2012 has been a year of change and we can expect more of the same in 2013. In the first issue of DataONE NEWS, the official launch of DataONE on July 23, 2012 was announced. DataONE represents one of the key agents of change that holds great promise to provide data resources and tools that benefit the community and providing an infrastructure to serve science for many decades to come. In addition to DataONE, many other significant changes occurred during the past year throughout science and academia. As examples:

• “Big Data” entered the mainstream of science and many journal articles and conferences focused on elements of Big Data, including a 2012 National Research Council workshop that was instigated by the Defense Intelligence Agency and designed to review emerging capabilities and “review the subsequent increase in vulnerabilities over the capabilities gained and the significance to national security”.1

• Universities, large and small, re-designed buildings (including libraries) to create new types of informal learning spaces. I recently visited the main campus library at the University of Nevada Las Vegas which is entirely designed to promote learning through access to new types of learning spaces; the library was packed with students and the researchers I visited went out of their way to make sure that campus visitors saw the library first-hand. Informal learning spaces can be incorporated into all new buildings or remodeling efforts. See the newly released “Learning Space Toolkit”2 which was created by North Carolina State Universities and partners for more information.

• MOOCs entered our vocabulary and are in the midst of changing the face of education. “MOOCs”, which stands for Massive Open Online Courses, were highlighted in a March 4, 2012 article by Tamar Lewin in the New York Times in which it was argued that they were “a tool for democratizing higher education.” Many of our elite universities now offer free courses online including Harvard, MIT, UC Berkeley, and universities throughout the world are assessing how to respond to this new phenomenon.

The arrival of Big Data, new types of learning spaces, and new ways of learning (i.e., MOOCs) will undoubtedly impact research and education

Reflecting on 2012 and agents of changefor many years to come. However, there is one change that occurred in 2012 that is truly a watershed event—the emergence of the citizen science movement. Citizen science is also known as “participatory science” and “public participation in scientific research” (or PPSR). Citizen science was highlighted in a new book of the same title3, the August 2012 “Special Issue: Citizen Science – new pathways to public investment in research” of Frontiers in Ecology and the Environment, and numerous magazine and newspaper article that focused on citizen science findings. These writings principally illustrate citizen involvement in specific projects such as eBird4 or Project Budburst5 and the impacts they have on research, conservation and education.

There are many other mechanisms, however, for how citizens can be involved in scientific research. First, citizens may donate the processing power and storage capabilities of their home computers through projects like SETI@home and other volunteer distributed computing activities.6 Second, citizens may fund specific scientific research projects through crowdfunding sites like Rocket Hub.7

Actively engaging citizens in all aspects of research from funding, through data collection, and analysis, visualization and interpretation of results offers the opportunity to transform science, education, and decision-making globally. For example, as more individuals participate in research and learn

how science really works, it is likely that there will be renewed interest in STEM education and, importantly, that science skeptics will have a diminished voice in setting policy and education standards.

DataONE is pleased to be playing a role in advancing citizen science through co-sponsoring the first national PPSR conference in August 2012 in Portland, Oregon and through the work of its PPSR Working Group, which is surveying and recommending best citizen science practices and policies. In particular, be on the lookout for a forthcoming guide in 2013 that describes the best practices for managing citizen science data.

The year 2013 looks to be every bit as exciting as 2012, and I look forward to providing a look at DataONE developments and broader emerging trends in the New Year.

— Bill MichenerProject Director, DataONE

1 National Research Council, 2012. Big Data: A Workshop Report Committee for Science and Technology Challenges to U.S. National Security Interests, Division on Engineering and Physical Sciences, National Research Council, Washington, DC. 34 pp. ISBN 978-0-309-26688-8. 2 Learning Space Toolkit (http://learningspacetoolkit.org)3 Dickinson, J.L. and R. Bonney (eds.). 2012. Citizen Science: Public Participation in Environmental Research. Cornell University Press, Ithaca, NY. 279 pp. ISBN 978-0-8014-4911-6.4 ebird.org5 neoninc.org/budburst/6 Korpela, E.J. 2012. SETI@home, BOINC, and volunteer distributed computing. Annual Review of Earth and Planetary Science 40:69-87.7 rockehub.com

Volume 1 Issue 2

©2012 DataONE 1312 Basehart SE University of New Mexico Albuquerque NM 87106

UpcomingEVENTS Members of the DataONE Team will be at the following events. Full information on training activities can be found at bit.ly/D1Training and our calendar is available at bit.ly/D1Events.

Jan. 8-10Federation of Earth Science Information Partners (ESIP) Winter Meeting Washington, DCwww.esipfed.org/meetings

Jan. 14-16International Digital Curation Conference Amsterdam, Netherlandswww.dcc.ac.uk/events/idcc13

Mar. 18-20 Research Data Alliance Meeting Gothenburg, Swedenrd-alliance.org

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FeaturedRESOURCE

DataONE provides education modules for all stages of the data life cycle

In response to increasing attention towards data management planning and other critical functions of protecting, sharing, and re-use of data, the DataONE Community Engagement and Education Working Group (CEEWG) built a series of data management educational materials for the broader community. Data management education should have solid foundations in the classroom, allowing students to carry good data habits into careers as science professionals, and the materials produced by the working group meet that need for all stages of the Data Life Cycle.

The DataONE Data Management Education Modules include:

• Why Data Management?• Data Sharing• Data Management Planning• Data Entry and Manipulation• Data Quality Control and Assurance• Data Protection and Backups• Metadata• How to Write Quality Metadata• Data Citation• AnalysisandWorkflows

Targeted towards graduate-level Earth science audiences, each lesson comprises learning objectives, reference to the data lifecycle, in-depth content, best practices, a lesson summary and references for further reading. Available for viewing via slideshare (bit.ly/D1Slides) the full slide set can be downloaded from the DataONE website (bit.ly/D1Modules).

These materials form the basis of many of the DataONE training activities and in May 2012 a 2-day workshop for approximately 20 PhD students was hosted in Santa Barbara, CA. Through extensive surveying the workshop enabled us to measure learning progress of each student, identify potential areas of improvement and make appropriate edits ot the materials. The results of the surveys can be viewed at bit.ly/D1Modules.

If you are engaged in data management education we strongly recommend you take a look at these resources for your teaching practice or personal development.

TheDUGout It’s now been 5 months since our last DataONE Users Group annual meeting, and high-time to recover from the rush of the academic semester and refocus on opportunities for engagement with the DataONE Users Group. Although we only meet as a full group once a year, we aim to foster year-round dialogue and engagement among the membership for continuous evaluation, conversation, and generation of feedback on the value that DataONE can and does offer to the broader community of users. This dialogue and engagement may come in many forms, including panels, birds-of-a-feather sessions, lunch discussion, or any variety of other activities at any assortment of disciplinary or domain conferences, meetings, and events. As with many things, what you put in is what you will get out.

To facilitate the organization and initiation of these events, we’ve prepared a survey to develop a list of where DUG members are professionally active, and where and when groups may begin to coalesce. I will work with others to analyze the survey responses, will reach out to individuals to encourage specific activities, and then will report back to the overall membership on the outcomes and plans. I’m hopeful that this will help us jump-start these activities and make forward progress before the next annual meeting. Please complete the survey at bit.ly/DUGSurvey by January 21st 2013.

We’ll reconvene as a full group in Chapel Hill in July 2013, once again paired with the Federation of Earth Science Information Partners (ESIP) meeting. I’ll look forward to seeing you all at that time or sooner

— Andrew Sallans

Vice-Chair, DataONE Users Group Head, Strategic Data Initiatives, University of Virginia

Libraries

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CyberSPOTCyberInfrastructure UpdateDataONE infrastructure continues to operate in production mode with no downtime, and now has a total of nine Member Nodes and a total of more than 128,000 data packages. The latest addition to the production Member Nodes represents Cornell’s Lab of Ornithology Avian Knowledge Network. Another Member Node, ONEShare which works directly with the DataUp tool described in the Investigator Toolkit section, is in the final stages of testing and expected to be available before the end of the year.

Development activity has focussed on some infrastructure changes to improve the discovery of content and ensure these enhanced search capabilities are available to investigator tools through the Coordinating Node service interfaces. Additional enhancements to the infrastructure including full Member Node to Member Node replication capabilities and log aggregation services have also been completed and are undergoing final testing. Version 1.1 of the infrastructure which includes all these enhancements is expected to be deployed before the end of 2012, and will enable ongoing development efforts to be focussed on streamlining the process for adding new Member Nodes, increasing the coverage of the Investigator Toolkit and facilitate further progressive enhancements to the DataONE infrastructure capabilities.

About the Investigator ToolkitThe Investigator Toolkit provides a suite of software tools that interoperate with DataONE service interface to provide seamless access to the archive and preservation services it offers. Different tools in the toolkit address different aspects of the data lifecycle, and combined together, enable investigators to fully utilize the DataONE services for all their data management needs.

An application or tool becomes part of the Investigator Toolkit when it is enhanced through a plugin or extension that enables it to utilize the DataONE service interfaces. In the simplest case, a researcher may have an identifier for a data package to be retrieved.

Using a DataONE enabled tool, this process would be as simple as the normal “File | Open” menu choices, except instead of selecting a file, the user would provide the identifier instead. The application would then interact with the DataONE services to locate the components of the DataPackage, and download them to the local storage for further use.

A more enhanced tool might take advantage of further capabilities such as search to help with the discovery of relevant data packages, and the ability to write back to Member Nodes. This later operation is similar to saving content to local storage, but instead the content is stored to a Member Node and assigned a new unique identifier with which the content can always be identified with.

In some cases, an entirely new tool may be created from scratch in order to take full advantage of the DataONE services. This was the case for the DataONE CLI (command line interface, source available at https://repository.dataone.org/software/cicore/trunk/itk/d1_client_cli/ ), which is a tool primarily intended for developers and technical users and enables full interaction with all DataONE services at a very low level. Another tool developed from scratch for DataONE is the ONEDrive, a cross platform file system driver that enables one to mount the DataONE infrastructure as a network drive from their desktop, and explore and retrieve content from the Coordinating and Member Nodes directly. The ONEDrive is currently under active development and is in a functional prototype stage.

Other tools in the Investigator Toolkit exemplify the process of extending existing functionality rather than creating something new. For example the ONEMercury search interface is a modified version of the well tested Mercury (http://mercury.ornl.gov/) search interface, and the production version is available on any Coordinating Node at https://cn.dataone.org/onemercury to search through content indexed by DataONE. ONE-R is a plugin for the widely used R statistical and analysis package (http://www.r-project.org/)to enable read and write access to DataONE, as well as providing some data package management capabilities. ONE-R is currently

in late prototype stage and is functional though not yet released. ONE-R is being developed at https://repository.dataone.org/software/cicore /trunk/itk/d1_client_r/ .

Another Investigator Tool currently under active development is the DataUp extension for Microsoft Excel. DataUp (http://dataup.cdlib.org/) is an open source tool developed by The California Digital Library and Microsoft Research to help researchers document, manage, and archive tabular data that might typically be held in Microsoft’s Excel spreadsheet. DataUp currently uploaded content to ONEShare, a new Member Node for DataONE, and in this way enables contribution of data directly to DataONE. Direct support for DataONE services is planned for DataUp, and is expected in the tools next iteration of development.

Through ongoing internal and community efforts, DataONE will continue to expand the number of applications in the Investigator Toolkit, adressing all aspects of the science data lifecycle from planning through publication. n

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WorkingGroupFOCUS The data lifecycle that informs the DataONE architecture is aimed primarily at the collection, curation, validation, and long-term preservation and accessibility of high-volume datasets. Alongside the data, a wealth of machine-processable provenance metadata can also be collected and later used to provide DataONE users with additional value at each phase of the lifecycle. Broadly speaking, the term refers to “the sources of information, such as people, entities, and processes, involved in producing, influencing, or delivering a piece of data”1. More specifically, provenance complements the metadata records associated with data stored in DataONE’s Member Nodes, by providing an account of how the data emerged from the execution of complex processes carried out both in the lab and by computational means.

A typical example is that of a data analysis pipeline (a.k.a. scientific workflow) designed to process observational data through a sequence of steps. The provenance of the data that emerges from the pipeline is a detailed account of the transformations operated on the data by each of the steps, including details of those steps. Provenance recorded by scientific workflow systems can be used, e.g., to interpret and “debug” workflow results, and to identify which data products have been influenced by what data inputs, parameter settings, etc. For example, if a dataset has been found as problematic, all affected derived products that depend on this data can be automatically found through querying provenance data.

Provenance is also a key enabler for effective and efficient data sharing, e.g. through a large-scale data repository as that provided by DataONE. Provenance analysis and mining helps scientists who are searching through a data repository, to decide whether the data they find is to be trusted, or is of sufficient quality to be further used and analysed. In general, adding provenance to the metadata search capabilities of DataONE nodes makes for more accurate and sophisticated data retrieval, an important feature at the scale of hundreds of thousand available datasets.

Delivering such value to users, however, requires that process descriptions and provenance metadata become first-class citizens in the DataONE space. The mission of the Provenance Working Group is to address the conceptual, modelling, and architectural issues associated with all phases of DataONE’s data and provenance lifecycle. These include harvesting provenance through observations obtained by instrumenting the processes (scientific workflows) that produce the data, and creating a provenance repository with query capabilities tailored to large graphs. Additional features of provenance management include combining provenance fragments from different processes that share some of their data, and privacy-aware provenance publishing2.

The WG has been focusing specifically on scientific processes that are realized through the use of scientific workflows, a programming paradigm that has become popular amongst computational scientists. A number of workflow systems come equipped with provenance capture sub-systems, and are therefore a natural initial target for this

group. Additionally, the group has recently begun to focus on provenance-enabling other programming languages, such as the very popular R, an open source, library-rich scripting language for statistical computing.

The Working Group draws its strength from the diversity of backgrounds and competences brought to the group by its core members, straddling both sides of the Atlantic and with a common denominator in their interest in scientific data and metadata management. Some of the members are from research groups that develop scientific workflow managers, including Kepler (UCSD, UC Davis), VisTrails (NYU-Poly, formerly Utah), Taverna (myGrid, UK), e-Science Central (Newcastle, UK), and Wings (UCSC-ISI). Others are scientists working in Ecology (UC Berkeley), Databases (Gonzaga University, UC Davis) and Programming Languages (Edinburgh, UK). Over its lifetime, the group has also hosted invited guests who brought additional expertise and diversity to the group.

The accomplishments of the WG to date are documented through a string of

cont’d next page ›››

Lifecycle of data incorporating primary data, derived data, and workflow artifacts with corresponding provenance metadata for quality assessment.

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Each Member Node within the DataONE federation completes a description document summarizing the content, technical characteristics and policies of their resources. These documents can be found on the DataONE.org site at bit.ly/D1CMNs. In each newsletter issue we will highlight one of our current Member Nodes.

KNB: Knowledge Network for Biocomplexity knb.ecoinformatics.org

The Knowledge Network for Biocomplexity is a globally interconnected set of Metacat data and metadata management servers that are linked via the DataONE federation. Two sites serve as the central index for all metadata on the KNB (at NCEAS in California and LTER in New Mexico), and other Metacat instances are distributed worldwide. Data held in the KNB are principally from the ecological and environmental sciences but also include data from allied disciplines such as genomics, physiology, behavior, evolution, economics, sociology and policy. Data represent survey and monitoring information as well asmanytypesof manipulateddatafromfieldandlaboratoryexperiments.Typesof data include relational data, vector and matrix data, raster images, vector images and audio. Contributors include independent scientists working in the environmental sciencesaswellasfield stationsandresearchnetworks suchasthe Organization of Biological Field Stations and the University of California Natural Reserve System.

Data and metadata availability (rights, licensing, restrictions):

1. Intellectual rights subject to laws in the US, but otherwise set by contributor.

2. System provides separate access control for metadata and data objects and provides mechanism for provider to state intellectual rights requirements.

3. There is an option for embargo acomplished through fine-grained access controll as specified by data contributor.

With over 4,900 registered users the KNB comprises over 28,300 data packages. Just under half of these are between 1-10 MB, ~4% between 10-200 MB, with the remainder less than 1 MB.

Metadata standards (incl. provenance):

• Ecological Metadata Language (EML)

• Biological Data Profile (BDP)

• ISO 19139

• Modeling Markup Language (MoML)

• others ..

MemberNodeDESCRIPTION publications over the past three years (see references below). All of this work could not have been accomplished without the help of several summer interns during 2010, 2011, and 2012, and, as of this year, of a post-doctoral researcher (Victor Cuevas).

The group is currently engaged on multiple fronts, with a common focus of implementing new features that integrate with, and add value to, the DataONE data preservation architecture by making it provenance-aware. We plan to demonstrate initial results of these multiple efforts early in 2013.

Our approach is grounded in a model for provenance that extends the new PROV model promoted by the W3C (www.w3.org/2011/prov/wiki/). A reference implementation of the draft model (called D-OPM, extending the PROV-precursor model: OPM) and its query capabilities have been described3.

The WG is also collaborating with the DataONE EVA WG, which contributes a set of use cases, implemented using VisTrails workflows, for Multi-Scale Synthesis & Terrestrial Model Intercomparison Project (MsTMIP). Here we are using the provenance-capture capabilities of the VisTrails workflow system4 to produce provenance traces which will enhance the DataONE search capabilities (for instance ONEMercury) to “search by provenance”. For example, it will be possible to users to retrieve datasets that were produced using a particular service, or that were derived from other specific datasets.

Other ongoing efforts include the development of Prov-Explorer, a tool for provenance querying and visualization that leverages work done by our interns over the summer, and a study to enable provenance recording from executions of R programs. n

1 Luc Moreau, Paul Groth, Simon Miles, Javier Vazquez, John Ibbotson, Sheng Jiang, Steve Munroe, Omer Rana, Andreas Schreiber, Victor Tan, and Laszlo Varga. The Provenance of Electronic Data. Communications of the ACM, 51:52–58, 2008.2 Dey S, Zinn D, Ludäscher B. ProPub: Towards a Declarative Approach for Publishing Customized, Policy-Aware Provenance. Intl. Conf. on Scientific and Statistical Database Management (SSDBM): 225-243, 2011.3 Victor Cuevas-Vicenttin, Saumen Dey and Bertram Ludäscher. Modeling and Querying Scientific Workflow Provenance in the D-OPM. 7th Workshop on Workflows in Support of Large-Scale Science (WORKS), Salt Lake City, November 10-16, 2012.4 Juliana Freire, David Koop, Emanuele Santos, and Cláudio T. Silva, Provenance for Computational Tasks: A Survey, Computing in Science & Engineering, 10(3), pp. 11-21, 2008.

WorkingGroupFOCUS cont’d

OutreachUPDATE DataONE has been active at the Ecological Society of America meetings for a number of years with multiple presentations, posters, special sessions and workshops. We have also hosted a booth in the exhibit hall for the last two years. Having a booth (and handing out chocolate) gives us greater visibility and provides us with an opportunity to meet more users and potential users within the environmental sciences community. It also provides a ‘go-to’ spot for people to ask additional questions following our training events. It would seem logical therefore, to grow our presence at other meetings.

Although always in attendance at the American Geophysical Union meeting, this was the first year that DataONE had a booth. We took a small first step, at a huge long-standing meeting, and occupied part of a

booth graciously hosted by our friends from NEON. Despite the size of the meeting, the atmosphere in the hall was relaxed and we had many valuable conversations with people interested in working with Big Data, in bringing data to DataONE as a Member Node, in using DataONE and in providing suggestions for additional development. We enjoyed meeting all of you and engaging more of the Earth science community.

Released just in advance of the AGU was an issue of International Innovation on ‘A new dawn for environmental data networks’ that highlights DataONE. The article provides an excellent background to the project as well as discussing the scope of DataONE within a climate where the speed and scale of data processing has increased exponentially and management of data is becoming increasingly time-consuming. As the name suggests, the issue reports on multiple international

projects and illustrates how the Earth sciences are entering a new era of coherence and integration. Both the article and the issue can be accessed here: bit.ly/D1IntIn.

In other outreach news, to further distribute our teaching modules (see featured resource in this issue) and to provide a home for the many presentations given on DataONE, we have now established our presence on slideshare. This means you can flick through the slide deck prior to download to see if the module meets your teaching needs. We’re pretty confident they will, but just in case a tweak is needed for your specific audience, they have been released under a CC0 license so that you can edit them as needed. Maybe pop over to slideshare.net/DataONE now to take a look and start following us? And while browsing, did you know we can also be found on Vimeo, YouTube, Facebook, Twitter .... n

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1312 Basehart SEUniversity of New MexicoAlbuquerque, NM 87106

Fax: 505.246.6007

DataONE is a collaboration among many partner organizations, and is funded by the US National Science Foundation (NSF) under

a Cooperative Agreement.

Project Director:

William Michenerwmichene@unm.edu

505.814.7601

Executive Director:

Rebecca Koskelarkoskela@unm.edu

505.382.0890

Director of Community Engagement and Outreach:

Amber Buddenaebudden@dataone.unm.edu

505.205.7675

Director of Development and Operations

Dave Vieglaisdave.vieglias@gmail.com

Above: DataONE exhibit at AGU Below: Current issue of International Innovation featuring DataONE