ABSTRACT
Biodiversity databases have recently become widely available to the public
and to other researchers. To retrieve information from these
resources, users must understand the underlying data schemas. Many other domains share this problem.
We developed an interface, TaxonTree, to visualize the taxonomic hierarchy
of animal names. We applied integrated
searching and browsing so that users need not have complete knowledge either of appropriate
keywords or the organization of the data.
Our qualitative user study of TaxonTree in an undergraduate course is the first to describe usage patterns and issues in the biodiversity domain. Users indicated that the system was usable and tree visualization aided their understanding of the data. Interestingly, users with different levels of interest in the domain had different interaction preferences. The study provides further evidence for the value of integrated searching and browsing and interactive tree visualization for information retrieval and understanding.
Author Keywords
Biodiversity, Hierarchy/Tree Visualization, Information Retrieval, Searching, Browsing,
Animation.
ACM Classification Keywords
H.5.2 [Information Interfaces and Presentation]: User Interfaces – Graphical user interfaces (GUI), Interaction styles, Screen design, Evaluation/methodology; H.3.3 [Information Storage and retrieval]: Information Search and Retrieval – Information filtering, Search process; J. 3 [Life and Medical Sciences]: Biology and genetics.
INTRODUCTION
Biodiversity Information
An understanding of the nature and magnitude of biological diversity is fundamental to most pressing environmental and conservation debates; the budding field of biodiversity informatics is poised to transform those debates [10, 21]. Biodiversity can be defined as “the variety of life” [27]. Typical questions in this field include: What kinds of organisms exist? What is their genetic diversity? How are these organisms distributed geographically? What processes are responsible for generating, maintaining, or changing this variety? How is this variety important to us and how do we affect it?
Biodiversity databases designed to answer these questions are as complex as molecular and medical biology resources. They contain organism-related information such as distribution, taxonomy, natural history, and conservation data. Perhaps the best developed are a number of taxonomic databases for researchers. The Species 2000 [1] initiative seeks a comprehensive index of the world’s species. Museums provide online access to their specimen databases, often through gateways such as the Ocean Biogeographic Information System [4], and are currently developing tools for knowledge discovery and distributed querying (e.g. Mammal Networked Information System [3]).
Unlike molecular biology databases, many biodiversity databases serve a broad audience. Wildlife departments and agricultural extension agencies maintain state-specific websites on local game, non-game, and pest species. These resources as well as commercial and private non-profit natural history databases are aimed at concerned citizens and outdoor enthusiasts.
Whether they present primary data or encyclopedic summaries, these resources share a reliance on taxonomy, or the scientific naming of organisms. Biologists give organisms scientific names, usually Latin or Latin-like, that must follow certain rules to be considered official by the scientific community. Organisms thought to be close relatives are put into a group together, and related groups are likewise grouped, forming a nested hierarchy, which can be visualized as a tree. In a Linnaean classification, groupings receive a rank, such as Kingdom, Phylum, Class, Order, Family, Genus, and Species (in decreasing order of inclusiveness). Common names, on the other hand, are informal ways of referring to organisms. While they are not standardized (they differ according to language and dialect of the laypeople using them) common names can be very useful for non-experts.
Biodiversity Information Retrieval
Current interfaces to online biodiversity resources can be
divided into three broad categories, none of which take advantage of current
advances in HCI research such as dynamic queries [6], query previews [16], and visualization [13]. The simplest interfaces involve one or two
free text query fields and either pull-down or radio button lists of categories
to be searched. The current Animal
Diversity Web interface is an example of such a simple structured query
interface (Figure 1). This
interface has the advantage of simplicity, but may not be used effectively by
users who do not know currently recognized scientific or common names.
Figure 1. ADW Simple
Structured Query Interface
Some interfaces rely heavily on controlled keyword choices and combine all possible structured queries on the same page. This kind of interface is typical of large conservation data resources, and is no doubt driven by the complexity of the different query types expected from users. While it reduces the need to know keywords for each category, a novice user or even a professional may be stymied by how much or how little of the page may be used for their search, and by the role of free text in the search.
The third and most complex kind combines the first two by
providing different pages for different kinds of structured queries. This makes it possible to support many kinds
of queries without including them all on the same page, but a user must
determine which page has the query form of interest. It is possible to build a complex query across query types, but
the user must build queries across pages.
These interfaces all have problems for end-users. First, they require that the users have
significant knowledge about the underlying database, and the structure of the
website search interface. Without such
knowledge, users regularly get empty, or alternatively, huge search
results – which are often meaningless. Second, they
often provide search results in the form of a long list (Figure 1). Such lists
lack biological context that could help users not only find specific
information but understand the larger biodiversity patterns in the
results. Our approach to solving
these problems is to create an interface that couples browsing and searching of
the taxonomic hierarchy using modern visualization techniques in order to help users build
an understanding of the underlying data.
Below we review research on visualizing hierarchies, integrating searching and browsing, and other techniques that motivate our approach. We then describe the tool we designed, TaxonTree. Finally, we describe a user study of TaxonTree designed to characterize user behavior in the biodiversity domain.
Related
work
Visualizing
Hierarchies
Over the last 20 years there has been much
research on how to effectively display and interact with hierarchies. One approach is node-link diagrams. Cone Trees, a 3D hierarchy visualization,
makes the context easily visible with a focus and context display [24]. However, it has limits on scale and suffers
from occlusions. Hyperbolic Browser
solves the occlusion problems of Cone Trees by using hyperbolic space instead
of 3D perspective [19]. WebTOC displays
a hierarchical representation of the documents local to the site as a Table of
Contents [22]. Users can expand and collapse the
hierarchy. Links are listed with
a bar that represents the volume of information available when
following that link. PadPrints
provides a dynamically built hierarchical history of visited web pages to aid
web navigation [18]. Zooming enables users to control how much context is viewed. SpaceTree combines the node-link tree diagram with a
zooming environment that dynamically lays out
branches of the tree to best fit the available screen space [23]. Degree of Interest Trees compute a degree of interest value for each node
in the hierarchy and display an overview of the complete hierarchy
by only showing detail for nodes with high DOI values [12].
Integrated
Searching and Browsing
A number of projects have integrated searching
and browsing. SuperBook shows the
number of occurrences of the search word to the left of section titles in a
table of contents [15]. However, users still have to expand items
manually to see how the words are distributed in the lower levels. AMIT
(Animated Multiscale Interactive TreeViewer) integrates fisheye treebrowsing
with search and filtering for web information access [28]. It displays only the paths to the matching
nodes and the size of a node represent its relevance score. LifeLines searches can be
performed on a patient record and highlights all parts of the record that match
[7]. SpaceTree supports dynamic filters and searches
showing results in the node link diagram [23].
Yee et al. applied integrated searching and browsing to user interfaces
for large image collections within their web-based Flamenco system [29].
DateLens is a fisheye calendar that highlights the days containing
appointments that match the search criteria [9]. In
addition, its attribute-mapped scrollbar represents which days are highlighted
outside of the current view.
Animation
Bederson and Boltman [8] found that animation improves
users’ ability to reconstruct the information space, and does not increase task
performance time. In applications where the user navigates around fixed spatial data
space, they recommend brief animated transitions. Others have made similar recommendations [11].
SpaceTree
Because SpaceTree [23] (Figure 2) combines hierarchy visualization, integrated
searching and browsing, and animation, we chose to adapt it to the biodiversity domain as a first
step towards understanding the domain and its users. SpaceTree
is a tree browser that enables users to explore large trees by adding a zooming environment
that dynamically lays out branches to best fit the conventional node-link
diagram to
the available screen space.
Figure 2. SpaceTree
Users can navigate the tree by clicking on nodes or by using
the arrow keys. Based on user feedback that they
didn’t want to open the tree one level at a time when there was room to
open more levels at once, SpaceTree maximizes the number of lower levels to be opened. When users change the focus, the tree
is animated to its new layout, which makes full use of screen space, in three main
steps: 1) trims the tree of the branches that would overlap the new branch to
be opened; 2) moves the trimmed tree so that the new tree layout will center on the window, 3) expands
the branch out of the new focus point. While
animating, SpaceTree retains landmarks to help users maintain their
orientation. It uses the current focus
and the path up to the root as landmarks and highlights the ancestor path of the
current focus. SpaceTree provides icons to preview
the topology of branches that cannot be fully opened because of lack of
space.
SpaceTree also supports filters and searches. As users type a string, SpaceTree highlights the relevant nodes within
the tree. Users can see a filtered view
of the tree, displaying only the paths to the matching nodes.
Figure 3. TaxonTree lays
out branches of the classification tree to best fit the available screen space.
Magnified node shows synapomorphies (evolutionarily significant, diagnostic
characteristics) and “more” triangle indicating further nodes.
Taxontree
To visualize the Linnaean classification for taxonomic names
in Kingdom Animalia, we developed TaxonTree (Figure
3) by extending SpaceTree. TaxonTree[1]
allows users to browse and search a tree of about 200,000 animal names that we
constructed by integrating data from a number of public and private sources [2, 5, 25, 26]. TaxonTree,
like SpaceTree, uses animated zooming and integrated searching and browsing. Search results are presented in the
larger context of their classification tree to help users understand patterns in
biodiversity information.
Target
Audience and Design Partners
There have been no studies to describe usage patterns and issues
in the biodiversity information domain. We
used methodologies adapted from collaborative design [14] to explore the domain and develop an interface
for it in concert with its users.
Our target audience is students taking a second-year college course
University of Maryland entitled Introduction to Animal Diversity. As biology majors, they are becoming familiar
with the biological content but cannot be considered experts. To incorporate the extra detail that the
course covered about known evolutionary relationships, we constructed a
specialized tree of animal names that further resolved the Linnaean
classification at higher levels [17, 20].
We worked with a five member team of “design partners”, invited from the
Animal Diversity course; seven women participated as design partners during
this study.
Extensions
While major functionalities are inherited from SpaceTree, TaxonTree
expands on its capabilities in several ways.
Domain-specific visualization
We changed the visualization of TaxonTree to support biodiversity
data. First, we displayed common names with a
different font style so that users can distinguish them easily. Second, we provided links to
external web pages on three different, publicly available websites (Animal
Diversity Web, Tree of Life, University of California Museum of Paleontology). Thus, TaxonTree can be used as a portal to
public biodiversity resources.
Some features were designed explicitly for the University of Maryland Animal Diversity course, such as visual distinction of required course material from non-required material, bookmarks of names for future reference, and display of biological ranks for each node as tool tips. We also added a way to see 'synapomorphies' (Figure 3) - attributes of nodes that show how that node is distinguished from its siblings.
Figure 4. TaxonTree
presents search results in the larger biological context of their
classification tree. All squids are mollusks but there
are several subgroups.
Search
Users can search both scientific and common names in the
classification. They can also search on
synapomorphies and the full text of the Animal Diversity Web showing results
within TaxonTree. TaxonTree presents search results in
the biological context of their classification tree and highlights them (Error! Reference source not
found.). It helps users
understand patterns in the results, for example animals with a common name
including “dolphin” appear in two very different branches of the tree. Since the search result tree is often larger
than screen, TaxonTree automatically zooms out to fit it in.
Modified
interaction
As explained above, at the first step of animation when users change the
focus, SpaceTree automatically closes the branches that would overlap a newly
opened branch. This has two major
advantages: 1) the screen is less cluttered; 2) the siblings of the focus node
are always adjacent. However, we found
that some of the more sophisticated 'auto-layout' features of SpaceTree were
confusing and disorienting to our undergraduate design partners and project
members with content experience and interest.
We thus introduced an optional simpler navigation mode whereby users can
open and close nodes manually.
Scaling
up
To show the overview of the whole tree and to provide dynamic filters, SpaceTree requires the entire tree to be loaded into memory. Since this is impractical for large trees, we built TaxonTree to access the data from a database. In this way we are able to scale up to support interaction with very large trees and multiple attributes.
USER
STUDY
In May 2003 we conducted a qualitative study with three main
goals. First, we wanted to characterize
how users of this domain think about biodiversity information in general. Are they more likely to look for information
using scientific or common names? What
taxonomic rank (species or higher) are they more likely to target? What kind of information are they most
interested in? Second, we investigated
the usability and interaction preferences with this particular software. Are users comfortable with integrated
searching and browsing, and with animation and zooming? Finally, we examined how this kind of information retrieval
interface can assist information understanding in this domain. Do students use the tree visualization to
successfully complete tasks that require interpretation and understanding of
the underlying data structure?
We chose a qualitative methodology because user behavior in this domain has never been studied. Also, the aim of TaxonTree is to foster content understanding so standard metrics of efficiency are unlikely to be appropriate. Insights gained from this study should guide both design and quantitative assessment of future tools.
Participants
We recruited 18 undergraduate volunteers (8 male: 10 female,
18 to 20 years old) from the Animal Diversity course at University of
Maryland. None of them were part of the above described
design partner team. Each participant
was given ten dollars for his/her participation. We tested five pairs of users and eight single users for a
total of 13 sessions, or user “teams.” The study
occurred at the end of the semester so participants were largely familiar with
the biological content but could not be considered experts. The software had been demonstrated in
lecture and distributed to all students on CD-ROM for personal use two
weeks prior to the study. Users reported
they had used the program for an average time of half an hour, and eight out of
18 had not used it at all.
Procedure
Each session lasted 30 to 45 minutes. Each user filled out a survey to determine their computer usage background and amount of time previously spent with TaxonTree. They were seated in front of a 2GHz Windows XP laptop with an ordinary mouse, a 1280x1024 pixel display and 512MB RAM, placed on a standard office desk. We videotaped the computer screen throughout the testing to capture both the actions and verbal comments of the users.
Users then received a brief demonstration of TaxonTree features. User teams were asked to perform nine information retrieval tasks, described below. When user teams had obvious difficulty completing the tasks we gave them prompts. At the end of the tasks, we asked open-ended questions about what each user liked and found difficult about the software. We asked what kind of information they generally would like about animals.
Tasks
Two tasks were general and open-ended, without single correct answers. We designed these tasks to assess user
preferences for information targets and strategies to reach them. We asked them
to use TaxonTree to find information about an animal of their choice. Depending on the strategy they took in task
1 (searching or browsing), we asked them in task 2 to choose another target
animal and use the other strategy to find it.
The other seven tasks were more specific and had a limited number of
correct answers. These tasks assessed a
user’s ability to use most of the features of the software, to further examine
their preferences for information-seeking strategies, and to examine the role
of the interface in understanding the information.
These tasks were: 3. Find an extinct taxon. 4. Count how
many extinct taxa you might need to know about for the final exam. 5.
Find and name the taxon whose members are all united with the synapomorphy
"Lactation." 6. What is the sister group to this group of lactating
animals? 7. Now try searching on the common name, “dolphin.” What do you notice about the results? 8.
Find some victims or carriers of malaria. 9. What do you notice about these
victims or
carriers?
results
Characterizing Users In This Domain
We
noticed during testing sessions that some users were clearly interested in the
content. These users verbally expressed
prior content knowledge as they worked on tasks, or asked questions indicating
curiosity about information beyond the task.
For example, a user asked “Why isn’t there anything about mosquitoes?”
when looking at result for a search about malaria. In contrast, some users never departed from the tasks at hand. Figure
5 illustrates how often users offered extra
information indicating content interest.
Guided by Figure
5, we defined high interest users as those who
spontaneously offered extra content information during at least 4 of the 9 tasks; the others
were labeled low interest. Consistent
with this categorization, three users labeled low interest in this way had
stated they lacked interest in the Animal Diversity course.
Figure 5. Distribution of 13 user teams based on the
number of tasks in which they spontaneously offered additional biological
information, indicating their interest in the domain
Low interest and high interest users reported similar hours of experience with the application and similar levels of comfort with computing. Six of the 8 males in the study were in low-interest user teams; two of the ten females were in low interest teams.
Users tended to be interested in looking for animals using common names and above the species level e.g. “frogs.” (Order Anura). Specifically, when asked to choose any animal to find, users gave 20 out of 26 initial targets as common names rather than scientific names. Many targets were clearly above the species level (14 of 26 targets) while 4 were ambiguous and 8 were species level. Fewer than half of the search targets (12 of 26 targets) were required course content. Low and high interest user teams had similar search targets. When asked the kinds of information they were interested in, 5 of 13 teams mentioned way of life (food habits, behavior, ecology). Five of 13 teams noted that they enjoyed learning unique characteristics of animals – interesting superlatives or what sets an animal apart from others. Four user teams mentioned that they wanted only the information necessary to pass their course. Two user teams mentioned an interest in evolutionary relationships. One user wanted information to distinguish dangerous from harmless animals.
Usability
The interface seemed comfortable to users once they knew what
features were available. Interaction
with and interpretation of the nodes was apparently intuitive, because even
users who had never used the program immediately began opening and closing
nodes. Few users needed prompts
explaining “more” triangles (shown in Figure
3), panning or zooming, node-clicking, or the
ability to search. About 85% of the
prompts we gave related instead to using our specific search categories and
controlling the view options: how much of the tree was displayed (all nodes as
they are opened, just the subset required for the course as they are opened, or
all required nodes at once). Low interest users actually needed, on average,
fewer prompts per session (4.3) to complete tasks compared to high interest
users (7.4).
Searching and Browsing
Most users used both searching and browsing strategies together in at least one task. Only four user teams always used a single strategy within each task; three of these four teams were low interest users. Five of 13 teams browsed the tree before choosing a target or changed their target while browsing.
Most users preferred browsing the tree over searching. Only three of 13 teams used searching as an initial strategy – these were all high interest users who probably had better ideas of what search terms to use. Even after a successful search, 10 out of 11 subject teams returned to a strategy of browsing. When asked why, they told us it was more fun than searching, that they wanted to refresh their memories, and that they didn’t know exactly what to search on.
Task Completion
Users completed 92% of all tasks without prompts to interpret results shown onscreen.
Some tasks that asked for direct interpretation of the tree were very easy for the user teams. In task 5, 12 of 13 teams needed no prompts to correctly associate an attribute (the synapomorphy “lactation”) with the name of the appropriate node. All but one team successfully completed task 6, identifying a sister group from a search result by opening a nearby node. Only three teams needed a prompt. Task 9, what do you notice about victims and carriers of malaria, was readily answered. Eleven of 13 teams gave an immediate answer relating to the tree structure (such as, the search results were in related branches of the tree). Task 4 asked “Count how many extinct taxa you might need to know about for the final exam.” All but one team immediately moved from displaying all overview nodes, including 16 color-coded as extinct, to task completion (counting all the nodes that were color coded as extinct).
However, some tasks were clearly harder than others. Task 7 asked users to draw inferences from a search on the common name “dolphin.” We scored understanding success if the users gave at least one of two answers. First, there are many kinds of organisms whose common name includes the word “dolphin.” Second, organisms with a common name including the word “dolphin” appear in more than one very different branches of the animal kingdom. The first inference was immediately drawn by all but one of the 13 user teams. Such an inference would be nearly impossible to make quickly using a typical list of search engine results. The second, however, requires the more sophisticated inference requiring an understanding of biological relationships. This inference was only mentioned by five of 13 user teams.
Task 8 asked users to conduct a search for carriers or victims of malaria. This task posed particular difficulty because of its sensitivity to both the search terms chosen and the category of search that was run. Three users were unable to complete the search without more than two prompts. These plus an additional two user teams failed to look at the web pages in the results to be sure that their search terms were in the appropriate context. However, 8 of 13 teams did check for relevance.
Tree visualization was very effective in helping users complete tasks. Task 9, “What do you notice about these victims or carriers?” could be completed either by interpreting a tree visualization of search results or by applying prior knowledge to those search results. For example, a user response such as “all of these victims seem to live in forests” would be an example of prior knowledge, while “all of these victims are in the vertebrate part of the tree” indicates use of the tree to interpret the results. Although three or four user teams did use prior knowledge, only one set of users used it as their first answer to the question. All the others gave tree information for their first answer.
Domain interest seemed correlated with domain expertise as high interest users
but not low interest users tended to effectively use their prior knowledge to
help solve tasks.
User
Comments
User responses to open ended questions are summarized in Table 1. Users said
that TaxonTree was usable and had desirable content (synapomorphies, external
web links, course information). Several mentioned explicitly that TaxonTree’s
visualization would be more useful to them than accessing the same information
in their lecture notes or in the textbook.
Users had difficulty with unfamiliar features (search categories and view menu options). The other negative comments related to information quantity. Some users noted the difficulties inherent in displaying large amounts of information (font sizes, zooming problems). Some wanted more refined search results, while others felt that merely having so much information available to browse or search was daunting.
All but two user teams offered spontaneous positive comments while
completing tasks. Visualizing
search results in the tree structure elicited the most positive comments
(6 user teams), along with the availability of web pages with more information
(4). Four user teams also were excited
about the ability to see an overview tree of the information necessary for
their course.
|
What users liked |
# of user teams |
|
Easy to learn and use |
9 |
|
Tree visualization |
9 |
|
Synapomorphies |
7 |
|
Ability to search different categories |
6 |
|
How evolutionary history is presented |
4 |
|
How tree is interactive |
3 |
|
Links to external web sites. |
3 |
|
Seeing which content required for their
course. |
3 |
|
What users found difficult |
|
|
Search categories were hard to understand |
4 |
|
Font too small, especially when zoomed
out |
3 |
|
Too much information, too many search
results |
3 |
|
Had problems zooming |
3 |
|
Had problems understanding view menu |
3 |
Table 1. User comments
to open-ended questions. Responses
given by fewer than three user teams are not included.
discussion
AND CONCLUSION
Our qualitative user study of TaxonTree is the first to describe usage
patterns
and issues in the biodiversity domain. TaxonTree provides the value of
interactive tree visualization with integrated searching and browsing in the
biodiversity domain.
Characterizing
Biodiversity Users
We recognize that our self-selected user sample is not representative of
the public at large, but we can begin to characterize a subset of biodiversity
information seekers – learners. Interest in biodiversity was female-biased.
Even interested biology majors primarily seek information using common
names. They often look for names at
levels above the species level, and have some prior knowledge about what they
are looking for. Basic natural history
is of interest, as well as particular characteristics distinguishing an animal
from others. Though drawn from a
course focusing on evolutionary relationships, only two user teams mentioned a special
interest in them. This suggests a general lack of appreciation for the value of these
relationships in understanding biodiversity. Our users expressed little concern over practical information such as which
animals are dangerous, or how to care for pets.
Role of Content Knowledge/Interest
It is difficult to separate effects of content interest from
content knowledge but we think this is a promising area for future
research. In our study, high interest
users often used prior knowledge to solve tasks. Was
this because they had more knowledge (a consequence of their content interest)
or were they more willing to use it?
After formal testing we asked some user teams to try the auto node
closing feature. Their comments were
consistent with the impressions we gained during our design phase: high
interest user teams, like our design partners and our staff biologists,
preferred manually closing and opening branches. Low interest users preferred
auto-closing. We plan further tests to
determine if this difference in interaction preference is really related to
content expertise, and if so whether individuals transition from one style to
the other as they become experts.
Figure 6. Example of tree
diagram currently familiar to biology students
Interactive Tree Visualization
A style of tree diagram (shown in Figure
6) that biology students are currently familiar with is
very different from TaxonTree’s style in the following ways: 1) it shows names
only at leaves; 2) internal nodes are labeled with brackets outside of the
tree; 3) every branch has a fixed angle. Despite these differences, users
easily understood TaxonTree’s tree structure.
We conclude that our combination of interaction style and tree
representation is very intuitive. It
could therefore be useful in many other domains, but a closer look at the
trade-offs of the different representations is warranted.
One possible explanation for why users prefer to browse the tree rather than
search it is that they enjoyed the animated interaction. If this is true, making interaction fun may
be another benefit of animation.
It is beneficial to present search results in an interactive classification
tree that shows biological context. Users
easily interpreted search results, quickly using the tree structure to discover
the quantity of biologically unique results. They
often made more sophisticated inferences about relationships among the
search results, which is nearly impossible by using a typical list of search
results. They also carried out additional browsing,
giving them a better sense of the search result by further browsing.
ACKNOWLEDGMENTS
This work builds on our colleagues’ work on SpaceTree, and
we especially thank Catherine Plaisant and Jesse Grosjean for their ongoing
efforts with that. We thank our design partners for
their insight and assistance. Jeff
Jensen provided invaluable content advice and access to his undergraduate
course. This work is supported
in part by NSF #0219492.
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