DataONE 2014 Summer Internship Program
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.
Interns undertake a 9 week program of work centered around one of the projects listed below. Each intern will be paired with one primary mentor and, in some cases, secondary and tertiary 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, maintain frequent communication throughout the program and interns are required to work in an open notebook environment.
February 17 - Application period opens
March 18 - Deadline for receipt of applications at midnight Mountain time
Apr 1 - Notification of acceptance and scheduling of face-to-face meetings (schedules permitting)
May 26 - Program begins*
June 24 - Midterm evaluations
July 25 - Program concludes**
* Some allowance will be made for students who are unavailable during these dates due to their school calendar.
** Program may not extend beyond Aug 15 2014.
The program is open to undergraduate students, graduate students, and postgraduates who have received their degree 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 U.S. 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 above) with significant availability during the normal business hours. Interns from previous years are eligible to participate.
Interns will receive a stipend of $5,000 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.
Projects cover a range of topic areas and vary in the extent and type of prior background required of the intern. Not all projects are guaranteed funding and the interests and expertise of the applicants will, in part, determine which projects will be selected for the program. The titles and descriptions of this year’s projects are posted below.
2014 Project Titles
- Enabling Data Annotation: Integrating User Management into the DataONE Metadata Environment
- Integrating Ontology Search and Recommendation into the DataONE Metadata Environment
- Scaffolding Citizen Science Projects to Improve the Quality of Citizen Science Data Through Targeted APIs Supporting Collection and Integration of Citizen Science Data into DataONE Member Nodes
- Tuning the Citizen Science “Instrument” for Gathering Data While Documenting Data Quality
- Providing Provenance Trace in OPeNDAP Hyrax Served Science Data in a DataONE Member Node
- Understanding and Using Provenance from Digital Notebooks
- Community Sustainable Scientific Metadata Standards Directory
- SimilarityExplorer: Inspire Climate Science Discovery Through Advanced Big Data Analysis
- Implementing Add-On Features that add Value to DataONE and Center for Open Science Communities
- Creating Engaging Video Shorts for Stories About Data Management and Sharing
- Developing Screencast Tutorials for DataONE Tools and Resources
Project 1: Enabling Data Annotation: Integrating User Management into the DataONE Metadata Environment
Project Description: This project aims to make data ingest and annotation easy for a wide range of users. A Semantic Annotator was prototyped last summer to provide vision for how this can work in the DataONE environment. That tool enables earth and environmental scientists to annotate their data and link their data to relevant ontology concepts. However, users have to complete the whole process in one continuous session, and there are no security policies to protect their privacy. One way to solve the problem is to integrate user management into the Semantic Annotator to help preserve annotation results for re-use between sessions. This will make it easier to annotate data at the appropriate time and also allow multiple people to collaborate on the task. It also preserves provenance so that the systems may maintain a record of who made updates and when. The scope of this task includes:
- enabling user account functionality, whereby users have to register in order to have permission to use the application. Usage history data will be stored for later reuse.
- enabling the loading of user-specified enhancement parameters to link into existing datasets in order to reduce repetitive annotation, which will be very useful when processing datasets with same or similar metadata.
- enabling “semantic palette"/"my favorites" facets to preserve user's frequently used classes or properties for re-use between sessions, which will improve the efficiency of users’ annotation.
- implementing data access/security, so that users can control who sees the data.
The successful candidate will have finished developing the software by the deadline and tested the software with the DataONE data to ensure that it is robust.
The candidate will also assist with related publications and presentation preparation.
Primary Mentor: Deborah L. McGuinness (Rensselaer Polytechnic Institute)
Secondary Mentor: Xixi Luo (
Additional Mentors: Evan Patton, Paulo Pinheiro da Silva
Necessary Prerequisites: Computer Science major
Desirable Skills / Qualifications: Demonstrated experience in Semantic Web technologies. Programming skills to work independently and meet deadlines.
Expected Outcomes: Broadly usable version of the Semantic Annotator with user account functionality which will make data annotation easier for domain scientists. Demonstration of the tool for selected DataONE content. Associated material suitable for a publication.
Project 2: Integrating Ontology Search and Recommendation into the DataONE Metadata Environment
Project Description: This project aims to make data ingest and annotation easy for a wide range of users. A Semantic Annotator was prototyped last summer to provide a vision of how this would work. That tool provided a predefined set of ontologies including OBO-E, PROV-O and others into which earth and environmental scientists could link their data. It also enabled users to load and use additional ontologies. This functionality required users to be familiar with the specific ontologies they intended to use. However, there are some situations in which users may wish to discover relevant ontologies in the process of creating annotations. Integrating ontology search and recommendation into the Semantic Annotator can help to achieve this goal. The scope of this task includes:
- enabling Ontology Search (internal or external), integrated with external services such as the search feature of Linked Open Vocabularies, to enable user search based on keywords in order to find appropriate classes, properties, etc.
- enabling the "individuals" facet, which allows the user to click on a class in order to see instances of the class.
- enabling ontology recommendation based on usage history. If a dataset has the same or similar metadata as a previous dataset, then the previously discovered or used ontologies will be recommended to the current user.
The successful candidate will have finished developing the software by the deadline and tested the software with the DataONE data to ensure that it is robust. The candidate will also assist with related publications and presentation preparation.
Primary Mentor: Deborah L. McGuinness (Rensselaer Polytechnic Institute)
Secondary Mentor: Xixi Luo (DataONE postdoc, Rensselaer Polytechnic Institute)
Additional Mentors: Evan Patton, Paulo Pinheiro da Silva
Necessary Prerequisites: Computer Science major
Desirable Skills / Qualifications: Demonstrated experience in Semantic Web technologies. Programming skills to work independently and meet deadlines
Expected Outcomes: Broadly usable version of the Semantic Annotator with ontology search and recommendation functionality which will make data annotation easier for domain scientists. Demonstration of the tool for selected DataONE content. Associated material suitable for a publication.
Project 3: Scaffolding Citizen Science Projects to Improve the Quality of Citizen Science Data Through Targeted APIs Supporting Collection and Integration of Citizen Science Data into DataONE Member Nodes
Project Description: This project aims to extend the Application Programming Interfaces (APIs) and website capabilities of CitSci.org to transform this platform into a more service-based architecture with increased data and systems interoperability with DataONE nodes. CitSci.org is a comprehensive cyberinfrastructure that supports over 50+ citizen science projects by providing project managers access to online tools that centralize project management and collect, analyze, model, and visualize data. Data collected by CitSci.org projects are available for download online, yet lack a robust API to synchronize and share datasets across remote data repositories such as DataONE Member Nodes.
The intern will assist in developing a API/web service tools that promote CitSci.org data exchange with DataONE Member Nodes while integrating domain-specific data exchange standards (e.g., Darwin Core) and establishing data exchange protocols (e.g., PPSR_CORE Program Data Model and Observation data Model) to share these data.
The successful candidate will work with an interdisciplinary ecological informatics team and the PPSR WG team members to develop Application Programming Interfaces (APIs) and associated web services to extend the service-based architecture of CitSci.org and similar platforms. The intern will also assist in developing the Occurrence Data Model of the PPSR_CORE data exchange protocol and build APIs for platforms to share program level metadata with CitizenScience.org and SciStarter.com through the existing PPSR_CORE Program Data Model. The intern will also begin development of APIs to support citizen science occurrence data exchange with DataONE Member Nodes through the PPSR_CORE Occurrence Data Model.
Primary Mentor: Greg Newman (Natural Resource Ecology Laboratory, Colorado State University)
Secondary Mentor: Andrea Wiggins (DataONE postdoc, Cornell University)
Additional Mentors: Jennifer Shirk, Russell Scarpino, Nicole Kaplan, and Brian Fauver
Expected Outcomes: Broadly usable and interoperable citizen science data shared with DataONE Member Nodes, scientists, and the public accessible through APIs and web services. Potential products may include: additional website capabilities on the CitSci.org infrastructure, mobile applications, and embeddable widgets for existing citizen science programs with their own website(s) to use to help guide (scaffold and structure) their activities to improve overall data quality.
Project 4: Tuning the Citizen Science “Instrument” for Gathering Data While Documenting Data Quality
Project Description: Citizen science is a novel “instrument” that can gather data unavailable to scientists using traditional methods. Recent advances in internet technologies and mobile computing are accelerating the possibilities to engage the public in participating in scientific research. Projects such as eBird and CoCoRaHS are providing data that is proving exceptionally to scientists. Nevertheless both the scientific community and the public remain skeptical about the quality of citizen science data because citizens by definition are not “certified” scientists. To overcome this hurdle citizen science projects use variety of approaches to improve and document data quality. The candidate will mine the web and use a literature review process (perhaps combined with web surveys and/or personal interviews) to document citizen participation, data collection procedures, and analysis tasks undertaken by citizens. Building on the efforts of Wiggins et al 2011
(Mechanisms for Data Quality and Validation in Citizen Science), the candidate will document methods to improve and document data quality. One goal will be to define data models and data quality models that can guide citizen science projects in the future. A second goal will be to better understand and define the essential tension between improving the data quality (having citizens work like scientists) and keeping the citizens engaged in the project.
Primary Mentor: Robert D Stevenson (University of Massachusetts Boston)
Secondary Mentor: Greg Newman (Natural Resource Ecology Laboratory, Colorado State University)
Additional Mentors: Andrea Wiggins
Necessary Prerequisites: Background in informatics with interest in data and citizen science
Desirable Skills / Qualifications: Experience with meta analysis / Designing web and interview surveys / interest in Biodiversity
Expected Outcomes: Review paper and recommendations for citizen science programs to document data quality
Project 5: Providing Provenance Trace in OPeNDAP Hyrax Served Science Data in a DataONE Member Node
Project Description: This project aims to enable and provide provenance tracing during the access of science data using the OPeNDAP Hyrax software stack. The provenance provided will express the various OPeNDAP Hyrax components that have been used to generate data products that will be used by scientists and researchers. In addition to providing provenance for the data access component, scientists and researchers will also have the ability to ping-back (using the W3C provenance working group recommendation) to the repository in order to provide attribution and citation of the data products and originating data sets used in publications and papers. The successful candidate will complete the development of software by the deadline, tested the software with DataONE sites using OPeNDAP Hyrax for data access to ensure the robustness of the resulting work, and document the work that they have completed by writing implementation documentation and blogs related to their experience in the project. The candidate will also assist with any related publications.
Primary Mentor: Patrick West (Rensselaer Polytechnic Institute)
Secondary Mentor: Deborah McGuinness, Tim Lebo ( (Rensselaer Polytechnic Institute)
Necessary Prerequisites: C, C++, Computer Science major
Desirable Skills / Qualifications: Demonstrated experience in Semantic Web Technologies. Programming skills to work independently and in a small team environment and meet deadlines
Expected Outcomes: Development of an OPeNDAP Hyrax Back-End Server module to provide provenance capture in the data access workflow and ping-back services for attributing and citing works using OPeNDAP data products.
Project 6: Understanding and Using Provenance from Digital Notebooks
Project Description: Capturing and analysis of provenance from scientific workflow environments and databases is well studied and understood. Scientists can employ provenance information to better understand, debug, and document their findings, and thus greatly simplify and enhance reproducible science. Digital notebooks such as iPython can be understood as a new way of marrying ideas from high-level scripting, interactive workflows, and even “executable papers”: similar to the idea of Literate Programming, the digital notebook combines both documentation (the paper) and the code to produce the results. Thus, by design, they are self-documenting and greatly enhance transparency and reproducibility. However, the provenance models used for digital notebooks are less well studied than those for databases and workflow systems. Capturing the provenance of data obtained during multiple interactive sessions is therefore one of the enablers of emerging, dynamic models of scholarly communication. Based on existing and to-be-developed notebooks, the project will investigate the potential for provenance capture in these environments, identifying technical challenges and assessing both complexities and opportunities. Furthermore, it will explore the modeling of provenance in such contexts and the adaptation of existing querying and analysis techniques.
Primary Mentor: Bertram Ludaescher (University of California Davis)
Secondary Mentor: Paolo Missier (Newcastle University)
Additional Mentors/Collaborators: Victor Cuevas, James Cheney, Duncan Temple Lang, Karthik Ram
Necessary Prerequisites: Knowledge of Python, general programming (i.e., Java), CS background
Desirable Skills / Qualifications: Working experience with Python, R. Familiarity of provenance management. Knowledge of iPython and/or Knitr (http://yihui.name/knitr/)
Expected Outcomes: The project aims to extend the scope of and knowledge about provenance management to the increasingly popular digital notebooks based on Python and R, respectively.
Project 7: Community Sustainable Scientific Metadata Standards Directory
Project Description: The Research Data Alliance (RDA) Metadata Standards Directory Working Group (MSDWG) is developing a prototype wiki-based directory to list metadata standards applicable to scientific data. The initial emphasis is on widely-used and domain community-endorsed metadata standards and schemas with significant interoperation/re-use capability. The Digital Curation Centre (DCC) has compiled a catalog of Disciplinary Metadata Standards, use cases for these standards, as well as tools to implement the standards. This catalog can be found on the DCC Disciplinary Metadata page at (http://www.dcc.ac.uk/resources/metadata-standards). The RDA MSDWG has joined with the DCC to extend this catalog of disciplinary metadata. The MDSWG has circulated a survey requesting additional information about metadata standards; the tools and use cases associated with them, and additional information that shows where and how scientists use them worldwide. The goal of this summer project is continue work on a prototype system that would allow respondents to the survey to directly fill out a template, similar to a wiki, that standardizes the presentation of information and eliminates the need to transfer information from the survey. The prototype system will also enable updates and corrections to the catalog of disciplinary metadata. The long-term goal of the MSDWG is a community sustainable directory of metadata standards. The goals of this summer project are to continue work on a prototype catalog system that:
- is wiki based, supporting community participation
- standardizes the presentation of information about metadata standards, tools, and use cases
- enables catalog contributions, updates, and corrections by directly filling out a template,
- conduct a survey, building off of the fall 2013 survey, to:
- gather new information and updates about metadata standards, tools, and uses cases
- tests the functionality of the prototype catalog
The long-term goal of the MSDWG is a community sustainable directory of metadata standards
Primary Mentor: Rebecca Koskela (DataONE, University of New Mexico)
Secondary Mentor: Jane Greenberg (University of North Carolina Chapel Hill)
Additional Mentors: Alex Ball, Keith Jeffery
Necessary Prerequisites: Software development experience; familiarity with GitHub
Desirable Skills / Qualifications: Familiarity with scientific metadata; some knowledge of registry challenges or interest in learning.
Expected Outcomes: Functional prototype system for adding additional information and correcting existing information in the Disciplinary Metadata catalog
Project 8: SimilarityExplorer: Inspire Climate Science Discovery Through Advanced Big Data Analysis
Project Description: Numerical modeling has become an important technique for extrapolating local observations and understanding of the Earth system and climate change, to larger spatial and temporal regions. For example, terrestrial biosphere models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. But due to the complexity of the Earth system, TBMs estimates vary widely. Regional and global scale TBMs run simulations at millions of locations at thousands of time steps. They require not only extensive computing resources (e.g. supercomputers), but also generate terabytes of output data. Inter-comparison among different TBMs estimates and comparing them against observations are important to find model-model and model-observation agreements/disagreements, which can further provide feedbacks to the modeling community for model skills improvements.
Analyzing such a huge amount of data is a typical big data challenge. The DataONE Exploration, Analysis, and Visualization (EVA) working group has been leveraging advanced big data exploration and visualization techniques to tackle this challenge and started the design and development of SimilarityExplorer. The SimilarityExplorer tool leverages multi-dimensional projection and synchronized spatiotemporal correlation techniques to allow people to conveniently explorer and visualize the similarity/difference among complex multi-dimension, multi-scale, and multi-variable environmental data and how the similarity/difference change across regions and along time.
Figure 1. Screenshot of current SimilarityExplorer
The DataONE EVA working group proposes to make further improvements to the SimilarityExplorer tool and apply the improved tool in real terrestrial biosphere research scenarios. Specifically, the proposed summer intern project will:
- Integrate Taylor Diagram (Taylor 2001) into SimilarityExplorer to better support one-to-many model comparison.
- Improve the tool to allow exploring data similarity in user defined regions and time periods.
- Make usability improvements to SimilarityExplorer.
- Propose and implement new innovative techniques to explore complex environmental science data to analyze their similarities and differences.
- Work with climate scientists and terabytes of modeling and observational data from the Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) and develop innovative usage scenarios for the SimilarityExplorer tool.
- Collaborate with the DataONE Provenance working group and capture the provenance in standard format (e.g. ProvONE) associated with modeling data processing and analysis.
Taylor, K.E.: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183-7192, 2001 (also see PCMDI Report 55, http://www-pcmdi.llnl.gov/publications/ab55.html)
Primary Mentor: Bob Cook (Oak Ridge National Laboratory)
Secondary Mentor: Yaxing Wei (Oak Ridge National Laboratory)
Additional Mentors: Christopher Schwalm
Necessary Prerequisites: B.S. in computer science or statistics, strong programming background.
Desirable Skills / Qualifications: Proficient in Java programming; training and interest in environmental and climate modeling sciences; interest in analyzing and visualize spatiotemporal data; strong communication skills.
Expected Outcome: The outcomes of this project will be (1) an improved SimilarityExplorer tool that can help climate-modeling scientists to explore complex multi-dimensional, multi-scale, and multi-variable climate data, analyze their similarities/differences, and find how, when, where, and why they are similar/different; (2) a number of usage scenarios of the SimilarityExplorer tool based on the MsTMIP data and research scope.
Project 9: Implementing Add-On Features that add Value to DataONE and Center for Open Science Communities
Project Description: Identify and implement add-on features to the Open Science Framework (OSF; http://osf.io) which will provide mutual benefit to and expand the value of DataONE and Center for Open Science assets. The Center for Open Science (COS; http://cos.io) is a new non-profit tech start-up in Charlottesville, VA, aimed at promoting integrity, reproducibility, and transparency in the scientific workflow. COS accomplishes this mission by building infrastructure (ie. OSF), building communities of best practice, and conducting metascience research to study the alignment (or misalignment) of scientific values and practices. COS has focused primarily on reaching social science researchers since launching in 2013. This internship will support development of further partnership in mission between DataONE and COS by specifically targeting issues of scientific reproducibility for earth science research that intersects with national laboratories. Seeking a graduate student with experience developing open source software and APIs. Internship location: offices of COS in Charlottesville, VA.
Primary Mentor: Andrew Sallans (DataONE Users Group Chair / Partnerships and Collaborations Lead, Center for Open Science
Secondary Mentor: Jeff Spies (Technical Lead and Architect, Center for Open Science)
Additional Mentors: Amber Budden
Desirable Skills / Qualifications: Prior experience with open source software development and consuming/developing APIs
Expected Outcomes: Development of new connections between the DataONE infrastructure and Open Science Framework, demonstrated by increased integration of datasets, services, tools, etc.
Project 10: Creating Engaging Video Shorts for Stories about Data Management and Sharing
Project Description: The intern will be responsible for creating short videos that present stories about the real-life experiences of researchers. The videos will be based on stories that have been collected as part of DataONE’s Data Stories project, and can be accessed online at https://notebooks.dataone.org/data-stories/. The intern selected for this project will work in close collaboration with the Data Stories project leader during the concept development and review stages of the video project.
CREATIVE LICENSE The intern will have broad creative freedom regarding the style and technologies used to create the videos. Intern may wish to use online video creation tools (e.g., PowToon, Voki) or other software intern has access to.
For ideas about the possible range of video styles, check out the following:
- The Sparky Awards (http://www.sparkyawards.org/entries/index.shtml)
- Creature Cast (http://creaturecast.org)
- 3-part video on data management (http://www.youtube.com/watch?v=RVZbk3GEVSw)
- NESCent Film Festival (http://filmfestival.nescent.org/2013-entries/)
- “The Day the Data Died” (http://www.funnyordie.com/videos/8086a40dbf/the-day-the-data-diedloose-b...)
- Simply Storage: Management (http://www.youtube.com/watch?v=bJPSOWJjWqo)
- Librarians Do Gaga (http://www.youtube.com/watch?v=a_uzUh1VT98)
Primary Mentor: Stacy Rebich Hespanha (DataONE postdoc, National Center for Ecological Analysis and Synthesis, University of California-Santa Barbara)
Secondary Mentor: Amber Budden (DataONE)
Necessary Prerequisites: Experience with creating online video content, ability to tell engaging stories using video
Desirable Skills / Qualifications: Knowledge about key issues in data management and sharing
Expected Outcomes: Development, creation, review, and revision of video shorts (3 minutes or less) based on stories about the real-life experiences of researchers. Videos created for this project will be covered under the Creative Commons 3.0 License (CC-BY) and distributed through the DataONE website.
Project 11: Developing Screencast Tutorials for DataONE Tools and Resources
Project Description: DataONE has developed significant tools and resources of value to the research community. Many of these have documentation associated with them, but none have readily available public demos or screencasts. As part of this project the intern will develop a process for creating screencast tutorials, including identification of appropriate software, workflow process and timeline. Screencasts for a single tool/resource will be broken down into multiple chapters and the intern will also explore appropriate timings for a positive user experience. Draft screencasts will be tested / evaluated by members of the DataONE community and the intern will coordinate this survey / feedback effort, incorporating suggestions into additional development activity. Completed screencasts will be published on the DataONE public website and uploaded to Vimeo / YouTube under a CC-0 license. Potential tools / resources include: ONEMercury (https://cn.dataone.org/onemercury/), ONE-R (http://releases.dataone.org/online/dataone_r/), DataONE Best Practices Database (http://www.dataone.org/best-practices), ONEDrive (not yet released), Ask DataONE (https://ask.dataone.org/questions/). Evaluation of suitability and prioritization of these resources will be one of the first activities of the intern in collaboration with the DataONE community. Note: It is not anticipated that all the above resources will be covered during the period of the internship.
Primary Mentor: Amber Budden (DataONE)
Secondary Mentor: Carly Strasser (California Digital Library)
Necessary Prerequisites: Experience teaching / presenting to a general or undergraduate audience, experience writing for a general audience (not academic papers or business reports), interest in online media and materials development.
Desirable Skills / Qualifications: Experience with open source or other screencast, video editing, sound production tools.
Expected Outcomes: Story-boarding, scripting and production of several narrated screencasts for DataONE resources that will be posted on the public website.
If you are a U.S. citizen, please complete the form here.
If you are a non citizen that currently holds a valid U.S. work or study visa that extends throughout the internship period, please complete the form here.
Note: DataONE can not sponsor a visa for internship applicants.
Applications must be completed by 11:59 PM (Mountain time) on March 18th. You will be asked to upload a cover letter and resume, both in PDF format. Applicants should also provide a letter of reference. The letter of reference should be sent directly by its author to firstname.lastname@example.org by the application deadline.
- The cover letter should address the following questions:
- Which DataONE Summer Internship project(s) 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 email@example.com 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.
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.
The Summer Internships are supported by a National Science Foundation Award (NSF Award 0830944): "DataNetONE (Observation Network for Earth)".
For more information
If you have questions or problems about the application process or internship program in general, please send e-mail to firstname.lastname@example.org.