[BiO BB] DataONE Summer Internship Opportunity
Hilmar Lapp
hlapp at gmx.net
Tue Mar 15 15:14:49 EDT 2011
The Data Observation Network for Earth (DataONE) is a virtual
organization dedicated to providing open, persistent, robust, and
secure access to biodiversity and environmental data, supported by the
U.S. National Science Foundation. DataONE is pleased to announce the
availability of summer research internships for undergraduates,
graduate students and recent postgraduates.
Program Structure
Up to eight interns will be accepted in 2011, each paired with one
primary mentor and, in some cases, secondary mentors. Interns need not
necessarily be at the same location or institution as their mentor(s).
Interns and mentors are expected to have a face-to-face meeting at the
beginning of the summer, and interns are encouraged to attend the
DataONE All-Hands Meeting in the fall to present the results of their
work. DataONE will pay all necessary travel expenses.
Schedule
March 15 - Application period opens
April 8 - Deadline for receipt of applications at midnight Pacific time
April 15 - Notification of acceptance. Scheduling of face-to-face
kickoff meetings based on availability of interns and mentors
May 23 - Program begins*
June 27 - Midterm evaluations
July 29 - Program concludes
October 18-20 - DataONE All-Hands-Meeting, New Mexico (attendance
encouraged)
* Allowance will be made for students who are unavailable during these
date due to their school calendar.
Eligibility
The program is open to all undergraduate students, graduate students,
and postgraduates who have received their masters or doctorate within
the past five years. Given the broad range of projects, there are no
restrictions on academic backgrounds or field of study. Interns must
be at least 18 years of age by the program start date, must be
currently enrolled or employed at a university or other research
institution and must currently reside in, and be eligible to work in,
the United States. Interns are expected to be available approximately
40 hours/week during the internship period (noted below) with
significant availability during the normal business hours. Interns
from previous years are eligible to participate.
Financial Support
Interns will receive a stipend of $4,500 for participation, paid in
two installments (one at the midterm and one at the conclusion of the
program). In addition, required travel expenses will be borne by
DataONE. Participation in the program after the mid-term is contingent
on satisfactory performance. The University of New Mexico will
administer funds. Interns will need to supply their own computing
equipment and Internet connection. For students who are not US
citizens or permanent residents, complete visa information will be
required, and it may be necessary for the funds to be paid through the
student’s university or research institution. In such cases, the
student will need to provide the necessary contact information for
their organization.
Project Ideas
Projects cover a range of topic areas and vary in the extent and type
of prior background required of the intern. The interests and
expertise of the applicants will, in part, determine which projects
will be selected for the program. Off-list projects are also eligible,
in which case potential applicants are strongly encouraged to contact
the organizers and/or potential mentors with their ideas prior to
applying. The titles of this year’s projects (see below for more
detailed descriptions) are:
DATA MANAGEMENT: Best practices of data management for public
participation in science and research
DATA MANAGEMENT: Online learning modules related to best practices
throughout the data lifecycle
EDUCATION: Accessing and analyzing environmental data in the classroom
SOCIOLOGY OF SCIENCE: Understanding how scientists analyze data
DATA SCIENCE: How much ecological data is out there?
DATA SCIENCE: Tracking the reuse of 1000 datasets
PROGRAMMING: Subsetting and publishing “dynamic” scientific datasets
PROGRAMMING: Scientific workflow provenance repository and publishing
toolkit
PROGRAMMING: Integrating loosely structured data into the Linked Open
Data cloud
SCIENCE COMMUNICATION: Developing video animations for DataONE
community engagement
To Apply
Application materials should be sent to internship at dataone.org by
11:59 PM (Pacific time) on April 8th, and should include a cover
letter, resume and letter of reference all in PDF format. The
applicant should send the cover letter and resume, while the letter
of reference should be sent directly by its author.
The cover letter should address the following questions:
What DataONE Summer Internship projects are you most interested in and
why?
What contributions do you expect to be able to make to the project(s)?
What background do you have which is relevant to the project(s)?
What do you expect to learn and/or achieve by participating?
What are your thoughts and ideas about the project, including
particular suggestions for ways of achieving the project objectives?
How will participation in this program help you achieve your
educational and career objectives?
Are there any factors that would affect your ability to participate,
including other summer employment, university schedules, and other
commitments?
The resume should include the applicant’s educational history, current
position, any publications or honors, and full contact information
(including phone number, e-mail address, and mailing address).
The letter of reference should be sent directly to internship at dataone.org
and should be from a professor, supervisor, or mentor.
Evaluation of applications
Applications will be judged by the following criteria:
The academic and technical qualifications of the applicant.
Evidence of strong written and oral communication skills.
The extent to which the applicant can provide substantive
contributions to one or more projects, including the applicant’s ideas
for project implementation.
The extent to which the internship would be of value to the career
development of the applicant
The availability of the applicant during the period of the internship.
Intellectual Property
DataONE is predicated on openness and universal access. Software is
developed under one of several open source licenses, and copyrightable
content produced during the course of the project will made available
under a Creative Commons (CC-BY 3.0) license. Where appropriate,
projects may result in published articles and conference
presentations, on which the intern is expected to make a substantive
contribution, and receive credit for that contribution.
Funding acknowledgement
The Summer Internships are supported by The National Science
Foundation: "INTEROP: Creation of an International Virtual Data Center
for the Biodiversity, Ecological and Environmental Sciences" (NSF
Award 0753138) and "DataNet Full Proposal: DataNetONE (Observation
Network for Earth)" (NSF Award 0830944).
For more information
If you have questions or problems about the application process or
internship program in general, please send e-mail to internship at dataone.org
.
Project Ideas
Best practices of data management for public participation in science
and research
Description: The DataONE Citizen Science Working Group (CSWG) is
working to organize and develop best practices for management of data
and information for the increasing number of local, regional and
national projects that focus on “Public Participation in Science and
Research (PPSR),” also called Citizen Science projects. The 2011 CSWG
intern will assist in the inventory and description of data practices
for PPSR projects, based on the response from an earlier survey
conducted as part of the CSWG. The goals of the intern project are to
develop a metadata description for key aspects of the data held by
each group, and make this information available back to the CSWG as a
small database. The intern will then help identify and document best
practices for data management by PPSR projects, assist in vetting the
best practice documents across the PPSR community, and work with CSWG
to make the best practices available via the DataONE website as well
as other outlets. Products will include a suite of best practices for
data management by PPSR projects; in addition, the intern will be
encouraged to give a formal presentation at a scientific, data
management or PPSR conference or meeting. Local work preferred, at
Tucson or Ithaca, though remote work would be possible for outstanding
candidates (though one trip for an organization meeting would be
required).
Qualifications needed: Undergraduate or graduate student or
equivalent; simple database management (e.g., MS Access) skills
preferred; public engagement; writing; organization; small project
management
Skills to be learned: Metadata management; best practices template;
database management; communications and outreach; project management
Primary mentor: Jake Weltzin (USA National Phenology Network)
Secondary mentor: Rick Bonney (Cornell Laboratory of Ornithology)
Developing online learning modules related to the best practices
throughout the data lifecycle
Description: DataONE is developing online learning modules designed to
educate DataONE users in various aspects of the data lifecycle. This
project involves: 1) researching and acquiring software that can
produce high quality online learning; 2) developing online learning
modules using pre-prepared power point slides produced by the DataONE
Community Engagement and Education Working Group; 3) adding content
about data management 4) participating in a workshop hosted by DataONE
to refine and add additional content to educational modules (July,
2011).
Qualifications needed: A science data management background;
Familiarity with aspects of the data lifecycle; Ability to quickly
learn new software; Some work in development of educational materials
helpful
Skills to be learned: Creative ways to educate a varied audience on
data lifecycle; familiarity in use of chosen software used to develop
online learning modules; collaboration techniques with dispersed
working group.
Primary mentor: Viv Hutchison (USGS NBII)
Secondary mentors: Stephanie Hampton (National Center for Ecological
Analysis and Synthesis), Carly Strasser (National Center for
Ecological Analysis and Synthesis)
Understanding how scientists analyze data
Description: Scientists use a wide variety of tools and techniques to
manage and analyze data. However, to our knowledge no one has taken a
systematic look at how scientists do their work. In this project, we
will examine a large number of the scientific workflows that have been
constructed. We will develop a way of categorizing workflows based on
their complexity, types of processing steps employed, and other
factors. The goal is to develop new and significant understanding of
the scientific process and how it is being enabled by science workflows.
Qualifications needed: Self-starter, determined, enthusiastic, willing
to keep a research notebook up-to-date openly online. Experience with
a modern programming language, statistics and data analysis, and R
would be helpful.
Skills to be learned: Kepler and Taverna workflow languages, research
methods, research analysis, keeping an open science research notebook,
communicating research results. A peer-reviewed publication is
envisioned.
Primary mentor: William Michener (University New Mexico)
Secondary mentors: Rebecca Koskela (University of New Mexico), Bertram
Ludaescher (University of California Davis)
Accessing and analyzing environmental data in the classroom
Description: A graduate student intern will create an educational
module for use in undergraduate classrooms – the module will be
designed to teach basic principles in ecology or environmental science
using data that are publicly available through the DataONE network.
The student will work with mentors to choose appropriate data sets,
questions and analyses, and create a simple program to access and
analyze the data in R. The student will create documentation that
accompanies the exercise, potentially in multimedia formats, to train
instructors to use the exercise in classrooms.
Qualifications needed: Basic background in ecology or environmental
science, and statistics is necessary. Experience implementing
statistics in a scripted statistical package such as R, Matlab or SAS
is necessary. Experience with online training materials and multimedia
presentation – e.g., screencasts - is useful.
Skills to be learned: The student will hone skills in statistical
analysis, programming in R, working with large data sets, and creating
teaching materials. The student will gain a well-rounded perspective
on the importance of all aspects of the data life cycle in
environmental sciences, and build a diverse professional network with
leaders in environmental informatics and data-driven environmental
science research.
Primary mentor: Stephanie Hampton (National Center for Ecological
Analysis and Synthesis)
Secondary mentors: Carly Strasser (National Center for Ecological
Analysis and Synthesis), Amber Budden (University of New Mexico)
How much ecological data is out there?
Description: No one is certain how much ecological data exists, or how
this amount compares to the volume of data currently housed in
repositories such as Knowledge Network for Biocomplexity (KNB). It
would be useful to determine this for designing infrastructure, but
also as a call to arms for ecologists to start sharing this “dark
data”. For this project, we will develop a method for estimating the
amount of ecological data being generated, with a focus on “small
science” projects. Initially this project will involve brainstorming
about the best way to estimate such a complex figure, and the intern
will then be tasked with producing the estimate using the decided upon
methods. Potential methods for estimation may include sampling
publications, surveying scientists, or exploring existing databases.
We foresee that results from this project will be highly cited since
such an estimate is useful for discussions about data sharing, data
reuse, and repository development in Ecology.
Qualifications needed: Applicants should be graduate students, have a
strong background in the field of ecology or environmental science,
and have statistics experience. Experience using computer scripts for
data retrieval would be helpful, along with programming experience in
R and/or MATLAB. The intern will need to be creative and excited about
tackling complex problems.
Skills to be learned: The student will be exposed to topics in data
management, reuse, and archiving, and will learn to work with
ecological databases. They will learn to work collaboratively on
complex problems with several members of the DataONE team, and have
the opportunity to write a peer-reviewed publication with the
potential for high citation rates. Particular skills related to
computer scripting, statistics, and data mining will be specific to
the methods determined by the student and mentors.
Primary mentor: Carly Strasser (National Center for Ecological
Analysis and Synthesis)
Secondary mentor: Stephanie Hampton (National Center for Ecological
Analysis and Synthesis)
Tracking the reuse of 1000 datasets
Description: We believe that openly archiving raw data facilitates
valuable reuse. Can we measure this? What contribution does data reuse
make to the published literature? Who reanalyzes data? For what? Does
this vary across disciplines and repositories? These questions are the
focus of an exploratory study, "Tracking data reuse: Following one
thousand datasets from public repositories into the published
literature." In this internship you'll work directly with Heather to
collect, extract, annotate, and analyze data to explore these
important questions. See http://bit.ly/cPsek0 for more info on the
project.
Qualifications needed: Self-starter, determined, enthusiastic, willing
to keep a research notebook up-to-date openly online. Experience with
statistics, the academic literature, PubMed, ISI Web of Science,
Python, R, and blogging would be helpful.
Skills to be learned:Research methods, research data collection, text
extraction from the scientific literature, keeping an open science
research notebook, communicating research results
Primary mentor: Heather Piwowar (National Evolutionary Synthesis Center)
Secondary mentor: Todd Vision (University of North Carolina Chapel
Hill/National Evolutionary Synthesis Center)
Subsetting and publishing “dynamic” scientific datasets
Description: The Avian Knowledge Network (AKN) is a federation of bird
monitoring datasets, the largest and most dynamic of which is eBird.
Datasets such as these, that are constantly being edited and expanded,
are challenging to incorporate into the DataONE framework because of
the way they are currently published. This project involves
researching issues around dataset subsetting and duplication to
recommend a publishing approach that works for “dynamic” datasets.
Expected outcomes: (1) Implement that strategy by migrating the AKN
repository to a DataONE–integrated Metacat deployment, making AKN into
a DataONE Member Node; (2) Produce a case-study article that captures
the implementation process that could act as a guide to future Member
Nodes making similar efforts.
Qualifications needed: metadata mapping; high level programming
language (e.g., Perl, Java); SQL; shell scripting
Skills to be learned: data repository implementation; scientific data
organization and publishing
Primary mentor: Paul Allen (Cornell Laboratory of Ornithology)
Secondary mentors: Kevin Webb (Cornell Laboratory of Ornithology)
Scientific workflow provenance repository and publishing toolkit
Description: Scientific workflow systems are increasingly used to
automate scientific computations and data analysis and visualization
pipelines. An important feature of scientific workflow systems is
their ability to record and subsequently query and visualize
provenance information. Provenance includes the processing history and
lineage of data, and can be used, e.g., to validate/invalidate
outputs, debug workflows, document authorship and attribution chains,
etc. and thus facilitate “reproducible science”. We aim to develop (1)
a provenance repository system for publishing and sharing data
provenance collected from runs of a number of scientific workflow
systems (Kepler, Taverna, Vistrails), together with (2) a provenance
trace publication system that allows scientists to interactively and
graphically select relevant fragments of a provenance trace for
publishing. The selection may be driven by the need to protect private
information, thus including hiding, abstracting, or anonymizing
irrelevant or sensitive parts. Part (1) will be based on a DataONE-
extension of the Open Provenance Model (D1-OPM) and leverage an
earlier Summer of Code project. In particular, the provenance toolkit
includes an API for managing workflow provenance (i.e., uploading into
and retrieving from a data storage back-end). Part (2) will implement
a new policy-aware approach to publishing provenance, which aims at
reconciling a user’s (selective) provenance publication requests, with
agreed upon provenance integrity constraints. For an existing rule-
based backend, a graphical user environment needs to be developed that
lets users select, abstract, hide, and anonymize provenance graph
fragments prior to their publication.
Qualifications needed: For Part 1, applicants should have experience
in SQL and Java or a scripting language (e.g., Python or Perl). For
Part 2, programming of GUIs with Rich Internet Application (RIA)
technologies (e.g., GWT) is a plus.
Skills to be learned: : Collaborative open source software development
using state-of-the-art languages and tools (databases, workflow
systems, interactive information visualization).
Primary mentor: Bertram Ludaescher (University of California Davis)
Secondary mentor: Paolo Missier (Newcastle University)
Integrating loosely structured data into the Linked Open Data cloud
Description: The Linked Data conventions describe four principles that
allow data of any kind and from any online source to form a global
interconnected web of data: i) name every "thing" that has some data
or information associated with it; ii) use HTTP URIs to do so; iii)
provide useful information or data in Resource Description Framework
(RDF) format to someone looking up such URIs; and iv) within
information provided this way, link to other common "things", such as
points or axes of reference, and use common vocabularies to attach
meaning to links wherever possible. These seemingly simple principles
have nonetheless been highly effective in facilitating the creation of
large, globally distributed, and constantly growing aggregations of
Linked Open Data (LOD), a unversally applicable framework for machines
and users alike to integrate, navigate, and discover data by following
links that are semantically of interest. Trying to apply the Linked
Data principles to data holdings of non-specialized digital
repositories, such as DataONE and many of its member nodes, is
challenging. These data are often highly heterogenous, and not
natively expressed in RDF, or a format structured enough that would
lend itself to automatic conversion to RDF. Instead, they are
typically represented in formats that are either loosely structured in
an ad-hoc manner (such as spreadsheets), or according to one of a
myriad of formats output by instruments or analysis programs. It is
thus not clear what the universe of "things" to name is, what are
common points or axes of reference, what kinds (semantics) of links
are needed, and how data archived in this way can be exposed in RDF
such that the conversion can be automated, yet is still useful for
science-motivated discovery and integration. The idea of this project
is to develop an exploratory prototype, and practical recommendations
resulting from it, for how the heterogeneous and loosely structured
data held in non-specialized DataONE member nodes can be exposed to
the Linked (Open) Data cloud. The approach would consist of obtaining
a sufficiently representative sample of data sets from DataONE's
initial 3 member nodes (Dryad, KNB, and ORNL-DAAC), and using them as
instance data for which to define the RDF predicate vocabularies,
domain ontologies, resource URIs, and conversion mechanisms that are
necessary to create a LOD representation of these data. This
representation can then be uploaded to, navigated, and queried in
either one of the web-based LOD browsers (such as URIburner), or for
example in a local installation of OpenLink Virtuoso.
Qualifications needed: Knowledge of RDF and one of its widely used
serializations (XML, N3). Familiarity with either C or Java
programming, or a scripting language that has good support for RDF and
OWL, will be needed. Familiarity with Linked Data, and experience with
metadata vocabularies and domain ontologies in RDF and OWL will be
very helpful.
Skills to be learned: Designing and executing an exploratory study
through all phases. Identifying and communicating alternatives and
their advantages and drawbacks. Developing practical semantic web
resources for existing instance data.
Primary Mentor: Hilmar Lapp (National Evolutionary Synthesis Center)
Developing video animations for DataONE community engagement
Description: DataONE wishes to develop a set of video animations to
help explain DataONE's value and capabilities to a range of audiences.
Several topics have been identified for these short animations, a
couple of storyboards have been developed, and one animation created.
The intern will work with the mentors to continue building this set of
animations according to the principles of ‘universal design’.
Qualifications needed: Applicants should have strong visual design
skills and a high level of expertise in development of digital
animation. Expertise in communicating scientific information to a
variety of audiences is desirable.
Skills to be learned: Video / animation development; science
communications.
Primary mentor: Paul Allen (Cornell Laboratory of Ornithology)
Secondary mentors: Amber Budden (University of New Mexico), Will
Morris (Cornell Laboratory of Ornithology)
This information is also available at: http://www.dataone.org/content/2011-summer-internship-program
Rebecca Koskela
Executive Director, DataONE
University of New Mexico
1312 Basehart SE
Albuquerque, NM 87106
Email: rkoskela at unm.edu
Office (M,W,F): (505) 814-1111
Cell & Office (T,Th): (505) 382-0890
Fax: (505) 246-6007
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