[BiO BB] DataONE Summer Internship Opportunity

[Hilmar Lapp]

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