Part of the Global Unified Open Data Architecture (GUODA) infrastructure hosted at iDigBio is providing Jupyter Notebooks to biodiversity researchers to facilitate analyzing large datasets with Apache Spark. This talk was delivered at the TDWG 2016 Annual Conference in Santa Clara de San Carlos, Costa Rica.

1. iDigBio is funded by a grant from the National Science Foundation’s Advancing Digitization of
Biodiversity Collections Program. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect the views of
the National Science Foundation.
GUODA: A Unified Platform for
Large-Scale Computational
Research on Open-Access
Biodiversity Data
Matthew Collins, Alexander Thompson, Jorrit
Poelen, Jennifer Hammock

2. 2
What is GUODA?
Global Unified Open Data Access
An informal collaboration between technologists from
organizations like EOL , ePANDDA, and iDigBio as well as
independent biodiversity informaticists. We share data use
cases, best practices, infrastructure, code, and ideas
around the science that can be done by analyzing large open-
access biodiversity datasets.
http://guoda.bio

3. 3
What our members are interested in
Computation with biodiversity data
• Research at scale
• Lowering barriers to accessing computation
• Reproducibility
Matthew Collins
Technical Operations
Manager - iDigBio
Jorrit Poelen
Independant
Alexander Thompson
Software Products
Lead - iDigBio
Jennifer Hammock
Marine Theme
Coordinator - EOL
Nathan Bird
Software
Developer - iDigBio

4. 4
An example use of GUODA
Does anyone use catalog numbers in
remarks fields to document relationships
between specimen records in iDigBio?
(We’re at TDWG so we’ve got to do
something with identifiers, right?)

5. 5
A term-document index of iDigBio
(idb_df
.select(idb_df["uuid"],
idb_df["uuid"])
.where(sql.column("note") != "")
.withColumn("tokens",
udf_tokenize(sql.column("note")))
.select(sql.explode(sql.column("tokens")))
.groupBy(sql.column("uuid"),
sql.column("token"))
.count()
)

6. 6
What terms match catalognumber?
joined = (idb_df_ids
.join(idb_tf_df,
on=idb_df_ids["idb_catalognumber"]
== idb_tf_df["token"])
.join(idb_df_notes,
on=sql.column("uuid") ==
idb_df_notes["note_uuid"])
.withColumn("catalognumber_len",
sql.length(sql.column("idb_catalognumber")
))
)

7. 7
What do we find?
A few things like record
bd347847…
Has a remark
Part of Collection at FH:
barcode-00374180.
Which matches record
826da57a...
Histogram of matching
catalognumber length

8. 8
How long did that take to write?
< 200 lines of code (including whitespace
and comments)
1 intermittent day of coding
https://github.com/iDigBio/idb-spark

9. 9
How long did that take to run?
73.5 million records in iDigBio
to 151 million document:term:counts
40 minutes
Joined back to iDigBio resulting in 2.9 billion
terms found in the catalognumber field
3 hours 40 minutes

10. 10
Good tools in the hands of people
with good ideas:
IDEAS RESULTSWORK

11. 11
Servers!
Mesos
HDFS
Spark
Marathon
Docker
Cassandra
Infrastructure
Advanced Computing and Information Systems Lab
http://acis.ufl.edu

12. 12
Data is half the tool
Copies of whole datasets
• Stored locally
• Refreshed automatically
Re-represent datasets in a useful structure for
high performance computing - parquet on
HDFS:
https://github.com/bio-guoda/guoda-datasets

13. 13
Interfaces to GUODA
• Jupyter Notebooks for end-users
• Containers for API and web services
• Persistent storage for application state
• Hangouts calls every 2-4 weeks

14. 14
The front door to GUODA
Notebooks
“Literate Programming”
Comments, code, and outputs all
together in a readable document
that describes what is being done

15. 15
Here’s what it looks like

16. 16
GUODA Jupyter notebook interface

17. 17
What would you do with it?
Have a Github account and want to write
code? This is an alpha quality system.
http://jupyter.idigbio.org
Or talk to us if you want to host an
application on our systems
mcollins@acis.ufl.edu godfoder@acis.ufl.edu

18. iDigBio is funded by a grant from the National Science Foundation’s Advancing Digitization of
Biodiversity Collections Program. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect the views of
the National Science Foundation.
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