only at that particular study.
Let’s check out an example.
ggplot(pie_crab, aes(x = date, y = size, color = site))+
geom_point(size = 2) +
facet_wrap(. ~ site) +
theme_bw() +
theme(legend.position = "none")ggplot2 function that assumes you want panels laid out in a regular grid. There are other facet_... alternatives that let you specify row versus column arrangement. You could also facet by multiple variables by putting something to the left of the tilde
In a plot grid, each panel is completely independent of all others. These are often used in publications where you want to highlight several different relationships that have some thematic connection. In synthesis work, your hypotheses may be more complicated than in primary research and such a plot grid would then be necessary to put all visual evidence for a hypothesis in the same location. On a practical note, plot grids are also a common way of circumventing figure number limits enforced by journals.
Let’s check out an example that relies on the cowplot library.
# Load a needed library
library(cowplot)
# Create the first graph
crab_p1 <- ggplot(pie_crab, aes(x = site, y = size, fill = site)) +
geom_violin() +
coord_flip() +
theme_bw() +
theme(legend.position = "none")
# Create the second
crab_p2 <- ggplot(pie_crab, aes(x = air_temp, y = water_temp)) +
geom_errorbar(aes(ymax = water_temp + water_temp_sd, ymin = water_temp - water_temp_sd),
width = 0.1) +
geom_errorbarh(aes(xmax = air_temp + air_temp_sd, xmin = air_temp - air_temp_sd),
width = 0.1) +
geom_point(aes(fill = site), pch = 23, size = 3) +
theme_bw()
# Assemble into a plot grid
cowplot::plot_grid(crab_p1, crab_p2, labels = "AUTO", nrow = 1)labels = "AUTO" argument means that each panel of the plot grid gets the next sequential capital letter. You could also substitute that for a vector with labels of your choosing
